1
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He H, Chen S, Yu Y, Fan Z, Qian Y, Dong Y, Song Y, Zhong C, Sun X, Cao Q, Li S, Huang W, Li W, Zhuang M, Yang J, Wang X, Wang J, Wu D, Wang H, Wen W. Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer. Gut 2024; 73:1529-1542. [PMID: 38719336 DOI: 10.1136/gutjnl-2023-331773] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/20/2024] [Indexed: 08/10/2024]
Abstract
OBJECTIVE Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.
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Affiliation(s)
- Huisi He
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Shuzhen Chen
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Yong Yu
- Department I of Biliary Tract Surgery, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Zhecai Fan
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Youwen Qian
- Department of Pathology, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Yaping Dong
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuting Song
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Caiming Zhong
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Xiaojuan Sun
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Qiqi Cao
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Shiyao Li
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Weihan Huang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Wenxin Li
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Mingzhu Zhuang
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jinxian Yang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Xianming Wang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaqian Wang
- Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, China
| | - Dongfang Wu
- Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, China
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun-Yat-sen University, Guangzhou, China
| | - Hongyang Wang
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- International Cooperation Laboratory on Signal Transduction, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wen Wen
- National Center for Liver Cancer, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
- Department of Laboratory Diagnosis, Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
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2
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Xu L, Zhang Y, Lin Z, Deng X, Ren X, Huang M, Li S, Zhou Q, Fang F, Yang Q, Zheng G, Chen Z, Wu Z, Sun X, Lin J, Shen J, Guo J, Li X, Xue T, Tan J, Lin X, Tan L, Peng H, Shen S, Peng S, Li S, Liang L, Cleary JM, Lai J, Xie Y, Kuang M. FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma. J Hepatol 2024; 81:265-277. [PMID: 38508240 DOI: 10.1016/j.jhep.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/03/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND & AIMS Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.
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Affiliation(s)
- Lixia Xu
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Ying Zhang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhilong Lin
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinlang Deng
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoxue Ren
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Mingle Huang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shangru Li
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qianying Zhou
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fei Fang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qingxia Yang
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Gaomin Zheng
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zebin Chen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhongdao Wu
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xi Sun
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jie Lin
- Second Department of General Surgery, Shunde Hospital, Southern Medical University, Foshan, China
| | - Jingxian Shen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianping Guo
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoxing Li
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianchen Xue
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jing Tan
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China; Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Xiaoxuan Lin
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Li Tan
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hong Peng
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shunli Shen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaoqiang Li
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lijian Liang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - James M Cleary
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA
| | - Jiaming Lai
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Yubin Xie
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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3
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Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J, Band N, James BT, Babu S, Galiana-Melendez F, Louderback K, Prokopenko D, Tanzi RE, Bennett DA, Tsai LH, Kellis M. Single-cell multiregion dissection of Alzheimer's disease. Nature 2024:10.1038/s41586-024-07606-7. [PMID: 39048816 DOI: 10.1038/s41586-024-07606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/24/2024] [Indexed: 07/27/2024]
Abstract
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.
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Affiliation(s)
- Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Leyla Anne Akay
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ziting Xia
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, MIT, Cambridge, MA, USA
| | | | - Ayesha P Ng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ghada Abdelhady
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neil Band
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudhagar Babu
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Fabiola Galiana-Melendez
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Kate Louderback
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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4
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Ozturk K, Panwala R, Sheen J, Ford K, Jayne N, Portell A, Zhang DE, Hutter S, Haferlach T, Ideker T, Mali P, Carter H. Interface-guided phenotyping of coding variants in the transcription factor RUNX1. Cell Rep 2024; 43:114436. [PMID: 38968069 DOI: 10.1016/j.celrep.2024.114436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Panwala
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Jeanna Sheen
- School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Kyle Ford
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Nathan Jayne
- School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Andrew Portell
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Dong-Er Zhang
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Hutter
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 Munich, Germany
| | - Torsten Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 Munich, Germany
| | - Trey Ideker
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
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5
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Hou Y, Lin B, Xu T, Jiang J, Luo S, Chen W, Chen X, Wang Y, Liao G, Wang J, Zhang J, Li X, Xiang X, Xie Y, Wang J, Peng S, Lv W, Liu Y, Xiao H. The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer. Nat Commun 2024; 15:5555. [PMID: 39030177 PMCID: PMC11271530 DOI: 10.1038/s41467-024-49824-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 06/20/2024] [Indexed: 07/21/2024] Open
Abstract
Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP's function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.
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MESH Headings
- Tumor Microenvironment/immunology
- Humans
- Thyroid Neoplasms/genetics
- Thyroid Neoplasms/metabolism
- Thyroid Neoplasms/immunology
- Thyroid Neoplasms/pathology
- Calcitonin Gene-Related Peptide/metabolism
- Carcinoma, Neuroendocrine/genetics
- Carcinoma, Neuroendocrine/metabolism
- Carcinoma, Neuroendocrine/pathology
- Carcinoma, Neuroendocrine/immunology
- Dendritic Cells/immunology
- Dendritic Cells/metabolism
- Thyroid Cancer, Papillary/metabolism
- Thyroid Cancer, Papillary/immunology
- Thyroid Cancer, Papillary/genetics
- Thyroid Cancer, Papillary/pathology
- Receptors, Calcitonin Gene-Related Peptide/metabolism
- Cyclic AMP/metabolism
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Neurotransmitter Agents/metabolism
- Gene Expression Regulation, Neoplastic
- Cell Line, Tumor
- Calcitonin Gene-Related Peptide Receptor Antagonists/pharmacology
- Single-Cell Analysis
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Affiliation(s)
- Yingtong Hou
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bo Lin
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianyi Xu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Juan Jiang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shuli Luo
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wanna Chen
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xinwen Chen
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuanqi Wang
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guanrui Liao
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianping Wang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiayuan Zhang
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xuyang Li
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiao Xiang
- Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yubin Xie
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ji Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weiming Lv
- Department of Thyroid Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yihao Liu
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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6
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Xu Y, Wang Y, Ma S. SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer. CELL REPORTS METHODS 2024; 4:100813. [PMID: 38971150 PMCID: PMC11294836 DOI: 10.1016/j.crmeth.2024.100813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/23/2024] [Accepted: 06/13/2024] [Indexed: 07/08/2024]
Abstract
Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.
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Affiliation(s)
- Yupu Xu
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yuzhou Wang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shisong Ma
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; School of Data Science, University of Science and Technology of China, Hefei, China.
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7
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Verhey TB, Seo H, Gillmor A, Thoppey-Manoharan V, Schriemer D, Morrissy S. mosaicMPI: a framework for modular data integration across cohorts and -omics modalities. Nucleic Acids Res 2024; 52:e53. [PMID: 38813827 PMCID: PMC11229337 DOI: 10.1093/nar/gkae442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.
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Affiliation(s)
- Theodore B Verhey
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Heewon Seo
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Aaron Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Varsha Thoppey-Manoharan
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - David Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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8
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Luo Y, Xia Y, Liu D, Li X, Li H, Liu J, Zhou D, Dong Y, Li X, Qian Y, Xu C, Tao K, Li G, Pan W, Zhong Q, Liu X, Xu S, Wang Z, Liu R, Zhang W, Shan W, Fang T, Wang S, Peng Z, Jin P, Jin N, Shi S, Chen Y, Wang M, Jiao X, Luo M, Gong W, Wang Y, Yao Y, Zhao Y, Huang X, Ji X, He Z, Zhao G, Liu R, Wu M, Chen G, Hong L, Ma D, Fang Y, Liang H, Gao Q. Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors. Cell 2024:S0092-8674(24)00653-6. [PMID: 38971151 DOI: 10.1016/j.cell.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/12/2024] [Accepted: 06/10/2024] [Indexed: 07/08/2024]
Abstract
Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.
