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Zhong Y, Cui S, Yang Y, Cai JJ. Controlled Noise: Evidence of epigenetic regulation of Single-Cell expression variability. Bioinformatics 2024; 40:btae457. [PMID: 39018178 PMCID: PMC11283284 DOI: 10.1093/bioinformatics/btae457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/24/2024] [Accepted: 07/16/2024] [Indexed: 07/19/2024] Open
Abstract
MOTIVATION Understanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap. RESULTS This paper introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level. AVAILABILITY The source code and data used in this paper can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Zhong
- School of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China
| | - Siwei Cui
- School of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China
| | - Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, United States
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, United States
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2
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Zhang T, Wu Z, Li L, Ren J, Zhang Z, Wang G. CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data. Brief Bioinform 2024; 25:bbae198. [PMID: 38678387 PMCID: PMC11056015 DOI: 10.1093/bib/bbae198] [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: 01/31/2024] [Revised: 03/08/2024] [Accepted: 04/10/2024] [Indexed: 04/30/2024] Open
Abstract
In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Zhenao Wu
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Liangyu Li
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Jixiang Ren
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Ziheng Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
- Faculty of Computing, Harbin Institute of Technology Harbin, 150001, China
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3
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Osorio D, Capasso A, Eckhardt SG, Giri U, Somma A, Pitts TM, Lieu CH, Messersmith WA, Bagby SM, Singh H, Das J, Sahni N, Yi SS, Kuijjer ML. Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data. NATURE COMPUTATIONAL SCIENCE 2024; 4:237-250. [PMID: 38438786 DOI: 10.1038/s43588-024-00597-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 01/17/2024] [Indexed: 03/06/2024]
Abstract
Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.
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Affiliation(s)
- Daniel Osorio
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
| | - Anna Capasso
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - S Gail Eckhardt
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Uma Giri
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Alexander Somma
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Todd M Pitts
- Division of Medical Oncology, University of Colorado Cancer Center, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Christopher H Lieu
- Division of Medical Oncology, University of Colorado Cancer Center, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Wells A Messersmith
- Division of Medical Oncology, University of Colorado Cancer Center, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Stacey M Bagby
- Division of Medical Oncology, University of Colorado Cancer Center, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Harinder Singh
- Department of Immunology, Center for Systems Immunology, University of Pittsburg, Pittsburg, PA, USA
| | - Jishnu Das
- Department of Immunology, Center for Systems Immunology, University of Pittsburg, Pittsburg, PA, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - S Stephen Yi
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
- Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX, USA.
- Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.
| | - Marieke L Kuijjer
- Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway.
- Department of Pathology, Leiden University Medical Center (LUMC), Leiden University, Leiden, The Netherlands.
- Leiden Center for Computational Oncology, Leiden University Medical Center (LUMC), Leiden University, Leiden, The Netherlands.
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4
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Liu J, Kreimer A, Li WV. Differential variability analysis of single-cell gene expression data. Brief Bioinform 2023; 24:bbad294. [PMID: 37598422 PMCID: PMC10516347 DOI: 10.1093/bib/bbad294] [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: 04/10/2023] [Revised: 07/18/2023] [Accepted: 07/29/2023] [Indexed: 08/22/2023] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.
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Affiliation(s)
- Jiayi Liu
- Graduate Programs in Molecular Biosciences, Rutgers, The State University of New Jersey, 604 Allison Rd, Piscataway, 08854, NJ, USA
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, 08854, NJ, USA
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ, USA
| | - Anat Kreimer
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, 08854, NJ, USA
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ, USA
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 900 University Ave, Riverside, 92521, CA, USA
- Previous affiliation where part of the work was completed: Department of Biostatistics and Epidemiology, Rutgers, The State University of New Jersey, 683 Hoes Lane West, Piscataway, 08854, NJ, USA
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5
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Wolf S, Melo D, Garske KM, Pallares LF, Lea AJ, Ayroles JF. Characterizing the landscape of gene expression variance in humans. PLoS Genet 2023; 19:e1010833. [PMID: 37410774 DOI: 10.1371/journal.pgen.1010833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023] Open
Abstract
Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.
