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Xia Y, Liu Y, Li T, He S, Chang H, Wang Y, Zhang Y, Ge W. Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data. Methods 2024; 228:12-21. [PMID: 38759908 DOI: 10.1016/j.ymeth.2024.05.007] [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: 04/12/2024] [Revised: 04/28/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024] Open
Abstract
Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.
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Affiliation(s)
- Yucheng Xia
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China
| | - Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Tianhao Li
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Sihan He
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Hong Chang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yaqing Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Wenyi Ge
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
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2
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Logotheti S, Pavlopoulou A, Rudsari HK, Galow AM, Kafalı Y, Kyrodimos E, Giotakis AI, Marquardt S, Velalopoulou A, Verginadis II, Koumenis C, Stiewe T, Zoidakis J, Balasingham I, David R, Georgakilas AG. Intercellular pathways of cancer treatment-related cardiotoxicity and their therapeutic implications: the paradigm of radiotherapy. Pharmacol Ther 2024; 260:108670. [PMID: 38823489 DOI: 10.1016/j.pharmthera.2024.108670] [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: 11/11/2023] [Revised: 05/16/2024] [Accepted: 05/25/2024] [Indexed: 06/03/2024]
Abstract
Advances in cancer therapeutics have improved patient survival rates. However, cancer survivors may suffer from adverse events either at the time of therapy or later in life. Cardiovascular diseases (CVD) represent a clinically important, but mechanistically understudied complication, which interfere with the continuation of best-possible care, induce life-threatening risks, and/or lead to long-term morbidity. These concerns are exacerbated by the fact that targeted therapies and immunotherapies are frequently combined with radiotherapy, which induces durable inflammatory and immunogenic responses, thereby providing a fertile ground for the development of CVDs. Stressed and dying irradiated cells produce 'danger' signals including, but not limited to, major histocompatibility complexes, cell-adhesion molecules, proinflammatory cytokines, and damage-associated molecular patterns. These factors activate intercellular signaling pathways which have potentially detrimental effects on the heart tissue homeostasis. Herein, we present the clinical crosstalk between cancer and heart diseases, describe how it is potentiated by cancer therapies, and highlight the multifactorial nature of the underlying mechanisms. We particularly focus on radiotherapy, as a case known to often induce cardiovascular complications even decades after treatment. We provide evidence that the secretome of irradiated tumors entails factors that exert systemic, remote effects on the cardiac tissue, potentially predisposing it to CVDs. We suggest how diverse disciplines can utilize pertinent state-of-the-art methods in feasible experimental workflows, to shed light on the molecular mechanisms of radiotherapy-related cardiotoxicity at the organismal level and untangle the desirable immunogenic properties of cancer therapies from their detrimental effects on heart tissue. Results of such highly collaborative efforts hold promise to be translated to next-generation regimens that maximize tumor control, minimize cardiovascular complications, and support quality of life in cancer survivors.
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Affiliation(s)
- Stella Logotheti
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, 15780, Athens, Greece; Biomedical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Athanasia Pavlopoulou
- Izmir Biomedicine and Genome Center, Izmir, Turkey; Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
| | | | - Anne-Marie Galow
- Institute for Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany
| | - Yağmur Kafalı
- Izmir Biomedicine and Genome Center, Izmir, Turkey; Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
| | - Efthymios Kyrodimos
- First Department of Otorhinolaryngology, Head and Neck Surgery, Hippocrateion General Hospital Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Aris I Giotakis
- First Department of Otorhinolaryngology, Head and Neck Surgery, Hippocrateion General Hospital Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Stephan Marquardt
- Institute of Translational Medicine for Health Care Systems, Medical School Berlin, Hochschule Für Gesundheit Und Medizin, 14197 Berlin, Germany
| | - Anastasia Velalopoulou
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioannis I Verginadis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thorsten Stiewe
- Institute of Molecular Oncology, Philipps-University, 35043 Marburg, Germany; German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), 35043 Marburg, Germany; Genomics Core Facility, Philipps-University, 35043 Marburg, Germany; Institute for Lung Health (ILH), Justus Liebig University, 35392 Giessen, Germany
| | - Jerome Zoidakis
- Department of Biotechnology, Biomedical Research Foundation, Academy of Athens, Athens, Greece; Department of Biology, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Robert David
- Department of Cardiac Surgery, Rostock University Medical Center, 18057 Rostock, Germany; Department of Life, Light & Matter, Interdisciplinary Faculty, Rostock University, 18059 Rostock, Germany
| | - Alexandros G Georgakilas
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, 15780, Athens, Greece.
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3
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Qin T, Zhong J, Li P, Liang J, Li M, Zhang G. Matrix Metalloproteinase and Aortic Aneurysm: A Two-sample Mendelian Randomization Study. Ann Vasc Surg 2024; 105:227-235. [PMID: 38609009 DOI: 10.1016/j.avsg.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/12/2024] [Accepted: 02/04/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Studies have linked matrix metalloproteinases (MMPs) to both thoracic aortic aneurysm and abdominal aortic aneurysm (TAA and AAA). The precise MMPs entailed in this procedure, however, were still unknown. This study used a two-sample Mendelian randomization (MR) analysis to look into the causal relationship between MMPs and the risk of TAA and AAA. METHODS Eight MMPs, including MMP-1, MMP-2, MMP-3, MMP-8, MMP-9, MMP-10, MMP-12, and MMP-13, were found among people of European ancestry with accessible Genome-Wide Association Studies (GWAS). We employed the findings from Genome-Wide Association Studies (GWAS) for 8 MMPs, and TAA and AAA from the FinnGen consortiums (3,201 cases and 317,899 controls, respectively) were used in a two-sample MR analysis. The primary method of analysis for MR was the inverse variance weighted (IVW) method, along with analyses of heterogeneity and horizontal pleiotropy. 31 single-nucleotide polymorphisms connected to MMP were retrieved. RESULTS IVW demonstrated a negative causal association between TAA and AAA and serum MMP-12 levels. The incidence of TAA decreased by 1.031% for every 1 ng/mL increase in serum MMP-12 [odds ratio (OR) = 0.897, 95% confidence interval (CI): 0.831-0.968, P = 0.005]. The incidence of AAA fell by 1.653% (OR = 0.835, 95% CI: 0.752-0.926, P = 0.001) for every 1 ng/mL increase in serum MMP-12. There was no horizontal pleiotropy or heterogeneity in the MR data (P > 0.05). CONCLUSIONS The levels of TAA and AAA and serum MMP-12 are causally related. MMP-12 is a factor that reduces the risk of AAA and TTA. Our study suggested that MMP-12 level is causally associated with a decreased risk of TAA and AAA.
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MESH Headings
- Humans
- Aortic Aneurysm, Abdominal/genetics
- Aortic Aneurysm, Abdominal/enzymology
- Aortic Aneurysm, Abdominal/diagnostic imaging
- Aortic Aneurysm, Abdominal/blood
- Aortic Aneurysm, Abdominal/epidemiology
- Aortic Aneurysm, Thoracic/genetics
- Aortic Aneurysm, Thoracic/enzymology
- Aortic Aneurysm, Thoracic/blood
- Aortic Aneurysm, Thoracic/diagnostic imaging
- Aortic Aneurysm, Thoracic/epidemiology
- Case-Control Studies
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Incidence
- Matrix Metalloproteinase 12/genetics
- Matrix Metalloproteinase 12/blood
- Matrix Metalloproteinases/genetics
- Matrix Metalloproteinases/blood
- Mendelian Randomization Analysis
- Phenotype
- Polymorphism, Single Nucleotide
- Protective Factors
- Risk Assessment
- Risk Factors
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Affiliation(s)
- Tao Qin
- Department of Ultrasound, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China
| | - Jiankai Zhong
- Department of Cardiology, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China
| | - Pinglan Li
- Department of Ultrasound, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China
| | - Jianlin Liang
- Department of Cardiology, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China
| | - Meijun Li
- Department of Cardiology, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China
| | - Guangjun Zhang
- Department of Ultrasound, Shunde Hospital, Southern Medical University, (The First People's Hospital of Shunde Foshan), Foshan, China.
