Publications by authors named "Yunshun Chen"

16 Publications

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A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast.

EMBO J 2021 Jun 5;40(11):e107333. Epub 2021 May 5.

ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic, Australia.

To examine global changes in breast heterogeneity across different states, we determined the single-cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1 tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post-menopausal women. Single-cell profiling of 34 treatment-naive primary tumors, including estrogen receptor (ER) , HER2 , and triple-negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1 tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8 T cells characterized triple-negative and HER2 cancers but not ER tumors, while all subtypes comprised cycling tumor-associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large-scale integration of patient samples provides a high-resolution map of cell diversity in normal and cancerous human breast.
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http://dx.doi.org/10.15252/embj.2020107333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8167363PMC
June 2021

Intraclonal Plasticity in Mammary Tumors Revealed through Large-Scale Single-Cell Resolution 3D Imaging.

Cancer Cell 2019 04 28;35(4):618-632.e6. Epub 2019 Mar 28.

Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia. Electronic address:

Breast tumors are inherently heterogeneous, but the evolving cellular organization through neoplastic progression is poorly understood. Here we report a rapid, large-scale single-cell resolution 3D imaging protocol based on a one-step clearing agent that allows visualization of normal tissue architecture and entire tumors at cellular resolution. Imaging of multicolor lineage-tracing models of breast cancer targeted to either basal or luminal progenitor cells revealed profound clonal restriction during progression. Expression profiling of clones arising in Pten/Trp53-deficient tumors identified distinct molecular signatures. Strikingly, most clones harbored cells that had undergone an epithelial-to-mesenchymal transition, indicating widespread, inherent plasticity. Hence, an integrative pipeline that combines lineage tracing, 3D imaging, and clonal RNA sequencing technologies offers a comprehensive path for studying mechanisms underlying heterogeneity in whole tumors.
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http://dx.doi.org/10.1016/j.ccell.2019.02.010DOI Listing
April 2019

Foxp1 Is Indispensable for Ductal Morphogenesis and Controls the Exit of Mammary Stem Cells from Quiescence.

Dev Cell 2018 12 25;47(5):629-644.e8. Epub 2018 Oct 25.

Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia. Electronic address:

Long-lived quiescent mammary stem cells (MaSCs) are presumed to coordinate the dramatic expansion of ductal epithelium that occurs through the different phases of postnatal development, but little is known about the molecular regulators that underpin their activation. We show that ablation of the transcription factor Foxp1 in the mammary gland profoundly impairs ductal morphogenesis, resulting in a rudimentary tree throughout life. Foxp1-deficient glands were highly enriched for quiescent Tspan8 MaSCs, which failed to become activated even in competitive transplantation assays, thus highlighting a cell-intrinsic defect. Foxp1 deletion also resulted in aberrant expression of basal genes in luminal cells, inferring a role in cell-fate decisions. Notably, Foxp1 was uncovered as a direct repressor of Tspan8 in basal cells, and deletion of Tspan8 rescued the defects in ductal morphogenesis elicited by Foxp1 loss. Thus, a single transcriptional regulator Foxp1 can control the exit of MaSCs from dormancy to orchestrate differentiation and development.
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http://dx.doi.org/10.1016/j.devcel.2018.10.001DOI Listing
December 2018

Mutant TRP53 exerts a target gene-selective dominant-negative effect to drive tumor development.

Genes Dev 2018 11 26;32(21-22):1420-1429. Epub 2018 Oct 26.

Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.

Mutations in , prevalent in human cancer, are reported to drive tumorigenesis through dominant-negative effects (DNEs) over wild-type TRP53 function as well as neomorphic gain-of-function (GOF) activity. We show that five TRP53 mutants do not accelerate lymphomagenesis on a TRP53-deficient background but strongly synergize with c-MYC overexpression in a manner that distinguishes the hot spot mutations. RNA sequencing revealed that the mutant TRP53 DNE does not globally repress wild-type TRP53 function but disproportionately impacts a subset of wild-type TRP53 target genes. Accordingly, TRP53 mutant proteins impair pathways for DNA repair, proliferation, and metabolism in premalignant cells. This reveals that, in our studies of lymphomagenesis, mutant TRP53 drives tumorigenesis primarily through the DNE, which modulates wild-type TRP53 function in a manner advantageous for neoplastic transformation.
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http://dx.doi.org/10.1101/gad.314286.118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217734PMC
November 2018

Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR.

F1000Res 2017;6:2055. Epub 2017 Nov 28.

The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.
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http://dx.doi.org/10.12688/f1000research.13196.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5747346PMC
November 2017

Construction of developmental lineage relationships in the mouse mammary gland by single-cell RNA profiling.

Nat Commun 2017 11 20;8(1):1627. Epub 2017 Nov 20.

ACRF Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.