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Affiliation(s)
- Scott Wolf
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Diogo Melo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kristina M Garske
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Luisa F Pallares
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Amanda J Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Child and Brain Development, Canadian Institute for Advanced Research, Toronto, Canada
| | - Julien F Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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6
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Yang Y, Li G, Zhong Y, Xu Q, Lin YT, Roman-Vicharra C, Chapkin RS, Cai JJ. scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs. Cell Syst 2023; 14:302-311.e4. [PMID: 36787742 PMCID: PMC10121998 DOI: 10.1016/j.cels.2023.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/22/2022] [Accepted: 01/20/2023] [Indexed: 02/16/2023]
Abstract
We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.
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Affiliation(s)
- Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Yan Zhong
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
| | - Qian Xu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Yu-Te Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Cristhian Roman-Vicharra
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Robert S Chapkin
- Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases, Texas A&M University, College Station, TX 77843, USA.
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA; Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA; Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, USA.
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7
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Yan B, Wang C, Chakravorty S, Zhang Z, Kadadi SD, Zhuang Y, Sirit I, Hu Y, Jung M, Sahoo SS, Wang L, Shao K, Anderson NL, Trujillo‐Ochoa JL, Briggs SD, Liu X, Olson MR, Afzali B, Zhao B, Kazemian M. A comprehensive single cell data analysis of lymphoblastoid cells reveals the role of super-enhancers in maintaining EBV latency. J Med Virol 2023; 95:e28362. [PMID: 36453088 PMCID: PMC10027397 DOI: 10.1002/jmv.28362] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 12/02/2022]
Abstract
We probed the lifecycle of Epstein-Barr virus (EBV) on a cell-by-cell basis using single cell RNA sequencing (scRNA-seq) data from nine publicly available lymphoblastoid cell lines (LCLs). While the majority of LCLs comprised cells containing EBV in the latent phase, two other clusters of cells were clearly evident and were distinguished by distinct expression of host and viral genes. Notably, both were high expressors of EBV LMP1/BNLF2 and BZLF1 compared to another cluster that expressed neither gene. The two novel clusters differed from each other in their expression of EBV lytic genes, including glycoprotein gene GP350. The first cluster, comprising GP350- LMP1hi cells, expressed high levels of HIF1A and was transcriptionally regulated by HIF1-α. Treatment of LCLs with Pevonedistat, a drug that enhances HIF1-α signaling, markedly induced this cluster. The second cluster, containing GP350+ LMP1hi cells, expressed EBV lytic genes. Host genes that are controlled by super-enhancers (SEs), such as transcription factors MYC and IRF4, had the lowest expression in this cluster. Functionally, the expression of genes regulated by MYC and IRF4 in GP350+ LMP1hi cells were lower compared to other cells. Indeed, induction of EBV lytic reactivation in EBV+ AKATA reduced the expression of these SE-regulated genes. Furthermore, CRISPR-mediated perturbation of the MYC or IRF4 SEs in LCLs induced the lytic EBV gene expression, suggesting that host SEs and/or SE target genes are required for maintenance of EBV latency. Collectively, our study revealed EBV-associated heterogeneity among LCLs that may have functional consequence on host and viral biology.