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4
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Zeng Z, Ma Y, Hu L, Tan B, Liu P, Wang Y, Xing C, Xiong Y, Du H. OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing. Nat Commun 2024; 15:5983. [PMID: 39013860 PMCID: PMC11252408 DOI: 10.1038/s41467-024-50194-3] [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: 06/19/2023] [Accepted: 06/28/2024] [Indexed: 07/18/2024] Open
Abstract
Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.
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Affiliation(s)
- Zehua Zeng
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
| | - Yuqing Ma
- Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong Province, China
- Institute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China
| | - Lei Hu
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Bowen Tan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Peng Liu
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yixuan Wang
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
| | - Cencan Xing
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
| | - Yuanyan Xiong
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun-Yat-Sen University, Guangzhou, Guangdong, China.
| | - Hongwu Du
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
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5
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Grobecker P, Sakoparnig T, van Nimwegen E. Identifying cell states in single-cell RNA-seq data at statistically maximal resolution. PLoS Comput Biol 2024; 20:e1012224. [PMID: 38995959 DOI: 10.1371/journal.pcbi.1012224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/04/2024] [Indexed: 07/14/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.
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Affiliation(s)
- Pascal Grobecker
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Thomas Sakoparnig
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Erik van Nimwegen
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
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6
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Magaña-López G, Calzone L, Zinovyev A, Paulevé L. scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics. PLoS Comput Biol 2024; 20:e1011620. [PMID: 38976751 PMCID: PMC11257695 DOI: 10.1371/journal.pcbi.1011620] [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: 10/22/2023] [Revised: 07/18/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.
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Affiliation(s)
| | - Laurence Calzone
- Institut Curie, Université PSL, Paris, France
- INSERM, U900, Paris, France
- Mines ParisTech, Université PSL, Paris, France
| | | | - Loïc Paulevé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France
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7
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Guo X, Zhao Y, You F. MOI is a comprehensive database collecting processed multi-omics data associated with viral infection. Sci Rep 2024; 14:14725. [PMID: 38926513 PMCID: PMC11208532 DOI: 10.1038/s41598-024-65629-6] [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: 01/26/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Viral infections pose significant public health challenges, exemplified by the global impact of COVID-19 caused by SARS-CoV-2. Understanding the intricate molecular mechanisms governing virus-host interactions is pivotal for effective intervention strategies. Despite the burgeoning multi-omics data on viral infections, a centralized database elucidating host responses to viruses remains lacking. In response, we have developed a comprehensive database named 'MOI' (available at http://www.fynn-guo.cn/ ), specifically designed to aggregate processed Multi-Omics data related to viral Infections. This meticulously curated database serves as a valuable resource for conducting detailed investigations into virus-host interactions. Leveraging high-throughput sequencing data and metadata from PubMed and Gene Expression Omnibus (GEO), MOI comprises over 3200 viral-infected samples, encompassing human and murine infections. Standardized processing pipelines ensure data integrity, including bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). MOI offers user-friendly interfaces presenting comprehensive cell marker tables, gene expression data, and epigenetic landscape charts. Analytical tools for DNA sequence conversion, FPKM calculation, differential gene expression, and Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment enhance data interpretation. Additionally, MOI provides 16 visualization plots for intuitive data exploration. In summary, MOI serves as a valuable repository for researchers investigating virus-host interactions. By centralizing and facilitating access to multi-omics data, MOI aims to advance our understanding of viral pathogenesis and expedite the development of therapeutic interventions.
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Affiliation(s)
- Xuefei Guo
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China.
| | - Yang Zhao
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China
| | - Fuping You
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China
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8
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Debuysschere C, Nekoua MP, Alidjinou EK, Hober D. The relationship between SARS-CoV-2 infection and type 1 diabetes mellitus. Nat Rev Endocrinol 2024:10.1038/s41574-024-01004-9. [PMID: 38890459 DOI: 10.1038/s41574-024-01004-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/23/2024] [Indexed: 06/20/2024]
Abstract
Environmental factors, in particular viral infections, are thought to have an important role in the pathogenesis of type 1 diabetes mellitus (T1DM). The COVID-19 pandemic reinforced this hypothesis as many observational studies and meta-analyses reported a notable increase in the incidence of T1DM following infection with SARS-CoV-2 as well as an association between SARS-CoV-2 infection and the risk of new-onset T1DM. Experimental evidence suggests that human β-cells express SARS-CoV-2 receptors and that SARS-CoV-2 can infect and replicate in β-cells, resulting in structural or functional alterations of these cells. These alterations include reduced numbers of insulin-secreting granules, impaired pro-insulin (or insulin) secretion, and β-cell transdifferentiation or dedifferentiation. The inflammatory environment induced by local or systemic SARS-CoV-2 infection might result in a set of signals (such as pro-inflammatory cytokines) that lead to β-cell alteration or apoptosis or to a bystander activation of T cells and disruption of peripheral tolerance that triggers autoimmunity. Other mechanisms, such as viral persistence, molecular mimicry and activation of endogenous human retroviruses, are also likely to be involved in the pathogenesis of T1DM following SARS-CoV-2 infection. This Review addresses the issue of the involvement of SARS-CoV-2 infection in the development of T1DM using evidence from epidemiological, clinical and experimental studies.
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Affiliation(s)
- Cyril Debuysschere
- Université de Lille, CHU Lille, Laboratoire de virologie ULR3610, Lille, France
| | | | | | - Didier Hober
- Université de Lille, CHU Lille, Laboratoire de virologie ULR3610, Lille, France.
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9
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Cottrell S, Hozumi Y, Wei GW. K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis. Comput Biol Med 2024; 175:108497. [PMID: 38678944 PMCID: PMC11090715 DOI: 10.1016/j.compbiomed.2024.108497] [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: 03/02/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.
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Affiliation(s)
- Sean Cottrell
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Yuta Hozumi
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
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10
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Singh A, Khiabanian H. Feature selection followed by a novel residuals-based normalization simplifies and improves single-cell gene expression analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.02.530891. [PMID: 38328133 PMCID: PMC10849523 DOI: 10.1101/2023.03.02.530891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.
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Affiliation(s)
- Amartya Singh
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
| | - Hossein Khiabanian
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey
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11
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Hong H, Wang Y, Menard M, Buckley J, Zhou L, Volpicelli-Daley L, Standaert D, Qin H, Benveniste E. Suppression of the JAK/STAT Pathway Inhibits Neuroinflammation in the Line 61-PFF Mouse Model of Parkinson's Disease. RESEARCH SQUARE 2024:rs.3.rs-4307273. [PMID: 38766241 PMCID: PMC11100885 DOI: 10.21203/rs.3.rs-4307273/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Parkinson's disease (PD) is characterized by neuroinflammation, progressive loss of dopaminergic neurons, and accumulation of a-synuclein (a-Syn) into insoluble aggregates called Lewy pathology. The Line 61 a-Syn mouse is an established preclinical model of PD; Thy-1 is used to promote human a-Syn expression, and features of sporadic PD develop at 9-18 months of age. To accelerate the PD phenotypes, we injected sonicated human a-Syn preformed fibrils (PFFs) into the striatum, which produced phospho-Syn (p-a-Syn) inclusions in the substantia nigra pars compacta and significantly increased MHC Class II-positive immune cells. Additionally, there was enhanced infiltration and activation of innate and adaptive immune cells in the midbrain. We then used this new model, Line 61-PFF, to investigate the effect of inhibiting the JAK/STAT signaling pathway, which is critical for regulation of innate and adaptive immune responses. After administration of the JAK1/2 inhibitor AZD1480, immunofluorescence staining showed a significant decrease in p-a-Syn inclusions and MHC Class II expression. Flow cytometry showed reduced infiltration of CD4+ T-cells, CD8+ T-cells, CD19+ B-cells, dendritic cells, macrophages, and endogenous microglia into the midbrain. Importantly, single-cell RNA-Sequencing analysis of CD45+ cells from the midbrain identified 9 microglia clusters, 5 monocyte/macrophage (MM) clusters, and 5 T-cell (T) clusters, in which potentially pathogenic MM4 and T3 clusters were associated with neuroinflammatory responses in Line 61-PFF mice. AZD1480 treatment reduced cell numbers and cluster-specific expression of the antigen-presentation genes H2-Eb1, H2-Aa, H2-Ab1, and Cd74 in the MM4 cluster and proinflammatory genes such as Tnf, Il1b, C1qa, and C1qc in the T3 cluster. Together, these results indicate that inhibiting the JAK/STAT pathway suppresses the activation and infiltration of innate and adaptive cells, reducing neuroinflammation in the Line 61-PFF mouse model.