The mammary epithelium comprises two primary cellular lineages, but the degree of heterogeneity within these compartments and their lineage relationships during development remain an open question. Here we report single-cell RNA profiling of mouse mammary epithelial cells spanning four developmental stages in the post-natal gland. Notably, the epithelium undergoes a large-scale shift in gene expression from a relatively homogeneous basal-like program in pre-puberty to distinct lineage-restricted programs in puberty. Interrogation of single-cell transcriptomes reveals different levels of diversity within the luminal and basal compartments, and identifies an early progenitor subset marked by CD55. Moreover, we uncover a luminal transit population and a rare mixed-lineage cluster amongst basal cells in the adult mammary gland. Together these findings point to a developmental hierarchy in which a basal-like gene expression program prevails in the early post-natal gland prior to the specification of distinct lineage signatures, and the presence of cellular intermediates that may serve as transit or lineage-primed cells.
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http://dx.doi.org/10.1038/s41467-017-01560-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696379PMC
November 2017

c-Myb Regulates the T-Bet-Dependent Differentiation Program in B Cells to Coordinate Antibody Responses.

Cell Rep 2017 04;19(3):461-470

Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia; Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia. Electronic address:

Humoral immune responses are tailored to the invading pathogen through regulation of key transcription factors and their networks. This is critical to establishing effective antibody-mediated responses, yet it is unknown how B cells integrate pathogen-induced signals to drive or suppress transcriptional programs specialized for each class of pathogen. Here, we detail the key role of the transcription factor c-Myb in regulating the T-bet-mediated anti-viral program. Deletion of c-Myb in mature B cells significantly increased serum IgG2c and CXCR3 expression by upregulating T-bet, normally suppressed during Th2-cell-mediated responses. Enhanced expression of T-bet resulted in aberrant plasma cell differentiation within the germinal center, mediated by CXCR3 expression. These findings identify a dual role for c-Myb in limiting inappropriate effector responses while coordinating plasma cell differentiation with germinal center egress. Identifying such intrinsic regulators of specialized antibody responses can assist in vaccine design and therapeutic intervention in B-cell-mediated immune disorders.
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http://dx.doi.org/10.1016/j.celrep.2017.03.060DOI Listing
April 2017

Lung Basal Stem Cells Rapidly Repair DNA Damage Using the Error-Prone Nonhomologous End-Joining Pathway.

PLoS Biol 2017 01 26;15(1):e2000731. Epub 2017 Jan 26.

ACRF Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

Lung squamous cell carcinoma (SqCC), the second most common subtype of lung cancer, is strongly associated with tobacco smoking and exhibits genomic instability. The cellular origins and molecular processes that contribute to SqCC formation are largely unexplored. Here we show that human basal stem cells (BSCs) isolated from heavy smokers proliferate extensively, whereas their alveolar progenitor cell counterparts have limited colony-forming capacity. We demonstrate that this difference arises in part because of the ability of BSCs to repair their DNA more efficiently than alveolar cells following ionizing radiation or chemical-induced DNA damage. Analysis of mice harbouring a mutation in the DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a key enzyme in DNA damage repair by nonhomologous end joining (NHEJ), indicated that BSCs preferentially repair their DNA by this error-prone process. Interestingly, polyploidy, a phenomenon associated with genetically unstable cells, was only observed in the human BSC subset. Expression signature analysis indicated that BSCs are the likely cells of origin of human SqCC and that high levels of NHEJ genes in SqCC are correlated with increasing genomic instability. Hence, our results favour a model in which heavy smoking promotes proliferation of BSCs, and their predilection for error-prone NHEJ could lead to the high mutagenic burden that culminates in SqCC. Targeting DNA repair processes may therefore have a role in the prevention and therapy of SqCC.
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http://dx.doi.org/10.1371/journal.pbio.2000731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268430PMC
January 2017

From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.

F1000Res 2016 20;5:1438. Epub 2016 Jun 20.

The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia; Department of Mathematics and Statistics, The University of Melbourne, Victoria, 3010, Australia.

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934518PMC
http://dx.doi.org/10.12688/f1000research.8987.2DOI Listing
August 2016

It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR.

Methods Mol Biol 2016 ;1418:391-416

Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.

RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq experiments using the Rsubread and edgeR software packages. The basic pipeline includes read alignment and counting, filtering and normalization, modelling of biological variability and hypothesis testing. For hypothesis testing, we describe particularly the quasi-likelihood features of edgeR. Some more advanced downstream analysis steps are also covered, including complex comparisons, gene ontology enrichment analyses and gene set testing. The code required to run each step is described, along with an outline of the underlying theory. The chapter includes a case study in which the pipeline is used to study the expression profiles of mammary gland cells in virgin, pregnant and lactating mice.
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http://dx.doi.org/10.1007/978-1-4939-3578-9_19DOI Listing
December 2016

Integration of microRNA signatures of distinct mammary epithelial cell types with their gene expression and epigenetic portraits.

