Biology subjects

Kharchenko, P.

Publications and source records attributed to Kharchenko, P..

17 recordsLinked to original sources

bioRxiv2026

Stoichiometric transcription factor partnerships specify GABAergic neuron subtype identity

Combinatorial transcription factor (TF) codes specify neuronal fates, yet how quantitative differences in interacting TFs shape these decisions remains unresolved. We address this in the developing basal ganglia, where a pool of undifferentiated progenitors gives rise to both D1 and D2 medium spiny neurons (MSNs). Combining sparse in vivo CRISPR perturbation, lineage barcoding, and single-cell transcriptomics in mice, we find that loss of Sp9 shifts clonal fate bias from D2 toward D1 MSNs and intercalated cells. Mechanistically, chromatin profiling and biochemical assays show that at GC-rich promoters, SP9 binds DNA directly and activates transcription. At distal enhancers, SP9 binds indirectly, tethered by DLX factors, represses activity, and associates with the NuRD corepressor complex. The relative abundance of SP9 and DLX selects between these modes. These findings extend the combinatorial TF code beyond factor identity to relative proportions, with relevance to neurodevelopmental disorders.

developmental biology↗

bioRxiv2026

Sex differences in the genetic basis of human recombination within 190,000 parent-child pairs

Structured AbstractO_ST_ABSIntroductionC_ST_ABSSexual reproduction uses a specialized cell division called meiosis, in which a single round of DNA replication is followed by two cell divisions to create hap-loid gametes. Genetic recombination in meiosis assures faithful segregation of chromosomes and establishes patterns of genetic linkage and inheritance. Meiotic recombination is thus a fundamental genomic process that shapes major features of the genomic landscape, influences mutation, and creates genetic diversity. RationaleThe genetic basis of between-individual variation in the occurrence and genomic location of meiotic recombination events remains poorly understood in humans. In particular, we lack an understanding of whether the same DNA regions shape vari-ation in recombination rates across studies. We address this by curating 112,144 maternal and 78,653 paternal meiosis events (parent-child pairs) across cohorts from four countries, creating the largest dataset of its kind. ResultsWe identify 58 independent genomic regions associated with the occurrence and genomic location of recombination events, 24 of which are unique across phe-notypes. Of the 24 unique regions, 6 are novel and specific to female meiotic recombination harboring genes linked to ovarian function, a 7th novel region is linked to male recombination. Many regions have significant sex-dependent ef-fects across studies. We estimate the between-sex genetic correlation for meiotic recombination rate to be 0.374 (0.072 SD), which captures the extent to which the genetic factors influencing a trait in one sex also impact the same trait in the other. 30-40% of the individual-level variation in recombination rate is at-tributable to DNA markers, of which only 37.7% (2.7% SD) and 47.1% (3.8% SD) is attributable to previously known global modulators of meiotic recombi-nation, in females and males respectively. We create a polygenic predictor that significantly predicts recombination rates of both women and men after adjusting for age and year of birth. We find significant correlations between female meiotic recombination, reproductive outcomes and reproductive aging, after adjusting for study ascertainment. ConclusionsIn the largest meta-analysis to date of human meiotic recombination, we identify novel loci with sex-specific effects that influence male and female recombination rate across multiple cohorts.

genomics↗

bioRxiv2025

Single-cell multiomic human brain atlas reveals regulatory drivers of cortical regionality

Distinct regional functionality of the human cortex is orchestrated by diverse cellular and molecular processes, yet the underlying regulatory mechanisms remain poorly understood. We performed multiomic single-cell and spatial characterization of nine regions of the human cortex to define the gene regulatory networks and transcription factors that govern cell-type and region specificity. With the combined data of over three million cells, two striking patterns of cortical neuron specialization were uncovered: a rostral-caudal gradient of calcium regulatory machinery, and subunit switching of multiple signaling receptor families across the transmodal-sensory axis. Gene regulatory network analysis revealed putative transcriptional regulators of cortical neuron specialization with cell-type- and region-specific gene regulation patterns. While regionalization was observed in gene expression, chromatin accessibility, and spatial distributions, these modalities exhibited distinct cortical patterns. Our findings illuminate critical neuronal pathways that vary throughout the cortex and the gene regulatory networks that establish cortical regionalization in the human brain.