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12
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Marx V. scRNA-seq: oh, the joys. Nat Methods 2024; 21:750-753. [PMID: 38654084 DOI: 10.1038/s41592-024-02263-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
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13
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Yabo YA, Heiland DH. Understanding glioblastoma at the single-cell level: Recent advances and future challenges. PLoS Biol 2024; 22:e3002640. [PMID: 38814900 PMCID: PMC11139343 DOI: 10.1371/journal.pbio.3002640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.
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Affiliation(s)
- Yahaya A Yabo
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Dieter Henrik Heiland
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, Faculty of Medicine, Medical Center University of Freiburg, Freiburg, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- German Cancer Consortium (DKTK) partner site, Freiburg, Germany
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14
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Gao CF, Vaikuntanathan S, Riesenfeld SJ. Dissection and integration of bursty transcriptional dynamics for complex systems. Proc Natl Acad Sci U S A 2024; 121:e2306901121. [PMID: 38669186 PMCID: PMC11067469 DOI: 10.1073/pnas.2306901121] [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: 04/26/2023] [Accepted: 03/06/2024] [Indexed: 04/28/2024] Open
Abstract
RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.
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Affiliation(s)
- Cheng Frank Gao
- Department of Chemistry, University of Chicago, Chicago, IL60637
| | - Suriyanarayanan Vaikuntanathan
- Department of Chemistry, University of Chicago, Chicago, IL60637
- Institute for Biophysical Dynamics, University of Chicago, Chicago, IL60637
| | - Samantha J. Riesenfeld
- Institute for Biophysical Dynamics, University of Chicago, Chicago, IL60637
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL60637
- Department of Medicine, University of Chicago, Chicago, IL60637
- Committee on Immunology, Biological Sciences Division, University of Chicago, Chicago, IL60637
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15
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Kim H, Chang W, Chae SJ, Park JE, Seo M, Kim JK. scLENS: data-driven signal detection for unbiased scRNA-seq data analysis. Nat Commun 2024; 15:3575. [PMID: 38678050 DOI: 10.1038/s41467-024-47884-3] [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: 10/18/2023] [Accepted: 04/14/2024] [Indexed: 04/29/2024] Open
Abstract
High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.
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Affiliation(s)
- Hyun Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, 45221, USA
| | - Seok Joo Chae
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Minseok Seo
- Department of Computer and Information Science, Korea University, Sejong, 30019, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea.
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16
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Ning L, Quan C, Wang Y, Wu Z, Yuan P, Xie N. scRNA-seq characterizing the heterogeneity of fibroblasts in breast cancer reveals a novel subtype SFRP4 + CAF that inhibits migration and predicts prognosis. Front Oncol 2024; 14:1348299. [PMID: 38686196 PMCID: PMC11056562 DOI: 10.3389/fonc.2024.1348299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/27/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly impact the tumor microenvironment and therapeutic responses in breast cancer (BC). Despite their importance, the comprehensive profile of CAFs in BC remains to be fully elucidated. Methods To address this gap, we utilized single-cell RNA sequencing (scRNA-seq) to delineate the CAF landscape within 14 BC normal-tumor paired samples. We further corroborated our findings by analyzing several public datasets, thereby validating the newly identified CAF subtype. Additionally, we conducted coculture experiments with BC cells to assess the functional implications of this CAF subtype. Results Our scRNA-seq analysis unveiled eight distinct CAF subtypes across five tumor and six adjacent normal tissue samples. Notably, we discovered a novel subtype, designated as SFRP4+ CAFs, which was predominantly observed in normal tissues. The presence of SFRP4+ CAFs was substantiated by two independent scRNA-seq datasets and a spatial transcriptomics dataset. Functionally, SFRP4+ CAFs were found to impede BC cell migration and the epithelial-mesenchymal transition (EMT) process by secreting SFRP4, thereby modulating the WNT signaling pathway. Furthermore, we established that elevated expression levels of SFRP4+ CAF markers correlate with improved survival outcomes in BC patients, yet paradoxically, they predict a diminished response to neoadjuvant chemotherapy in cases of triple-negative breast cancer. Conclusion This investigation sheds light on the heterogeneity of CAFs in BC and introduces a novel SFRP4+ CAF subtype that hinders BC cell migration. This discovery holds promise as a potential biomarker for refined prognostic assessment and therapeutic intervention in BC.
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Affiliation(s)
- Lvwen Ning
- Biobank, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuntao Quan
- Biobank, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yue Wang
- Biobank, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zhijie Wu
- Biobank, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China
| | - Peixiu Yuan
- College of Materials and Energy, South China Agricultural University, Guangzhou, China
| | - Ni Xie
- Biobank, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China
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17
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Fay M, Clavijo PE, Allen CT. Heterogeneous characterization of neutrophilic cells in head and neck cancers. Head Neck 2024. [PMID: 38622975 DOI: 10.1002/hed.27774] [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: 12/08/2023] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Neutrophilic cells are among the most abundant immune populations within the head and neck tumor microenvironment (TME) and harbor multiple mechanisms of immunosuppression. Despite these important features, neutrophilic cells may be underrepresented in contemporary studies that aim to comprehensively characterize the immune landscape of the TME due to discrepancies in tissue processing and analysis techniques. Here, we review the role of pathologically activated neutrophilic cells within the TME and pitfalls of various approaches used to study their frequency and function in clinical samples. METHODS The literature was identified by searching PubMed for "immune landscape" and "tumor immune microenvironment" in combination with keywords describing solid tumor malignancies. Key publications that assessed the immune composition of solid tumors derived from human specimens were included. The tumor and blood processing methodologies in each study were reviewed in depth and correlated with the reported abundance of neutrophilic cells. RESULTS Neutrophilic cells do not survive cryopreservation, and many studies fail to identify and study neutrophilic cell populations due to cryopreservation of clinical samples for practical reasons. Additional single-cell transcriptomic studies filter out neutrophilic cells due to low transcriptional counts. CONCLUSIONS This report can help readers critically interpret studies aiming to comprehensively study the immune TME that fail to identify and characterize neutrophilic cells.
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Affiliation(s)
- Magdalena Fay
- Surgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul E Clavijo
- Surgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Clint T Allen
- Surgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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18
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Boakye Serebour T, Cribbs AP, Baldwin MJ, Masimirembwa C, Chikwambi Z, Kerasidou A, Snelling SJB. Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries. Eur J Hum Genet 2024:10.1038/s41431-024-01564-4. [PMID: 38565638 DOI: 10.1038/s41431-024-01564-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 04/04/2024] Open
Abstract
The advent of single-cell resolution sequencing and spatial transcriptomics has enabled the delivery of cellular and molecular atlases of tissues and organs, providing new insights into tissue health and disease. However, if the full potential of these technologies is to be equitably realised, ancestrally inclusivity is paramount. Such a goal requires greater inclusion of both researchers and donors in low- and middle-income countries (LMICs). In this perspective, we describe the current landscape of ancestral inclusivity in genomic and single-cell transcriptomic studies. We discuss the collaborative efforts needed to scale the barriers to establishing, expanding, and adopting single-cell sequencing research in LMICs and to enable globally impactful outcomes of these technologies.
