Biology subjects

Leslie, C. S.

Publications and source records attributed to Leslie, C. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

We present a novel gene-level regulatory model called SCARlink that predicts single-cell gene expression from single-cell chromatin accessibility within and flanking (+/-250kb) the genic loci by training on multiome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on a peak atlas. SCARlink significantly outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across across high-coverage multiome data sets while giving comparable to improved performance on low-coverage data sets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 8x-35x enriched in fine-mapped eQTLs and 22x-35x enriched in fine-mapped GWAS variants across 83 UK Biobank traits. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

bioinformatics↗

bioRxiv2023

ERG-driven prostate cancer emerges from basal-luminal hybrid cells

To gain insight into how ERG translocations cause prostate cancer, we performed single cell transcriptional profiling of an autochthonous mouse model at an early stage of disease initiation. Despite broad expression of ERG in all prostate epithelial cells, proliferation was enriched in a small, stem-like population with mixed-luminal basal identity (called intermediate cells). Through a series of lineage tracing and primary prostate tissue transplantation experiments, we find that tumor initiating activity resides in a subpopulation of basal cells that co-express the luminal genes Tmprss2 and Nkx3.1 (called BasalLum) but not in the larger population of classical Krt8+ luminal cells. Upon ERG activation, BasalLum cells give rise to the highly proliferative intermediate state, which subsequently transitions to the larger population of Krt8+ luminal cells characteristic of ERG-positive human cancers. Furthermore, this proliferative population is characterized by an ERG-specific chromatin state enriched for NFkB, AP-1, STAT and NFAT binding, with implications for TF cooperativity. The fact that the proliferative potential of ERG is enriched in a small stem-like population implicates the chromatin context of these cells as a critical variable for unmasking its oncogenic activity.

cancer biology↗

bioRxiv2023

3D Chromatin Dynamics during Innate and Adaptive Immune Memory Acquisition

Immune cells responding to pathogens undergo molecular changes that are intimately linked to genome organization. Recent work has demonstrated that natural killer (NK) and CD8+ T cells experience substantial transcriptomic and epigenetic rewiring during their differentiation from naive to effector to memory cells. Whether these molecular adaptations are accompanied by changes in three-dimensional (3D) chromatin architecture is unknown. In this study, we combine histone profiling, ATAC-seq, RNA-seq and high-throughput chromatin capture (HiC) assay to investigate the dynamics of one-dimensional (1D) and 3D chromatin during the differentiation of innate and adaptive lymphocytes. To this end, we discovered a coordinated 1D and 3D epigenetic remodeling during innate immune memory differentiation, and demonstrate that effector CD8+ T cells adopt an NK-like architectural program that is maintained in memory cells. Altogether, our study reveals the dynamic nature of the 1D and 3D genome during the formation of innate and adaptive immunological memory.

immunology↗

bioRxiv2022

An epigenetic barrier sets the timing of human neuronal maturation

The pace of human brain development is highly protracted compared to most other species. The maturation of cortical neurons is particularly slow, extending over months to years to reach adult-like functions. Remarkably, such protracted timing is retained in cortical neurons derived from human pluripotent stem cell (hPSC) during in vitro differentiation or upon transplantation into the murine brain in vivo. Those findings suggest the presence of a cell intrinsic clock that sets the pace of neuronal maturation, though the molecular nature of such a clock has remained elusive. Here, we identify an epigenetic developmental program which sets the timing of human neuronal maturation. First, we developed a human PSC-based approach to synchronize the birth of cortical neurons in vitro which allowed us to define a detailed atlas of progressive morphological, functional, and molecular maturation in human cortical neurons. Interestingly, we observed a slow, temporal unfolding of maturation programs that is limited by the retention of a specific set of epigenetic factors. Loss-of-function studies for several of those factors in cortical neurons enables precocious molecular and physiological maturation. Remarkably, transient inhibition of EZH2, EHMT1/2 or DOT1L, at the progenitor stage primes newly born neurons to rapidly acquire mature properties upon differentiation. Therefore, our findings reveal that the rate at which human neurons mature is set well before neurogenesis through the establishment of an "epigenetic barrier" in progenitor cells. Mechanistically, this barrier acts by holding transcriptional maturation programs in a poised state that gets gradually released during neuronal differentiation to ensure the prolonged timeline characteristic of human cortical neuron maturation.