neuroscience↗

bioRxiv2024

Semaphorin 4A maintains functional diversity of the hematopoietic stem cell pool

Somatic stem cell pools are comprised of diverse, highly specialized subsets whose individual contribution is critical for the overall regenerative function. In the bone marrow, myeloid-biased HSC (myHSC) are indispensable for replenishment of myeloid cells and platelets during inflammatory response but at the same time, become irreversibly damaged during inflammation and aging. Here, we identify an extrinsic factor, Semaphorin 4A (Sema4A), which non cell-autonomously confers myHSC resilience to inflammatory stress. We show that the absence of Sema4A, myHSC inflammatory hyper-responsiveness in young mice drives excessive myHSC expansion, myeloid bias and profound loss of regenerative function with age. Mechanistically, Sema4A is mainly produced by neutrophils, signals via a cell surface receptor Plexin D1 and safeguards myHSC epigenetic state. Our study shows that by selectively protecting a distinct stem cell subset, an extrinsic factor preserves functional diversity of somatic stem cell pool throughout organismal lifespan.

cell biology↗

bioRxiv2023

Mapping Human Tissues with Highly Multiplexed RNA in situ Hybridization

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. Recently there has been a surge of multiplexed RNA in situ techniques but their application to human tissues and clinical biopsies has been limited due to their large size, general lower tissue quality and high background autofluorescence. Here we report DART-FISH, a versatile padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections at cellular resolution. We introduced an omni-cell type cytoplasmic stain, dubbed RiboSoma that substantially improves the segmentation of cell bodies. We developed a computational decoding-by-deconvolution workflow to extract gene spots even in the presence of optical crowding. Our enzyme-free isothermal decoding procedure allowed us to image 121 genes in a large section from the human neocortex in less than 10 hours, where we successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. Additionally, we demonstrated the detection of transcripts as short as 461 nucleotides, including neuropeptides and discovered new cortical layer markers. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.

genomics↗

bioRxiv2022

Case-control analysis of single-cell RNA-seq studies

Single-cell RNA-seq (scRNA-seq) assays are being increasingly utilized to investigate specific hypotheses in both basic biology and clinically-applied studies. The design of most such studies can be often reduced to a comparison between two or more groups of samples, such as disease cases and healthy controls, or treatment and placebo. Comparative analysis between groups of scRNA-seq samples brings additional statistical considerations, and currently there is a lack of tools to address this common scenario. Based on our experience with comparative designs, here we present a computational suite (Cacoa - case-control analysis) to carry out statistical tests, exploration, and visualization of scRNA-seq sample cohorts. Using multiple example datasets, we demonstrate how application of these techniques can provide additional insights, and avoid issues stemming from inter-individual variability, limited sample size, and high dimensionality of the data.

bioinformatics↗

bioRxiv2022

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Tissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts "multicellular gene expression patterns" that capture how sample-specific expression states of a cell type are correlated with the expression states of other cell types. Such multicellular patterns can reveal molecular mechanisms underlying coordinated changes of different cell types within the tissue, and can be used to stratify individuals in a clinically-relevant and reproducible manner. We first validated the performance of scITD using in vitro experimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern linked to nephritis, which was characterized by an expansion of activated memory B cells along with helper T cell activation. Our approach also sheds light on ligand-receptor interactions potentially mediating these multicellular patterns. As validation, we demonstrated that these expression patterns also stratified donors from a pediatric SLE dataset by the same phenotypic attributes. Lastly, we found the interferon multicellular pattern and others to be conserved in a COVID-19 dataset, pointing to the presence of both general and disease-specific patterns of inter-individual immune variation. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

bioinformatics↗

bioRxiv2022

Haplotype-enhanced inference of somatic copy number profiles from single-cell transcriptomes

