1
|
Polonsky M, Gerhardt LMS, Yun J, Koppitch K, Colón KL, Amrhein H, Wold B, Zheng S, Yuan GC, Thomson M, Cai L, McMahon AP. Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease. Nat Commun 2024; 15:7010. [PMID: 39237549 DOI: 10.1038/s41467-024-51186-z] [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: 01/02/2024] [Accepted: 08/01/2024] [Indexed: 09/07/2024] Open
Abstract
Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, together with injury-invoked inflammation and fibrosis. Deciphering the molecular pathways and cellular interactions driving these processes is challenging due to the complex tissue structure. Here, we apply single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics reveals injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicts Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and their neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis identifies cellular microenvironments resembling early tertiary lymphoid structures and associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease.
Collapse
Affiliation(s)
- Michal Polonsky
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Louisa M S Gerhardt
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
- Fifth Department of Medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Jina Yun
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Katsuya Lex Colón
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Henry Amrhein
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Shiwei Zheng
- Department of Genetics and Genomic Sciences and Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences and Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Long Cai
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
| |
Collapse
|
2
|
White BS, de Reyniès A, Newman AM, Waterfall JJ, Lamb A, Petitprez F, Lin Y, Yu R, Guerrero-Gimenez ME, Domanskyi S, Monaco G, Chung V, Banerjee J, Derrick D, Valdeolivas A, Li H, Xiao X, Wang S, Zheng F, Yang W, Catania CA, Lang BJ, Bertus TJ, Piermarocchi C, Caruso FP, Ceccarelli M, Yu T, Guo X, Bletz J, Coller J, Maecker H, Duault C, Shokoohi V, Patel S, Liliental JE, Simon S, Saez-Rodriguez J, Heiser LM, Guinney J, Gentles AJ. Community assessment of methods to deconvolve cellular composition from bulk gene expression. Nat Commun 2024; 15:7362. [PMID: 39191725 PMCID: PMC11350143 DOI: 10.1038/s41467-024-50618-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 07/11/2024] [Indexed: 08/29/2024] Open
Abstract
We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.
Collapse
Affiliation(s)
- Brian S White
- Sage Bionetworks, Seattle, WA, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Aurélien de Reyniès
- Centre de Recherche des Cordeliers, INSERM U1138, Université Paris Cité, Paris, France
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Joshua J Waterfall
- INSERM U830 and Translational Research Department, Institut Curie, PSL Research University, Paris, France
| | | | - Florent Petitprez
- Programme Cartes d'Identité des Tumeurs, Ligue Nationale Contre le Cancer, Paris, France
- MRC Centre for Reproductive Health, the Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Yating Lin
- Xiamen University, Xiamen, Fujian, China
| | | | - Martin E Guerrero-Gimenez
- Institute of Biochemistry and Biotechnology, School of Medicine, National University of Cuyo, Mendoza, Argentina
| | | | - Gianni Monaco
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, AV, Italy
| | | | | | - Daniel Derrick
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Alberto Valdeolivas
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Haojun Li
- Xiamen University, Xiamen, Fujian, China
| | - Xu Xiao
- Xiamen University, Xiamen, Fujian, China
| | - Shun Wang
- Department of Pathology, Cancer Hospital, Chinese Aacdemy of Medical Science, Beijing, China
| | | | | | - Carlos A Catania
- Laboratory of Intelligent Systems (LABSIN), Engineering School, National University of Cuyo, Mendoza, Argentina
| | - Benjamin J Lang
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | | | - Francesca P Caruso
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, AV, Italy
| | - Michele Ceccarelli
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, AV, Italy
- Sylvester Comprehensive Cancer Center, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA
| | | | | | | | - John Coller
- Stanford Functional Genomics Facility, Stanford University School of Medicine, Stanford, CA, USA
| | - Holden Maecker
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Caroline Duault
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Vida Shokoohi
- Stanford Functional Genomics Facility, Stanford University School of Medicine, Stanford, CA, USA
| | - Shailja Patel
- Translational Applications Service Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Joanna E Liliental
- Translational Applications Service Center, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Laura M Heiser
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | | | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University, Stanford, CA, USA.
| |
Collapse
|
3
|
Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
Collapse
Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
| |
Collapse
|
4
|
Foley KG, Adli M, Kim JJ. Single-nuclei sequencing of uterine serous carcinoma reveals racial differences in immune signaling. Proc Natl Acad Sci U S A 2024; 121:e2402998121. [PMID: 39133838 PMCID: PMC11348309 DOI: 10.1073/pnas.2402998121] [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: 02/15/2024] [Accepted: 07/15/2024] [Indexed: 08/29/2024] Open
Abstract
Significant racial disparities exist between Black and White patients with uterine serous carcinoma (USC). While the reasons for these disparities are unclear, several studies have demonstrated significantly different rates of driver mutations between racial groups, including TP53. However, limited research has investigated the transcriptional differences of tumors or the composition of the tumor microenvironment (TME) between these groups. Here, we report the single-nuclei RNA-sequencing profiles of primary USC tumors from diverse racial backgrounds. We find that there are significant differences between the tumors of Black and White patients. Tumors from Black patients exhibited higher expression of specific genes associated with aggressiveness, such as PAX8, and axon guidance and synaptic signaling pathways. We also demonstrated that T cell populations are reduced in the tumor tissue compared to matched benign, while anti-inflammatory macrophage populations are retained within the TME. Furthermore, we investigated the connection between PAX8 overexpression and immunosuppression in USC through regulation of several cytokines and chemokines. Notably, we show that PAX8 activity can influence macrophage gene expression and protein secretion. These studies provide a detailed understanding of the USC transcriptome and TME, and identify differences in tumor biology from patients of different racial backgrounds.
Collapse
Affiliation(s)
- K. Grace Foley
- Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Robert H. Lurie Cancer Center, Northwestern University, Chicago, IL60611
| | - Mazhar Adli
- Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Robert H. Lurie Cancer Center, Northwestern University, Chicago, IL60611
| | - J. Julie Kim
- Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Robert H. Lurie Cancer Center, Northwestern University, Chicago, IL60611
| |
Collapse
|
5
|
Gural B, Kirkland L, Hockett A, Sandroni P, Zhang J, Rosa-Garrido M, Swift SK, Chapski D, Flinn MA, O'Meara CC, Vondriska TM, Patterson M, Jensen BC, Rau CD. Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.09.607400. [PMID: 39149394 PMCID: PMC11326268 DOI: 10.1101/2024.08.09.607400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Recent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives. Methods We have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific α1A adrenergic receptor (CM-α1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-α1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope. Results Uncorrected DEGs for the CM-α1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the α1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-α1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5). Conclusions Our algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.
Collapse
Affiliation(s)
- Brian Gural
- Department of Genetics and Computational Medicine Program, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Logan Kirkland
- McAllister Heart Institute, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Medicine, Division of Cardiology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Abbey Hockett
- Department of Genetics and Computational Medicine Program, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Peyton Sandroni
- Department of Pharmacology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiandong Zhang
- McAllister Heart Institute, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Medicine, Division of Cardiology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Manuel Rosa-Garrido
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Samantha K Swift
- Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Douglas Chapski
- Departments of Anesthesiology & Perioperative Medicine, Medicine/Cardiology, and Physiology, David Geffen School of Medicine; Molecular Biology Institute; University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael A Flinn
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Caitlin C O'Meara
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Thomas M Vondriska
- Departments of Anesthesiology & Perioperative Medicine, Medicine/Cardiology, and Physiology, David Geffen School of Medicine; Molecular Biology Institute; University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michaela Patterson
- Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Brian C Jensen
- McAllister Heart Institute, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Medicine, Division of Cardiology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Pharmacology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christoph D Rau
- Department of Genetics and Computational Medicine Program, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- McAllister Heart Institute, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
6
|
Calanca N, Faldoni FLC, Souza CP, Souza JS, de Souza Alves BE, Soares MBP, Wong DVT, Lima-Junior RCP, Marchi FA, Rainho CA, Rogatto SR. Inflammatory breast cancer microenvironment repertoire based on DNA methylation data deconvolution reveals actionable targets to enhance the treatment efficacy. J Transl Med 2024; 22:735. [PMID: 39103878 DOI: 10.1186/s12967-024-05553-5] [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: 05/17/2024] [Accepted: 07/28/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Although the clinical signs of inflammatory breast cancer (IBC) resemble acute inflammation, the role played by infiltrating immune and stromal cells in this aggressive disease is uncharted. The tumor microenvironment (TME) presents molecular alterations, such as epimutations, prior to morphological abnormalities. These changes affect the distribution and the intricate communication between the TME components related to cancer prognosis and therapy response. Herein, we explored the global DNA methylation profile of IBC and surrounding tissues to estimate the microenvironment cellular composition and identify epigenetically dysregulated markers. METHODS We used the HiTIMED algorithm to deconvolve the bulk DNA methylation data of 24 IBC and six surrounding non-tumoral tissues (SNT) (GSE238092) and determine their cellular composition. The prognostic relevance of cell types infiltrating IBC and their relationship with clinicopathological variables were investigated. CD34 (endothelial cell marker) and CD68 (macrophage marker) immunofluorescence staining was evaluated in an independent set of 17 IBC and 16 non-IBC samples. RESULTS We found lower infiltration of endothelial, stromal, memory B, dendritic, and natural killer cells in IBC than in SNT samples. Higher endothelial cell (EC) and stromal cell content were related to better overall survival. EC proportions positively correlated with memory B and memory CD8+ T infiltration in IBC. Immune and EC markers exhibited distinct DNA methylation profiles between IBC and SNT samples, revealing hypermethylated regions mapped to six genes (CD40, CD34, EMCN, HLA-G, PDPN, and TEK). We identified significantly higher CD34 and CD68 protein expression in IBC compared to non-IBC. CONCLUSIONS Our findings underscored cell subsets that distinguished patients with better survival and dysregulated markers potentially actionable through combinations of immunotherapy and epigenetic drugs.