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Affiliation(s)
- Yikai Luo
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yu Xia
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dan Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiong Li
- Department of Gynecology & Obstetrics, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China
| | - Huayi Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiahao Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dongchen Zhou
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yu Dong
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xin Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yiyu Qian
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cheng Xu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Kangjia Tao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guannan Li
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wen Pan
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qing Zhong
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xingzhe Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sen Xu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhi Wang
- Department of Gynecology & Obstetrics, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China
| | - Ronghua Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei Zhang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wanying Shan
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tian Fang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Siyuan Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zikun Peng
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ping Jin
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ning Jin
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shennan Shi
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuxin Chen
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mengjie Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiaofei Jiao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mengshi Luo
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenjian Gong
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ya Wang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yue Yao
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Yi Zhao
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xinlin Huang
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Xuwo Ji
- Precision Scientific (Beijing) Co., Ltd., Beijing 100085, China
| | - Zhaoren He
- BioMap (Beijing) Intelligence Technology Limited, Beijing 100089, China
| | - Guangnian Zhao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Rong Liu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Mingfu Wu
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gang Chen
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Hong
- Department of Gynecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Ding Ma
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Yong Fang
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Qinglei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education, Hubei Provincial Key Laboratory of Tumor Invasion and Metastasis), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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9
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Aguirre M, Spence JP, Sella G, Pritchard JK. Gene regulatory network structure informs the distribution of perturbation effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602130. [PMID: 39005431 PMCID: PMC11245109 DOI: 10.1101/2024.07.04.602130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford CA
| | | | - Guy Sella
- Department of Biological Sciences, Columbia University, New York NY
- Program for Mathematical Genomics, Columbia University, New York NY
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
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10
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Boe RH, Triandafillou CG, Lazcano R, Wargo JA, Raj A. Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.30.601416. [PMID: 39005406 PMCID: PMC11244927 DOI: 10.1101/2024.06.30.601416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Resistance to cancer therapy is driven by both cell-intrinsic and microenvironmental factors. Previous work has revealed that multiple resistant cell fates emerge in melanoma following treatment with targeted therapy and that, in vitro, these resistant fates are determined by the transcriptional state of individual cells prior to exposure to treatment. What remains unclear is whether these resistant fates are shared across different genetic backgrounds and how, if at all, these resistant fates interact with the tumor microenvironment. Through spatial transcriptomics and single-cell RNA sequencing, we uncovered distinct resistance programs in melanoma cells shaped by both intrinsic cellular states and the tumor microenvironment. Consensus non-negative matrix factorization revealed shared intrinsic resistance programs across different cell lines, highlighting the presence of universal and unique resistance pathways. In patient samples, we demonstrated that these resistance programs coexist within individual tumors and associate with diverse immune signatures, suggesting that the tumor microenvironment and distribution of resistant fates are closely connected. Single-cell resolution spatial transcriptomics in xenograft models revealed both intrinsically determined and extrinsically influenced resistant fates. Overall, this work demonstrates that each therapy resistant fate coexists with a distinct immune microenvironment in tumors and that, in vivo, tissue features, such as regions of necrosis, can influence which resistant fate is adopted.
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Affiliation(s)
- Ryan H. Boe
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Catherine G. Triandafillou
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA
| | - Rossana Lazcano
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jennifer A. Wargo
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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11
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Chen X, Zhao J, Yue S, Li Z, Duan X, Lin Y, Yang Y, He J, Gao L, Pan Z, Yang X, Su X, Huang M, Li X, Zhao Y, Zhang X, Li Z, Hu L, Tang J, Hao Y, Tian Q, Wang Y, Xu L, Huang Q, Cao Y, Chen Y, Zhu B, Li Y, Bai F, Zhang G, Ye L. An oncolytic virus delivering tumor-irrelevant bystander T cell epitopes induces anti-tumor immunity and potentiates cancer immunotherapy. NATURE CANCER 2024; 5:1063-1081. [PMID: 38609488 PMCID: PMC11286533 DOI: 10.1038/s43018-024-00760-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/15/2024] [Indexed: 04/14/2024]
Abstract
Tumor-specific T cells are crucial in anti-tumor immunity and act as targets for cancer immunotherapies. However, these cells are numerically scarce and functionally exhausted in the tumor microenvironment (TME), leading to inefficacious immunotherapies in most patients with cancer. By contrast, emerging evidence suggested that tumor-irrelevant bystander T (TBYS) cells are abundant and preserve functional memory properties in the TME. To leverage TBYS cells in the TME to eliminate tumor cells, we engineered oncolytic virus (OV) encoding TBYS epitopes (OV-BYTE) to redirect the antigen specificity of tumor cells to pre-existing TBYS cells, leading to effective tumor inhibition in multiple preclinical models. Mechanistically, OV-BYTE induced epitope spreading of tumor antigens to elicit more diverse tumor-specific T cell responses. Remarkably, the OV-BYTE strategy targeting human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell memory efficiently inhibited tumor progression in a human tumor cell-derived xenograft model, providing important insights into the improvement of cancer immunotherapies in a large population with a history of SARS-CoV-2 infection or coronavirus disease 2019 (COVID-19) vaccination.
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Affiliation(s)
- Xiangyu Chen
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
- Changping Laboratory, Beijing, China
| | - Jing Zhao
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Shuai Yue
- Institute of Immunology, Third Military Medical University, Chongqing, China
- Cancer Center, Daping Hospital and Army Medical Center of PLA, Third Military Medical University, Chongqing, China
| | - Ziyu Li
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Xiang Duan
- The State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Model Animal Research Center, Medical School of Nanjing University, Nanjing, China
| | - Yao Lin
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Yang Yang
- Guangdong Provincial Key Laboratory of Immune Regulation and Immunotherapy, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Junjian He
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Leiqiong Gao
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Zhiwei Pan
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Xiaofan Yang
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Xingxing Su
- Department of Hepatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Min Huang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xiao Li
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Ye Zhao
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xuehui Zhang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhirong Li
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Li Hu
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Jianfang Tang
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Yaxing Hao
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Qin Tian
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Yifei Wang
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
| | - Lifan Xu
- Institute of Immunology, Third Military Medical University, Chongqing, China
| | - Qizhao Huang
- Institute of Immunological Innovation and Translation, Chongqing Medical University, Chongqing, China
| | - Yingjiao Cao
- Guangdong Provincial Key Laboratory of Immune Regulation and Immunotherapy, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Yaokai Chen
- Department of Infectious Diseases, Chongqing Public Health Medical Center, Chongqing, China
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Yan Li
- The State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Model Animal Research Center, Medical School of Nanjing University, Nanjing, China.
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China.
| | - Guozhong Zhang
- Key Laboratory of Animal Epidemiology of the Ministry of Agriculture, College of Veterinary Medicine, China Agricultural University, Beijing, China.
| | - Lilin Ye
- Changping Laboratory, Beijing, China.
- Institute of Immunology, Third Military Medical University, Chongqing, China.
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12
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Zheng J, Lu W, Wang C, Chen S, Zhang Q, Su C. Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC. Aging (Albany NY) 2024; 16:10943-10971. [PMID: 38944814 PMCID: PMC11272124 DOI: 10.18632/aging.205974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/16/2024] [Indexed: 07/01/2024]
Abstract
The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate meta programs of cancer cells. The bulk RNA sequencing (RNA-seq) data revealed high clinical prognostic values of functionally heterogeneous cancer cells of three meta programs, with transcription factor regulatory network and motif activities revealing the key transcription factors that regulate functionally heterogeneous ccRCC cells. The interactions between varying meta programs and other cell subpopulations in the microenvironment were investigated. Finally, we assessed the sensitivity of cancer cells of disparate meta programs to different anti-cancer agents. Our findings inform on the intra-tumor heterogeneity of ccRCC and its regulatory networks and offers new perspectives to facilitate the designs of rational therapeutic strategies.
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Affiliation(s)
- Jie Zheng
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Wenhao Lu
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Chengbang Wang
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Shaohua Chen
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Qingyun Zhang
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Cheng Su
- Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
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13
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Goodspeed A, Bodlak A, Duffy AB, Nelson-Taylor S, Oike N, Porfilio T, Shirai R, Walker D, Treece A, Black J, Donaldson N, Cost C, Garrington T, Greffe B, Luna-Fineman S, Demedis J, Lake J, Danis E, Verneris M, Adams DL, Hayashi M. Characterization of transcriptional heterogeneity and novel therapeutic targets using single cell RNA-sequencing of primary and circulating Ewing sarcoma cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576251. [PMID: 38293103 PMCID: PMC10827204 DOI: 10.1101/2024.01.18.576251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Ewing sarcoma is the second most common bone cancer in children, accounting for 2% of pediatric cancer diagnoses. Patients who present with metastatic disease at the time of diagnosis have a dismal prognosis, compared to the >70% 5-year survival of those with localized disease. Here, we utilized single cell RNA-sequencing to characterize the transcriptional landscape of primary Ewing sarcoma tumors and surrounding tumor microenvironment (TME). Copy-number analysis identified subclonal evolution within patients prior to treatment. Primary tumor samples demonstrate a heterogenous transcriptional landscape with several conserved gene expression programs, including those composed of genes related to proliferation and EWS targets. Single cell RNA-sequencing and immunofluorescence of circulating tumor cells at the time of diagnosis identified TSPAN8 as a novel therapeutic target.