developmental biology↗

bioRxiv2022

Terminal differentiation and persistence of effector regulatory T cells essential for the prevention of intestinal inflammation

Regulatory T (Treg) cells represent a specialized CD4+ T cell lineage with essential anti-inflammatory functions. Recent studies of the adaptations of Treg cells to non-lymphoid tissues which enable their specialized immunosuppressive and tissue supportive functions raise questions about the underlying mechanisms of these adaptations and whether they represent stable differentiation or reversible activation states. Using novel genetic tools, we characterized the transcriptional programs of distinct colonic effector Treg cell types. We found that attenuated T cell receptor (TCR) signaling and acquisition of substantial TCR independent functionality appears to facilitate the terminal differentiation of a population of colonic effector Treg cells distinguished by stable expression of immunomodulatory cytokine interleukin-10 (IL-10). Functional studies revealed that this subset of effector Treg cells, but not their expression of IL-10, was indispensable for colonic health. These findings suggest core features of terminal differentiation of effector Treg cells in non-lymphoid tissues and their function therein.

immunology↗

bioRxiv2022

Genome-wide CRISPR guide RNA design and specificity analysis with GuideScan2

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly gRNA/library design in custom genomes. GuideScan2 analysis identified widespread confounding effects of low-specificity gRNAs in published CRISPR knockout, interference and activation screens and enabled construction of a ready-to-use gRNA library that reduced off-target effects in a novel gene essentiality screen. GuideScan2 also enabled the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome.

genomics↗

bioRxiv2022

Scalable sequence-informed embedding of single-cell ATAC-seq data with CellSpace

Standard scATAC-seq analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. We present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space. CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and scores the activity of transcription factors in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors, or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.

bioinformatics↗

bioRxiv2022

Heterogeneity of Inflammation-associated Synovial Fibroblasts in Rheumatoid Arthritis and Its Drivers

Inflammation of non-barrier immunologically quiescent tissues is associated with a massive influx of blood-borne innate and adaptive immune cells. Cues from the latter are likely to alter and expand the spectrum of states observed in cells that are constitutively resident. However, local communications between immigrant and resident cell types in human inflammatory disease remain poorly understood. Here, we explored heterogeneity of synovial fibroblasts (FLS) in inflamed joints of rheumatoid arthritis (RA) patients using paired single cell RNA and ATAC sequencing (scRNA/ATAC-seq), multiplexed imaging, and spatial transcriptomics along with in vitro modeling of cell extrinsic factor signaling. These analyses suggest that local exposures to myeloid and T cell derived cytokines, TNF, IFN{gamma}, IL-1{beta}, or lack thereof, drive six distinct FLS states some of which closely resemble fibroblast states in other disease-affected tissues including skin and colon. Our results highlight a role for concurrent, spatially distributed cytokine signaling within the inflamed synovium.

immunology↗

bioRxiv2021

Epiphany: predicting Hi-C contact maps from 1D epigenomic signals

Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and indeed do not capture cell-type-specific differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from five epigenomic tracks that are already available in hundreds of cell types and tissues: DNase I hypersensitive sites and ChIP-seq for CTCF, H3K27ac, H3K27me3, and H3K4me3. Epiphany uses 1D convolutional layers to learn local representations from the input tracks, a bidirectional long short-term memory (Bi-LSTM) layers to capture long term dependencies along the epigenome, as well as a generative adversarial network (GAN) architecture to encourage contact map realism. To improve the usability of predicted contact matrices, we trained and evaluated models using multiple normalization and matrix balancing techniques including KR, ICE, and HiC-DC+ Z-score and observed-over-expected count ratio. Epiphany is trained with a combination of MSE and adversarial (i.a., a GAN) loss to enhance its ability to produce realistic Hi-C contact maps for downstream analysis. Epiphany shows robust performance and generalization to held-out chromosomes within and across cell types and species, and its predicted contact matrices yield accurate TAD and significant interaction calls. At inference time, Epiphany can be used to study the contribution of specific epigenomic peaks to 3D architecture and to predict the structural changes caused by perturbations of epigenomic signals.