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of CNVs from scRNA-seq data. To resolve tumor clonal architecture, Numbat exploits the evolutionary relationships between subclones to iteratively infer the single-cell copy number profiles and tumor clonal phylogeny. Analyzing 21 tumor samples composed of multiple myeloma, breast, and thyroid cancers, we show that Numbat can accurately reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We uncover additional subclonal complexity contributed by allele-specific alterations, and identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. We hope that the increased power to characterize genomic aberrations and tumor subclonal phylogenies provided by Numbat will help delineate contributions of genetic and non-genetic mechanisms in cancer.

bioinformatics↗

bioRxiv2022

Myeloid-biased HSC require Semaphorin 4A from the bone marrow niche for self-renewal under stress and life-long persistence

Tissue stem cells are hierarchically organized. Those that are most primitive serve as key drivers of regenerative response but the signals that selectively preserve their functional integrity are largely unknown. Here, we identify a secreted factor, Semaphorin 4A (Sema4A), as a specific regulator of myeloid-biased hematopoietic stem cells (myHSC), which are positioned at the top of the HSC hierarchy. Lack of Sema4A leads to exaggerated myHSC (but not downstream "balanced" HSC) proliferation after acute inflammatory stress, indicating that Sema4A enforces myHSC quiescence. Strikingly, aged Sema4A knock-out myHSC expand but almost completely lose reconstitution capacity. The effect of Sema4A is non cell-autonomous, since upon transplantation into Sema4A-deficient environment, wild-type myHSC excessively proliferate but fail to engraft long-term. Sema4A constrains inflammatory signaling in myHSC and acts via a surface receptor Plexin-D1. Our data support a model whereby the most primitive tissue stem cells critically rely on a dedicated signal from the niche for self-renewal and life-long persistence.

cell biology↗

bioRxiv2021

Single-nuclei transcriptomes from human adrenal gland reveals distinct cellular identities of low and high-risk neuroblastoma tumors.

Childhood neuroblastoma has a remarkable variability in outcome. Age at diagnosis is one of the most important prognostic factors, with children less than 1 year old having favorable outcomes. We studied single-cell and single-nuclei transcriptomes of neuroblastoma with different clinical risk groups and stages, including healthy adrenal gland. We compared tumor cell populations with embryonic mouse sympatho-adrenal derivatives, and post-natal human adrenal gland. We provide evidence that low and high-risk neuroblastoma have different cell identities, representing two disease entities. Low-risk neuroblastoma presents a transcriptome that resembles sympatho- and chromaffin cells, whereas malignant cells enriched in high-risk neuroblastoma resembles an unknown subtype of TRKB+ cholinergic progenitor population identified in human post-natal gland. Analyses of these populations revealed different gene expression programs for worst and better survival in correlation with age at diagnosis. Our findings reveal two cellular identities and a composition of human neuroblastoma tumors reflecting clinical heterogeneity and outcome.

cancer biology↗

bioRxiv2020

Single cell transcriptomics of primate sensory neurons identifies cell types associated with human chronic pain

Distinct types of dorsal root ganglion sensory neurons may have unique contributions to chronic pain. Identification of primate sensory neuron types is critical for understanding the cellular origin and heritability of chronic pain. However, molecular insights into the primate sensory neurons are missing. Here we classify non-human primate dorsal root ganglion sensory neurons based on their transcriptome and map human pain heritability to neuronal types. First, we identified cell correlates between two major datasets for mouse sensory neuron types. Machine learning exposes an overall cross-species conservation of somatosensory neurons between primate and mouse, although with differences at individual gene level, highlighting the importance of primate data for clinical translation. We map genomic loci associated with chronic pain in human onto primate sensory neuron types to identify the cellular origin of human chronic pain. Genome-wide associations for chronic pain converge on two different neuronal types distributed between pain disorders that display different genetic susceptibilities, suggesting both unique and shared mechanisms between different pain conditions.