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14
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Chapple RH, Liu X, Natarajan S, Alexander MIM, Kim Y, Patel AG, LaFlamme CW, Pan M, Wright WC, Lee HM, Zhang Y, Lu M, Koo SC, Long C, Harper J, Savage C, Johnson MD, Confer T, Akers WJ, Dyer MA, Sheppard H, Easton J, Geeleher P. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. Genome Biol 2024; 25:161. [PMID: 38898465 PMCID: PMC11186099 DOI: 10.1186/s13059-024-03309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 06/14/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. RESULTS Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. CONCLUSIONS Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.
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Affiliation(s)
- Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xueying Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Sivaraman Natarajan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Margaret I M Alexander
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yuna Kim
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Anand G Patel
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Christy W LaFlamme
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Min Pan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - William C Wright
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Hyeong-Min Lee
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yinwen Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Meifen Lu
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Selene C Koo
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Courtney Long
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Harper
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Chandra Savage
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Melissa D Johnson
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Thomas Confer
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Walter J Akers
- Department of Biomedical Engineering, University of Texas Southwestern Medical School, Dallas, TX, 75390, USA
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA
| | - Heather Sheppard
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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15
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Danino-Levi M, Goldberg T, Keter M, Akselrod N, Shprach-Buaron N, Safra M, Singer G, Alon S. Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events. RNA (NEW YORK, N.Y.) 2024; 30:749-759. [PMID: 38575346 PMCID: PMC11182005 DOI: 10.1261/rna.079801.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Cancer cells can manipulate immune cells and escape from the immune system response. Quantifying the molecular changes that occur when an immune cell touches a tumor cell can increase our understanding of the underlying mechanisms. Recently, it became possible to perform such measurements in situ-for example, using expansion sequencing, which enabled in situ sequencing of genes with super-resolution. We systematically examined whether individual immune cells from specific cell types express genes differently when in physical proximity to individual tumor cells. First, we demonstrated that a dense mapping of genes in situ can be used for the segmentation of cell bodies in 3D, thus improving our ability to detect likely touching cells. Next, we used three different computational approaches to detect the molecular changes that are triggered by proximity: differential expression analysis, tree-based machine learning classifiers, and matrix factorization analysis. This systematic analysis revealed tens of genes, in specific cell types, whose expression separates immune cells that are proximal to tumor cells from those that are not proximal, with a significant overlap between the different detection methods. Remarkably, an order of magnitude more genes are triggered by proximity to tumor cells in CD8 T cells compared to CD4 T cells, in line with the ability of CD8 T cells to directly bind major histocompatibility complex (MHC) class I on tumor cells. Thus, in situ sequencing of an individual biopsy can be used to detect genes likely involved in immune-tumor cell-cell interactions. The data used in this manuscript and the code of the InSituSeg, machine learning, cNMF, and Moran's I methods are publicly available at doi:10.5281/zenodo.7497981.
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Affiliation(s)
- Michal Danino-Levi
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Tal Goldberg
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Maya Keter
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Nikol Akselrod
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Noa Shprach-Buaron
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Modi Safra
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Gonen Singer
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shahar Alon
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
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16
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Cheng J, Xiao M, Meng Q, Zhang M, Zhang D, Liu L, Jin Q, Fu Z, Li Y, Chen X, Xie H. Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction. World J Surg Oncol 2024; 22:156. [PMID: 38872167 PMCID: PMC11170806 DOI: 10.1186/s12957-024-03435-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.
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Affiliation(s)
- Junpeng Cheng
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Meizhu Xiao
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Qingkang Meng
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Min Zhang
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Denan Zhang
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Lei Liu
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Qing Jin
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Zhijin Fu
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Yanjiao Li
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China
| | - Xiujie Chen
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.
| | - Hongbo Xie
- Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.
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17
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Winter PS, Ramseier ML, Navia AW, Saksena S, Strouf H, Senhaji N, DenAdel A, Mirza M, An HH, Bilal L, Dennis P, Leahy CS, Shigemori K, Galves-Reyes J, Zhang Y, Powers F, Mulugeta N, Gupta AJ, Calistri N, Van Scoyk A, Jones K, Liu H, Stevenson KE, Ren S, Luskin MR, Couturier CP, Amini AP, Raghavan S, Kimmerling RJ, Stevens MM, Crawford L, Weinstock DM, Manalis SR, Shalek AK, Murakami MA. Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597767. [PMID: 38915726 PMCID: PMC11195125 DOI: 10.1101/2024.06.06.597767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.
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Affiliation(s)
- Peter S. Winter
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michelle L. Ramseier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Andrew W. Navia
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Sachit Saksena
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Haley Strouf
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Nezha Senhaji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alan DenAdel
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Mahnoor Mirza
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Hyun Hwan An
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Bilal
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Peter Dennis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Catharine S. Leahy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kay Shigemori
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jennyfer Galves-Reyes
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Ye Zhang
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Foster Powers
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nolawit Mulugeta
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Nicholas Calistri
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Alex Van Scoyk
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kristen Jones
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Huiyun Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Siyang Ren
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Marlise R. Luskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Charles P. Couturier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Srivatsan Raghavan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mark M. Stevens
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research, Cambridge, MA, USA
| | - David M. Weinstock
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Current Address: Merck and Co., Rahway, NJ, USA
| | - Scott R. Manalis
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Alex K. Shalek
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Mark A. Murakami
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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18
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Liu S, Zhang S, Dong H, Jin X, Sun J, Zhou H, Jin Y, Li Y, Wu G. CD63 + tumor-associated macrophages drive the progression of hepatocellular carcinoma through the induction of epithelial-mesenchymal transition and lipid reprogramming. BMC Cancer 2024; 24:698. [PMID: 38849760 PMCID: PMC11157766 DOI: 10.1186/s12885-024-12472-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Tumor-associated macrophages (TAMs) constitute a substantial part of human hepatocellular carcinoma (HCC). The present study was devised to explore TAM diversity and their roles in HCC progression. METHODS Through the integration of multiple 10 × single-cell transcriptomic data derived from HCC samples and the use of consensus nonnegative matrix factorization (an unsupervised clustering algorithm), TAM molecular subtypes and expression programs were evaluated in detail. The roles played by these TAM subtypes in HCC were further probed through pseudotime, enrichment, and intercellular communication analyses. Lastly, vitro experiments were performed to validate the relationship between CD63, which is an inflammatory TAM expression program marker, and tumor cell lines. RESULTS We found that the inflammatory expression program in TAMs had a more obvious interaction with HCC cells, and CD63, as a marker gene of the inflammatory expression program, was associated with poor prognosis of HCC patients. Both bulk RNA-seq and vitro experiments confirmed that higher TAM CD63 expression was associated with the growth of HCC cells as well as their epithelial-mesenchymal transition, metastasis, invasion, and the reprogramming of lipid metabolism. CONCLUSIONS These analyses revealed that the TAM inflammatory expression program in HCC is closely associated with malignant tumor cells, with the hub gene CD63 thus representing an ideal target for therapeutic intervention in this cancer type.
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Affiliation(s)
- Shiqi Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Shuairan Zhang
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Hang Dong
- Phase I Clinical Trails Center, The People's Hospital of China Medical University, Shenyang, People's Republic of China
| | - Xiuli Jin
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Jing Sun
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Haonan Zhou
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Yifan Jin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China
| | - Yiling Li
- Department of Gastroenterology, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.
| | - Gang Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.
