Development of a multiomics and functional drug sensitivity platform to investigate cell type specific drug effects in AML.

e19013 Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.

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CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

BMC Bioinformatics ◽

10.1186/s12859-021-04186-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Kai Kang ◽

Caizhi Huang ◽

Yuanyuan Li ◽

David M. Umbach ◽

Leping Li

Keyword(s):

Gene Expression ◽

Cell Types ◽

Biological Tissues ◽

Specific Gene ◽

Specific Cell ◽

Specific Information ◽

Expression Data ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific

Abstract Background Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. Result We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Conclusions The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell–cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

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Variation in Stem Cell Driven Hierarchies Underlies Clinical Outcome and Drug Response in AML

Blood ◽

10.1182/blood-2020-140895 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 27-28

Author(s):

Andy G.X. Zeng ◽

Amanda Mitchell ◽

Oleksandr Galkin ◽

Jean C.Y. Wang ◽

James A. Kennedy ◽

...

Keyword(s):

Stem Cell ◽

Relative Abundance ◽

Drug Response ◽

Drug Sensitivity ◽

Immune Cell ◽

Cell Types ◽

Chemotherapy Response ◽

Specific Cell ◽

Cell Type ◽

Cellular Hierarchy

AML is a stem cell disease wherein the properties of the disease-driving leukemia stem cells (LSCs) are reflected in the cellular hierarchies that they generate. We sought to understand how these hierarchies vary across patients and whether their characteristics are clinically relevant. To evaluate cellular hierarchies in AML, we re-analyzed the scRNA-seq data of 13,653 cells from 12 AML patients at diagnosis (van Galen, Cell 2019) and in particular the stem and progenitor blast populations. We identified three novel leukemia stem populations differing in their depth of quiescence, inflammatory signaling, and myeloid priming. Using the signatures of 7 malignant populations as well as 7 immune cell populations from this scRNA-seq data, we applied CIBERSORTx deconvolution (Newman, Nat Biotechnol 2019) on bulk RNA-seq data from multiple patient cohorts. This enabled determination of the relative abundance of each cell type in each patient, thereby capturing the "shape" of the leukemic hierarchies of hundreds of AML patients. AML patients within these cohorts clustered into 4 groups defined by different proportions of stem, progenitor, and mature blasts - each of which differed in their underlying genomic alterations and overall survival. Differences in chemotherapy response were mediated by specific cell types, notably a GMP-like blast population and a quiescent stem-like population (qLSPC). Dominance of the GMP-like population was associated with longer survival (HR -3.1, p=0.002), and these blasts were enriched among younger patients (<65 years) and those with favorable risk cytogenetics. In contrast, qLSPCs were associated with poor survival (HR 2.3, p=0.02; TCGA) and were more abundant in older patients (> 65 years) and patients with adverse cytogenetic alterations. Critically, this qLSPC population was enriched at relapse as well as within a subset of pediatric AML patients that failed to respond to induction chemotherapy. We confirmed that qLSPCs were also enriched among functionally validated leukemia engrafting (LSC+) sorted AML fractions. Accordingly, a high LSC17 score was strongly correlated with qLSPC abundance and anti-correlated with GMP-like abundance, suggesting that the score may reflect the underlying cellular hierarchy of each AML patient. Next, we generated drug sensitivity profiles for each cell type by correlating ex vivo drug sensitivity data for each of 112 inhibitors screened in the BEAT-AML trial with the relative abundance of each cell type across individual patients. In particular, Venetoclax sensitivity correlated with the abundance of primitive cell types and anti-correlated with mature cell types, suggesting that the composition of the leukemic hierarchy in AML patients is associated with drug response. We previously demonstrated that a high LSC17 score identifies patients who do not benefit from standard chemotherapy (Ng, Nature 2016). To develop a diagnostic tool for therapy selection amongst these poor prognosis patients, we retrained the LSC17 genes against cell type abundance and derived a subscore (LSC-7) to map patients along an axis of primitive vs mature leukemic hierarchies. We show that AML samples with a high LSC-7 score (more stem-like blasts) were more sensitive to Venetoclax whereas AML patients with a low LSC-7 score (more mature blasts) benefited from treatment with Gemtuzumab-Ozogamicin (GO). Used together, the LSC17 and LSC-7 scores enable risk stratification as well as subsequent drug selection for high-risk patients, and can both be measured by a single rapid NanoString assay. To apply this approach more broadly, we identified several published studies with drug screening data from primary patient samples and accompanying RNAseq data. By correlating cell type abundance with drug response, we were able to map the critical cell types that mediate sensitivity and resistance to inhibitors of mitochondrial metabolism, histone demethylation, and the CD47-SIRPa axis, among others. Our data establish that scRNA-seq informed deconvolution of bulk expression data permits characterization of the cellular hierarchy of individual AML patients. This framework can enhance our understanding of many aspects of biological, genomic, and clinical heterogeneity in AML, and represents a powerful tool to enable personalized therapeutic decision-making in AML. Figure Disclosures Wang: Trilium Therapeutics: Patents & Royalties. Dick:Bristol-Myers Squibb/Celgene: Research Funding.