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Affiliation(s)
- Tracy Boakye Serebour
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Adam P Cribbs
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Mathew J Baldwin
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Collen Masimirembwa
- The African Institute of Biomedical Science and Technology, Harare, Zimbabwe
| | - Zedias Chikwambi
- The African Institute of Biomedical Science and Technology, Harare, Zimbabwe
| | - Angeliki Kerasidou
- The Ethox Centre and the Wellcome Centre for Ethics and Humanities, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah J B Snelling
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
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19
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Rogalla S, Holman D, Rubin S, Ferenc M, Holman E, Koron A, Daniel R, Boland B, Nolan G, Chang J. Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis. RESEARCH SQUARE 2024:rs.3.rs-3965505. [PMID: 38559236 PMCID: PMC10980100 DOI: 10.21203/rs.3.rs-3965505/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.
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20
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Zhan X, Yin Y, Zhang H. BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework. Bioinformatics 2024; 40:btae127. [PMID: 38439545 PMCID: PMC10942801 DOI: 10.1093/bioinformatics/btae127] [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: 10/30/2023] [Revised: 02/24/2024] [Accepted: 02/29/2024] [Indexed: 03/06/2024] Open
Abstract
MOTIVATION Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. RESULTS In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION The code implemented in Python and the data used for experiments have been released on GitHub (https://github.com/zhanglabNKU/BERMAD) and Zenodo (https://zenodo.org/records/10695073) with detailed instructions.
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Affiliation(s)
- Xiangxin Zhan
- Department of Intelligence Engineering, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Yanbin Yin
- Department of Food Science and Technology, University of Nebraska – Lincoln, Lincoln, NE 68588, United States
| | - Han Zhang
- Department of Intelligence Engineering, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
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21
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Guo R, Kong J, Tang P, Wang S, Sang L, Liu L, Guo R, Yan K, Qi M, Bian Z, Song Y, Jiang Z, Li Y. Unbiased Single-Cell Sequencing of Hematopoietic and Immune Cells from Aplastic Anemia Reveals the Contributors of Hematopoiesis Failure and Dysfunctional Immune Regulation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304539. [PMID: 38145351 PMCID: PMC10933602 DOI: 10.1002/advs.202304539] [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: 07/05/2023] [Revised: 10/24/2023] [Indexed: 12/26/2023]
Abstract
Aplastic anemia (AA) is a bone marrow (BM) failure syndrome mediated by hyperactivated T-cells with heterogeneous pathogenic factors. The onset of BM failure cannot be accurately determined in humans; therefore, exact pathogenesis remains unclear. In this study, a cellular atlas and microenvironment interactions is established using unbiased single-cell RNA-seq, along with multi-omics analyses (mass cytometry, cytokine profiling, and oxidized fatty acid metabolomics). A new KIR+ CD8+ regulatory T cells (Treg) subset is identified in patients with AA that engages in immune homeostasis. Conventional CD4+ T-cells differentiate into highly differentiated T helper cells with type 2 cytokines (IL-4, IL-6, and IL-13), GM-SCF, and IL-1β. Immunosuppressive homeostasis is impaired by enhanced apoptosis of activated Treg cells. Pathological Vδ1 cells dominated the main fraction of γδ T-cells. The B/plasma, erythroid, and myeloid lineages also exhibit substantial pathological features. Interactions between TNFSF12-TNFRSF12A, TNF-TNFRSF1A, and granzyme-gasdermin are associated with the cell death of hematopoietic stem/progenitor (HSPCs), Treg, and early erythroid cells. Ferroptosis, a major driver of HSPCs destruction, is identified in patients with AA. Furthermore, a case of twins with AA is reported to enhance the persuasiveness of the analysis. These results collectively constitute the cellular atlas and microenvironment interactions in patients with AA and provide novel insights into the development of new therapeutic opportunities.
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Affiliation(s)
- Rongqun Guo
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
- Academy of Medical ScienceHenan Medical College of Zhengzhou UniversityZhengzhouHenan450052China
| | - Jingjing Kong
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Ping Tang
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Shuya Wang
- Department of Blood TransfusionThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Lina Sang
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Liu Liu
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Rong Guo
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Ketai Yan
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
- Academy of Medical ScienceHenan Medical College of Zhengzhou UniversityZhengzhouHenan450052China
| | - Mochu Qi
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Zhilei Bian
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Yongping Song
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Zhongxing Jiang
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Yingmei Li
- Department of HematologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
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22
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [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: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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23
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Kim HD, Jung S, Lim HY, Ryoo BY, Ryu MH, Chuah S, Chon HJ, Kang B, Hong JY, Lee HC, Moon DB, Kim KH, Kim TW, Tai D, Chew V, Lee JS, Finn RS, Koh JY, Yoo C. Regorafenib plus nivolumab in unresectable hepatocellular carcinoma: the phase 2 RENOBATE trial. Nat Med 2024; 30:699-707. [PMID: 38374347 PMCID: PMC10957471 DOI: 10.1038/s41591-024-02824-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: 06/27/2023] [Accepted: 01/21/2024] [Indexed: 02/21/2024]
Abstract
Regorafenib has anti-tumor activity in patients with unresectable hepatocellular carcinoma (uHCC) with potential immunomodulatory effects, suggesting that its combination with immune checkpoint inhibitor may have clinically meaningful benefits in patients with uHCC. The multicenter, single-arm, phase 2 RENOBATE trial tested regorafenib-nivolumab as front-line treatment for uHCC. Forty-two patients received nivolumab 480 mg every 4 weeks and regorafenib 80 mg daily (3-weeks-on/1-week-off schedule). The primary endpoint was the investigator-assessed objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. The secondary endpoints included safety, progression-free survival (PFS) and overall survival (OS). ORR per RECIST version 1.1 was 31.0%, meeting the primary endpoint. The most common adverse events were palmar-plantar erythrodysesthesia syndrome (38.1%), alopecia (26.2%) and skin rash (23.8%). Median PFS was 7.38 months. The 1-year OS rate was 80.5%, and the median OS was not reached. Exploratory single-cell RNA sequencing analyses of peripheral blood mononuclear cells showed that long-term responders exhibited T cell receptor repertoire diversification, enrichment of genes representing immunotherapy responsiveness in MKI67+ proliferating CD8+ T cells and a higher probability of M1-directed monocyte polarization. Our data support further clinical development of the regorafenib-nivolumab combination as front-line treatment for uHCC and provide preliminary insights on immune biomarkers of response. ClinicalTrials.gov identifier: NCT04310709 .
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Affiliation(s)
- Hyung-Don Kim
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seyoung Jung
- Genome Insight, Inc., San Diego, La Jolla, CA, USA
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Ho Yeong Lim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Baek-Yeol Ryoo
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Min-Hee Ryu
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Samuel Chuah
- Translational Immunology Institute, SingHealth-Duke-NUS Academic Medical Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Hong Jae Chon
- Department of Medical Oncology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Beodeul Kang
- Department of Medical Oncology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Jung Yong Hong
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Han Chu Lee
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Deok-Bog Moon
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ki-Hun Kim
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Tae Won Kim
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - David Tai
- Division of Medical Oncology, National Cancer Centre, Singapore, Singapore
| | - Valerie Chew
- Translational Immunology Institute, SingHealth-Duke-NUS Academic Medical Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Jeong Seok Lee
- Genome Insight, Inc., San Diego, La Jolla, CA, USA
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Richard S Finn
- Division of Hematology-Oncology, Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Changhoon Yoo
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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24
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Khodosevich K, Dragicevic K, Howes O. Drug targeting in psychiatric disorders - how to overcome the loss in translation? Nat Rev Drug Discov 2024; 23:218-231. [PMID: 38114612 DOI: 10.1038/s41573-023-00847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 12/21/2023]
Abstract
In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.