Collapse
Affiliation(s)
- Naiade Calanca
- Department of Clinical Genetics, University Hospital of Southern Denmark, Beriderbakken 4, Vejle, DK, 7100, Denmark
- Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu, SP, 18618-689, Brazil
| | - Flavia Lima Costa Faldoni
- Department of Clinical Genetics, University Hospital of Southern Denmark, Beriderbakken 4, Vejle, DK, 7100, Denmark
| | - Cristiano Pádua Souza
- Medical Oncology Department, Barretos Cancer Hospital, Pio XII Foundation, Barretos, SP, 14784-400, Brazil
| | | | - Bianca Elen de Souza Alves
- Department of Physiology and Pharmacology, Drug Research and Development Center (NPDM), Faculty of Medicine, Federal University of Ceará, Fortaleza, 60430-270, Brazil
| | - Milena Botelho Pereira Soares
- Health Technology Institute, SENAI CIMATEC, Salvador, BA, 41650-010, Brazil
- Gonçalo Moniz Institute, FIOCRUZ, Salvador, BA, 40296-710, Brazil
| | - Deysi Viviana Tenazoa Wong
- Department of Physiology and Pharmacology, Drug Research and Development Center (NPDM), Faculty of Medicine, Federal University of Ceará, Fortaleza, 60430-270, Brazil
| | - Roberto César Pereira Lima-Junior
- Department of Physiology and Pharmacology, Drug Research and Development Center (NPDM), Faculty of Medicine, Federal University of Ceará, Fortaleza, 60430-270, Brazil
| | - Fabio Albuquerque Marchi
- Department of Head and Neck Surgery, University of São Paulo Medical School, São Paulo, SP, 05402-000, Brazil
- Center for Translational Research in Oncology, Cancer Institute of the State of São Paulo (ICESP), São Paulo, SP, 01246-000, Brazil
| | - Claudia Aparecida Rainho
- Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu, SP, 18618-689, Brazil
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, University Hospital of Southern Denmark, Beriderbakken 4, Vejle, DK, 7100, Denmark.
- Institute of Regional Health Research, University of Southern Denmark, Odense, 5000, Denmark.
- Botucatu Medical School Hospital, São Paulo State University (UNESP), Botucatu, SP, Brazil.
| |
Collapse
|
7
|
Bai X, Chen S, Chi X, Xie B, Guo X, Feng H, Wei P, Zhang D, Xie S, Xie T, Chen Y, Gou M, Qiao Q, Liu X, Jin W, Xu W, Zhao Z, Xing Q, Wang X, Zhang X, Dong C. Reciprocal regulation of T follicular helper cells and dendritic cells drives colitis development. Nat Immunol 2024; 25:1383-1394. [PMID: 38942990 DOI: 10.1038/s41590-024-01882-1] [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: 06/07/2023] [Accepted: 05/22/2024] [Indexed: 06/30/2024]
Abstract
The immunological mechanisms underlying chronic colitis are poorly understood. T follicular helper (TFH) cells are critical in helping B cells during germinal center reactions. In a T cell transfer colitis model, a lymphoid structure composed of mature dendritic cells (DCs) and TFH cells was found within T cell zones of colonic lymphoid follicles. TFH cells were required for mature DC accumulation, the formation of DC-T cell clusters and colitis development. Moreover, DCs promoted TFH cell differentiation, contributing to colitis development. A lineage-tracing analysis showed that, following migration to the lamina propria, TFH cells transdifferentiated into long-lived pathogenic TH1 cells, promoting colitis development. Our findings have therefore demonstrated the reciprocal regulation of TFH cells and DCs in colonic lymphoid follicles, which is critical in chronic colitis pathogenesis.
Collapse
Affiliation(s)
- Xue Bai
- New Cornerstone Science Laboratory, Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Sijie Chen
- Bioinformatics Division, BNRIST and Department of Automation, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| | - Xinxin Chi
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Bowen Xie
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Xinyi Guo
- New Cornerstone Science Laboratory, Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Han Feng
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Peng Wei
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Di Zhang
- Department of Pathology, The First Hospital of China Medical University and College of Basic Medical Sciences of China Medical University, Shenyang, China
| | - Shan Xie
- Department of Gastroenterology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Tian Xie
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Yongzhen Chen
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Mengting Gou
- New Cornerstone Science Laboratory, Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China
| | - Qin Qiao
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Xinwei Liu
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Wei Jin
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Wei Xu
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Zixuan Zhao
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Qi Xing
- New Cornerstone Science Laboratory, Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Xiaohu Wang
- Institute of Immunology and School of Medicine, Tsinghua University, Beijing, China
| | - Xuegong Zhang
- Bioinformatics Division, BNRIST and Department of Automation, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
- Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing, China
| | - Chen Dong
- New Cornerstone Science Laboratory, Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China.
- Research Unit of Immune Regulation and Immune Diseases of Chinese Academy of Medical Sciences, Shanghai Jiao Tong University School of Medicine-Affiliated Renji Hospital, Shanghai, China.
- Westlake University School of Medicine, Hangzhou, China.
| |
Collapse
|
8
|
Chow A, Lareau CA. Concepts and new developments in droplet-based single cell multi-omics. Trends Biotechnol 2024:S0167-7799(24)00184-7. [PMID: 39095258 DOI: 10.1016/j.tibtech.2024.07.006] [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: 02/01/2024] [Revised: 05/31/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024]
Abstract
Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.
Collapse
Affiliation(s)
- Arthur Chow
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Caleb A Lareau
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| |
Collapse
|
9
|
Schott M, León-Periñán D, Splendiani E, Strenger L, Licha JR, Pentimalli TM, Schallenberg S, Alles J, Samut Tagliaferro S, Boltengagen A, Ehrig S, Abbiati S, Dommerich S, Pagani M, Ferretti E, Macino G, Karaiskos N, Rajewsky N. Open-ST: High-resolution spatial transcriptomics in 3D. Cell 2024; 187:3953-3972.e26. [PMID: 38917789 DOI: 10.1016/j.cell.2024.05.055] [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: 01/04/2024] [Revised: 04/05/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
Collapse
Affiliation(s)
- Marie Schott
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Daniel León-Periñán
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Elena Splendiani
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Leon Strenger
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Jan Robin Licha
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Tancredi Massimo Pentimalli
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Jonathan Alles
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sarah Samut Tagliaferro
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Anastasiya Boltengagen
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sebastian Ehrig
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Stefano Abbiati
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Steffen Dommerich
- Department of Otorhinolaryngology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin 13353, Germany
| | - Massimiliano Pagani
- IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | | | - Giuseppe Macino
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Cellular Biotechnologies and Hematology, La Sapienza University of Rome, 00161 Rome, Italy.
| | - Nikos Karaiskos
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany.
| | - Nikolaus Rajewsky
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Charité - Universitätsmedizin, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Site Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Berlin, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Site Berlin, Berlin, Germany.
| |
Collapse
|
10
|
Wang Z, Li Z, Luan T, Cui G, Shu S, Liang Y, Zhang K, Xiao J, Yu W, Cui J, Li A, Peng G, Fang Y. A spatiotemporal molecular atlas of mouse spinal cord injury identifies a distinct astrocyte subpopulation and therapeutic potential of IGFBP2. Dev Cell 2024:S1534-5807(24)00394-0. [PMID: 39029468 DOI: 10.1016/j.devcel.2024.06.016] [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: 10/09/2023] [Revised: 03/26/2024] [Accepted: 06/20/2024] [Indexed: 07/21/2024]
Abstract
Spinal cord injury (SCI) triggers a cascade of intricate molecular and cellular changes that determine the outcome. In this study, we resolve the spatiotemporal organization of the injured mouse spinal cord and quantitatively assess in situ cell-cell communication following SCI. By analyzing existing single-cell RNA sequencing datasets alongside our spatial data, we delineate a subpopulation of Igfbp2-expressing astrocytes that migrate from the white matter (WM) to gray matter (GM) and become reactive upon SCI, termed Astro-GMii. Further, Igfbp2 upregulation promotes astrocyte migration, proliferation, and reactivity, and the secreted IGFBP2 protein fosters neurite outgrowth. Finally, we show that IGFBP2 significantly reduces neuronal loss and remarkably improves the functional recovery in a mouse model of SCI in vivo. Together, this study not only provides a comprehensive molecular atlas of SCI but also exemplifies how this rich resource can be applied to endow cells and genes with functional insight and therapeutic potential.