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Affiliation(s)
- Bingyu Yan
- Department of BiochemistryPurdue UniversityWest LafayetteIndianaUSA
| | - Chong Wang
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | | | - Zonghao Zhang
- Department of Agricultural and Biological EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Simran D. Kadadi
- Department of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Yuxin Zhuang
- Department of BiochemistryPurdue UniversityWest LafayetteIndianaUSA
| | - Isabella Sirit
- Department of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yonghua Hu
- Department of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Minwoo Jung
- Department of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | | | - Luopin Wang
- Department of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Kunming Shao
- Department of Agricultural and Biological EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Nicole L. Anderson
- Department of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Jorge L. Trujillo‐Ochoa
- Immunoregulation Section, Kidney Diseases BranchNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIHBethesdaMarylandUSA
| | - Scott D. Briggs
- Department of BiochemistryPurdue UniversityWest LafayetteIndianaUSA
| | - Xing Liu
- Department of BiochemistryPurdue UniversityWest LafayetteIndianaUSA
| | - Matthew R. Olson
- Department of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Behdad Afzali
- Immunoregulation Section, Kidney Diseases BranchNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIHBethesdaMarylandUSA
| | - Bo Zhao
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Majid Kazemian
- Department of BiochemistryPurdue UniversityWest LafayetteIndianaUSA
- Department of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
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8
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SoRelle ED, Dai J, Reinoso-Vizcaino NM, Barry AP, Chan C, Luftig MA. Time-resolved transcriptomes reveal diverse B cell fate trajectories in the early response to Epstein-Barr virus infection. Cell Rep 2022; 40:111286. [PMID: 36044865 PMCID: PMC9879279 DOI: 10.1016/j.celrep.2022.111286] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/07/2022] [Accepted: 08/08/2022] [Indexed: 01/28/2023] Open
Abstract
Epstein-Barr virus infection of B lymphocytes elicits diverse host responses via well-adapted transcriptional control dynamics. Consequently, this host-pathogen interaction provides a powerful system to explore fundamental processes leading to consensus fate decisions. Here, we use single-cell transcriptomics to construct a genome-wide multistate model of B cell fates upon EBV infection. Additional single-cell data from human tonsils reveal correspondence of model states to analogous in vivo phenotypes within secondary lymphoid tissue, including an EBV+ analog of multipotent activated precursors that can yield early memory B cells. These resources yield exquisitely detailed perspectives of the transforming cellular landscape during an oncogenic viral infection that simulates antigen-induced B cell activation and differentiation. Thus, they support investigations of state-specific EBV-host dynamics, effector B cell fates, and lymphomagenesis. To demonstrate this potential, we identify EBV infection dynamics in FCRL4+/TBX21+ atypical memory B cells that are pathogenically associated with numerous immune disorders.
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Affiliation(s)
- Elliott D SoRelle
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Joanne Dai
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Nicolás M Reinoso-Vizcaino
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Ashley P Barry
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Micah A Luftig
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA.
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9
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Cancer: More than a geneticist’s Pandora’s box. J Biosci 2022. [DOI: 10.1007/s12038-022-00254-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Bentzur A, Alon S, Shohat-Ophir G. Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. Int J Mol Sci 2022; 23:3811. [PMID: 35409169 PMCID: PMC8998543 DOI: 10.3390/ijms23073811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/26/2022] [Accepted: 03/29/2022] [Indexed: 12/10/2022] Open
Abstract
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
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Affiliation(s)
- Assa Bentzur
- The Mina & Everard Goodman Faculty of Life Sciences, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology, Bar-Ilan University, Ramat Gan 5290002, Israel;
- The Alexander Kofkin Faculty of Engineering, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shahar Alon
- The Alexander Kofkin Faculty of Engineering, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Galit Shohat-Ophir
- The Mina & Everard Goodman Faculty of Life Sciences, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology, Bar-Ilan University, Ramat Gan 5290002, Israel;
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11
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M Ascensión A, Ibáñez-Solé O, Inza I, Izeta A, Araúzo-Bravo MJ. Triku: a feature selection method based on nearest neighbors for single-cell data. Gigascience 2022; 11:6547682. [PMID: 35277963 PMCID: PMC8917514 DOI: 10.1093/gigascience/giac017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 09/24/2021] [Indexed: 01/03/2023] Open
Abstract
Background Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset. Results Triku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes. Conclusion Triku is developed in Python 3 and is available at https://github.com/alexmascension/triku.