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Affiliation(s)
- Konstantin Khodosevich
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
| | - Katarina Dragicevic
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Oliver Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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25
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Liao YM, Hsu SH, Chiou SS. Harnessing the Transcriptional Signatures of CAR-T-Cells and Leukemia/Lymphoma Using Single-Cell Sequencing Technologies. Int J Mol Sci 2024; 25:2416. [PMID: 38397092 PMCID: PMC10889174 DOI: 10.3390/ijms25042416] [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: 12/22/2023] [Revised: 02/02/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
Chimeric antigen receptor (CAR)-T-cell therapy has greatly improved outcomes for patients with relapsed or refractory hematological malignancies. However, challenges such as treatment resistance, relapse, and severe toxicity still hinder its widespread clinical application. Traditional transcriptome analysis has provided limited insights into the complex transcriptional landscape of both leukemia cells and engineered CAR-T-cells, as well as their interactions within the tumor microenvironment. However, with the advent of single-cell sequencing techniques, a paradigm shift has occurred, providing robust tools to unravel the complexities of these factors. These techniques enable an unbiased analysis of cellular heterogeneity and molecular patterns. These insights are invaluable for precise receptor design, guiding gene-based T-cell modification, and optimizing manufacturing conditions. Consequently, this review utilizes modern single-cell sequencing techniques to clarify the transcriptional intricacies of leukemia cells and CAR-Ts. The aim of this manuscript is to discuss the potential mechanisms that contribute to the clinical failures of CAR-T immunotherapy. We examine the biological characteristics of CAR-Ts, the mechanisms that govern clinical responses, and the intricacies of adverse events. By exploring these aspects, we hope to gain a deeper understanding of CAR-T therapy, which will ultimately lead to improved clinical outcomes and broader therapeutic applications.
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Affiliation(s)
- Yu-Mei Liao
- Division of Hematology-Oncology, Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Shih-Hsien Hsu
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center of Applied Genomics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Shyh-Shin Chiou
- Division of Hematology-Oncology, Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center of Applied Genomics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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26
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Cai X, Zhang W, Zheng X, Xu Y, Li Y. scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data. Interdiscip Sci 2024:10.1007/s12539-023-00601-y. [PMID: 38368575 DOI: 10.1007/s12539-023-00601-y] [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: 06/12/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 02/19/2024]
Abstract
With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.
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Affiliation(s)
- Xianxian Cai
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Wei Zhang
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China.
| | - Xiaoying Zheng
- Operations research and planning department, Naval University of Engineering, Wuhan, 430033, China
| | - Yaxin Xu
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Yuanyuan Li
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, China
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27
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Sehgal M, Ramu S, Vaz JM, Ganapathy YR, Muralidharan S, Venkatraghavan S, Jolly MK. Characterizing heterogeneity along EMT and metabolic axes in colorectal cancer reveals underlying consensus molecular subtype-specific trends. Transl Oncol 2024; 40:101845. [PMID: 38029508 PMCID: PMC10698572 DOI: 10.1016/j.tranon.2023.101845] [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: 08/06/2023] [Revised: 11/01/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
Colorectal cancer (CRC) is highly heterogeneous with variable survival outcomes and therapeutic vulnerabilities. A commonly used classification system in CRC is the Consensus Molecular Subtypes (CMS) based on gene expression patterns. However, how these CMS categories connect to axes of phenotypic plasticity and heterogeneity remains unclear. Here, in our analysis of CMS-specific TCGA data and 101 bulk transcriptomic datasets, we found the epithelial phenotype score to be consistently positively correlated with scores of glycolysis, OXPHOS and FAO pathways, while PD-L1 activity scores positively correlated with mesenchymal phenotype scoring, revealing possible interconnections among plasticity axes. Single-cell RNA-sequencing analysis of patient samples revealed that that CMS2 and CMS3 subtype samples were relatively more epithelial as compared to CMS1 and CMS4. CMS1 revealed two subpopulations: one close to CMS4 (more mesenchymal) and the other closer to CMS2 or CMS3 (more epithelial), indicating a partial EMT-like behavior. Consistent observations were made in single-cell analysis of metabolic axes and PD-L1 activity scores. Together, our results quantify the patterns of two functional interconnected axes of phenotypic heterogeneity - EMT and metabolic reprogramming - in a CMS-specific manner in CRC.
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Affiliation(s)
- Manas Sehgal
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Soundharya Ramu
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Joel Markus Vaz
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India; School of Biological Sciences, Georgia Institute of Technology, Atlanta 30332, United States
| | | | - Srinath Muralidharan
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | | | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India.
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28
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Deng Y, Lu Y, Li M, Shen J, Qin S, Zhang W, Zhang Q, Shen Z, Li C, Jia T, Chen P, Peng L, Chen Y, Zhang W, Liu H, Zhang L, Rong L, Wang X, Chen D. SCAN: Spatiotemporal Cloud Atlas for Neural cells. Nucleic Acids Res 2024; 52:D998-D1009. [PMID: 37930842 PMCID: PMC10767991 DOI: 10.1093/nar/gkad895] [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: 08/14/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.
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Affiliation(s)
- Yushan Deng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yubao Lu
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Mengrou Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wei Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Qiang Zhang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Zhaoyang Shen
- Life Sciences and Technology College, China Pharmaceutical University, Nanjing 211198, China
| | - Changxiao Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Tengfei Jia
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Peixin Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Lingmin Peng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yangfeng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wensheng Zhang
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Liangming Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Limin Rong
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Dongsheng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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29
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Weidemann BJ, Marcheva B, Kobayashi M, Omura C, Newman MV, Kobayashi Y, Waldeck NJ, Perelis M, Lantier L, McGuinness OP, Ramsey KM, Stein RW, Bass J. Repression of latent NF-κB enhancers by PDX1 regulates β cell functional heterogeneity. Cell Metab 2024; 36:90-102.e7. [PMID: 38171340 PMCID: PMC10793877 DOI: 10.1016/j.cmet.2023.11.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 07/17/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Interactions between lineage-determining and activity-dependent transcription factors determine single-cell identity and function within multicellular tissues through incompletely known mechanisms. By assembling a single-cell atlas of chromatin state within human islets, we identified β cell subtypes governed by either high or low activity of the lineage-determining factor pancreatic duodenal homeobox-1 (PDX1). β cells with reduced PDX1 activity displayed increased chromatin accessibility at latent nuclear factor κB (NF-κB) enhancers. Pdx1 hypomorphic mice exhibited de-repression of NF-κB and impaired glucose tolerance at night. Three-dimensional analyses in tandem with chromatin immunoprecipitation (ChIP) sequencing revealed that PDX1 silences NF-κB at circadian and inflammatory enhancers through long-range chromatin contacts involving SIN3A. Conversely, Bmal1 ablation in β cells disrupted genome-wide PDX1 and NF-κB DNA binding. Finally, antagonizing the interleukin (IL)-1β receptor, an NF-κB target, improved insulin secretion in Pdx1 hypomorphic islets. Our studies reveal functional subtypes of single β cells defined by a gradient in PDX1 activity and identify NF-κB as a target for insulinotropic therapy.