Breast Cancer Res 2015 Jun 18;17:85. Epub 2015 Jun 18.

ACRF Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.

Introduction: MicroRNAs (miRNAs) have been implicated in governing lineage specification and differentiation in multiple organs; however, little is known about their specific roles in mammopoiesis. We have determined the global miRNA expression profiles of functionally distinct epithelial subpopulations in mouse and human mammary tissue, and compared these to their cognate transcriptomes and epigenomes. Finally, the human miRNA signatures were used to interrogate the different subtypes of breast cancer, with a view to determining miRNA networks deregulated during oncogenesis.

Methods: RNA from sorted mouse and human mammary cell subpopulations was subjected to miRNA expression analysis using the TaqMan MicroRNA Array. Differentially expressed (DE) miRNAs were correlated with gene expression and histone methylation profiles. Analysis of miRNA signatures of the intrinsic subtypes of breast cancer in The Cancer Genome Atlas (TCGA) database versus those of normal human epithelial subpopulations was performed.

Results: Unique miRNA signatures characterized each subset (mammary stem cell (MaSC)/basal, luminal progenitor, mature luminal, stromal), with a high degree of conservation across species. Comparison of miRNA and transcriptome profiles for the epithelial subtypes revealed an inverse relationship and pinpointed key developmental genes. Interestingly, expression of the primate-specific miRNA cluster (19q13.4) was found to be restricted to the MaSC/basal subset. Comparative analysis of miRNA signatures with H3 lysine modification maps of the different epithelial subsets revealed a tight correlation between active or repressive marks for the top DE miRNAs, including derepression of miRNAs in Ezh2-deficient cellular subsets. Interrogation of TCGA-identified miRNA profiles with the miRNA signatures of different human subsets revealed specific relationships.

Conclusions: The derivation of global miRNA expression profiles for the different mammary subpopulations provides a comprehensive resource for understanding the interplay between miRNA networks and target gene expression. These data have highlighted lineage-specific miRNAs and potential miRNA-mRNA networks, some of which are disrupted in neoplasia. Furthermore, our findings suggest that key developmental miRNAs are regulated by global changes in histone modification, thus linking the mammary epigenome with genome-wide changes in the expression of genes and miRNAs. Comparative miRNA signature analyses between normal breast epithelial cells and breast tumors confirmed an important linkage between luminal progenitor cells and basal-like tumors.
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http://dx.doi.org/10.1186/s13058-015-0585-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497411PMC
June 2015

Regulation of germinal center responses and B-cell memory by the chromatin modifier MOZ.

Proc Natl Acad Sci U S A 2014 Jul 16;111(26):9585-90. Epub 2014 Jun 16.

Divisions of Immunology,Departments of Medical Biology and

Memory B cells and long-lived bone marrow-resident plasma cells maintain humoral immunity. Little is known about the intrinsic mechanisms that are essential for forming memory B cells or endowing them with the ability to rapidly differentiate upon reexposure while maintaining the population over time. Histone modifications have been shown to regulate lymphocyte development, but their role in regulating differentiation and maintenance of B-cell subsets during an immune response is unclear. Using stage-specific deletion of monocytic leukemia zinc finger protein (MOZ), a histone acetyltransferase, we demonstrate that mutation of this chromatin modifier alters fate decisions in both primary and secondary responses. In the absence of MOZ, germinal center B cells were significantly impaired in their ability to generate dark zone centroblasts, with a concomitant decrease in both cell-cycle progression and BCL-6 expression. In contrast, there was increased differentiation to IgM and low-affinity IgG1(+) memory B cells. The lack of MOZ affected the functional outcome of humoral immune responses, with an increase in secondary germinal centers and a corresponding decrease in secondary high-affinity antibody-secreting cell formation. Therefore, these data provide strong evidence that manipulating epigenetic modifiers can regulate fate decisions during humoral responses, and thus could be targeted for therapeutic intervention.
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http://dx.doi.org/10.1073/pnas.1402485111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4084455PMC
July 2014

voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.

Genome Biol 2014 Feb 3;15(2):R29. Epub 2014 Feb 3.

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
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http://dx.doi.org/10.1186/gb-2014-15-2-r29DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053721PMC
February 2014

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor.

Nat Protoc 2013 Sep 22;8(9):1765-86. Epub 2013 Aug 22.

Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.
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http://dx.doi.org/10.1038/nprot.2013.099DOI Listing
September 2013

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.

Nucleic Acids Res 2012 May 28;40(10):4288-97. Epub 2012 Jan 28.

Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia.

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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http://dx.doi.org/10.1093/nar/gks042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3378882PMC
May 2012