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19
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Huycke TR, Häkkinen TJ, Miyazaki H, Srivastava V, Barruet E, McGinnis CS, Kalantari A, Cornwall-Scoones J, Vaka D, Zhu Q, Jo H, Oria R, Weaver VM, DeGrado WF, Thomson M, Garikipati K, Boffelli D, Klein OD, Gartner ZJ. Patterning and folding of intestinal villi by active mesenchymal dewetting. Cell 2024; 187:3072-3089.e20. [PMID: 38781967 PMCID: PMC11166531 DOI: 10.1016/j.cell.2024.04.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/30/2023] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Tissue folds are structural motifs critical to organ function. In the intestine, bending of a flat epithelium into a periodic pattern of folds gives rise to villi, finger-like protrusions that enable nutrient absorption. However, the molecular and mechanical processes driving villus morphogenesis remain unclear. Here, we identify an active mechanical mechanism that simultaneously patterns and folds the intestinal epithelium to initiate villus formation. At the cellular level, we find that PDGFRA+ subepithelial mesenchymal cells generate myosin II-dependent forces sufficient to produce patterned curvature in neighboring tissue interfaces. This symmetry-breaking process requires altered cell and extracellular matrix interactions that are enabled by matrix metalloproteinase-mediated tissue fluidization. Computational models, together with in vitro and in vivo experiments, revealed that these cellular features manifest at the tissue level as differences in interfacial tensions that promote mesenchymal aggregation and interface bending through a process analogous to the active dewetting of a thin liquid film.
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Affiliation(s)
- Tyler R Huycke
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Teemu J Häkkinen
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Hikaru Miyazaki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Vasudha Srivastava
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Emilie Barruet
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Christopher S McGinnis
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Ali Kalantari
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jake Cornwall-Scoones
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Dedeepya Vaka
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Qin Zhu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Roger Oria
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Krishna Garikipati
- Departments of Mechanical Engineering, and Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Dario Boffelli
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Ophir D Klein
- Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA.
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
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20
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Lim J, Rodriguez R, Williams K, Silva J, Gutierrez AG, Tyler P, Baharom F, Sun T, Lin E, Martin S, Kayser BD, Johnston RJ, Mellman I, Delamarre L, West NR, Müller S, Qu Y, Heger K. The Exonuclease TREX1 Constitutes an Innate Immune Checkpoint Limiting cGAS/STING-Mediated Antitumor Immunity. Cancer Immunol Res 2024; 12:663-672. [PMID: 38489753 PMCID: PMC11148535 DOI: 10.1158/2326-6066.cir-23-1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/15/2024] [Accepted: 03/15/2024] [Indexed: 03/17/2024]
Abstract
The DNA exonuclease three-prime repair exonuclease 1 (TREX1) is critical for preventing autoimmunity in mice and humans by degrading endogenous cytosolic DNA, which otherwise triggers activation of the innate cGAS/STING pathway leading to the production of type I IFNs. As tumor cells are prone to aberrant cytosolic DNA accumulation, we hypothesized that they are critically dependent on TREX1 activity to limit their immunogenicity. Here, we show that in tumor cells, TREX1 restricts spontaneous activation of the cGAS/STING pathway, and the subsequent induction of a type I IFN response. As a result, TREX1 deficiency compromised in vivo tumor growth in mice. This delay in tumor growth depended on a functional immune system, systemic type I IFN signaling, and tumor-intrinsic cGAS expression. Mechanistically, we show that tumor TREX1 loss drove activation of CD8+ T cells and NK cells, prevented CD8+ T-cell exhaustion, and remodeled an immunosuppressive myeloid compartment. Consequently, TREX1 deficiency combined with T-cell-directed immune checkpoint blockade. Collectively, we conclude that TREX1 is essential to limit tumor immunogenicity, and that targeting this innate immune checkpoint remodels the tumor microenvironment and enhances antitumor immunity by itself and in combination with T-cell-targeted therapies. See related article by Toufektchan et al., p. 673.
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Affiliation(s)
| | | | | | - John Silva
- Genentech Inc., South San Francisco, California
| | | | - Paul Tyler
- Genentech Inc., South San Francisco, California
| | | | - Tao Sun
- Genentech Inc., South San Francisco, California
| | - Eva Lin
- Genentech Inc., South San Francisco, California
| | | | | | | | - Ira Mellman
- Genentech Inc., South San Francisco, California
| | | | | | | | - Yan Qu
- Genentech Inc., South San Francisco, California
| | - Klaus Heger
- Genentech Inc., South San Francisco, California
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21
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Nussbaum YI, Hossain KSMT, Kaifi J, Warren WC, Shyu CR, Mitchem JB. Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation. J Biomed Inform 2024; 154:104644. [PMID: 38631462 DOI: 10.1016/j.jbi.2024.104644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/29/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-specific gene expression programs (GEPs), the characterization of GEPs associated with biological processes and stimuli responses remains limited. In this study, we aim to infer biologically meaningful GEPs that are associated with both cellular phenotypes and activity programs directly from scRNA-seq data. METHODS We applied linear CorEx, a machine-learning-based approach, to infer GEPs by grouping genes based on total correlation optimization function in simulated and real-world scRNA-seq datasets. Additionally, we utilized a transfer learning approach to project CorEx-inferred GEPs to other scRNA-seq datasets. RESULTS By leveraging total correlation optimization, linear CorEx groups genes and demonstrates superior performance in identifying cell types and activity programs compared to similar methods using simulated data. Furthermore, we apply this same approach to real-world scRNA-seq data from the mouse dentate gyrus and embryonic colon development, uncovering biologically relevant GEPs related to cell types, developmental ages, and cell cycle programs. We also demonstrate the potential for transfer learning by evaluating similar datasets, showcasing the cross-species sensitivity of linear CorEx. CONCLUSION Our findings validate linear CorEx as a valuable tool for comprehensively analyzing complex signals in scRNA-seq data, leading to deeper insights into gene expression dynamics, cellular heterogeneity, and regulatory mechanisms.
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Affiliation(s)
- Yulia I Nussbaum
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA
| | - K S M Tozammel Hossain
- Department of Information Science, University of North Texas, 3940 N Elm St, Denton, TX 76203, USA
| | - Jussuf Kaifi
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA; Department of Surgery, University of Missouri Hospital, 1 Hospital Dr., Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, 800 Hospital Dr., Columbia, MO 65201, USA; Siteman Cancer Center, Washington University School of Medicine, 4921 Parkview Pl, St. Louis, MO 63110, USA
| | - Wesley C Warren
- Department of Surgery, University of Missouri Hospital, 1 Hospital Dr., Columbia, MO 65212, USA; Bond Life Sciences Center, University of Missouri, 1201 Rollin St., Columbia, MO 65211, USA
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA
| | - Jonathan B Mitchem
- VA Northeast Ohio Healthcare System, 10701 East Boulevard, Cleveland, OH 44106, USA; Department of Colon and Rectal Surgery, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA; Department of Inflammation and Immunity, Lerner Research Institute, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
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22
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Prakash A, Dion E, Banerjee TD, Monteiro A. The molecular basis of scale development highlighted by a single-cell atlas of Bicyclus anynana butterfly pupal forewings. Cell Rep 2024; 43:114147. [PMID: 38662541 DOI: 10.1016/j.celrep.2024.114147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/26/2024] [Accepted: 04/09/2024] [Indexed: 06/01/2024] Open
Abstract
Butterfly wings display a diversity of cell types, including large polyploid scale cells, yet the molecular basis of such diversity is poorly understood. To explore scale cell diversity at a transcriptomic level, we employ single-cell RNA sequencing of ∼5,200 large cells (>6 μm) from 22.5- to 25-h male pupal forewings of the butterfly Bicyclus anynana. Using unsupervised clustering, followed by in situ hybridization, immunofluorescence, and CRISPR-Cas9 editing of candidate genes, we annotate various cell types on the wing. We identify genes marking non-innervated scale cells, pheromone-producing glandular cells, and innervated sensory cell types. We show that senseless, a zinc-finger transcription factor, and HR38, a hormone receptor, determine the identity, size, and color of different scale cell types and are important regulators of scale cell differentiation. This dataset and the identification of various wing cell-type markers provide a foundation to compare and explore scale cell-type diversification across arthropod species.