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Affiliation(s)
- Benjamin J Weidemann
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Biliana Marcheva
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mikoto Kobayashi
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Chiaki Omura
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Marsha V Newman
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yumiko Kobayashi
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Nathan J Waldeck
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mark Perelis
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Ionis Pharmaceuticals, Carlsbad, CA 92010, USA
| | - Louise Lantier
- Vanderbilt-NIH Mouse Metabolic Phenotyping Center, Nashville, TN 37232, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Owen P McGuinness
- Vanderbilt-NIH Mouse Metabolic Phenotyping Center, Nashville, TN 37232, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kathryn Moynihan Ramsey
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Roland W Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joseph Bass
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
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30
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Li S, Zhang P, Chen W, Ye L, Brannan KW, Le NT, Abe JI, Cooke JP, Wang G. A relay velocity model infers cell-dependent RNA velocity. Nat Biotechnol 2024; 42:99-108. [PMID: 37012448 PMCID: PMC10545816 DOI: 10.1038/s41587-023-01728-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/28/2023] [Indexed: 04/05/2023]
Abstract
RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust performance in multiple kinetic regimes, high dropout ratio datasets and sparse datasets. We show that cellDancer overcomes the limitations of existing RNA velocity models in modeling erythroid maturation and hippocampus development. Moreover, cellDancer provides cell-specific predictions of transcription, splicing and degradation rates, which we identify as potential indicators of cell fate in the mouse pancreas.
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Affiliation(s)
- Shengyu Li
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Pengzhi Zhang
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Weiqing Chen
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology, Biophysics & Systems Biology, Weill Cornell Graduate School of Medical Science, Weill Cornell Medicine, Cornell University, Ithaca, NY, USA
| | - Lingqun Ye
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA
| | - Kristopher W Brannan
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Nhat-Tu Le
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jun-Ichi Abe
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John P Cooke
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA
| | - Guangyu Wang
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
- Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, USA.
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA.
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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31
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Park J, Cho Y, Yang D, Yang H, Lee D, Kubo M, Kang SJ. The transcription factor NFIL3/E4BP4 regulates the developmental stage-specific acquisition of basophil function. J Allergy Clin Immunol 2024; 153:132-145. [PMID: 37783432 DOI: 10.1016/j.jaci.2023.09.029] [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: 05/01/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Basophils are rare but important effector cells in many allergic disorders. Contrary to their early progenitors, the terminal developmental processes of basophils in which they gain their unique functional properties are unknown. OBJECTIVE We sought to identify a novel late-stage basophil precursor and a transcription factor regulating the terminal maturation of basophils. METHODS Using flow cytometry, transcriptome analysis, and functional assays, we investigated the identification and functionality of the basophil precursors as well as basophil development. We generated mice with basophil-specific deletion of nuclear factor IL-3 (NFIL3)/E4BP4 and analyzed the functional impairment of NFIL3/E4BP4-deficient basophils in vitro and in vivo using an oxazolone-induced murine model of allergic dermatitis. RESULTS We report a new mitotic transitional basophil precursor population (referred to as transitional basophils) that expresses the FcεRIα chain at higher levels than mature basophils. Transitional basophils are less responsive to IgE-linked degranulation but produce more cytokines in response to IL-3, IL-33, or IgE cross-linking than mature basophils. In particular, we found that the expression of NFIL3/E4BP4 gradually rises as cells mature from the basophil progenitor stage. Basophil-specific deletion of NFIL3/E4BP4 reduces the expression of genes necessary for basophil function and impairs IgE receptor signaling, cytokine secretion, and degranulation in the context of murine atopic dermatitis. CONCLUSIONS We discovered transitional basophils, a novel late-stage mitotic basophil precursor cell population that exists between basophil progenitors and postmitotic mature basophils. We demonstrated that NFIL3/E4BP4 augments the IgE-mediated functions of basophils, pointing to a potential therapeutic regulator for allergic diseases.
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Affiliation(s)
- Jiyeon Park
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Yuri Cho
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Dongchan Yang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Hanseul Yang
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Daeyoup Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Masato Kubo
- Division of Molecular Pathology, Research Institute for Biomedical Science, Tokyo University of Science, Noda, Japan; Laboratory for Cytokine Regulation, RIKEN Center for Integrative Medical Sciences, RIKEN Yokohama Institute, Yokohama, Japan
| | - Suk-Jo Kang
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
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32
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Bump P, Lubeck L. Marine Invertebrates One Cell at A Time: Insights from Single-Cell Analysis. Integr Comp Biol 2023; 63:999-1009. [PMID: 37188638 PMCID: PMC10714908 DOI: 10.1093/icb/icad034] [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: 02/27/2023] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 05/17/2023] Open
Abstract
Over the past decade, single-cell RNA-sequencing (scRNA-seq) has made it possible to study the cellular diversity of a broad range of organisms. Technological advances in single-cell isolation and sequencing have expanded rapidly, allowing the transcriptomic profile of individual cells to be captured. As a result, there has been an explosion of cell type atlases created for many different marine invertebrate species from across the tree of life. Our focus in this review is to synthesize current literature on marine invertebrate scRNA-seq. Specifically, we provide perspectives on key insights from scRNA-seq studies, including descriptive studies of cell type composition, how cells respond in dynamic processes such as development and regeneration, and the evolution of new cell types. Despite these tremendous advances, there also lie several challenges ahead. We discuss the important considerations that are essential when making comparisons between experiments, or between datasets from different species. Finally, we address the future of single-cell analyses in marine invertebrates, including combining scRNA-seq data with other 'omics methods to get a fuller understanding of cellular complexities. The full diversity of cell types across marine invertebrates remains unknown and understanding this diversity and evolution will provide rich areas for future study.
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Affiliation(s)
- Paul Bump
- Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA
| | - Lauren Lubeck
- Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA
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33
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Zhao H, Han R, Wang Z, Xian J, Bai X. Colorectal Cancer Stem Cells and Targeted Agents. Pharmaceutics 2023; 15:2763. [PMID: 38140103 PMCID: PMC10748092 DOI: 10.3390/pharmaceutics15122763] [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: 10/13/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Since their discovery, cancer stem cells have become a hot topic in cancer therapy research. These cells possess stem cell-like self-renewal and differentiation capacities and are important factors that dominate cancer metastasis, therapy-resistance and recurrence. Worse, their inherent characteristics make them difficult to eliminate. Colorectal cancer is the third-most common cancer and the second leading cause of cancer death worldwide. Targeting colorectal cancer stem cells (CR-CSCs) can inhibit colorectal cancer metastasis, enhance therapeutic efficacy and reduce recurrence. Here, we introduced the origin, biomarker proteins, identification, cultivation and research techniques of CR-CSCs, and we summarized the signaling pathways that regulate the stemness of CR-CSCs, such as Wnt, JAK/STAT3, Notch and Hh signaling pathway. In addition to these, we also reviewed recent anti-CR-CSC drugs targeting signaling pathways, biomarkers and other regulators. These will help researchers gain insight into the current agents targeting to CR-CSCs, explore new cancer drugs and propose potential therapies.
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Affiliation(s)
- Haobin Zhao
- Department of General Practice, People’s Hospital of Longhua, 38 Jinglong Jianshe Road, Shenzhen 518109, China; (H.Z.); (J.X.)
- Endocrinology Department, People’s Hospital of Longhua, 38 Jinglong Jianshe Road, Shenzhen 518109, China
| | - Ruining Han
- Obstetric Department, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518033, China;
| | - Zhankun Wang
- Emergency Department, People’s Hospital of Longhua, 38 Jinglong Jianshe Road, Shenzhen 518109, China;
| | - Junfang Xian
- Department of General Practice, People’s Hospital of Longhua, 38 Jinglong Jianshe Road, Shenzhen 518109, China; (H.Z.); (J.X.)
| | - Xiaosu Bai
- Endocrinology Department, People’s Hospital of Longhua, 38 Jinglong Jianshe Road, Shenzhen 518109, China
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34
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Wang Z, Xie X, Liu S, Ji Z. scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data. Life Sci Alliance 2023; 6:e202302103. [PMID: 37788907 PMCID: PMC10547911 DOI: 10.26508/lsa.202302103] [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: 04/20/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.
