scholarly journals Controlling for cellular heterogeneity using single-cell deconvolution of gene expression reveals novel markers of colorectal tumors exhibiting microsatellite instability

Oncotarget ◽  
2021 ◽  
Author(s):  
Matthew A.M. Devall ◽  
Graham Casey
Keyword(s):  
Gene Expression ◽  
Microsatellite Instability ◽  
Single Cell ◽  
Cellular Heterogeneity ◽  
Colorectal Tumors

scholarly journals A Single-Cell Transcriptome Atlas of the Mouse Glomerulus

2018 ◽  
Vol 29 (8) ◽  
pp. 2060-2068 ◽  
Author(s):  
Nikos Karaiskos ◽  
Mahdieh Rahmatollahi ◽  
Anastasiya Boltengagen ◽  
Haiyue Liu ◽  
Martin Hoehne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Mesangial Cells ◽  
Transcriptional Profiling ◽  
Wild Type Mouse ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Glomerular Cell

Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.

scholarly journals CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

2020 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Individual Gene ◽  
Genome Wide ◽  
Progression Of Disease ◽  
Cell Subpopulations ◽  
Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

scholarly journals The single-cell epigenetic regulatory landscape in mammalian perinatal testis development

2021 ◽  
Author(s):  
Jinyue Liao ◽  
Hoi Ching Suen ◽  
Shitao Rao ◽  
Alfred Chun Shui Luk ◽  
Ruoyu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Germ Cells ◽  
Somatic Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Cell Type

AbstractSpermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that scATAC-Seq allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution data also revealed putative stem cells within the Sertoli and Leydig cell populations. Further, we defined candidate target cell types and genes of several GWAS signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the ‘regulon’ of the mouse male germline and supporting somatic cells.

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals Beyond comparisons of means: understanding changes in gene expression at the single-cell level

2016 ◽  
Author(s):  
Catalina A Vallejos ◽  
Sylvia Richardson ◽  
John C Marioni
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Probabilistic Approach ◽  
Embryonic Stem ◽  
Cellular Heterogeneity ◽  
Bayesian Hierarchical ◽  
False Discovery ◽  
Rich Information ◽  
The Rich

Single-cell RNA sequencing (scRNA-seq) can be used to characterise differences in gene expression patterns between pre-specified populations of cells. Traditionally, differential expression tools are restricted to the study of changes in overall expression between cell populations. However, such analyses do not take full advantage of the rich information provided by scRNA-seq. In this article, we present a Bayesian hierarchical model which can be used to study changes in expression that lie beyond comparisons of means. In particular, our method can highlight genes that undergo changes in cell-to-cell heterogeneity between the populations but whose overall expression is preserved. Evidence supporting these changes is quantified using a probabilistic approach based on tail posterior probabilities, where a probability cut-off is calibrated through the expected false discovery rate. Our method incorporates a built-in normalisation strategy and quantifies technical artefacts by borrowing information from technical spike-in genes. Control experiments validate the performance of our approach. Finally, we compare expression patterns of mouse embryonic stem cells between different stages of the cell cycle, revealing substantial differences in cellular heterogeneity.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

2021 ◽  
Author(s):  
Rong Lu ◽  
HUMBERTO CONTRERAS-TRUJILLO ◽  
JIYA EERDENG ◽  
SAMIR AKRE ◽  
DU JIANG ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Dna Barcode ◽  
Treatment Resistance ◽  
Intratumoral Heterogeneity ◽  
Integrated System ◽  
Cellular Heterogeneity ◽  
Gene Expression Signatures ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression signatures respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover an unexpected yet common form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression signatures. Our integrated system directly and effectively interrogates the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2020 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Single cell transcriptomics reveals molecular subtype and functional heterogeneity in models of breast cancer

2018 ◽  
Author(s):  
Daniel L. Roden ◽  
Laura A. Baker ◽  
Benjamin Elsworth ◽  
Chia-Ling Chan ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Single Cell ◽  
Cell Line ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Cellular Heterogeneity ◽  
Breast Cancers ◽  
Intrinsic Subtypes ◽  
Pdx Model

AbstractBreast cancer has long been classified into a number of molecular subtypes that predict prognosis and therefore influence clinical treatment decisions. Cellular heterogeneity is also evident in breast cancers and plays a key role in the development, evolution and metastatic progression of many cancers. How clinical heterogeneity relates to cellular heterogeneity is poorly understood, so we approached this question using single cell gene expression analysis of well established in vitro and in vivo models of disease.To explore the cellular heterogeneity in breast cancer we first examined a panel of genes that define the PAM50 classifier of molecular subtype. Five breast cancer cell line models (MCF7, BT474, SKBR3, MDA-MB-231, and MDA-MB-468) were selected as representatives of the intrinsic molecular subtypes (luminal A and B, basal-like, and Her2-enriched). Single cell multiplex RT-PCR was used to isolate and quantify the gene expression of single cells from each of these models, and the PAM50 classifier applied. Using this approach, we identified heterogeneity of intrinsic subtypes at single-cell level, indicating that cells with different subtypes exist within a cell line. Using the Chromium 10X system, this study was extended into thousands of cells from the MCF7 cell-line and an ER+ patient derived xenograft (PDX) model and again identified significant intra-tumour heterogeneity of molecular subtype.Estrogen Receptor (ER) is an important driver and therapeutic target in many breast cancers. It is heterogeneously expressed in a proportion of clinical cases but the significance of this to ER activity is unknown. Significant heterogeneity in the transcriptional activation of ER regulated genes was observed within tumours. This differential activation of the ER cistrome aligned with expression of two known transcriptional co-regulatory factors of ER (FOXA1 and PGR).To examine the degree of heterogeneity for other important phenotypic traits, we used an unsupervised clustering approach to identify cellular sub-populations with diverse cancer associated transcriptional properties, such as: proliferation; hypoxia; and treatment resistance. In particular, we show that we can identify two distinct sub-populations of cells that may have denovo resistance to endocrine therapies in a treatment naïve PDX model of ER+ breast cancer. One of these consists of cells with a non-proliferative transcriptional phenotype that is enriched for transcriptional properties of ERBB2 tumours. The other is heavily enriched for components of the primary cilia. Gene regulatory networks were used to identify transcription factor regulons that are active in each cell, leading us to identify potential transcriptional drivers (such as E2F7, MYB and RFX3) of the cilia associated endocrine resistant cells. This rare subpopulation of cells also has a highly heterogenous mix of intrinsic subtypes highlighting a potential role of intra-tumour subtype heterogeneity in endocrine resistance and metastatic potential.Overall, These results suggest a high degree of cellular heterogeneity within breast cancer models, even cell lines, that can be functionally dissected into sub-populations of cells with transcriptional phenotypes of potential clinical relevance.

scholarly journals A Single-Cell Gene-Expression Profile Reveals Inter-Cellular Heterogeneity within Human Monocyte Subsets

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144351 ◽  
Author(s):  
Susanne T. Gren ◽  
Thomas B. Rasmussen ◽  
Sabina Janciauskiene ◽  
Katarina Håkansson ◽  
Jens G. Gerwien ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Human Monocyte ◽  
Cellular Heterogeneity ◽  
Monocyte Subsets ◽  
Cell Gene Expression ◽  
Cell Gene
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