Biology subjects

Kharchenko, P. V.

Publications and source records attributed to Kharchenko, P. V..

At least 19 recordsLinked to original sources

bioRxiv2026

In utero barcoding of mouse mesoderm reveals the clonal architecture of organ mesenchyme

The mesoderm coordinates organogenesis and generates the cardiovascular, visceral and musculoskeletal systems, yet in mammals this germ layer is difficult to access, impeding its genetic manipulation and study. Here, we establish embryonic day (E) 7.5 exocoelomic cavity nano-injection as a scalable route for selective genetic targeting of mouse mesoderm. Combining in utero lentiviral barcoding with single-cell and spatial transcriptomics, we reconstructed mesodermal and neural crest clonal relationships across >590,000 cells and 31,000 multicellular clones from E9.5-E10.5 embryos and E16.5 livers and hearts. In liver, capsular mesothelium was closely related to hepatic stellate cells, whereas fibroblast and vascular smooth muscle lineages diverged earlier. In heart, epicardium was clonally coupled to three spatially restricted mesenchymal sub-lineages but not to cardiomyocytes, and lineage tracing separated epicardial- and neural crest-derived populations within transcriptionally convergent valve mesenchyme. This approach enables Cre-independent manipulation of mammalian mesoderm and resolves organ mesenchyme development at clonal resolution.

developmental biology↗

bioRxiv2026

Clonal embeddings allow exploratory analysis of lineage-resolved single-cell data

Assays coupling high-throughput lineage tracing with single-cell transcriptomics are transforming studies of development and disease biology, revealing not only major differentiation routes but also continuous fate biases and their putative regulators. Yet, analysis of such data at scale presents challenges due to the sparse nature of clonal data and annotation dependencies. Towards that aim we developed a machine learning approach - clone2vec - which learns informative clone embeddings directly from the cellular expression manifold, bypassing discrete cell-type labels and remaining stable when clones are represented by few cells. This representation summarizes clonal variation as an interpretable geometry that supports exploration, statistics for clone-gene associations, and cross-dataset alignment. In prospective barcoding datasets spanning embryogenesis, tumorigenesis, and hematopoiesis, clone2vec recapitulates established clonal patterns and uncovers new axes of continuous variation that implicate regulatory programs and developmental pathways. In tumor microenvironments profiled with TCR sequencing, clone2vec robustly recovers distinct Treg lineages as well as conserved CD8+ T cell sublineages across cancer types, including several bystander-like clonal subsets. Overall, clone2vec provides a robust, general solution for the exploratory analysis of lineage-coupled scRNA-seq data.

bioinformatics↗

bioRxiv2026

High-resolution retrospective single cell lineage tracing with mutable homopolymers

Retrospective reconstruction of cell lineages from somatic mutations holds substantial promise for understanding development, tissue homeostasis, and disease. Yet, current approaches are often constrained by low marker density, technical noise, and incomplete molecular readouts that limit the resolution and accuracy of inferred lineages. Here, we present RETrace2, a single-cell dual-omic method for simultaneous lineage tracing and cell-type identification. By identifying and targeting highly mutable homopolymers, which we show are approximately twice as informative as conventional microsatellite repeats, combined with technical improvements to increase marker coverage and reduce experimental errors of reading and amplifying homopolymers, RETrace2 can trace lineage continuously at an estimated resolution of fewer than five cell divisions in microsatellite-unstable organisms. Simultaneously, sparse methylation profiles were obtained from the same cells for cell-type identification. We validated the method using in vitro ground-truth models and demonstrated its utility in vivo by reconstructing multi-organ lineage trees from the brain, kidney and liver of Msh2-deficient mice.

genetics↗

bioRxiv2025

A single-cell genetic colocalization test improves power and resolves disease-mediating cell types