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Affiliation(s)
- Alex M Ascensión
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
- Biodonostia Health Research Institute, Tissue Engineering Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
| | - Olga Ibáñez-Solé
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
- Biodonostia Health Research Institute, Tissue Engineering Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
| | - Iñaki Inza
- Intelligent Systems Group, Computer Science Faculty, University of the Basque Country, Donostia-San Sebastian, 20018, Spain
| | - Ander Izeta
- Biodonostia Health Research Institute, Tissue Engineering Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
| | - Marcos J Araúzo-Bravo
- Biodonostia Health Research Institute, Computational Biology and Systems Biomedicine Group, Paseo Dr. Begiristain, s/n, Donostia-San Sebastian, 20014, Spain
- Max Planck Institute for Molecular Biomedicine, Roentgenstr. 20, 48149 Muenster, German
- IKERBASQUE, Basque Foundation for Science, Euskadi plaza 5, Bilbao, 48009, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), 48940 Leioa, Spain
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12
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Sharon M, Vinogradov E, Argov CM, Lazarescu O, Zoabi Y, Hekselman I, Yeger-Lotem E. The differential activity of biological processes in tissues and cell subsets can illuminate disease-related processes and cell-type identities. Bioinformatics 2022; 38:1584-1592. [PMID: 35015838 DOI: 10.1093/bioinformatics/btab883] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/09/2021] [Accepted: 01/02/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION The distinct functionalities of human tissues and cell types underlie complex phenotype-genotype relationships, yet often remain elusive. Harnessing the multitude of bulk and single-cell human transcriptomes while focusing on processes can help reveal these distinct functionalities. RESULTS The Tissue-Process Activity (TiPA) method aims to identify processes that are preferentially active or under-expressed in specific contexts, by comparing the expression levels of process genes between contexts. We tested TiPA on 1579 tissue-specific processes and bulk tissue transcriptomes, finding that it performed better than another method. Next, we used TiPA to ask whether the activity of certain processes could underlie the tissue-specific manifestation of 1233 hereditary diseases. We found that 21% of the disease-causing genes indeed participated in such processes, thereby illuminating their genotype-phenotype relationships. Lastly, we applied TiPA to single-cell transcriptomes of 108 human cell types, revealing that process activities often match cell-type identities and can thus aid annotation efforts. Hence, differential activity of processes can highlight the distinct functionality of tissues and cells in a robust and meaningful manner. AVAILABILITY AND IMPLEMENTATION TiPA code is available in GitHub (https://github.com/moranshar/TiPA). In addition, all data are available as part of the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Moran Sharon
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Or Lazarescu
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Yazeed Zoabi
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.,The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
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13
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Ušaj M, Moretto L, Månsson A. Critical Evaluation of Current Hypotheses for the Pathogenesis of Hypertrophic Cardiomyopathy. Int J Mol Sci 2022; 23:2195. [PMID: 35216312 PMCID: PMC8880276 DOI: 10.3390/ijms23042195] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 02/04/2023] Open
Abstract
Hereditary hypertrophic cardiomyopathy (HCM), due to mutations in sarcomere proteins, occurs in more than 1/500 individuals and is the leading cause of sudden cardiac death in young people. The clinical course exhibits appreciable variability. However, typically, heart morphology and function are normal at birth, with pathological remodeling developing over years to decades, leading to a phenotype characterized by asymmetric ventricular hypertrophy, scattered fibrosis and myofibrillar/cellular disarray with ultimate mechanical heart failure and/or severe arrhythmias. The identity of the primary mutation-induced changes in sarcomere function and how they trigger debilitating remodeling are poorly understood. Support for the importance of mutation-induced hypercontractility, e.g., increased calcium sensitivity and/or increased power output, has been strengthened in recent years. However, other ideas that mutation-induced hypocontractility or non-uniformities with contractile instabilities, instead, constitute primary triggers cannot yet be discarded. Here, we review evidence for and criticism against the mentioned hypotheses. In this process, we find support for previous ideas that inefficient energy usage and a blunted Frank-Starling mechanism have central roles in pathogenesis, although presumably representing effects secondary to the primary mutation-induced changes. While first trying to reconcile apparently diverging evidence for the different hypotheses in one unified model, we also identify key remaining questions and suggest how experimental systems that are built around isolated primarily expressed proteins could be useful.