Collapse
Affiliation(s)
- Zeqing Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuxia Li
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianle Luan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guizhong Cui
- Guangzhou National Laboratory, Guangzhou 510005, China
| | - Shunpan Shu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiyao Liang
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China
| | - Kai Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingshu Xiao
- Guangzhou National Laboratory, Guangzhou 510005, China
| | - Wei Yu
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China
| | - Jihong Cui
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Ang Li
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China.
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yanshan Fang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
11
|
Xu Y, Wang Y, Ma S. SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer. CELL REPORTS METHODS 2024; 4:100813. [PMID: 38971150 PMCID: PMC11294836 DOI: 10.1016/j.crmeth.2024.100813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/23/2024] [Accepted: 06/13/2024] [Indexed: 07/08/2024]
Abstract
Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.
Collapse
Affiliation(s)
- Yupu Xu
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yuzhou Wang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shisong Ma
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China; School of Data Science, University of Science and Technology of China, Hefei, China.
| |
Collapse
|
12
|
Mistri SK, Hilton BM, Horrigan KJ, Andretta ES, Savard R, Dienz O, Hampel KJ, Gerrard DL, Rose JT, Sidiropoulos N, Majumdar D, Boyson JE. SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575073. [PMID: 38260519 PMCID: PMC10802474 DOI: 10.1101/2024.01.10.575073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM-SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire. SAP deficiency resulted in both a significant loss of an immature Gzma + Blk + Etv5 + Tox2 + γδT17 precursor population, and a significant increase in Cd4 + Cd8+ Rorc + Ptcra + Rag1 + thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data suggest that SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.
Collapse
Affiliation(s)
- Somen K Mistri
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Brianna M. Hilton
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Katherine J. Horrigan
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Emma S. Andretta
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Remi Savard
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Oliver Dienz
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Kenneth J Hampel
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont Medical Center, Burlington, Vermont 05405, USA
| | - Diana L. Gerrard
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont Medical Center, Burlington, Vermont 05405, USA
| | - Joshua T. Rose
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont Medical Center, Burlington, Vermont 05405, USA
| | - Nikoletta Sidiropoulos
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont Medical Center, Burlington, Vermont 05405, USA
| | - Devdoot Majumdar
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| | - Jonathan E. Boyson
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
| |
Collapse
|
13
|
Ravindran A, Holappa L, Niskanen H, Skovorodkin I, Kaisto S, Beter M, Kiema M, Selvarajan I, Nurminen V, Aavik E, Aherrahrou R, Pasonen-Seppänen S, Fortino V, Laakkonen JP, Ylä-Herttuala S, Vainio S, Örd T, Kaikkonen MU. Translatome profiling reveals Itih4 as a novel smooth muscle cell-specific gene in atherosclerosis. Cardiovasc Res 2024; 120:869-882. [PMID: 38289873 PMCID: PMC11218691 DOI: 10.1093/cvr/cvae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 12/01/2023] [Accepted: 12/12/2023] [Indexed: 02/01/2024] Open
Abstract
AIMS Vascular smooth muscle cells (SMCs) and their derivatives are key contributors to the development of atherosclerosis. However, studying changes in SMC gene expression in heterogeneous vascular tissues is challenging due to the technical limitations and high cost associated with current approaches. In this paper, we apply translating ribosome affinity purification sequencing to profile SMC-specific gene expression directly from tissue. METHODS AND RESULTS To facilitate SMC-specific translatome analysis, we generated SMCTRAP mice, a transgenic mouse line expressing enhanced green fluorescent protein (EGFP)-tagged ribosomal protein L10a (EGFP-L10a) under the control of the SMC-specific αSMA promoter. These mice were further crossed with the atherosclerosis model Ldlr-/-, ApoB100/100 to generate SMCTRAP-AS mice and used to profile atherosclerosis-associated SMCs in thoracic aorta samples of 15-month-old SMCTRAP and SMCTRAP-AS mice. Our analysis of SMCTRAP-AS mice showed that EGFP-L10a expression was localized to SMCs in various tissues, including the aortic wall and plaque. The TRAP fraction demonstrated high enrichment of known SMC-specific genes, confirming the specificity of our approach. We identified several genes, including Cemip, Lum, Mfge8, Spp1, and Serpina3, which are known to be involved in atherosclerosis-induced gene expression. Moreover, we identified several novel genes not previously linked to SMCs in atherosclerosis, such as Anxa4, Cd276, inter-alpha-trypsin inhibitor-4 (Itih4), Myof, Pcdh11x, Rab31, Serpinb6b, Slc35e4, Slc8a3, and Spink5. Among them, we confirmed the SMC-specific expression of Itih4 in atherosclerotic lesions using immunofluorescence staining of mouse aortic roots and spatial transcriptomics of human carotid arteries. Furthermore, our more detailed analysis of Itih4 showed its link to coronary artery disease through the colocalization of genome-wide association studies, splice quantitative trait loci (QTL), and protein QTL signals. CONCLUSION We generated a SMC-specific TRAP mouse line to study atherosclerosis and identified Itih4 as a novel SMC-expressed gene in atherosclerotic plaques, warranting further investigation of its putative function in extracellular matrix stability and genetic evidence of causality.
Collapse
MESH Headings
- Animals
- Female
- Humans
- Male
- Mice
- Aorta/metabolism
- Aorta/pathology
- Aortic Diseases/genetics
- Aortic Diseases/pathology
- Aortic Diseases/metabolism
- Apolipoprotein B-100/genetics
- Apolipoprotein B-100/metabolism
- Atherosclerosis/genetics
- Atherosclerosis/metabolism
- Atherosclerosis/pathology
- Disease Models, Animal
- Gene Expression Profiling
- Gene Expression Regulation
- Green Fluorescent Proteins/genetics
- Green Fluorescent Proteins/metabolism
- Mice, Inbred C57BL
- Mice, Knockout
- Mice, Transgenic
- Muscle, Smooth, Vascular/metabolism
- Muscle, Smooth, Vascular/pathology
- Myocytes, Smooth Muscle/metabolism
- Myocytes, Smooth Muscle/pathology
- Phenotype
- Plaque, Atherosclerotic
- Receptors, LDL/genetics
- Receptors, LDL/metabolism
- Ribosomal Proteins/genetics
- Ribosomal Proteins/metabolism
- Transcriptome
Collapse
Affiliation(s)
- Aarthi Ravindran
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Lari Holappa
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Henri Niskanen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Ilya Skovorodkin
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Susanna Kaisto
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Mustafa Beter
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Miika Kiema
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Ilakya Selvarajan
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Valtteri Nurminen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Einari Aavik
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Rédouane Aherrahrou
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
- Institute for Cardiogenetics, Universität zu Lübeck, 23562 Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Heart Centre Lübeck, 23562 Lübeck, Germany
| | - Sanna Pasonen-Seppänen
- Institute of Biomedicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Johanna P Laakkonen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Seppo Ylä-Herttuala
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Seppo Vainio
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Tiit Örd
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Minna U Kaikkonen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| |
Collapse
|
14
|
Yu Y, Hou W, Liu Y, Wang H, Dong L, Mai Y, Chen Q, Li Z, Sun S, Yang J, Cao Z, Zhang P, Zi Y, Liu R, Gao J, Zhang N, Li J, Ren L, Jiang H, Shang J, Zhu S, Wang X, Qing T, Bao D, Li B, Li B, Suo C, Pi Y, Wang X, Dai F, Scherer A, Mattila P, Han J, Zhang L, Jiang H, Thierry-Mieg D, Thierry-Mieg J, Xiao W, Hong H, Tong W, Wang J, Li J, Fang X, Jin L, Xu J, Qian F, Zhang R, Shi L, Zheng Y. Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling. Nat Biotechnol 2024; 42:1118-1132. [PMID: 37679545 PMCID: PMC11251996 DOI: 10.1038/s41587-023-01867-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/15/2023] [Indexed: 09/09/2023]
Abstract
Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.