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Affiliation(s)
- Zongqin Wang
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Xiaojun Xie
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
| | - Shouyang Liu
- https://ror.org/05td3s095 Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Zhiwei Ji
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
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35
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. Genome Biol 2023; 24:236. [PMID: 37858253 PMCID: PMC10588049 DOI: 10.1186/s13059-023-03067-9] [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: 03/03/2023] [Accepted: 09/20/2023] [Indexed: 10/21/2023] Open
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Statistics, University of Chicago, Chicago, IL, USA.
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36
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Wang L, Li W, Xie W, Wang R, Yu K. Dual-GCN-based deep clustering with triplet contrast for ScRNA-seq data analysis. Comput Biol Chem 2023; 106:107924. [PMID: 37487251 DOI: 10.1016/j.compbiolchem.2023.107924] [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: 03/01/2023] [Revised: 06/08/2023] [Accepted: 07/12/2023] [Indexed: 07/26/2023]
Abstract
Single-cell RNA sequencing (ScRNA-seq) technology reveals gene expression information at the cellular level. The critical tasks in ScRNA-seq data analysis are clustering and dimensionality reduction. Recent deep clustering algorithms are used to optimize the two tasks jointly, and their variations, graph-based deep clustering algorithms, are used to capture and preserve topological information in the process. However, the existing graph-based deep clustering algorithms ignore the distribution information of nodes when constructing cell graphs which leads to incomplete information in the embedding representation; and graph convolutional networks (GCN), which are most commonly used, often suffer from over-smoothing that leads to high sample similarity in the embedding representation and then poor clustering performance. Here, the dual-GCN-based deep clustering with Triplet contrast (scDGDC) is proposed for dimensionality reduction and clustering of scRNA-seq data. Two critical components are dual-GCN-based encoder for capturing more comprehensive topological information and triplet contrast for reducing GCN over-smoothing. The two components improve the dimensionality reduction and clustering performance of scDGDC in terms of information acquisition and model optimization, respectively. The experiments on eight real ScRNA-seq datasets showed that scDGDC achieves excellent performance for both clustering and dimensionality reduction tasks and is high robustness to parameters.
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Affiliation(s)
- LinJie Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Wei Li
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Ministry of Education, Shenyang 110000, China.
| | - WeiDong Xie
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Rui Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Kun Yu
- College of Medicine and Bioinformation Engineering, Northeastern University, Shenyang 110819, China.
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37
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.531029. [PMID: 36945441 PMCID: PMC10028846 DOI: 10.1101/2023.03.03.531029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
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38
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Song X, Na R, Peng N, Cao W, Ke Y. Exploring the role of macrophages in the progression from atypical hyperplasia to endometrial carcinoma through single-cell transcriptomics and bulk transcriptomics analysis. Front Endocrinol (Lausanne) 2023; 14:1198944. [PMID: 37780629 PMCID: PMC10537943 DOI: 10.3389/fendo.2023.1198944] [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: 04/02/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction In this study, we aimed to identify key genes in endometrial cancer by conducting single-cell analysis of macrophages. Methods We sourced clinical data from the TCGA database as well as supplementary datasets GSE201926 and GSE173682. Using bulk-seq data of atypical endometrial hyperplasia and endometrial cancer, we pinpointed key differentially expressed genes. Single-cell RNA sequencing was utilized for further gene expression analysis. Cluster analysis was conducted on TCGA tumor data, identifying two distinct subtypes. Statistical methods employed included LASSO regression for diagnostic modeling and various clustering algorithms for subtype identification. Results We found that subtype B was closely related to cellular metabolism. A diagnostic model was established using LASSO regression and was based on the genes CDH18, H19, PAGE2B, PXDN, and THRB. This model effectively differentiated the prognosis of cervical cancer. We also constructed a prognosis model and a column chart based on these key genes. Discussion Through CIBERSORT analysis, CDH18 and PAGE2B were found to be strongly associated with macrophage M0. We propose that these genes influence the transformation from atypical endometrial hyperplasia to endometrial cancer by affecting macrophage M0. In conclusion, these key genes may serve as therapeutic targets for endometrial cancer. A new endometrial cancer risk prognosis model and column chart have been constructed based on these genes, offering a reliable direction for future cervical cancer treatment.
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Affiliation(s)
| | | | | | - Wenming Cao
- Department of Gynecology, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China
| | - Yan Ke
- Department of Gynecology, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China
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Liao P, Huang Q, Zhang J, Su Y, Xiao R, Luo S, Wu Z, Zhu L, Li J, Hu Q. How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy. Front Immunol 2023; 14:1238454. [PMID: 37671151 PMCID: PMC10475738 DOI: 10.3389/fimmu.2023.1238454] [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: 06/11/2023] [Accepted: 08/03/2023] [Indexed: 09/07/2023] Open
Abstract
Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.
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Affiliation(s)
- Pu Liao
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Huang
- Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, National Health Commission (NHC) Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiwei Zhang
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Su
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, National Health Commission (NHC) Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rui Xiao
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Pathophysiology, School of Basic Medicine; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shengquan Luo
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Pathophysiology, School of Basic Medicine; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zengbao Wu
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liping Zhu
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Pathophysiology, School of Basic Medicine; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiansha Li
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qinghua Hu
- Key Laboratory of Pulmonary Diseases of Ministry of Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Pathophysiology, School of Basic Medicine; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Parker JB, Valencia C, Akras D, DiIorio SE, Griffin MF, Longaker MT, Wan DC. Understanding Fibroblast Heterogeneity in Form and Function. Biomedicines 2023; 11:2264. [PMID: 37626760 PMCID: PMC10452440 DOI: 10.3390/biomedicines11082264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Historically believed to be a homogeneous cell type that is often overlooked, fibroblasts are more and more understood to be heterogeneous in nature. Though the mechanisms behind how fibroblasts participate in homeostasis and pathology are just beginning to be understood, these cells are believed to be highly dynamic and play key roles in fibrosis and remodeling. Focusing primarily on fibroblasts within the skin and during wound healing, we describe the field's current understanding of fibroblast heterogeneity in form and function. From differences due to embryonic origins to anatomical variations, we explore the diverse contributions that fibroblasts have in fibrosis and plasticity. Following this, we describe molecular techniques used in the field to provide deeper insights into subpopulations of fibroblasts and their varied roles in complex processes such as wound healing. Limitations to current work are also discussed, with a focus on future directions that investigators are recommended to take in order to gain a deeper understanding of fibroblast biology and to develop potential targets for translational applications in a clinical setting.
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Affiliation(s)
- Jennifer B. Parker
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Caleb Valencia
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
| | - Deena Akras
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
| | - Sarah E. DiIorio
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle F. Griffin
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
| | - Michael T. Longaker
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
| | - Derrick C. Wan
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA (M.F.G.)
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41
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Abstract
Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.
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Affiliation(s)
- Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, United States of America
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 137] [Impact Index Per Article: 137.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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43
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Laub V, Devraj K, Elias L, Schulte D. Bioinformatics for wet-lab scientists: practical application in sequencing analysis. BMC Genomics 2023; 24:382. [PMID: 37420172 DOI: 10.1186/s12864-023-09454-7] [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: 03/23/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. CONCLUSION Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.
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Affiliation(s)
- Vera Laub
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany.
| | - Kavi Devraj
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, Telangana, India
| | - Lena Elias
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Dorothea Schulte
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [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: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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Li Q. scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics. Genome Biol 2023; 24:149. [PMID: 37353848 PMCID: PMC10290357 DOI: 10.1186/s13059-023-02988-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/13/2023] [Indexed: 06/25/2023] Open
Abstract
Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of cellular dynamics with minimal influence from batch effects. For inference, scTour simultaneously estimates the developmental pseudotime, delineates the vector field, and maps the transcriptomic latent space under a single, integrated framework. For prediction, scTour precisely reconstructs the underlying dynamics of unseen cellular states or a new independent dataset. scTour's functionalities are demonstrated in a variety of biological processes from 19 datasets.