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CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

10.1101/2021.01.30.428954 ◽

2021 ◽

Author(s):

Kai Kang ◽

Caizhi David Huang ◽

Yuanyuan Li ◽

David M. Umbach ◽

Leping Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

In Silico ◽

Cell Types ◽

R Package ◽

Biological Tissues ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific

AbstractBackgroundBiological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and with a new function to aid interpretation of deconvolution outcomes. The R package would be of interest for the broader R community.ResultWe developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating CDSeq-estimated cell types using publicly available single-cell RNA sequencing (scRNA-seq) data (single-cell data from 20 major organs are included in the R package). This function allows users to readily interpret and visualize the CDSeq-estimated cell types. We carried out additional validations of the CDSeqR software with in silico and in vitro mixtures and with real experimental data including RNA-seq data from the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) project.ConclusionsThe existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. However, bulk level analyses neglect tissue heterogeneity and hinder investigation in a cell-type-specific fashion. The CDSeqR package can be viewed as providing in silico single-cell dissection of bulk measurements. It enables researchers to gain cell-type-specific information from bulk RNA-seq data.

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Shisa6 mediates cell-type specific regulation of depression in the nucleus accumbens

Molecular Psychiatry ◽

10.1038/s41380-021-01217-8 ◽

2021 ◽

Author(s):

Hee-Dae Kim ◽

Jing Wei ◽

Tanessa Call ◽

Nicole Teru Quintus ◽

Alexander J. Summers ◽

...

Keyword(s):

Nucleus Accumbens ◽

Molecular Mechanisms ◽

Cell Types ◽

Current Treatment ◽

Specific Cell ◽

Excitatory Synapses ◽

Cell Type ◽

Cell Type Specific ◽

Specific Regulation ◽

Circuit Function

AbstractDepression is the leading cause of disability and produces enormous health and economic burdens. Current treatment approaches for depression are largely ineffective and leave more than 50% of patients symptomatic, mainly because of non-selective and broad action of antidepressants. Thus, there is an urgent need to design and develop novel therapeutics to treat depression. Given the heterogeneity and complexity of the brain, identification of molecular mechanisms within specific cell-types responsible for producing depression-like behaviors will advance development of therapies. In the reward circuitry, the nucleus accumbens (NAc) is a key brain region of depression pathophysiology, possibly based on differential activity of D1- or D2- medium spiny neurons (MSNs). Here we report a circuit- and cell-type specific molecular target for depression, Shisa6, recently defined as an AMPAR component, which is increased only in D1-MSNs in the NAc of susceptible mice. Using the Ribotag approach, we dissected the transcriptional profile of D1- and D2-MSNs by RNA sequencing following a mouse model of depression, chronic social defeat stress (CSDS). Bioinformatic analyses identified cell-type specific genes that may contribute to the pathogenesis of depression, including Shisa6. We found selective optogenetic activation of the ventral tegmental area (VTA) to NAc circuit increases Shisa6 expression in D1-MSNs. Shisa6 is specifically located in excitatory synapses of D1-MSNs and increases excitability of neurons, which promotes anxiety- and depression-like behaviors in mice. Cell-type and circuit-specific action of Shisa6, which directly modulates excitatory synapses that convey aversive information, identifies the protein as a potential rapid-antidepressant target for aberrant circuit function in depression.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Cell type-specific biogenesis of novel vesicles containing viral products in human cytomegalovirus infection