Collapse
Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yi Zi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jian Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Chen Suo
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yan Pi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xia Wang
- National Institute of Metrology, Beijing, China
| | | | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | | | - Lijun Zhang
- Nanjing Vazyme Biotech Co. Ltd., Nanjing, China
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
- National Center of Gerontology, Beijing, China
| | - Xiang Fang
- National Institute of Metrology, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
| | - Feng Qian
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
- National Center of Gerontology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
| |
Collapse
|
15
|
Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections. Cancers (Basel) 2024; 16:2429. [PMID: 39001492 PMCID: PMC11240479 DOI: 10.3390/cancers16132429] [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: 05/25/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.
Collapse
Affiliation(s)
- Patrick G. Schupp
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Samuel J. Shelton
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Daniel J. Brody
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Rebecca Eliscu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Brett E. Johnson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Tali Mazor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kevin W. Kelley
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94143, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew B. Potts
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Michael W. McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Eric J. Huang
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
| | - Daniel A. Lim
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Russell O. Pieper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Joseph F. Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Joanna J. Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
| | - Michael C. Oldham
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| |
Collapse
|
16
|
Zvirblyte J, Nainys J, Juzenas S, Goda K, Kubiliute R, Dasevicius D, Kincius M, Ulys A, Jarmalaite S, Mazutis L. Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype. Commun Biol 2024; 7:780. [PMID: 38942917 PMCID: PMC11213875 DOI: 10.1038/s42003-024-06478-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/21/2024] [Indexed: 06/30/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.
Collapse
Affiliation(s)
- Justina Zvirblyte
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania
| | - Juozas Nainys
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania
- Droplet Genomics, Vilnius, 10257, Lithuania
| | - Simonas Juzenas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania
| | - Karolis Goda
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania
| | - Raimonda Kubiliute
- Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania
| | - Darius Dasevicius
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, Vilnius, 08406, Lithuania
| | | | - Albertas Ulys
- National Cancer Institute, Vilnius, 08660, Lithuania
| | - Sonata Jarmalaite
- Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania.
- National Cancer Institute, Vilnius, 08660, Lithuania.
| | - Linas Mazutis
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, 10257, Lithuania.
| |
Collapse
|
17
|
Yi L, Li Q, Zhu J, Cheng W, Xie Y, Huang Y, Zhao H, Hao M, Wei H, Zhao S. Single-nucleus RNA sequencing and lipidomics reveal characteristics of transcriptional and lipid composition in porcine longissimus dorsi muscle. BMC Genomics 2024; 25:622. [PMID: 38902599 PMCID: PMC11188186 DOI: 10.1186/s12864-024-10488-8] [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: 02/27/2024] [Accepted: 06/03/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Global per capita meat consumption continues to rise, especially pork. Meat quality is influenced by the content of intramuscular fat (IMF) as a key factor. The longissimus dorsi muscle of Dahe pigs (DHM, IMF: 7.98% ± 1.96%) and Dahe black pigs (DHBM, IMF: 3.30% ± 0.64%) was studied to explore cellular heterogeneity and differentially expressed genes (DEGs) associated with IMF deposition using single-nucleus RNA sequencing (snRNA-seq). The lipid composition was then analyzed using non-targeted lipidomics. RESULTS A total of seven cell subpopulations were identified, including myocytes, fibroblast/fibro/adipogenic progenitors (FAPs), satellite cells, endothelial cells, macrophages, pericytes, and adipocytes. Among them, FAPs and adipocytes were more focused because they could be associated with lipid deposition. 1623 DEGs in the FAPs subpopulation of DHBM were up-regulated compared with DHM, while 1535 were down-regulated. These DEGs enriched in the glycolysis/gluconeogenesis pathway. 109 DEGs were up-regulated and 806 were down-regulated in the adipocyte subpopulation of DHBM compared with DHM, which were mainly enriched in the PPAR signaling pathway and fatty acid (FA) biosynthesis. The expression level of PPARG, ABP4, LEP, and ACSL1 genes in DHM was higher than that in DHBM. Lipidomics reveals porcine lipid composition characteristics of muscle tissue. A total of 41 lipid classes and 2699 lipid species were identified in DHM and DHBM groups. The top ten relative peak areas of lipid classes in DHM and DHBM were triglyceride (TG), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), diglyceride (DG), cardiolipin (CL), ceramides (Cer), Simple Glc series (Hex1Cer), sphingomyelin (phSM), and phosphatidylinositol (PI). The relative peak areas of 35 lipid species in DHM were lower than DHBM, and 28 lipid species that were higher. There was a significant increase in the TG fatty acyl chains C6:0, C17:0, and C11:4, and a significant decrease in C16:0, C18:1, C18:2, and C22:4 in DHBM (p < 0.05). CONCLUSIONS C16:0 FA may downregulate the expression level of PPARG gene, which leads to the downregulation of fat metabolism-related genes such as ACSL, PLIN2, and FABP4 in DHBM compared with DHM. This may be the reason that the lipid deposition ability of Dahe pigs is stronger than that of Dahe black pigs, which need further investigation.
Collapse
Affiliation(s)
- Lanlan Yi
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Qiuyan Li
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Junhong Zhu
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Wenjie Cheng
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Yuxiao Xie
- College of Biology and Agriculture (College of Food Science and Technology), Zunyi Normal College, Zunyi, 563006, China
| | - Ying Huang
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Hongye Zhao
- Yunnan Province Key Laboratory for Porcine Gene Editing and Xenotransplantation, Yunnan Agricultural University, Kunming, 650201, China
| | - Meilin Hao
- College of Biology and Agriculture (College of Food Science and Technology), Zunyi Normal College, Zunyi, 563006, China
| | - Hongjiang Wei
- Yunnan Province Key Laboratory for Porcine Gene Editing and Xenotransplantation, Yunnan Agricultural University, Kunming, 650201, China.
| | - Sumei Zhao
- Yunnan Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, Kunming, 650201, Yunnan, China.
| |
Collapse
|
18
|
WANG Y, LUO B, WANG Z, QUE Z, JIANG L, TIAN J. [Advancements in Single-cell RNA Sequencing Technology
in the Study of the Tumor Microenvironment in Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2024; 27:441-450. [PMID: 39026495 PMCID: PMC11258646 DOI: 10.3779/j.issn.1009-3419.2024.101.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Indexed: 07/20/2024]
Abstract
The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
.
Collapse
|
19
|
Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [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: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
Collapse
Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| |
Collapse
|
20
|
Manchester AC, Ammons DT, Lappin MR, Dow S. Single cell transcriptomic analysis of the canine duodenum in chronic inflammatory enteropathy and health. Front Immunol 2024; 15:1397590. [PMID: 38933260 PMCID: PMC11199541 DOI: 10.3389/fimmu.2024.1397590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024] Open
Abstract
Chronic inflammatory enteropathy (CIE) is a common condition in dogs causing recurrent or persistent gastrointestinal clinical signs. Pathogenesis is thought to involve intestinal mucosal inflammatory infiltrates, but histopathological evaluation of intestinal biopsies from dogs with CIE fails to guide treatment, inform prognosis, or correlate with clinical remission. We employed single-cell RNA sequencing to catalog and compare the diversity of cells present in duodenal mucosal endoscopic biopsies from 3 healthy dogs and 4 dogs with CIE. Through characterization of 35,668 cells, we identified 31 transcriptomically distinct cell populations, including T cells, epithelial cells, and myeloid cells. Both healthy and CIE samples contributed to each cell population. T cells were broadly subdivided into GZMAhigh (putatively annotated as tissue resident) and IL7Rhigh (putatively annotated as non-resident) T cell categories, with evidence of a skewed proportion favoring an increase in the relative proportion of IL7Rhigh T cells in CIE dogs. Among the myeloid cells, neutrophils from CIE samples exhibited inflammatory (SOD2 and IL1A) gene expression signatures. Numerous differentially expressed genes were identified in epithelial cells, with gene set enrichment analysis suggesting enterocytes from CIE dogs may be undergoing stress responses and have altered metabolic properties. Overall, this work reveals the previously unappreciated cellular heterogeneity in canine duodenal mucosa and provides new insights into molecular mechanisms which may contribute to intestinal dysfunction in CIE. The cell type gene signatures developed through this study may also be used to better understand the subtleties of canine intestinal physiology in health and disease.