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Affiliation(s)
- Anupama Prakash
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
| | - Emilie Dion
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Tirtha Das Banerjee
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Antónia Monteiro
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
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23
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Liu Y, Carbonetto P, Willwerscheid J, Oakes SA, Macleod KF, Stephens M. Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.15.553436. [PMID: 37645713 PMCID: PMC10462040 DOI: 10.1101/2023.08.15.553436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and produce new therapeutically relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically relevant disease subtypes. Here we introduce a new statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can help decompose transcriptional heterogeneity into interpretable components - including patient-specific, dataset-specific and shared components relevant to disease subtypes - and that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data from three studies on pancreatic cancer adenocarcinoma (PDAC), GBCD produces a refined characterization of existing tumor subtypes (e.g., classical vs. basal), and identifies a new gene expression program (GEP) that is prognostic of poor survival independent of established prognostic factors such as tumor stage and subtype. The new GEP is enriched for genes involved in a variety of stress responses, and suggests a potentially important role for the integrated stress response in PDAC development and prognosis.
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24
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Rabadam G, Wibrand C, Flynn E, Hartoularos GC, Sun Y, Madubata C, Fragiadakis GK, Ye CJ, Kim S, Gartner ZJ, Sirota M, Neely J. Coordinated immune dysregulation in juvenile dermatomyositis revealed by single-cell genomics. JCI Insight 2024; 9:e176963. [PMID: 38743491 DOI: 10.1172/jci.insight.176963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
Juvenile dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I IFN response and autoantibodies. Treatment options are limited due to an incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of patients with JDM at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment toward an immature naive state as a hallmark of JDM at diagnosis. Furthermore, we find that these changes in B cells are paralleled by T cell signatures suggestive of Th2-mediated inflammation that persist despite disease quiescence. We applied network analysis to reveal that hyperactivation of the type I IFN response in all immune populations is coordinated with previously masked cell states including dysfunctional protein processing in CD4+ T cells and regulation of cell death programming in NK cells, CD8+ T cells, and γδ T cells. Together, these findings unveil the coordinated immune dysregulation underpinning JDM and provide insight into strategies for restoring balance in immune function.
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Affiliation(s)
- Gabrielle Rabadam
- UC Berkeley-UC San Francisco Graduate Program in Bioengineering, and
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, California, USA
| | - Camilla Wibrand
- Aarhus University, Aarhus, Denmark
- Division of Pediatric Rheumatology, Department of Pediatrics
| | | | - George C Hartoularos
- Graduate Program in Biological and Medical Informatics
- Division of Rheumatology, Department of Medicine
- Institute for Human Genetics
| | - Yang Sun
- Division of Rheumatology, Department of Medicine
| | - Chioma Madubata
- Division of Pediatric Rheumatology, Department of Pediatrics
- CoLabs
| | | | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine
- Institute for Human Genetics
- Department of Epidemiology and Biostatistics, and
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
| | - Susan Kim
- Division of Pediatric Rheumatology, Department of Pediatrics
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, California, USA
- Department of Pediatrics, UCSF, San Francisco, California, USA
| | - Jessica Neely
- Division of Pediatric Rheumatology, Department of Pediatrics
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25
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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26
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Wang J, Sun H, Mou L, Lu Y, Wu Z, Pu Z, Yang MM. Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning. Front Endocrinol (Lausanne) 2024; 15:1382896. [PMID: 38800474 PMCID: PMC11116564 DOI: 10.3389/fendo.2024.1382896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. Methods We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs. Results Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. Conclusion This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.
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Affiliation(s)
- Jun Wang
- Department of Endocrinology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Hongyan Sun
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Lisha Mou
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ying Lu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zijing Wu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zuhui Pu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ming-ming Yang
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
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Najibi AJ, Lane RS, Sobral MC, Bovone G, Kang S, Freedman BR, Gutierrez Estupinan J, Elosegui-Artola A, Tringides CM, Dellacherie MO, Williams K, Ijaz H, Müller S, Turley SJ, Mooney DJ. Durable lymph-node expansion is associated with the efficacy of therapeutic vaccination. Nat Biomed Eng 2024:10.1038/s41551-024-01209-3. [PMID: 38710838 DOI: 10.1038/s41551-024-01209-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/30/2024] [Indexed: 05/08/2024]
Abstract
Following immunization, lymph nodes dynamically expand and contract. The mechanical and cellular changes enabling the early-stage expansion of lymph nodes have been characterized, yet the durability of such responses and their implications for adaptive immunity and vaccine efficacy are unknown. Here, by leveraging high-frequency ultrasound imaging of the lymph nodes of mice, we report more potent and persistent lymph-node expansion for animals immunized with a mesoporous silica vaccine incorporating a model antigen than for animals given bolus immunization or standard vaccine formulations such as alum, and that durable and robust lymph-node expansion was associated with vaccine efficacy and adaptive immunity for 100 days post-vaccination in a mouse model of melanoma. Immunization altered the mechanical and extracellular-matrix properties of the lymph nodes, drove antigen-dependent proliferation of immune and stromal cells, and altered the transcriptional features of dendritic cells and inflammatory monocytes. Strategies that robustly maintain lymph-node expansion may result in enhanced vaccination outcomes.
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Affiliation(s)
- Alexander J Najibi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Ryan S Lane
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Miguel C Sobral
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Giovanni Bovone
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Shawn Kang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Benjamin R Freedman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Joel Gutierrez Estupinan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Alberto Elosegui-Artola
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Institute for Bioengineering of Catalonia, Barcelona, Spain
| | - Christina M Tringides
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Harvard Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Maxence O Dellacherie
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Katherine Williams
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Hamza Ijaz
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Sören Müller
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - Shannon J Turley
- Department of Cancer Immunology, Genentech, Inc., South San Francisco, CA, USA
| | - David J Mooney
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
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Kotliar D, Curtis M, Agnew R, Weinand K, Nathan A, Baglaenko Y, Zhao Y, Sabeti PC, Rao DA, Raychaudhuri S. Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592310. [PMID: 38746317 PMCID: PMC11092745 DOI: 10.1101/2024.05.03.592310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.
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Affiliation(s)
- Dylan Kotliar
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA
| | - Michelle Curtis
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan Agnew
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Center for Autoimmune Genetics and Etiology and Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
| | - Yu Zhao
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Pardis C. Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Organismic and Evolutionary Biology, FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Deepak A. Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Stadtmauer DJ, Basanta S, Maziarz JD, Cole AG, Dagdas G, Smith GR, van Breukelen F, Pavličev M, Wagner GP. Comparative single cell analysis reveals complex patterns of cell type and cell signaling innovations at the fetal-maternal interface. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.591945. [PMID: 38746137 PMCID: PMC11092578 DOI: 10.1101/2024.05.01.591945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The decidual-placental interface is one of the most diverse and rapidly evolving tissues in mammals. Its origin as a chimeric fetal-maternal tissue poses a unique evolutionary puzzle. We present single-cell RNA sequencing atlases from the fetal-maternal interfaces of the opossum, a marsupial, the Malagasy common tenrec, an afrotherian with primitive reproductive features, and mouse, guinea pig, and human. Invasive trophoblast shares a common transcriptomic signature across eutherians, which we argue represents a cell type family that radiated following the evolution of hemochorial placentation. We find evidence that the eutherian decidual stromal cell evolved stepwise from a predecidual state retained in Tenrec , followed by a second decidual cell type originating in Boreoeutheria with endocrine characteristics. We reconstruct ligand-receptor signaling to test evolutionary hypotheses at scale. Novel trophoblast and decidual cell types display strong integration into signaling networks compared to other cells. Additionally, we find consistent disambiguation between fetal and maternal signaling. Using phylogenetic analysis, we infer the cell-cell signaling network of the Placental common ancestor, and identify increased rates of signaling evolution in Euarchontoglires. Together, our findings reveal novel cell type identities and cell signaling dynamics at the mammalian fetal-maternal interface.
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30
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Rahimzadeh N, Srinivasan SS, Zhang J, Swarup V. Gene networks and systems biology in Alzheimer's disease: Insights from multi-omics approaches. Alzheimers Dement 2024; 20:3587-3605. [PMID: 38534018 PMCID: PMC11095483 DOI: 10.1002/alz.13790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/25/2024] [Accepted: 02/17/2024] [Indexed: 03/28/2024]
Abstract
Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.