genomics↗

bioRxiv2021

Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin

We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. For the task of retrieving motifs of expressed TFs for a given cell type, we find that BindVAE has a higher precision, albeit lower recall, compared to other motif discovery approaches.

bioinformatics↗

bioRxiv2021

Chromatin interaction aware gene regulatory modeling with graph attention networks

Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting non-coding genetic variation. Here we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays in order to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements up to 2Mb away in the genome, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than state-of-the-art deep learning methods for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both CNNs and the recently proposed Activity-by-Contact model. Sequence-based GraphReg also accurately predicts direct transcription factor (TF) targets as validated by CRISPRi TF knockout experiments via in silico ablation of TF binding motifs. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.

genomics↗

bioRxiv2021

Pancreatic cancer prognosis is predicted by a novel ATAC-array technology for assessing chromatin accessibility

We investigated tumor-cell-intrinsic chromatin accessibility patterns of pancreatic ductal adenocarcinoma (PDAC) by ATAC-seq on EpCAM+ PDAC malignant epithelial cells, sorted from 54 freshly resected human tumors, and discovered a signature of 1092 chromatin loci displaying differential accessibility between patients with disease free survival (DFS) < 1 year and patients with DFS > 1 year. Analyzing transcription factor (TF) binding motifs within these loci, we identified two TFs (ZKSCAN1 and HNF1b) displaying differential nuclear localization between patients with short vs. long DFS. We further developed a novel chromatin accessibility microarray methodology termed "ATAC-Array", an easy-to-use platform obviating the time and cost of next generation sequencing. Applying this novel methodology to the original ATAC-seq libraries as well as independent libraries generated from patient-derived organoids, we validated ATAC-array technology in both the original ATAC-Seq cohort as well as in an independent validation cohort. We conclude that PDAC prognosis can be predicted by ATAC-array, which represents a novel, lowcost, clinically feasible technology for assessing chromatin accessibility profiles.

cancer biology↗

bioRxiv2020

Single cell transcriptional profiling reveals helper, effector, and regulatory MAIT cell populations enriched during homeostasis and activation

Mucosal-associated invariant T (MAIT) cells are innate-like lymphocytes that recognize microbial vitamin B metabolites and have emerging roles in infectious disease, autoimmunity, and cancer. Although MAIT cells are identified by a semi-invariant T cell receptor, their phenotypic and functional heterogeneity is not well understood. Here we present an integrated single cell transcriptomic analysis of over 76,000 human MAIT cells during acute and chronic antigen-specific activation with the MR1 ligand 5-OP-RU and non-specific TCR stimulation. We show that MAIT cells span a broad range of homeostatic, effector, helper, tissue-infiltrating, regulatory, and exhausted phenotypes, with distinct gene expression programs associated with CD4+ or CD8+ co-expression. During acute activation, MAIT cells rapidly adopt a cytotoxic phenotype characterized by high expression of GZMB, IFNG and TNF. In contrast, chronic stimulation induces heterogeneous states defined by proliferation, cytotoxicity, immune modulation, and exhaustion. These scRNAseq-defined MAIT cell subtypes were also detected in individuals recently exposed to Mycobacterium tuberculosis infection, confirming their presence during human infection. Our study provides the first comprehensive atlas of human MAIT cells in activation conditions and defines substantial functional heterogeneity, suggesting complex roles in health and disease.

immunology↗

bioRxiv2020

HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

We present HiC-DC+, a software tool for Hi-C/Hi-ChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools, includes TAD and A/B compartment callers, and outperformed existing tools in H3K27ac HiChIP benchmarking as validated by CRISPRi-FlowFISH. Differential HiC-DC+ analysis recovered global principles of 3D organization during cohesin perturbation and differentiation, including TAD aggregation, enhancer hubs, and promoter-enhancer loop dynamics.