neuroscience↗

bioRxiv2020

Impact of metastatic prostate cancer on human bone marrow

Bone metastases are devastating complications of cancer. They are particularly common in prostate cancer, represent incurable disease and are refractory to immunotherapy. We sought to define distinct features of the bone marrow microenvironment by analyzing single cells from prostate cancer patients involved bone, uninvolved bone and distant bone sites as well as bone from cancer-free, orthopedic patients and healthy individuals. Metastatic prostate cancer was associated with multifaceted immune distortion, specifically exhaustion of distinct T cell subsets, appearance of macrophages with states specific to prostate cancer bone metastases. The chemokine CCL20 was notably overexpressed by myeloid cells, as was its cognate CCR6 receptor on T cells. Disruption of the CCL20-CCR6 axis in mice with syngeneic prostate bone metastases restored T cell reactivity and significantly prolonged animal survival. Comparative high resolution analysis of prostate cancer bone metastasis shows a targeted approach for relieving local immunosuppression for therapeutic effect.

cancer biology↗

bioRxiv2017

RNA velocity in single cells

RNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity--the time derivative of RNA abundance--can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

genomics↗

bioRxiv2017

Integrative Single-Cell Analysis By Transcriptional And Epigenetic States In Human Adult Brain

Detailed characterization of the cell types comprising the highly complex human brain is essential to understanding its function. Such tasks require highly scalable experimental approaches to examine different aspects of the molecular state of individual cells, as well as the computational integration to produce unified cell state annotations. Here we report the development of two highly scalable methods (snDrop-Seq and scTHS-Seq), that we have used to acquire nuclear transcriptome and DNA accessibility maps for thousands of single cells from the human adult visual and frontal cortex. This has led to the best-resolved human neuronal subtypes to date, identification of a majority of the non-neuronal cell types, as well as the cell-type specific nuclear transcriptome and DNA accessibility maps. Integrative analysis allowed us to identify transcription factors and regulatory elements shaping the state of different brain cell types, and to map genetic risk factors of human brain common diseases to specific pathogenic cell types and subtypes.

genomics↗

bioRxiv2017

Challenges And Emerging Directions In Single-Cell Analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

genomics↗

bioRxiv2017

UBiT2: a client-side web-application for gene expression data analysis

We present a purely client-side web-application, UBiT2 (User-friendly BioInformatics Tools), that provides installation-free, offline alignment, analysis, and visualization of RNA-sequencing as well as qPCR data. Analysis modules were designed with single cell transcriptomic analysis in mind. Using just a browser, users can perform standard analyses such as quality control, filtering, hierarchical clustering, principal component analysis, differential expression analysis, gene set enrichment testing, and more, all with interactive visualizations and exportable publication-quality figures. We apply UBiT2 to recapitulate findings from single cell RNA-seq and Fluidigm Biomark multiplex RT-qPCR gene expression datasets. UBiT2 is available at http://pklab.med.harvard.edu/jean/ubit2/index.html with open-source code available at https://github.com/JEFworks/ubit2.

bioinformatics↗

bioRxiv2017

Comparison of Principal Component Analysis and t-Stochastic Neighbor Embedding with Distance Metric Modifications for Single-cell RNA-sequencing Data Analysis

Recent developments in technological tools such as next generation sequencing along with peaking interest in the study of single cells has enabled single-cell RNA-sequencing, in which whole transcriptomes are analyzed on a single-cell level. Studies, however, have been hindered by the ability to effectively analyze these single cell RNA-seq datasets, due to the high-dimensional nature and intrinsic noise in the data. While many techniques have been introduced to reduce dimensionality of such data for visualization and subpopulation identification, the utility to identify new cellular subtypes in a reliable and robust manner remains unclear. Here, we compare dimensionality reduction visualization methods including principle component analysis and t-stochastic neighbor embedding along with various distance metric modifications to visualize single-cell RNA-seq datasets, and assess their performance in identifying known cellular subtypes. Our results suggest that selecting variable genes prior to analysis on single-cell RNA-seq data is vital to yield reliable classification, and that when variable genes are used, the choice of distance metric modification does not particularly influence the quality of classification. Still, in order to take advantage of all the gene expression information, alternative methods must be used for a reliable classification.

bioinformatics↗