10.1101/2020.12.10.420711 ◽

2020 ◽

Cited By ~ 1

Author(s):

Samina Momtaz ◽

Belen Molina ◽

Luwanika Mlera ◽

Felicia Goodrum ◽

Jean M. Wilson

Keyword(s):

Endothelial Cells ◽

Human Cytomegalovirus ◽

Biosynthetic Pathway ◽

Cell Types ◽

Endocytic Pathway ◽

Specific Cell ◽

Cell Type ◽

Subcellular Compartment ◽

Increased Risk ◽

Cell Type Specific

AbstractHuman cytomegalovirus (HCMV), while highly restricted for the human species, infects an unlimited array of cell types in the host. Patterns of infection are dictated by the cell type infected, but cell type-specific factors and how they impact tropism for specific cell types is poorly understood. Previous studies in primary endothelial cells showed that HCMV infection induces large multivesicular-like bodies that incorporate viral products including dense bodies and virions. Here we define the nature of these large vesicles using a recombinant virus where UL32, encoding the pp150 tegument protein, is fused in frame with green fluorescent protein (GFP, TB40/E-UL32-GFP). Cells were fixed and labeled with antibodies against subcellular compartment markers and imaged using confocal and super-resolution microscopy. In fibroblasts, UL32-GFP-positive vesicles were marked with classical markers of MVBs, including CD63 and lysobisphosphatidic acid (LBPA), both classical MVB markers, as well as the clathrin and LAMP1. Unexpectedly, UL32-GFP-positive vesicles in endothelial cells were not labeled by CD63, and LBPA was completely lost from infected cells. We defined these UL32-positive vesicles in endothelial cells using markers for the cis-Golgi (GM130), lysosome (LAMP1), and autophagy (LC3B). These findings suggest that virus-containing MVBs in fibroblasts are derived from the canonical endocytic pathway and takeover classical exosomal release pathway. Virus containing MVBs in HMVECs are derived from the early biosynthetic pathway and exploit a less characterized early Golgi-LAMP1-associated non-canonical secretory autophagy pathway. These results reveal striking cell-type specific membrane trafficking differences in host pathways that are exploited by HCMV.ImportanceHuman cytomegalovirus (HCMV) is a herpesvirus that, like all herpesvirus, that establishes a life long infection. HCMV remains a significant cause of morbidity and mortality in the immunocompromised and HCMV seropositivity is associated with increased risk vascular disease. HCMV infects many cells in the human and the biology underlying the different patterns of infection in different cell types is poorly understood. Endothelial cells are important target of infection that contribute to hematogenous spread of the virus to tissues. Here we define striking differences in the biogenesis of large vesicles that incorporate virions in fibroblasts and endothelial cells. In fibroblasts, HCMV is incorporated into canonical MVBs derived from an endocytic pathway, whereas HCMV matures through vesicles derived from the biosynthetic pathway in endothelial cells. This work defines basic biological differences between these cell types that may impact the outcome of infection.

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CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

10.1101/634097 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matthew N. Bernstein ◽

Zhongjie Ma ◽

Michael Gleicher ◽

Colin N. Dewey

Keyword(s):

Single Cell ◽

Web Application ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Training Set ◽

Sequence Read Archive ◽

Cell Ontology ◽

Cell Type Specific ◽

Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

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Detection of Cell Types Contributing to Cancer From Circulating, Cell-Free Methylated DNA

Frontiers in Genetics ◽

10.3389/fgene.2021.671057 ◽

2021 ◽

Vol 12 ◽

Author(s):

Megan E. Barefoot ◽

Netanel Loyfer ◽

Amber J. Kiliti ◽

A. Patrick McDeed ◽

Tommy Kaplan ◽

...