Collapse
Affiliation(s)
- Alison C. Manchester
- Colorado State University, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO, United States
| | - Dylan T. Ammons
- Colorado State University, Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO, United States
| | - Michael R. Lappin
- Colorado State University, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO, United States
| | - Steven Dow
- Colorado State University, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO, United States
- Colorado State University, Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO, United States
| |
Collapse
|
21
|
Hoedjes KM, Grath S, Posnien N, Ritchie MG, Schlötterer C, Abbott JK, Almudi I, Coronado-Zamora M, Durmaz Mitchell E, Flatt T, Fricke C, Glaser-Schmitt A, González J, Holman L, Kankare M, Lenhart B, Orengo DJ, Snook RR, Yılmaz VM, Yusuf L. From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology. Mol Ecol 2024:e17382. [PMID: 38856653 DOI: 10.1111/mec.17382] [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: 12/07/2023] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.
Collapse
Affiliation(s)
- Katja M Hoedjes
- Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sonja Grath
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Nico Posnien
- Department of Developmental Biology, Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
| | - Michael G Ritchie
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
| | | | | | - Isabel Almudi
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | | | - Esra Durmaz Mitchell
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Functional Genomics and Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Thomas Flatt
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Claudia Fricke
- Institute for Zoology/Animal Ecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | | | - Josefa González
- Institute of Evolutionary Biology, CSIC, UPF, Barcelona, Spain
| | - Luke Holman
- School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK
| | - Maaria Kankare
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Benedict Lenhart
- Department of Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Dorcas J Orengo
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Rhonda R Snook
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Vera M Yılmaz
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Leeban Yusuf
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
| |
Collapse
|
22
|
Melnik M, Miyoshi E, Ma R, Corrada M, Kawas C, Bohannan R, Caraway C, Miller CA, Hinman JD, John V, Bilousova T, Gylys KH. Simultaneous isolation of intact brain cells and cell-specific extracellular vesicles from cryopreserved Alzheimer's disease cortex. J Neurosci Methods 2024; 406:110137. [PMID: 38626853 DOI: 10.1016/j.jneumeth.2024.110137] [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: 12/15/2023] [Revised: 03/25/2024] [Accepted: 04/12/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND The neuronal and gliaI populations within the brain are tightly interwoven, making isolation and study of large populations of a single cell type from brain tissue a major technical challenge. Concurrently, cell-type specific extracellular vesicles (EVs) hold enormous diagnostic and therapeutic potential in neurodegenerative disorders including Alzheimer's disease (AD). NEW METHOD Postmortem AD cortical samples were thawed and gently dissociated. Following filtration, myelin and red blood cell removal, cell pellets were immunolabeled with fluorescent antibodies and analyzed by flow cytometry. The cell pellet supernatant was applied to a triple sucrose cushion for brain EV isolation. RESULTS Neuronal, astrocyte and microglial cell populations were identified. Cell integrity was demonstrated using calcein AM, which is retained by cells with esterase activity and an intact membrane. For some experiments cell pellets were fixed, permeabilized, and immunolabeled for cell-specific markers. Characterization of brain small EV fractions showed the expected size, depletion of EV negative markers, and enrichment in positive and cell-type specific markers. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS We optimized and integrated established protocols, aiming to maximize information obtained from each human autopsy brain sample. The uniqueness of our method lies in its capability to isolate cells and EVs from a single cryopreserved brain sample. Our results not only demonstrate the feasibility of isolating specific brain cell subpopulations for RNA-seq but also validate these subpopulations at the protein level. The accelerated study of EVs from human samples is crucial for a better understanding of their contribution to neuron/glial crosstalk and disease progression.
Collapse
Affiliation(s)
- Mikhail Melnik
- UCLA School of Nursing, Los Angeles, CA 90095, USA; Neuroscience Interdepartmental Program, UCLA School of Medicine, Los Angeles, CA 90095, USA
| | | | - Ricky Ma
- UCLA School of Nursing, Los Angeles, CA 90095, USA
| | - Maria Corrada
- Departments of Neurology, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine, CA 92697, USA
| | - Claudia Kawas
- Departments of Neurology, Irvine, CA 92697, USA; Neurobiology & Behavior, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine, CA 92697, USA
| | - Ryan Bohannan
- Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine, CA 92697, USA
| | - Chad Caraway
- Institute for Memory Impairments and Neurological Disorders, UC Irvine, Irvine, CA 92697, USA
| | | | - Jason D Hinman
- Mary S. Easton Center for Alzheimer's Research at UCLA, Los Angeles, CA 90073, USA; Departments of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Varghese John
- Mary S. Easton Center for Alzheimer's Research at UCLA, Los Angeles, CA 90073, USA; Departments of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Tina Bilousova
- UCLA School of Nursing, Los Angeles, CA 90095, USA; Mary S. Easton Center for Alzheimer's Research at UCLA, Los Angeles, CA 90073, USA; Departments of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA.
| | - Karen H Gylys
- UCLA School of Nursing, Los Angeles, CA 90095, USA; Mary S. Easton Center for Alzheimer's Research at UCLA, Los Angeles, CA 90073, USA; Neuroscience Interdepartmental Program, UCLA School of Medicine, Los Angeles, CA 90095, USA
| |
Collapse
|
23
|
Li X, Qiu P. Gene representation bias in spatial transcriptomics. J Bioinform Comput Biol 2024; 22:2450007. [PMID: 39036848 DOI: 10.1142/s0219720024500070] [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] [Indexed: 07/23/2024]
Abstract
For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.
Collapse
Affiliation(s)
- Xinling Li
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Peng Qiu
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| |
Collapse
|
24
|
Wen J. Single-cell transcriptomics reveals antigen-presenting capacity and therapeutic resistance potential of immunomodulatory endothelial cells in colorectal cancer. Immun Inflamm Dis 2024; 12:e1311. [PMID: 38874280 PMCID: PMC11177288 DOI: 10.1002/iid3.1311] [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: 11/24/2023] [Revised: 05/10/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND The heterogeneity of tumor endothelial cells (TECs) hinders the efficacy of antiangiogenic therapies (AATs). Only a small percentage of angiogenic TECs are considered effective targets for AATs. Immunomodulatory ECs (IMECs), as a newly focused functional subgroup of endothelial cells (ECs), are being evaluated for their ability to regulate tumor immune balance and influence existing AATs. METHODS Based on single-cell transcriptome data from colorectal cancer in a publicly available database, we conducted a wide array of bioinformatic approaches to study EC subsets that meet the IMECs definition. Our investigation encompassed the gene expression signatures of these subsets, cellular composition differences, cell-cell interactions. RESULTS Two subsets that meet the IMECs definition were found in tumors and para-cancerous tissues. Combined with the results of gene ontological analysis and interaction with CD4+ T cells, we found that IMECs can present MHC-II antigens to mature CD4+ T cells. There were differences in the level of interaction between IMECs and different types of mature CD4+ T cell subsets. In addition, IMEC subsets had different expression levels of angiogenesis related genes. The angiogenesis score of IMECs decreased after patients received immunotherapy. IMEC subsets do not depend on a single proangiogenic receptor and are involved in regulating angiogenesis, which may reduce the efficacy of AATs. The adverse effects of specific IMEC subsets on AATs were validated in the RNA-seq dataset of the bevacizumab treatment group. CONCLUSION Our study suggests the potential MHC-II antigen presentation capacity of IMECs and the enhanced angiogenesis characteristics within tumors. The function of IMECs in the vascular network may have a potentially adverse effect on AATs. Controlling the functional properties of IMECs may be a new angle for tumor therapy.
Collapse
Affiliation(s)
- Jingyi Wen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| |
Collapse
|
25
|
Scheuermann S, Hücker S, Engel A, Ludwig N, Lebhardt P, Langejürgen J, Kirsch S. A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing. SLAS Technol 2024; 29:100133. [PMID: 38583803 DOI: 10.1016/j.slast.2024.100133] [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: 11/29/2023] [Revised: 03/25/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.