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Affiliation(s)
- Qian Li
- Department of Pathology, University of Cambridge, Cambridge, UK.
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Rosales-Alvarez RE, Rettkowski J, Herman JS, Dumbović G, Cabezas-Wallscheid N, Grün D. VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells. Genome Biol 2023; 24:148. [PMID: 37353813 PMCID: PMC10290360 DOI: 10.1186/s13059-023-02974-1] [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: 09/27/2022] [Accepted: 05/18/2023] [Indexed: 06/25/2023] Open
Abstract
Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.
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Affiliation(s)
- Reyna Edith Rosales-Alvarez
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- International Max Planck Research School for Immunobiology, Epigenetics, and Metabolism (IMPRS-IEM), Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jasmin Rettkowski
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), Freiburg, Germany
| | - Josip Stefan Herman
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Gabrijela Dumbović
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Nina Cabezas-Wallscheid
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- CIBSS-Centre for Integrative Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- Helmholtz Institute for RNA-Based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany.
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Gao CF, Vaikuntanathan S, Riesenfeld SJ. Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.13.544828. [PMID: 37398022 PMCID: PMC10312759 DOI: 10.1101/2023.06.13.544828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-seq data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed a novel approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our novel use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.
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Affiliation(s)
| | | | - Samantha J Riesenfeld
- Institute for Biophysical Dynamics, University of Chicago, IL
- Pritzker School of Molecular Engineering, University of Chicago, IL
- Department of Medicine, University of Chicago, IL
- Committee on Immunology, University of Chicago, IL
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Zúñiga TM, Baker FL, Smith KA, Batatinha H, Lau B, Burgess SC, Gustafson MP, Katsanis E, Simpson RJ. Clonal Kinetics and Single-Cell Transcriptional Profiles of T Cells Mobilized to Blood by Acute Exercise. Med Sci Sports Exerc 2023; 55:991-1002. [PMID: 36719647 DOI: 10.1249/mss.0000000000003130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE Acute exercise redistributes large numbers of memory T cells, which may contribute to enhanced immune surveillance in regular exercisers. It is not known, however, if acute exercise promotes a broad or oligoclonal T-cell receptor (TCR) repertoire or evokes transcriptomic changes in "exercise-responsive" T-cell clones. METHODS Healthy volunteers completed a graded bout of cycling exercise up to 80% V̇O 2max . DNA was extracted from peripheral blood mononuclear cells collected at rest, during exercise (EX), and 1 h after (+1H) exercise, and processed for deep TCR-β chain sequencing and tandem single-cell RNA sequencing. RESULTS The number of unique clones and unique rearrangements was decreased at EX compared with rest ( P < 0.01) and +1H ( P < 0.01). Productive clonality was increased compared with rest ( P < 0.05) and +1H ( P < 0.05), whereas Shannon's Index was decreased compared with rest ( P < 0.05) and +1H ( P < 0.05). The top 10 rearrangements in the repertoire were increased at EX compared with rest ( P < 0.05) and +1H ( P < 0.05). Cross-referencing TCR-β sequences with a public database (VDJdb) revealed that exercise increased the number of clones specific for the most prevalent motifs, including Epstein-Barr virus, cytomegalovirus, and influenza A. We identified 633 unique exercise-responsive T-cell clones that were mobilized and/or egressed in response to exercise. Among these clones, there was an upregulation in genes related to cell death, cytotoxicity, and activation ( P < 0.05). CONCLUSIONS Acute exercise promotes an oligoclonal T-cell repertoire by preferentially mobilizing the most dominant clones, several of which are specific to known viral antigens and display differentially expressed genes indicative of cytotoxicity, activation, and apoptosis.
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MESH Headings
- Humans
- T-Lymphocytes
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/metabolism
- Epstein-Barr Virus Infections/metabolism
- Leukocytes, Mononuclear/metabolism
- Herpesvirus 4, Human/metabolism
- Receptors, Antigen, T-Cell, alpha-beta/genetics
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- Clone Cells/metabolism
- Exercise
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Affiliation(s)
- Tiffany M Zúñiga
- School of Nutritional Sciences and Wellness, The University of Arizona, Tucson, AZ
| | - Forrest L Baker
- School of Nutritional Sciences and Wellness, The University of Arizona, Tucson, AZ
| | - Kyle A Smith
- School of Nutritional Sciences and Wellness, The University of Arizona, Tucson, AZ
| | | | - Branden Lau
- The University of Arizona Genetics Core, The University of Arizona, Tucson, AZ
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Wagner VA, Deng G, Claflin KE, Ritter ML, Cui H, Nakagawa P, Sigmund CD, Morselli LL, Grobe JL, Kwitek AE. Cell-specific transcriptome changes in the hypothalamic arcuate nucleus in a mouse deoxycorticosterone acetate-salt model of hypertension. Front Cell Neurosci 2023; 17:1207350. [PMID: 37293629 PMCID: PMC10244568 DOI: 10.3389/fncel.2023.1207350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/08/2023] [Indexed: 06/10/2023] Open
Abstract
A common preclinical model of hypertension characterized by low circulating renin is the "deoxycorticosterone acetate (DOCA)-salt" model, which influences blood pressure and metabolism through mechanisms involving the angiotensin II type 1 receptor (AT1R) in the brain. More specifically, AT1R within Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) has been implicated in selected effects of DOCA-salt. In addition, microglia have been implicated in the cerebrovascular effects of DOCA-salt and angiotensin II. To characterize DOCA-salt effects upon the transcriptomes of individual cell types within the ARC, we used single-nucleus RNA sequencing (snRNAseq) to examine this region from male C57BL/6J mice that underwent sham or DOCA-salt treatment. Thirty-two unique primary cell type clusters were identified. Sub-clustering of neuropeptide-related clusters resulted in identification of three distinct AgRP subclusters. DOCA-salt treatment caused subtype-specific changes in gene expression patterns associated with AT1R and G protein signaling, neurotransmitter uptake, synapse functions, and hormone secretion. In addition, two primary cell type clusters were identified as resting versus activated microglia, and multiple distinct subtypes of activated microglia were suggested by sub-cluster analysis. While DOCA-salt had no overall effect on total microglial density within the ARC, DOCA-salt appeared to cause a redistribution of the relative abundance of activated microglia subtypes. These data provide novel insights into cell-specific molecular changes occurring within the ARC during DOCA-salt treatment, and prompt increased investigation of the physiological and pathophysiological significance of distinct subtypes of neuronal and glial cell types.
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Affiliation(s)
- Valerie A. Wagner
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
- Genetics Graduate Program, University of Iowa, Iowa City, IA, United States
| | - Guorui Deng
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, United States
| | - Kristin E. Claflin
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, United States
| | - McKenzie L. Ritter
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Huxing Cui
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, United States
- Obesity Research and Education Initiative, University of Iowa, Iowa City, IA, United States
- Fraternal Order of Eagles Diabetes Research Center, University of Iowa, Iowa City, IA, United States
| | - Pablo Nakagawa
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Curt D. Sigmund
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Neuroscience Research Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Lisa L. Morselli
- Department of Medicine, Division of Endocrinology and Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Justin L. Grobe
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Neuroscience Research Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Comprehensive Rodent Metabolic Phenotyping Core, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Anne E. Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, United States
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
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50
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Xu J, Zhang A, Liu F, Chen L, Zhang X. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. Brief Bioinform 2023:7169137. [PMID: 37200157 DOI: 10.1093/bib/bbad195] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/20/2023] Open
Abstract
Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Liang Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
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