Keyword(s):

Cancer Cells ◽

Cell Types ◽

Circulating Tumor Dna ◽

Cancer Diagnostics ◽

Tissue Homeostasis ◽

Specific Cell ◽

Cell Type ◽

Liquid Biopsies ◽

Methylated Dna ◽

Cell Type Specific

Detection of cellular changes in tissue biopsies has been the basis for cancer diagnostics. However, tissue biopsies are invasive and limited by inaccuracies due to sampling locations, restricted sampling frequency, and poor representation of tissue heterogeneity. Liquid biopsies are emerging as a complementary approach to traditional tissue biopsies to detect dynamic changes in specific cell populations. Cell-free DNA (cfDNA) fragments released into the circulation from dying cells can be traced back to the tissues and cell types they originated from using DNA methylation, an epigenetic regulatory mechanism that is highly cell-type specific. Decoding changes in the cellular origins of cfDNA over time can reveal altered host tissue homeostasis due to local cancer invasion and metastatic spread to distant organs as well as treatment responses. In addition to host-derived cfDNA, changes in cancer cells can be detected from cell-free, circulating tumor DNA (ctDNA) by monitoring DNA mutations carried by cancer cells. Here, we will discuss computational approaches to identify and validate robust biomarkers of changed tissue homeostasis using cell-free, methylated DNA in the circulation. We highlight studies performing genome-wide profiling of cfDNA methylation and those that combine genetic and epigenetic markers to further identify cell-type specific signatures. Finally, we discuss opportunities and current limitations of these approaches for implementation in clinical oncology.

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CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues

10.1101/847657 ◽

2019 ◽

Cited By ~ 1

Author(s):

Pawel F. Przytycki ◽

Katherine S. Pollard

Keyword(s):

Single Cell ◽

Cell Types ◽

Regulatory Elements ◽

Cell Labeling ◽

Open Chromatin ◽

Specific Cell ◽

Rna Seq ◽

Data Types ◽

Bulk Data ◽

Cell Type Specific

Single-cell and bulk genomics assays have complementary strengths and weaknesses, and alone neither strategy can fully capture regulatory elements across the diversity of cells in complex tissues. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene expression (RNA-seq) and other data types using a network model that simultaneously improves cell labeling in noisy scATAC-seq and annotates cell-type specific regulatory elements in bulk data. We demonstrate CellWalker’s robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing brain. We identify cells transitioning between transcriptional states, resolve enhancers to specific cell types, and observe that autism and other neurological traits can be mapped to specific cell types through their enhancers.

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Single-nucleus RNA-seq reveals dysregulation of striatal cell identity due to Huntington’s disease mutations

10.1101/2020.07.08.192880 ◽

2020 ◽

Author(s):

Sonia Malaiya ◽

Marcia Cortes-Gutierrez ◽

Brian R. Herb ◽

Sydney R. Coffey ◽

Samuel R.W. Legg ◽

...

Keyword(s):

Huntington's Disease ◽

Huntington’S Disease ◽

Neurodegenerative Disorder ◽

Cell Types ◽

Transcriptional Networks ◽

Rna Seq ◽

Cell Type ◽

Cell Identity ◽

Transcriptional Changes ◽

Cell Type Specific

ABSTRACTHuntington’s disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (Htt) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4,524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/+, and from wildtype controls. We used 14-15-month-old mice, a time point roughly equivalent to an early stage of symptomatic human disease. Cell type distributions indicated selective loss of D2 MSNs and increased microglia in aged HttQ175/+ mice. Thousands of differentially expressed genes were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell typespecific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the Polycomb Repressive Complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum.

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