Collapse
Affiliation(s)
| | - Sarah Hücker
- Biomarkers and innovative Technology Development, Division Personalized Tumor Therapy, Fraunhofer ITEM, Regensburg, Germany
| | - Annika Engel
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Nicole Ludwig
- Human Genetics, Saarland University, University Hospital, Saarbrücken, Germany
| | | | | | - Stefan Kirsch
- Biomarkers and innovative Technology Development, Division Personalized Tumor Therapy, Fraunhofer ITEM, Regensburg, Germany.
| |
Collapse
|
26
|
Borrelli C, Gurtner A, Arnold IC, Moor AE. Stress-free single-cell transcriptomic profiling and functional genomics of murine eosinophils. Nat Protoc 2024; 19:1679-1709. [PMID: 38504138 DOI: 10.1038/s41596-024-00967-3] [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: 04/17/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Eosinophils are a class of granulocytes with pleiotropic functions in homeostasis and various human diseases. Nevertheless, they are absent from conventional single-cell RNA sequencing atlases owing to technical difficulties preventing their transcriptomic interrogation. Consequently, eosinophil heterogeneity and the gene regulatory networks underpinning their diverse functions remain poorly understood. We have developed a stress-free protocol for single-cell RNA capture from murine tissue-resident eosinophils, which revealed distinct intestinal subsets and their roles in colitis. Here we describe in detail how to enrich eosinophils from multiple tissues of residence and how to capture high-quality single-cell transcriptomes by preventing transcript degradation. By combining magnetic eosinophil enrichment with microwell-based single-cell RNA capture (BD Rhapsody), our approach minimizes shear stress and processing time. Moreover, we report how to perform genome-wide CRISPR pooled genetic screening in ex vivo-conditioned bone marrow-derived eosinophils to functionally probe pathways required for their differentiation and intestinal maturation. These protocols can be performed by any researcher with basic skills in molecular biology and flow cytometry, and can be adapted to investigate other granulocytes, such as neutrophils and mast cells, thereby offering potential insights into their roles in both homeostasis and disease pathogenesis. Single-cell transcriptomics of eosinophils can be performed in 2-3 d, while functional genomics assays may require up to 1 month.
Collapse
Affiliation(s)
- Costanza Borrelli
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Alessandra Gurtner
- Institute of Experimental Immunology, University of Zürich, Zürich, Switzerland
| | - Isabelle C Arnold
- Institute of Experimental Immunology, University of Zürich, Zürich, Switzerland.
| | - Andreas E Moor
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
| |
Collapse
|
27
|
Muto Y, Yoshimura Y, Wu H, Chang-Panesso M, Ledru N, Woodward OM, Outeda P, Cheng T, Mahjoub MR, Watnick TJ, Humphreys BD. Multi-omics profiling of mouse polycystic kidney disease progression at a single cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.27.595830. [PMID: 38854144 PMCID: PMC11160654 DOI: 10.1101/2024.05.27.595830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to end-stage kidney disease. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single nucleus multimodal atlas of an orthologous mouse PKD model at early, mid and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalogue differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.
Collapse
Affiliation(s)
- Yoshiharu Muto
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Yasuhiro Yoshimura
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Monica Chang-Panesso
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Nicolas Ledru
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Owen M. Woodward
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Patricia Outeda
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Tao Cheng
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Moe R. Mahjoub
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Terry J. Watnick
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Benjamin D. Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
28
|
Thuilliez C, Moquin-Beaudry G, Khneisser P, Marques Da Costa ME, Karkar S, Boudhouche H, Drubay D, Audinot B, Geoerger B, Scoazec JY, Gaspar N, Marchais A. CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data. BIOINFORMATICS ADVANCES 2024; 4:vbae081. [PMID: 38915885 PMCID: PMC11194756 DOI: 10.1093/bioadv/vbae081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/02/2024] [Accepted: 05/29/2024] [Indexed: 06/26/2024]
Abstract
Motivation Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates. Results In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis. Availability and implementation CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.
Collapse
Affiliation(s)
- Corentin Thuilliez
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Gaël Moquin-Beaudry
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Pierre Khneisser
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Maria Eugenia Marques Da Costa
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Slim Karkar
- University Bordeaux, CNRS, IBGC, UMR, Bordeaux 33077, France
- Bordeaux Bioinformatic Center CBiB, University of Bordeaux, Bordeaux 33000, France
| | - Hanane Boudhouche
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Damien Drubay
- Office of Biostatistics and Epidemiology, Gustave Roussy, Université Paris-Saclay, Villejuif 94805, France
- Inserm, Université Paris-Saclay, CESP U1018, Oncostat, Labeled Ligue Contre le Cancer, Villejuif 94805, France
| | - Baptiste Audinot
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Birgit Geoerger
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Jean-Yves Scoazec
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Nathalie Gaspar
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Antonin Marchais
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| |
Collapse
|
29
|
Kumari P, Kaur M, Dindhoria K, Ashford B, Amarasinghe SL, Thind AS. Advances in long-read single-cell transcriptomics. Hum Genet 2024:10.1007/s00439-024-02678-x. [PMID: 38787419 DOI: 10.1007/s00439-024-02678-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.
Collapse
Affiliation(s)
- Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Manmeet Kaur
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Bruce Ashford
- Illawarra Shoalhaven Local Health District (ISLHD), NSW Health, Wollongong, NSW, Australia
| | - Shanika L Amarasinghe
- Monash Biomedical Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
- Walter and Eliza Hall Institute of Medical Research, 1G, Royal Parade, Parkville, VIC, 3025, Australia
| | - Amarinder Singh Thind
- Illawarra Shoalhaven Local Health District (ISLHD), NSW Health, Wollongong, NSW, Australia.
- The School of Chemistry and Molecular Bioscience (SCMB), University of Wollongong, Loftus St, Wollongong, NSW, 2500, Australia.
| |
Collapse
|
30
|
Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. SCIENCE ADVANCES 2024; 10:eadh2588. [PMID: 38781336 PMCID: PMC11114236 DOI: 10.1126/sciadv.adh2588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/05/2024] [Indexed: 05/25/2024]
Abstract
Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.
Collapse
Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Zhang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xuan Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M. Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F. Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E. Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Departments of Psychiatry and Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H. Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | | | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| |
Collapse
|
31
|
Nguyen H, Nguyen H, Tran D, Draghici S, Nguyen T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res 2024; 52:4761-4783. [PMID: 38619038 PMCID: PMC11109966 DOI: 10.1093/nar/gkae267] [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: 10/26/2023] [Revised: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
Collapse
Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Duc Tran
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Advaita Bioinformatics, Ann Arbor, MI, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| |
Collapse
|
32
|
Giarmarco M, Seto J, Brock D, Brockerhoff S. Spatial detection of mitochondrial DNA and RNA in tissues. Front Cell Dev Biol 2024; 12:1346778. [PMID: 38808224 PMCID: PMC11130414 DOI: 10.3389/fcell.2024.1346778] [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: 11/29/2023] [Accepted: 04/12/2024] [Indexed: 05/30/2024] Open
Abstract
Background Mitochondrial health has gained attention in a number of diseases, both as an indicator of disease state and as a potential therapeutic target. The quality and amount of mitochondrial DNA (mtDNA) and RNA (mtRNA) can be important indicators of mitochondrial and cell health, but are difficult to measure in complex tissues. Methods mtDNA and mtRNA in zebrafish retina samples were fluorescently labeled using RNAscope™ in situ hybridization, then mitochondria were stained using immunohistochemistry. Pretreatment with RNase was used for validation. Confocal images were collected and analyzed, and relative amounts of mtDNA and mtRNA were reported. Findings regarding mtDNA were confirmed using qPCR. Results Signals from probes detecting mtDNA and mtRNA were localized to mitochondria, and were differentially sensitive to RNase. This labeling strategy allows for quantification of relative mtDNA and mtRNA levels in individual cells. As a demonstration of the method in a complex tissue, single photoreceptors in zebrafish retina were analyzed for mtDNA and mtRNA content. An increase in mtRNA but not mtDNA coincides with proliferation of mitochondria at night in cones. A similar trend was measured in rods. Discussion Mitochondrial gene expression is an important component of cell adaptations to disease, stress, or aging. This method enables the study of mtDNA and mtRNA in single cells of an intact, complex tissue. The protocol presented here uses commercially-available tools, and is adaptable to a range of species and tissue types.
Collapse
Affiliation(s)
- Michelle Giarmarco
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
| | - Jordan Seto
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- Department of Biochemistry, University of Washington, Seattle, WA, United States
| | - Daniel Brock
- Department of Biochemistry, University of Washington, Seattle, WA, United States
| | - Susan Brockerhoff
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- Department of Biochemistry, University of Washington, Seattle, WA, United States
| |
Collapse
|
33
|
Huang S, Shi W, Li S, Fan Q, Yang C, Cao J, Wu L. Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis. LAB ON A CHIP 2024; 24:2601-2621. [PMID: 38669201 DOI: 10.1039/d4lc00105b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.