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Affiliation(s)
| | | | - Alf Månsson
- Department of Chemistry and Biomedical Sciences, Faculty of Health and Life Sciences, Linnaeus University, SE-39182 Kalmar, Sweden; (M.U.); (L.M.)
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14
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Schwabe D, Falcke M. On the relation between input and output distributions of scRNA-seq experiments. Bioinformatics 2022; 38:1336-1343. [PMID: 34908126 DOI: 10.1093/bioinformatics/btab841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/01/2021] [Accepted: 12/12/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing determines RNA copy numbers per cell for a given gene. However, technical noise poses the question how observed distributions (output) are connected to their cellular distributions (input). RESULTS We model a single-cell RNA sequencing setup consisting of PCR amplification and sequencing, and derive probability distribution functions for the output distribution given an input distribution. We provide copy number distributions arising from single transcripts during PCR amplification with exact expressions for mean and variance. We prove that the coefficient of variation of the output of sequencing is always larger than that of the input distribution. Experimental data reveals the variance and mean of the input distribution to obey characteristic relations, which we specifically determine for a HeLa dataset. We can calculate as many moments of the input distribution as are known of the output distribution (up to all). This, in principle, completely determines the input from the output distribution. AVAILABILITY AND IMPLEMENTATION Source code freely available at https://github.com/danielschw188/InputOutputSCRNASeq. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Schwabe
- Mathematical Cell Physiology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Martin Falcke
- Mathematical Cell Physiology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany.,Department of Physics, Humboldt University Berlin, 12489 Berlin, Germany
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15
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Buttler CA, Chuong EB. Emerging roles for endogenous retroviruses in immune epigenetic regulation. Immunol Rev 2022; 305:165-178. [PMID: 34816452 PMCID: PMC8766910 DOI: 10.1111/imr.13042] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/21/2021] [Accepted: 11/12/2021] [Indexed: 01/03/2023]
Abstract
In recent years, there has been significant progress toward understanding the transcriptional networks underlying mammalian immune responses, fueled by advances in regulatory genomic technologies. Epigenomic studies profiling immune cells have generated detailed genome-wide maps of regulatory elements that will be key to deciphering the regulatory networks underlying cellular immune responses and autoimmune disorders. Unbiased analyses of these genomic maps have uncovered endogenous retroviruses as an unexpected ally in the regulation of human immune systems. Despite their parasitic origins, studies are finding an increasing number of examples of retroviral sequences having been co-opted for beneficial immune function and regulation by the host cell. Here, we review how endogenous retroviruses have given rise to numerous regulatory elements that shape the epigenetic landscape of host immune responses. We will discuss the implications of these elements on the function, dysfunction, and evolution of innate immunity.
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16
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Wiggins GAR, Black MA, Dunbier A, Morley-Bunker AE, Pearson JF, Walker LC. Increased gene expression variability in BRCA1-associated and basal-like breast tumours. Breast Cancer Res Treat 2021; 189:363-375. [PMID: 34287743 PMCID: PMC8357684 DOI: 10.1007/s10549-021-06328-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/07/2021] [Indexed: 11/21/2022]
Abstract
Purpose Inherited variants in the cancer susceptibility genes, BRCA1 and BRCA2 account for up to 5% of breast cancers. Multiple gene expression studies have analysed gene expression patterns that maybe associated with BRCA12 pathogenic variant status; however, results from these studies lack consensus. These studies have focused on the differences in population means to identified genes associated with BRCA1/2-carriers with little consideration for gene expression variability, which is also under genetic control and is a feature of cellular function. Methods We measured differential gene expression variability in three of the largest familial breast cancer datasets and a 2116 breast cancer meta-cohort. Additionally, we used RNA in situ hybridisation to confirm expression variability of EN1 in an independent cohort of more than 500 breast tumours. Results BRCA1-associated breast tumours exhibited a 22.8% (95% CI 22.3–23.2) increase in transcriptome-wide gene expression variability compared to BRCAx tumours. Additionally, 40 genes were associated with BRCA1-related breast cancers that had ChIP-seq data suggestive of enriched EZH2 binding. Of these, two genes (EN1 and IGF2BP3) were significantly variable in both BRCA1-associated and basal-like breast tumours. RNA in situ analysis of EN1 supported a significant (p = 6.3 × 10−04) increase in expression variability in BRCA1-associated breast tumours. Conclusion Our novel results describe a state of increased gene expression variability in BRCA1-related and basal-like breast tumours. Furthermore, genes with increased variability may be driven by changes in DNA occupancy of epigenetic effectors. The variation in gene expression is replicable and led to the identification of novel associations between genes and disease phenotypes. Supplementary Information The online version contains supplementary material available at 10.1007/s10549-021-06328-y.