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Affiliation(s)
- Negin Rahimzadeh
- Mathematical, Computational, and Systems Biology (MCSB) ProgramUniversity of California IrvineIrvineCaliforniaUSA
| | - Shushrruth Sai Srinivasan
- Mathematical, Computational, and Systems Biology (MCSB) ProgramUniversity of California IrvineIrvineCaliforniaUSA
| | - Jing Zhang
- Department of Computer ScienceUniversity of CaliforniaIrvineCaliforniaUSA
| | - Vivek Swarup
- Department of Neurobiology and BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological Disorders (MIND)University of California IrvineIrvineCaliforniaUSA
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31
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An M, Mehta A, Min BH, Heo YJ, Wright SJ, Parikh M, Bi L, Lee H, Kim TJ, Lee SY, Moon J, Park RJ, Strickland MR, Park WY, Kang WK, Kim KM, Kim ST, Klempner SJ, Lee J. Early Immune Remodeling Steers Clinical Response to First-Line Chemoimmunotherapy in Advanced Gastric Cancer. Cancer Discov 2024; 14:766-785. [PMID: 38319303 PMCID: PMC11061611 DOI: 10.1158/2159-8290.cd-23-0857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/28/2023] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
Adding anti-programmed cell death protein 1 (anti-PD-1) to 5-fluorouracil (5-FU)/platinum improves survival in some advanced gastroesophageal adenocarcinomas (GEA). To understand the effects of chemotherapy and immunotherapy, we conducted a phase II first-line trial (n = 47) sequentially adding pembrolizumab to 5-FU/platinum in advanced GEA. Using serial biopsy of the primary tumor at baseline, after one cycle of 5-FU/platinum, and after the addition of pembrolizumab, we transcriptionally profiled 358,067 single cells to identify evolving multicellular tumor microenvironment (TME) networks. Chemotherapy induced early on-treatment multicellular hubs with tumor-reactive T-cell and M1-like macrophage interactions in slow progressors. Faster progression featured increased MUC5A and MSLN containing treatment resistance programs in tumor cells and M2-like macrophages with immunosuppressive stromal interactions. After pembrolizumab, we observed increased CD8 T-cell infiltration and development of an immunity hub involving tumor-reactive CXCL13 T-cell program and epithelial interferon-stimulated gene programs. Strategies to drive increases in antitumor immune hub formation could expand the portion of patients benefiting from anti-PD-1 approaches. SIGNIFICANCE The benefit of 5-FU/platinum with anti-PD-1 in first-line advanced gastric cancer is limited to patient subgroups. Using a trial with sequential anti-PD-1, we show coordinated induction of multicellular TME hubs informs the ability of anti-PD-1 to potentiate T cell-driven responses. Differential TME hub development highlights features that underlie clinical outcomes. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
- Minae An
- Experimental Therapeutics Development Center, Samsung Medical Center, Seoul, Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Arnav Mehta
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Byung Hoon Min
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | - Samuel J. Wright
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Milan Parikh
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Lynn Bi
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Hyuk Lee
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Tae Jun Kim
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Song-Yi Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeonghyeon Moon
- Departments of Neurology and Immunology, Yale School of Medicine, New Haven, Connecticut
| | - Ryan J. Park
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Division of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Matthew R. Strickland
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | | | - Won Ki Kang
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kyoung-Mee Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Samuel J. Klempner
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Jeeyun Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Vornholt E, Liharska LE, Cheng E, Hashemi A, Park YJ, Ziafat K, Wilkins L, Silk H, Linares LM, Thompson RC, Sullivan B, Moya E, Nadkarni GN, Sebra R, Schadt EE, Kopell BH, Charney AW, Beckmann ND. Characterizing cell type specific transcriptional differences between the living and postmortem human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306590. [PMID: 38746297 PMCID: PMC11092720 DOI: 10.1101/2024.05.01.24306590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human brains. As methods to obtain brain tissue from living individuals emerge, it is essential to characterize gene expression differences associated with tissue originating from either living or postmortem subjects using snRNA-seq, and to assess whether and how such differences may impact snRNA-seq studies of brain tissue. To address this, human prefrontal cortex single nuclei gene expression was generated and compared between 31 samples from living individuals and 21 postmortem samples. The same cell types were consistently identified in living and postmortem nuclei, though for each cell type, a large proportion of genes were differentially expressed between samples from postmortem and living individuals. Notably, estimation of cell type proportions by cell type deconvolution of pseudo-bulk data was found to be more accurate in samples from living individuals. To allow for future integration of living and postmortem brain gene expression, a model was developed that quantifies from gene expression data the probability a human brain tissue sample was obtained postmortem. These probabilities are established as a means to statistically account for the gene expression differences between samples from living and postmortem individuals. Together, the results presented here provide a deep characterization of both differences between snRNA-seq derived from samples from living and postmortem individuals, as well as qualify and account for their effect on common analyses performed on this type of data.
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O'Connor KW, Kishimoto K, Kuzma IO, Wagner KP, Selway JS, Roderick JE, Karna KK, Gallagher KM, Hu K, Liu H, Li R, Brehm MA, Zhu LJ, Curtis DJ, Tremblay CS, Kelliher MA. The role of quiescent thymic progenitors in TAL/LMO2-induced T-ALL chemotolerance. Leukemia 2024; 38:951-962. [PMID: 38553571 PMCID: PMC11073972 DOI: 10.1038/s41375-024-02232-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/08/2024] [Accepted: 03/14/2024] [Indexed: 05/08/2024]
Abstract
Relapse in T-cell acute lymphoblastic leukemia (T-ALL) may signify the persistence of leukemia-initiating cells (L-ICs). Ectopic TAL1/LMO expression defines the largest subset of T-ALL, but its role in leukemic transformation and its impact on relapse-driving L-ICs remain poorly understood. In TAL1/LMO mouse models, double negative-3 (DN3; CD4-CD8-CD25+CD44-) thymic progenitors harbored L-ICs. However, only a subset of DN3 leukemic cells exhibited L-IC activity, and studies linking L-ICs and chemotolerance are needed. To investigate L-IC heterogeneity, we used mouse models and applied single-cell RNA-sequencing and nucleosome labeling techniques in vivo. We identified a DN3 subpopulation with a cell cycle-restricted profile and heightened TAL1/LMO2 activity, that expressed genes associated with stemness and quiescence. This dormant DN3 subset progressively expanded throughout leukemogenesis, displaying intrinsic chemotolerance and enrichment in genes linked to minimal residual disease. Examination of TAL/LMO patient samples revealed a similar pattern in CD7+CD1a- thymic progenitors, previously recognized for their L-IC activity, demonstrating cell cycle restriction and chemotolerance. Our findings substantiate the emergence of dormant, chemotolerant L-ICs during leukemogenesis, and demonstrate that Tal1 and Lmo2 cooperate to promote DN3 quiescence during the transformation process. This study provides a deeper understanding of TAL1/LMO-induced T-ALL and its clinical implications in therapy failure.
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Affiliation(s)
- Kevin W O'Connor
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Medical Scientist Training Program, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Kensei Kishimoto
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Medical Scientist Training Program, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Irena O Kuzma
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Kelsey P Wagner
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Jonathan S Selway
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Justine E Roderick
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Keshab K Karna
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Kayleigh M Gallagher
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Kai Hu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Haibo Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Rui Li
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Michael A Brehm
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - David J Curtis
- Australian Centre for Blood Diseases (ACBD), Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - Cedric S Tremblay
- Department of Immunology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, R3E 0T5, Canada
- Paul Albrechtsen Research Institute CCMB, CancerCare Manitoba (CCMB), Winnipeg, MB, R3E 0V9, Canada
| | - Michelle A Kelliher
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
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34
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Chakraborty S, Guan Z, Kostrzewa CE, Shen R, Begg CB. Identifying somatic fingerprints of cancers defined by germline and environmental risk factors. Genet Epidemiol 2024. [PMID: 38686586 DOI: 10.1002/gepi.22565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/18/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.