Collapse
Affiliation(s)
- Shanqing Huang
- Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Weixiong Shi
- Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Shiyu Li
- Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qian Fan
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Chaoyong Yang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
- Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jiao Cao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Lingling Wu
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| |
Collapse
|
34
|
Ramnauth AD, Tippani M, Divecha HR, Papariello AR, Miller RA, Nelson ED, Pattie EA, Kleinman JE, Maynard KR, Collado-Torres L, Hyde TM, Martinowich K, Hicks SC, Page SC. Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.20.567883. [PMID: 38045413 PMCID: PMC10690172 DOI: 10.1101/2023.11.20.567883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we investigated age-associated changes in transcriptome-wide spatial gene expression in the human dentate gyrus across the lifespan. Genes associated with neurogenesis and the extracellular matrix were enriched in infants, while gene markers of inhibitory neurons and cell proliferation showed increases and decreases in post-infancy, respectively. While we did not find evidence for neural proliferation post-infancy, we did identify molecular signatures supporting protracted maturation of granule cells. We also identified a wide-spread hippocampal aging signature and an age-associated increase in genes related to neuroinflammation. Our findings suggest major changes to the putative neurogenic niche after infancy and identify molecular foci of brain aging in glial and neuropil enriched tissue.
Collapse
|
35
|
Upadhye A, Meza Landeros KE, Ramírez-Suástegui C, Schmiedel BJ, Woo E, Chee SJ, Malicki D, Coufal NG, Gonda D, Levy ML, Greenbaum JA, Seumois G, Crawford J, Roberts WD, Schoenberger SP, Cheroutre H, Ottensmeier CH, Vijayanand P, Ganesan AP. Intra-tumoral T cells in pediatric brain tumors display clonal expansion and effector properties. NATURE CANCER 2024; 5:791-807. [PMID: 38228835 DOI: 10.1038/s43018-023-00706-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/11/2023] [Indexed: 01/18/2024]
Abstract
Brain tumors in children are a devastating disease in a high proportion of patients. Owing to inconsistent results in clinical trials in unstratified patients, the role of immunotherapy remains unclear. We performed an in-depth survey of the single-cell transcriptomes and clonal relationship of intra-tumoral T cells from children with brain tumors. Our results demonstrate that a large fraction of T cells in the tumor tissue are clonally expanded with the potential to recognize tumor antigens. Such clonally expanded T cells display enrichment of transcripts linked to effector function, tissue residency, immune checkpoints and signatures of neoantigen-specific T cells and immunotherapy response. We identify neoantigens in pediatric brain tumors and show that neoantigen-specific T cell gene signatures are linked to better survival outcomes. Notably, among the patients in our cohort, we observe substantial heterogeneity in the degree of clonal expansion and magnitude of T cell response. Our findings suggest that characterization of intra-tumoral T cell responses may enable selection of patients for immunotherapy, an approach that requires prospective validation in clinical trials.
Collapse
Affiliation(s)
- Aditi Upadhye
- La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Kevin E Meza Landeros
- La Jolla Institute for Immunology, La Jolla, CA, USA
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Mexico
| | | | | | - Edwin Woo
- Southampton University Hospitals NHS Trust, Southampton, UK
| | - Serena J Chee
- Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Denise Malicki
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
- Rady Children's Hospital, San Diego, CA, USA
| | - Nicole G Coufal
- Rady Children's Hospital, San Diego, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - David Gonda
- Rady Children's Hospital, San Diego, CA, USA
- Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA
| | - Michael L Levy
- Rady Children's Hospital, San Diego, CA, USA
- Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA
| | | | | | - John Crawford
- Rady Children's Hospital, San Diego, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Children's Hospital Orange County, Irvine, CA, USA
| | - William D Roberts
- Rady Children's Hospital, San Diego, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | | | | | - Christian H Ottensmeier
- La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Clatterbridge Cancer Center NHS Foundation Trust, Liverpool, UK
| | - Pandurangan Vijayanand
- La Jolla Institute for Immunology, La Jolla, CA, USA.
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Anusha-Preethi Ganesan
- La Jolla Institute for Immunology, La Jolla, CA, USA.
- Rady Children's Hospital, San Diego, CA, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
36
|
Renaut S, Saavedra Armero V, Boudreau DK, Gaudreault N, Desmeules P, Thériault S, Mathieu P, Joubert P, Bossé Y. Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights. PLoS Genet 2024; 20:e1011301. [PMID: 38814983 PMCID: PMC11166281 DOI: 10.1371/journal.pgen.1011301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 06/11/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.
Collapse
Affiliation(s)
- Sébastien Renaut
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Victoria Saavedra Armero
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Dominique K. Boudreau
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Nathalie Gaudreault
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Patrice Desmeules
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Sébastien Thériault
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Patrick Mathieu
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Philippe Joubert
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
- Department of Molecular Medicine, Université Laval, Quebec City, Canada
| |
Collapse
|
37
|
Xie G, Toledo MP, Hu X, Yong HJ, Sanchez PS, Liu C, Naji A, Irianto J, Wang YJ. NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing. BMC Genomics 2024; 25:427. [PMID: 38689254 PMCID: PMC11059690 DOI: 10.1186/s12864-024-10335-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation. RESULTS We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed. CONCLUSIONS Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.
Collapse
Affiliation(s)
- Gengqiang Xie
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Maria Pilar Toledo
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Xue Hu
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Hyo Jeong Yong
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Pamela Sandoval Sanchez
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Chengyang Liu
- Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Ali Naji
- Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jerome Irianto
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Yue J Wang
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA.
| |
Collapse
|
38
|
Kidman J, Zemek RM, Sidhom JW, Correa D, Principe N, Sheikh F, Fear VS, Forbes CA, Chopra A, Boon L, Zaitouny A, de Jong E, Holt RA, Jones M, Millward MJ, Lassmann T, Forrest AR, Nowak AK, Watson M, Lake RA, Lesterhuis WJ, Chee J. Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes. Oncoimmunology 2024; 13:2345859. [PMID: 38686178 PMCID: PMC11057660 DOI: 10.1080/2162402x.2024.2345859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.
Collapse
MESH Headings
- Animals
- Immune Checkpoint Inhibitors/pharmacology
- Immune Checkpoint Inhibitors/therapeutic use
- Receptors, Antigen, T-Cell, alpha-beta/genetics
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- Mice
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/drug effects
- Lymphocytes, Tumor-Infiltrating/metabolism
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/drug effects
- CD8-Positive T-Lymphocytes/metabolism
- Humans
- Mice, Inbred C57BL
- Female
Collapse
Affiliation(s)
- Joel Kidman
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | | | | | - Debora Correa
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
| | - Nicola Principe
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | - Fezaan Sheikh
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | | | | | - Abha Chopra
- Medical Genomics Laboratories (IIID), Centre for Molecular Medicine and Innovative Therapeutics, Health Futures Institute, Murdoch University, Murdoch, Australia
| | | | - Ayham Zaitouny
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
- Department of Mathematical Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Emma de Jong
- Telethon Kids Institute, Perth, Australia
- Medical School, University of Western Australia, Perth, Australia
| | | | - Matt Jones
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | | | | | - Alistair R.R. Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | - Anna K. Nowak
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
- Medical School, University of Western Australia, Perth, Australia
| | - Mark Watson
- Medical Genomics Laboratories (IIID), Centre for Molecular Medicine and Innovative Therapeutics, Health Futures Institute, Murdoch University, Murdoch, Australia
| | - Richard A. Lake
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | - W. Joost Lesterhuis
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
- Telethon Kids Institute, Perth, Australia
| | - Jonathan Chee
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| |
Collapse
|
39
|
Dittmar DJ, Pielmeier F, Strieder N, Fischer A, Herbst M, Stanewsky H, Wenzl N, Röseler E, Eder R, Gebhard C, Schwarzfischer-Pfeilschifter L, Albrecht C, Herr W, Edinger M, Hoffmann P, Rehli M. Donor regulatory T cells rapidly adapt to recipient tissues to control murine acute graft-versus-host disease. Nat Commun 2024; 15:3224. [PMID: 38622133 PMCID: PMC11018811 DOI: 10.1038/s41467-024-47575-z] [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: 04/03/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
The adoptive transfer of regulatory T cells is a promising strategy to prevent graft-versus-host disease after allogeneic bone marrow transplantation. Here, we use a major histocompatibility complex-mismatched mouse model to follow the fate of in vitro expanded donor regulatory T cells upon migration to target organs. Employing comprehensive gene expression and repertoire profiling, we show that they retain their suppressive function and plasticity after transfer. Upon entering non-lymphoid tissues, donor regulatory T cells acquire organ-specific gene expression profiles resembling tissue-resident cells and activate hallmark suppressive and cytotoxic pathways, most evidently in the colon, when co-transplanted with graft-versus-host disease-inducing conventional T cells. Dominant T cell receptor clonotypes overlap between organs and across recipients and their relative abundance correlates with protection efficacy. Thus, this study reveals donor regulatory T cell selection and adaptation mechanisms in target organs and highlights protective features of Treg to guide the development of improved graft-versus-host disease prevention strategies.