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Affiliation(s)
- George A R Wiggins
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Michael A Black
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Anita Dunbier
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Arthur E Morley-Bunker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | | | - John F Pearson
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.,Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand
| | - Logan C Walker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.
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17
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Chen Z, Yang Z, Yuan X, Zhang X, Hao P. scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy. BMC Bioinformatics 2021; 22:211. [PMID: 33888056 PMCID: PMC8063398 DOI: 10.1186/s12859-021-04136-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/26/2021] [Indexed: 11/26/2022] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation. Result In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools. Conclusion Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04136-1.
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Affiliation(s)
- Zechuan Chen
- College of Life Sciences, Shanghai University, Shanghai, China.,Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Zeruo Yang
- Natural Medicine Institute of Zhejiang YangShengTang Co., Ltd., No. 181, Geyazhuang, Xihu District, Hangzhou, Zhejiang, China
| | - Xiaojun Yuan
- College of Life Sciences, Shanghai University, Shanghai, China
| | - Xiaoming Zhang
- Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China.
| | - Pei Hao
- Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China.
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18
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Osorio D, Zhong Y, Li G, Huang JZ, Cai JJ. scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data. PATTERNS (NEW YORK, N.Y.) 2020; 1:100139. [PMID: 33336197 PMCID: PMC7733883 DOI: 10.1016/j.patter.2020.100139] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 09/29/2020] [Accepted: 10/12/2020] [Indexed: 02/02/2023]
Abstract
We present scTenifoldNet-a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment-for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing. scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.
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Affiliation(s)
- Daniel Osorio
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Yan Zhong
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Jianhua Z. Huang
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - James J. Cai
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, USA
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19
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Mitchell S. What Will B Will B: Identifying Molecular Determinants of Diverse B-Cell Fate Decisions Through Systems Biology. Front Cell Dev Biol 2020; 8:616592. [PMID: 33511125 PMCID: PMC7835399 DOI: 10.3389/fcell.2020.616592] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/02/2020] [Indexed: 12/25/2022] Open
Abstract
B-cells are the poster child for cellular diversity and heterogeneity. The diverse repertoire of B lymphocytes, each expressing unique antigen receptors, provides broad protection against pathogens. However, B-cell diversity goes beyond unique antigen receptors. Side-stepping B-cell receptor (BCR) diversity through BCR-independent stimuli or engineered organisms with monoclonal BCRs still results in seemingly identical B-cells reaching a wide variety of fates in response to the same challenge. Identifying to what extent the molecular state of a B-cell determines its fate is key to gaining a predictive understanding of B-cells and consequently the ability to control them with targeted therapies. Signals received by B-cells through transmembrane receptors converge on intracellular molecular signaling networks, which control whether each B-cell divides, dies, or differentiates into a number of antibody-secreting distinct B-cell subtypes. The signaling networks that interpret these signals are well known to be susceptible to molecular variability and noise, providing a potential source of diversity in cell fate decisions. Iterative mathematical modeling and experimental studies have provided quantitative insight into how B-cells achieve distinct fates in response to pathogenic stimuli. Here, we review how systems biology modeling of B-cells, and the molecular signaling networks controlling their fates, is revealing the key determinants of cell-to-cell variability in B-cell destiny.
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