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Affiliation(s)
| | - Zoe Guan
- Mass General Research Institute, Boston, Massachusetts, USA
| | | | - Ronglai Shen
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Colin B Begg
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
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35
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Nelson ED, Tippani M, Ramnauth AD, Divecha HR, Miller RA, Eagles NJ, Pattie EA, Kwon SH, Bach SV, Kaipa UM, Yao J, Kleinman JE, Collado-Torres L, Han S, Maynard KR, Hyde TM, Martinowich K, Page SC, Hicks SC. An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590643. [PMID: 38712198 PMCID: PMC11071618 DOI: 10.1101/2024.04.26.590643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
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36
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Chapple RH, Liu X, Natarajan S, Alexander MIM, Kim Y, Patel AG, LaFlamme CW, Pan M, Wright WC, Lee HM, Zhang Y, Lu M, Koo SC, Long C, Harper J, Savage C, Johnson MD, Confer T, Akers WJ, Dyer MA, Sheppard H, Easton J, Geeleher P. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.13.536639. [PMID: 38712039 PMCID: PMC11071300 DOI: 10.1101/2023.04.13.536639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.
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37
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McCauley KB, Kukreja K, Tovar Walker AE, Jaffe AB, Klein AM. A map of signaling responses in the human airway epithelium. Cell Syst 2024; 15:307-321.e10. [PMID: 38508187 PMCID: PMC11031335 DOI: 10.1016/j.cels.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 11/14/2023] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Katherine B McCauley
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Respiratory Diseases, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA; Disease Area X, Biomedical Research, Novartis, Cambridge, MA 02139, USA
| | - Kalki Kukreja
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Aron B Jaffe
- Respiratory Diseases, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Allon M Klein
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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38
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Marmarelis MG, Littman R, Battaglin F, Niedzwiecki D, Venook A, Ambite JL, Galstyan A, Lenz HJ, Ver Steeg G. q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics. Commun Biol 2024; 7:400. [PMID: 38565955 PMCID: PMC11255321 DOI: 10.1038/s42003-024-06104-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
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Affiliation(s)
- Myrl G Marmarelis
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.
| | - Russell Littman
- University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Francesca Battaglin
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | | | - Alan Venook
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Jose-Luis Ambite
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Aram Galstyan
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Heinz-Josef Lenz
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
- University of California Riverside, Riverside, CA, 92521, USA
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39
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Chakraborty S, Guan Z, Begg CB, Shen R. Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach. Biometrics 2024; 80:ujae030. [PMID: 38682463 PMCID: PMC11056772 DOI: 10.1093/biomtc/ujae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 05/01/2024]
Abstract
Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.
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Affiliation(s)
- Saptarshi Chakraborty
- Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214, USA
| | - Zoe Guan
- Biostatistics Center, Mass General Research Institute, Boston, MA 02114, USA
| | - Colin B Begg
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
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40
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Liang Q, Soto LS, Haymaker C, Chen K. Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585725. [PMID: 38562886 PMCID: PMC10983986 DOI: 10.1101/2024.03.19.585725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. To achieve so, LSGI scrutinizes each sliding window employing non-negative matrix factorization (NMF) combined with linear regression. With LSGI, we demonstrated the identification of spatially proximal yet opposite directed pathway gradients in a glioblastoma dataset. We further applied LSGI to 87 tumor ST datasets reported from nine published studies and identified both pan-cancer and tumor-type specific pathways with gradated expression patterns, such as epithelial mesenchymal transition, MHC complex, and hypoxia. The local gradients were further categorized according to their association to tumor-TME (tumor microenvironment) interface, highlighting the pathways related to spatial transcriptional intratumoral heterogeneity. We conclude that LSGI enables highly interpretable STG analysis which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
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Affiliation(s)
- Qingnan Liang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
| | - Luisa Solis Soto
- Department of Translational Molecular Pathology, UT MD Anderson Cancer Center
| | - Cara Haymaker
- Department of Translational Molecular Pathology, UT MD Anderson Cancer Center
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
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41
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Zhou X, Seow WY, Ha N, Cheng TH, Jiang L, Boonruangkan J, Goh JJL, Prabhakar S, Chou N, Chen KH. Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes. Nat Commun 2024; 15:2342. [PMID: 38491027 PMCID: PMC10943009 DOI: 10.1038/s41467-024-46669-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.
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Affiliation(s)
- Xinrui Zhou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wan Yi Seow
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Norbert Ha
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Teh How Cheng
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Lingfan Jiang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jeeranan Boonruangkan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jolene Jie Lin Goh
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Nigel Chou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
| | - Kok Hao Chen
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
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42
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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43
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Danishuddin, Khan S, Kim JJ. Spatial transcriptomics data and analytical methods: An updated perspective. Drug Discov Today 2024; 29:103889. [PMID: 38244672 DOI: 10.1016/j.drudis.2024.103889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
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44
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Jogi HR, Smaraki N, Nayak SS, Rajawat D, Kamothi DJ, Panigrahi M. Single cell RNA-seq: a novel tool to unravel virus-host interplay. Virusdisease 2024; 35:41-54. [PMID: 38817399 PMCID: PMC11133279 DOI: 10.1007/s13337-024-00859-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 06/01/2024] Open
Abstract
Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.
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Affiliation(s)
- Harsh Rajeshbhai Jogi
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Nabaneeta Smaraki
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Dhaval J. Kamothi
- Division of Pharmacology and Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
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45
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, Morris K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024; 627:604-611. [PMID: 38448582 PMCID: PMC10954558 DOI: 10.1038/s41586-024-07109-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/23/2024] [Indexed: 03/08/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Robert E Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Vogelgsang
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA.
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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46
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Yasumizu Y, Takeuchi D, Morimoto R, Takeshima Y, Okuno T, Kinoshita M, Morita T, Kato Y, Wang M, Motooka D, Okuzaki D, Nakamura Y, Mikami N, Arai M, Zhang X, Kumanogoh A, Mochizuki H, Ohkura N, Sakaguchi S. Single-cell transcriptome landscape of circulating CD4 + T cell populations in autoimmune diseases. CELL GENOMICS 2024; 4:100473. [PMID: 38359792 PMCID: PMC10879034 DOI: 10.1016/j.xgen.2023.100473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024]
Abstract
CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.
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Affiliation(s)
- Yoshiaki Yasumizu
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan
| | - Daiki Takeuchi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Faculty of Medicine, Osaka University, Osaka, Japan
| | - Reo Morimoto
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yusuke Takeshima
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Tatsusada Okuno
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Makoto Kinoshita
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Takayoshi Morita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yasuhiro Kato
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Min Wang
- Clinical Immunology Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Daisuke Motooka
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Daisuke Okuzaki
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Yamami Nakamura
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Norihisa Mikami
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Masaya Arai
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Xuan Zhang
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Center for Infectious Diseases for Education and Research, Osaka University, Osaka, Japan
| | - Hideki Mochizuki
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan
| | - Naganari Ohkura
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Frontier Research in Tumor Immunology, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Shimon Sakaguchi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan; Department of Experimental Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan.
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47
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Licaj M, Mhaidly R, Kieffer Y, Croizer H, Bonneau C, Meng A, Djerroudi L, Mujangi-Ebeka K, Hocine HR, Bourachot B, Magagna I, Leclere R, Guyonnet L, Bohec M, Guérin C, Baulande S, Kamal M, Le Tourneau C, Lecuru F, Becette V, Rouzier R, Vincent-Salomon A, Gentric G, Mechta-Grigoriou F. Residual ANTXR1+ myofibroblasts after chemotherapy inhibit anti-tumor immunity via YAP1 signaling pathway. Nat Commun 2024; 15:1312. [PMID: 38346978 PMCID: PMC10861537 DOI: 10.1038/s41467-024-45595-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
Although cancer-associated fibroblast (CAF) heterogeneity is well-established, the impact of chemotherapy on CAF populations remains poorly understood. Here we address this question in high-grade serous ovarian cancer (HGSOC), in which we previously identified 4 CAF populations. While the global content in stroma increases in HGSOC after chemotherapy, the proportion of FAP+ CAF (also called CAF-S1) decreases. Still, maintenance of high residual CAF-S1 content after chemotherapy is associated with reduced CD8+ T lymphocyte density and poor patient prognosis, emphasizing the importance of CAF-S1 reduction upon treatment. Single cell analysis, spatial transcriptomics and immunohistochemistry reveal that the content in the ECM-producing ANTXR1+ CAF-S1 cluster (ECM-myCAF) is the most affected by chemotherapy. Moreover, functional assays demonstrate that ECM-myCAF isolated from HGSOC reduce CD8+ T-cell cytotoxicity through a Yes Associated Protein 1 (YAP1)-dependent mechanism. Thus, efficient inhibition after treatment of YAP1-signaling pathway in the ECM-myCAF cluster could enhance CD8+ T-cell cytotoxicity. Altogether, these data pave the way for therapy targeting YAP1 in ECM-myCAF in HGSOC.
