TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes.
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ABSTRACT:
SUBMITTER: Schwartz GW
PROVIDER: S-EPMC8409102 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Purpose: To characterize epigenomic heterogeneity in GSI-responsive and -resistant T-ALL cells Method: We generated GSI-resistant T-ALL DND41 cells and profiled their epigenome using single-cell RNA sequencing. Result: We report here the presence of resistant-like subpopulation in drug naïve DND41 T-ALL cells. Conclusion: While other popular scRNA-seq clustering algorithms failed, TooManyPeaks, a suite of clustering and visualization algorithm developed in our lab, could identify rare resistant-like DND41 T-ALL cells.
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Project description:Developmental history shapes the epigenome and biological function of differentiated cells. Epigenomic patterns have been broadly attributed to the three embryonic germ layers. Here we investigate how developmental origin influences epigenomes. We compare key epigenomes of cell types derived from surface ectoderm (SE), including keratinocytes and breast luminal and myoepithelial cells, against neural crest-derived melanocytes and mesoderm-derived dermal fibroblasts, to identify SE differentially methylated regions (SE-DMRs). DNA methylomes of neonatal keratinocytes share many more DMRs with adult breast luminal and myoepithelial cells than with melanocytes and fibroblasts from the same neonatal skin. This suggests that SE origin contributes to DNA methylation patterning, while shared skin tissue environment has limited effect on epidermal keratinocytes. Hypomethylated SE-DMRs are in proximity to genes with SE relevant functions. They are also enriched for enhancer- and promoter-associated histone modifications in SE-derived cells, and for binding motifs of transcription factors important in keratinocyte and mammary gland biology. Thus, epigenomic analysis of cell types with common developmental origin reveals an epigenetic signature that underlies a shared gene regulatory network.
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Project description:Identification of cell type-specific cis-regulatory elements (CREs) is crucial for understanding development and disease, although identification of functional regulatory elements remains challenging. We hypothesized that context-specific CREs could be identified by context-specific non-coding RNA (ncRNA) profiling, based on the observation that active CREs produce ncRNAs. We applied ncRNA profiling to identify rod and cone photoreceptor CREs from wild-type and mutant mouse retinas, defined by presence or absence, respectively, of the rod-specific transcription factor (TF) Nrl Nrl-dependent ncRNA expression strongly correlated with epigenetic profiles of rod and cone photoreceptors, identified thousands of candidate rod- and cone-specific CREs, and identified motifs for rod- and cone-specific TFs. Colocalization of NRL and the retinal TF CRX correlated with rod-specific ncRNA expression, whereas CRX alone favored cone-specific ncRNA expression, providing quantitative evidence that heterotypic TF interactions distinguish cell type-specific CRE activity. We validated the activity of novel Nrl-dependent ncRNA-defined CREs in developing cones. This work supports differential ncRNA profiling as a platform for the identification of cell type-specific CREs and the discovery of molecular mechanisms underlying TF-dependent CRE activity.
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Project description:The brain, spinal cord, and neural retina comprise the central nervous system (CNS) of vertebrates. Understanding the regulatory mechanisms that underlie the enormous cell-type diversity of the CNS is a significant challenge. Whole-genome mapping of DNase I-hypersensitive sites (DHSs) has been used to identify cis-regulatory elements in many tissues. We have applied this approach to the mouse CNS, including developing and mature neural retina, whole brain, and two well-characterized brain regions, the cerebellum and the cerebral cortex.For the various regions and developmental stages of the CNS that we analyzed, there were approximately the same number of DHSs; however, there were many DHSs unique to each CNS region and developmental stage. Many of the DHSs are likely to mark enhancers that are specific to the specific CNS region and developmental stage. We validated the DNase I mapping approach for identification of CNS enhancers using the existing VISTA Browser database and with in vivo and in vitro electroporation of the retina. Analysis of transcription factor consensus sites within the DHSs shows distinct region-specific profiles of transcriptional regulators particular to each region. Clustering developmentally dynamic DHSs in the retina revealed enrichment of developmental stage-specific transcriptional regulators. Additionally, we found reporter gene activity in the retina driven from several previously uncharacterized regulatory elements surrounding the neurodevelopmental gene Otx2. Identification of DHSs shared between mouse and human showed region-specific differences in the evolution of cis-regulatory elements.Overall, our results demonstrate the potential of genome-wide DNase I mapping to cis-regulatory questions regarding the regional diversity within the CNS. These data represent an extensive catalogue of potential cis-regulatory elements within the CNS that display region and temporal specificity, as well as a set of DHSs common to CNS tissues. Further examination of evolutionary conservation of DHSs between CNS regions and different species may reveal important cis-regulatory elements in the evolution of the mammalian CNS.
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Project description:BACKGROUND:Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. METHODS:We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. RESULTS:We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq. CONCLUSIONS:Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.
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