Collapse
Affiliation(s)
- David J Dittmar
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
- BioNTech SE, 82061, Neuried, Germany
| | - Franziska Pielmeier
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | | | - Alexander Fischer
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | - Michael Herbst
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
- Institute of Experimental Immunology, Research Unit Tumorimmunology, University of Zurich, Zurich, Switzerland
| | - Hanna Stanewsky
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | - Niklas Wenzl
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany
| | - Eveline Röseler
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany
| | - Rüdiger Eder
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | - Claudia Gebhard
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany
| | | | - Christin Albrecht
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | - Wolfgang Herr
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany
| | - Matthias Edinger
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany.
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany.
| | - Petra Hoffmann
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany.
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany.
| | - Michael Rehli
- Department of Internal Medicine III, University Hospital Regensburg, 93053, Regensburg, Germany.
- Leibniz Institute for Immunotherapy, 93053, Regensburg, Germany.
| |
Collapse
|
40
|
Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
Collapse
Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
| |
Collapse
|
41
|
Maden SK, Huuki-Myers LA, Kwon SH, Collado-Torres L, Maynard KR, Hicks SC. lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.04.588105. [PMID: 38617294 PMCID: PMC11014536 DOI: 10.1101/2024.04.04.588105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.
Collapse
Affiliation(s)
- Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| |
Collapse
|
42
|
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.
Collapse
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.
| |
Collapse
|
43
|
Yang B, Hu S, Jiang Y, Xu L, Shu S, Zhang H. Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases. Mol Neurobiol 2024:10.1007/s12035-024-04126-3. [PMID: 38564138 DOI: 10.1007/s12035-024-04126-3] [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: 11/29/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.
Collapse
Affiliation(s)
- Bingjie Yang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Shuqi Hu
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yiru Jiang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Lei Xu
- Department of Neurology, Zhejiang Rongjun Hospital, Jiaxing, Zhejiang, China
| | - Song Shu
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Hao Zhang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
44
|
Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [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: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
Collapse
Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
45
|
Reed AD, Pensa S, Steif A, Stenning J, Kunz DJ, Porter LJ, Hua K, He P, Twigger AJ, Siu AJQ, Kania K, Barrow-McGee R, Goulding I, Gomm JJ, Speirs V, Jones JL, Marioni JC, Khaled WT. A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast. Nat Genet 2024; 56:652-662. [PMID: 38548988 PMCID: PMC11018528 DOI: 10.1038/s41588-024-01688-9] [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: 03/28/2023] [Accepted: 02/09/2024] [Indexed: 04/17/2024]
Abstract
Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.
Collapse
Affiliation(s)
- Austin D Reed
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Sara Pensa
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Adi Steif
- CRUK, Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Jack Stenning
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | | | - Linsey J Porter
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Kui Hua
- CRUK, Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Peng He
- EMBL European Bioinformatics Institute, Hinxton, UK
- Sanger Institute, Hinxton, UK
| | - Alecia-Jane Twigger
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Abigail J Q Siu
- Department of Pharmacology, University of Cambridge, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Katarzyna Kania
- CRUK, Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Rachel Barrow-McGee
- Breast Cancer Now Tissue Bank, Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK
| | - Iain Goulding
- Breast Cancer Now Tissue Bank, Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK
| | - Jennifer J Gomm
- Breast Cancer Now Tissue Bank, Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK
| | - Valerie Speirs
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Aberdeen Cancer Centre, Aberdeen, UK
| | - J Louise Jones
- Breast Cancer Now Tissue Bank, Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK
| | - John C Marioni
- CRUK, Cambridge Institute, University of Cambridge, Cambridge, UK.
- EMBL European Bioinformatics Institute, Hinxton, UK.
- Sanger Institute, Hinxton, UK.
- Genentech, San Francisco, CA, USA.
| | - Walid T Khaled
- Department of Pharmacology, University of Cambridge, Cambridge, UK.
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
| |
Collapse
|
46
|
Jiménez-Gracia L, Marchese D, Nieto JC, Caratù G, Melón-Ardanaz E, Gudiño V, Roth S, Wise K, Ryan NK, Jensen KB, Hernando-Momblona X, Bernardes JP, Tran F, Sievers LK, Schreiber S, van den Berge M, Kole T, van der Velde PL, Nawijn MC, Rosenstiel P, Batlle E, Butler LM, Parish IA, Plummer J, Gut I, Salas A, Heyn H, Martelotto LG. FixNCut: single-cell genomics through reversible tissue fixation and dissociation. Genome Biol 2024; 25:81. [PMID: 38553769 PMCID: PMC10979608 DOI: 10.1186/s13059-024-03219-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/18/2024] [Indexed: 04/02/2024] Open
Abstract
The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.
Collapse
Affiliation(s)
- Laura Jiménez-Gracia
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Domenica Marchese
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Juan C Nieto
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Ginevra Caratù
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Elisa Melón-Ardanaz
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Victoria Gudiño
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Sara Roth
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Monash University Department of Surgery, Alfred Hospital, Melbourne, VIC, Australia
| | - Kellie Wise
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Australian Genomics Research Facility, Adelaide, South Australia, Australia
| | - Natalie K Ryan
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Kirk B Jensen
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Australian Genomics Research Facility, Adelaide, South Australia, Australia
| | - Xavier Hernando-Momblona
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
| | - Joana P Bernardes
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Florian Tran
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Laura Katharina Sievers
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Maarten van den Berge
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tessa Kole
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Petra L van der Velde
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Eduard Batlle
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Lisa M Butler
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Ian A Parish
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Jasmine Plummer
- St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ivo Gut
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Azucena Salas
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain.
- Universitat de Barcelona (UB), Barcelona, Spain.
- Omniscope, Barcelona, Spain.
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia.
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia.
- Omniscope, Barcelona, Spain.
| |
Collapse
|
47
|
Vathrakokoili Pournara A, Miao Z, Beker OY, Nolte N, Brazma A, Papatheodorou I. CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues. BIOINFORMATICS ADVANCES 2024; 4:vbae048. [PMID: 38638280 PMCID: PMC11023940 DOI: 10.1093/bioadv/vbae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/20/2024] [Accepted: 03/21/2024] [Indexed: 04/20/2024]
Abstract
Motivation Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods. Results In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods. Availability and implementation https://github.com/Papatheodorou-Group/CATD_snakemake.
Collapse
Affiliation(s)
- Anna Vathrakokoili Pournara
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 511436, China
| | - Ozgur Yilimaz Beker
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956, Turkey
| | - Nadja Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 121-1000, Slovenia
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| |
Collapse
|
48
|
Baker GJ, Novikov E, Zhao Z, Vallius T, Davis JA, Lin JR, Muhlich JL, Mittendorf EA, Santagata S, Guerriero JL, Sorger PK. Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565120. [PMID: 37961235 PMCID: PMC10634977 DOI: 10.1101/2023.11.01.565120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.
Collapse
Affiliation(s)
- Gregory J. Baker
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Edward Novikov
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Ziyuan Zhao
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA
| | - Tuulia Vallius
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Janae A. Davis
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Jeremy L. Muhlich
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Elizabeth A. Mittendorf
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jennifer L. Guerriero
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Peter K. Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| |
Collapse
|
49
|
Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [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: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
Collapse
Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| |
Collapse
|
50
|
Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.21.545365. [PMID: 37645893 PMCID: PMC10461981 DOI: 10.1101/2023.06.21.545365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.
Collapse
Affiliation(s)
- Patrick G. Schupp
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Samuel J. Shelton
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Daniel J. Brody
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Rebecca Eliscu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Brett E. Johnson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Tali Mazor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Kevin W. Kelley
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Matthew B. Potts
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Michael W. McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Eric J. Huang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Daniel A. Lim
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Russell O. Pieper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Joseph F. Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Joanna J. Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Department of Pathology, University of California, San Francisco, San Francisco, California, USA
| | - Michael C. Oldham
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| |
Collapse
|