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Affiliation(s)
- Monika Licaj
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Rana Mhaidly
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Yann Kieffer
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Hugo Croizer
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Claire Bonneau
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
- Department of Surgery, Institut Curie Hospital Group, 35 rue Dailly, 92210, Saint-Cloud, France
| | - Arnaud Meng
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Lounes Djerroudi
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, rue d'Ulm, F-75248, Paris, France
| | - Kevin Mujangi-Ebeka
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Hocine R Hocine
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Brigitte Bourachot
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Ilaria Magagna
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France
| | - Renaud Leclere
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, rue d'Ulm, F-75248, Paris, France
| | - Lea Guyonnet
- Cytometry platform, PSL University, Institut Curie, 75005, Paris, France
| | - Mylene Bohec
- ICGex Next-Generation Sequencing Platform, PSL University, Institut Curie, 75005, Paris, France
| | - Coralie Guérin
- Cytometry platform, PSL University, Institut Curie, 75005, Paris, France
| | - Sylvain Baulande
- ICGex Next-Generation Sequencing Platform, PSL University, Institut Curie, 75005, Paris, France
| | - Maud Kamal
- Department of Drug Development and Innovation, Institut Curie Hospital Group, 26, rue d'Ulm, F-75248, Paris, France
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, Institut Curie Hospital Group, 26, rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris-Saclay University, Institut Curie, 35 rue Dailly, 92210, Saint-Cloud, France
| | - Fabrice Lecuru
- Breast, gynecology and reconstructive surgery Department, Institut Curie Hospital Group, Paris Cité University, 26, rue d'Ulm, F-75248, Paris, France
| | - Véronique Becette
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 35 rue Dailly, 92210, Saint-Cloud, France
| | - Roman Rouzier
- Department of Surgery, Institut Curie Hospital Group, 35 rue Dailly, 92210, Saint-Cloud, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, rue d'Ulm, F-75248, Paris, France
| | - Geraldine Gentric
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France.
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France.
| | - Fatima Mechta-Grigoriou
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, 26, rue d'Ulm, F-75248, Paris, France.
- Inserm, U830, 26, rue d'Ulm, Paris, F-75005, France.
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48
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Sheng B, Pan S, Ye M, Liu H, Zhang J, Zhao B, Ji H, Zhu X. Single-cell RNA sequencing of cervical exfoliated cells reveals potential biomarkers and cellular pathogenesis in cervical carcinogenesis. Cell Death Dis 2024; 15:130. [PMID: 38346944 PMCID: PMC10861450 DOI: 10.1038/s41419-024-06522-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/15/2024]
Abstract
Cervical cancer (CC) is a common gynecological malignancy. Despite the current screening methods have been proved effectively and significantly decreased CC morbidity and mortality, deficiencies still exist. Single-cell RNA sequencing (scRNA-seq) approach can identify the complex and rare cell populations at single-cell resolution. By scRNA-seq, the heterogeneity of tumor microenvironment across cervical carcinogenesis has been mapped and described. Whether these alterations could be detected and applied to CC screening is unclear. Herein, we performed scRNA-seq of 56,173 cervical exfoliated cells from 15 samples, including normal cervix, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and malignancy. The present study delineated the alteration of immune and epithelial cells derived during the cervical lesion progression. A subset of lipid-associated macrophage was identified as a tumor-promoting element and could serve as a biomarker for predicting the progression of LSIL into HSIL, which was then verified by immunofluorescence. Furthermore, cell-cell communication analysis indicated the SPP1-CD44 axis might exhibit a protumor interaction between epithelial cell and macrophage. In this study, we investigated the cervical multicellular ecosystem in cervical carcinogenesis and identified potential biomarkers for early detection.
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Affiliation(s)
- Bo Sheng
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Shuya Pan
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Miaomiao Ye
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Hejing Liu
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Jiamin Zhang
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Bo Zhao
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Huihui Ji
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
| | - Xueqiong Zhu
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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49
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Schnitzler GR, Kang H, Fang S, Angom RS, Lee-Kim VS, Ma XR, Zhou R, Zeng T, Guo K, Taylor MS, Vellarikkal SK, Barry AE, Sias-Garcia O, Bloemendal A, Munson G, Guckelberger P, Nguyen TH, Bergman DT, Hinshaw S, Cheng N, Cleary B, Aragam K, Lander ES, Finucane HK, Mukhopadhyay D, Gupta RM, Engreitz JM. Convergence of coronary artery disease genes onto endothelial cell programs. Nature 2024; 626:799-807. [PMID: 38326615 PMCID: PMC10921916 DOI: 10.1038/s41586-024-07022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/03/2024] [Indexed: 02/09/2024]
Abstract
Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.
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Affiliation(s)
- Gavin R Schnitzler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Helen Kang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Shi Fang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ramcharan S Angom
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Vivian S Lee-Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - X Rosa Ma
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Ronghao Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Tony Zeng
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Martin S Taylor
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Shamsudheen K Vellarikkal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aurelie E Barry
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Oscar Sias-Garcia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Glen Munson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Tung H Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Drew T Bergman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Stephen Hinshaw
- Department of Chemical and Systems Biology, ChEM-H, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nathan Cheng
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Faculty of Computing and Data Sciences, Departments of Biology and Biomedical Engineering, Biological Design Center, and Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Krishna Aragam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Debabrata Mukhopadhyay
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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50
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Zhao L, Cunningham CM, Andruska AM, Schimmel K, Ali MK, Kim D, Gu S, Chang JL, Spiekerkoetter E, Nicolls MR. Rat microbial biogeography and age-dependent lactic acid bacteria in healthy lungs. Lab Anim (NY) 2024; 53:43-55. [PMID: 38297075 PMCID: PMC10834367 DOI: 10.1038/s41684-023-01322-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 12/21/2023] [Indexed: 02/02/2024]
Abstract
The laboratory rat emerges as a useful tool for studying the interaction between the host and its microbiome. To advance principles relevant to the human microbiome, we systematically investigated and defined the multitissue microbial biogeography of healthy Fischer 344 rats across their lifespan. Microbial community profiling data were extracted and integrated with host transcriptomic data from the Sequencing Quality Control consortium. Unsupervised machine learning, correlation, taxonomic diversity and abundance analyses were performed to determine and characterize the rat microbial biogeography and identify four intertissue microbial heterogeneity patterns (P1-P4). We found that the 11 body habitats harbored a greater diversity of microbes than previously suspected. Lactic acid bacteria (LAB) abundance progressively declined in lungs from breastfed newborn to adolescence/adult, and was below detectable levels in elderly rats. Bioinformatics analyses indicate that the abundance of LAB may be modulated by the lung-immune axis. The presence and levels of LAB in lungs were further evaluated by PCR in two validation datasets. The lung, testes, thymus, kidney, adrenal and muscle niches were found to have age-dependent alterations in microbial abundance. The 357 microbial signatures were positively correlated with host genes in cell proliferation (P1), DNA damage repair (P2) and DNA transcription (P3). Our study established a link between the metabolic properties of LAB with lung microbiota maturation and development. Breastfeeding and environmental exposure influence microbiome composition and host health and longevity. The inferred rat microbial biogeography and pattern-specific microbial signatures could be useful for microbiome therapeutic approaches to human health and life quality enhancement.
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Affiliation(s)
- Lan Zhao
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA.
- VA Palo Alto Health Care System, Palo Alto, CA, USA.
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA.
| | - Christine M Cunningham
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Adam M Andruska
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Katharina Schimmel
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Md Khadem Ali
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Dongeon Kim
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Shenbiao Gu
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Jason L Chang
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Edda Spiekerkoetter
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Mark R Nicolls
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford, CA, USA.
- VA Palo Alto Health Care System, Palo Alto, CA, USA.
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA.
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