bioinformatics↗

bioRxiv2020

A unified atlas of CD8 T cell dysfunctional states in cancer and infection

CD8 T cells play an essential role in defense against viral and bacterial infections and in tumor immunity. Deciphering T cell loss of functionality is complicated by the conspicuous heterogeneity of CD8 T cell states described across different experimental and clinical settings. By carrying out a unified analysis of over 300 ATAC-seq and RNA-seq experiments from twelve independent studies of CD8 T cell dysfunction in cancer and infection we defined a shared differentiation trajectory towards terminal dysfunction and its underlying transcriptional drivers and revealed a universal early bifurcation of functional and dysfunctional T cell activation states across models. Experimental dissection of acute and chronic viral infection using scATAC-seq and allele-specific scRNA-seq identified state-specific transcription factors and captured the early emergence of highly similar TCF1+ progenitor-like populations at an early branch point, at which epigenetic features of functional and dysfunctional T cells diverge. Our atlas of CD8 T cell states will facilitate mechanistic studies of T cell immunity and translational efforts.

genomics↗

bioRxiv2020

Sequential CRISPR gene editing in human iPSCs charts the clonal evolution of leukemia

Human cancers arise through an evolutionary process whereby cells acquire somatic mutations that drive them to outgrow normal cells and create successive clonal populations. "Bottom-up" human cancer evolution models could help illuminate this process, but their creation has faced significant challenges. Here we combined human induced pluripotent stem cell (iPSC) and CRISPR/Cas9 technologies to develop a model of the clonal evolution of acute myeloid leukemia (AML). Through the sequential introduction of 3 disease-causing mutations (ASXL1 C-terminus truncation, SRSF2P95L and NRASG12D), we obtained single, double and triple mutant iPSC lines that, upon hematopoietic differentiation, exhibit progressive dysplasia with increasing number of mutations, capturing distinct premalignant stages, including clonal hematopoiesis, myelodysplastic syndrome, and culminating in a transplantable leukemia. iPSC-derived clonal hematopoietic stem/progenitor cells recapitulate transcriptional and chromatin accessibility signatures of normal and malignant hematopoiesis found in primary human cells. By mapping dynamic changes in transcriptomes and chromatin landscapes, we characterize transcriptional programs driving specific stage transitions and identify vulnerabilities for early therapeutic targeting. Such synthetic "de novo oncogenesis" models can empower the investigation of multiple facets of the malignant transformation of human cells.

cancer biology↗

bioRxiv2019

High-resolution in vivo identification of miRNA targets by Halo-Enhanced Ago2 Pulldown

The identification of miRNA targets by Ago2 crosslinking-immunoprecipitation (CLIP) methods has provided major insights into the biology of this important class of non-coding RNAs. However, these methods are technically challenging and not easily translated to an in vivo setting. To overcome these limitations and to facilitate the investigation of miRNA functions in mice, we have developed a method (HEAP: for Halo-Enhanced Ago2 Pulldown) to map miRNA-mRNA binding sites. This method is based on a novel genetically engineered mouse harboring a conditional, Cre-regulated, Halo-Ago2 allele expressed from the endogenous Ago2 locus. By using a resin conjugated to the HaloTag ligand, Ago2-miRNA-mRNA complexes can be efficiently purified from cells and tissues expressing the endogenous Halo-Ago2 allele. We demonstrate the reproducibility and sensitivity of this method in mouse embryonic stem cells, in developing embryos, in adult tissues and in autochthonous mouse models of human brain and lung cancers.\n\nThe tools and the datasets we have generated will serve as a valuable resource to the scientific community and will facilitate the characterization of miRNA functions under physiological and pathological conditions.

molecular biology↗

bioRxiv2019

A machine learning approach predicts essential genes and pharmacological targets in cancer

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

cancer biology↗