Statistical colocalization testing methods can determine if the same single-nucleotide polymorphism (SNP) underlies both a genome-wide association study (GWAS) locus as well as an expression quantitative trait (eQTL) locus. This can nominate potential mechanistic pathways from SNPs to genes to traits, while providing cell type or tissue context. Surprisingly, systematic colocalization testing with bulk-tissue eQTLs fails to link the majority of GWAS loci with gene expression changes. Mapping eQTLs with single-cell expression data has the potential to reveal the missing regulatory effects of GWAS variants. However, current pseudobulk cluster-based approaches may be underpowered when clustering accuracy is imperfect or with an incorrectly selected cluster resolution. To improve power of single-cell colocalization tests, we developed a cluster-free method, scJLIM. By modeling eQTL interactions with continuous cell states (e.g., principal components), scJLIM estimates eQTL significance and colocalization in individual cells. We benchmarked our method with simulated data, demonstrating improvements in power over pseudobulk methods. In our main applications, we used scJLIM to analyze blood and brain scRNA-seq datasets paired with autoimmune and neurological disease GWAS, respectively. We identified nearly twice as many total colocalizations compared with traditional pseudobulk analyses carried out within the major cell populations of these tissues. Aligning with a recent experimental study, we highlighted an example of the ETS2 gene colocalizing with an inflammatory bowel disease GWAS locus in a subset of myeloid cells. For Parkinsons disease (PD), our results pointed to TRPV2 as a potential gene of interest, corroborated by transcriptional changes in both post-mortem PD brains and iPSC-derived neuronal models of alpha-synucleinopathy.

genomics↗

bioRxiv2025

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney

Building upon a foundational Human Kidney resource, we present a comprehensive multi-modal atlas that defines spatially resolved versus unresolved repair states and mechanisms in human kidney disease. Homeostatic interactions between injured kidney epithelium and its surrounding milieu determine successful repair outcomes, while pathogenic signaling promotes unresolved inflammation and fibrosis leading to chronic disease. We integrated multiple single-cell and spatial modalities across [~]700 samples from >350 patients ([~]250 research biopsies), analyzing [~]1.7 million cells alongside complementary mouse multi-omic profiles spanning acute-to-chronic injury and aging (>300,000 cells) and spatial transcriptomic analysis of >150 human biopsies. This cross-species atlas delineates functional pathways and druggable targets across the nephron and defines gene regulatory networks and chromatin landscapes governing tubular, fibroblast, and immune cell transitions from injury to either recovery or failed repair states. We identified distinct cellular states associated with specific pathological features that show dynamic distributions between acute kidney injury (AKI) and chronic kidney disease (CKD), organized within unique spatial niches that reveal progression mechanisms from early injury to unresolved disease. Gene regulatory analyses prioritized key transcription factor activities (SOX4, SOX9, NFKB1, REL, KLFs) and their target networks establishing disease states and tissue microenvironments. These regulatory programs were directly linked to clinical outcomes, identifying molecular signatures of recovery and secreted biomarkers predictive of AKI-to-CKD progression, providing a key resource for therapeutic development and precision medicine approaches in kidney disease.

molecular biology↗

bioRxiv2025

Impact of Segmentation Errors in Analysis of Spatial Transcriptomics Data.

Spatial transcriptomics aims to elucidate cell coordination within biological tissues by linking the state of the cell with its local tissue microenvironment. Imaging-based assays are particularly promising for exploring such interdependencies, as they can resolve molecular and cellular features with subcellular resolution in three dimensions. Quantification and analysis of cellular state in such data, however, ultimately depends on the ability to recognize which molecules belong to each cell. Despite computational and experimental progress, this cell segmentation task remains challenging. Here we re-analyze data from multiple tissues and platforms and find that segmentation errors currently confound most downstream analysis of cellular state, including analysis of differential expression, inference of neighboring cell influence, and ligand-receptor interactions. The extent to which mis-segmented molecules impact the results can be striking, often dominating the set of top hits. We show that factorization of molecular neighborhoods can be effective at isolating such molecular admixtures and minimizing their impact on downstream analysis, analogous to doublet filtering of scRNA-seq data. As applications of spatial transcriptomics assays become more widespread, we expect corrections for the confounding effect of segmentation errors to become increasingly important for being able to resolve molecular mechanisms of tissue biology.

bioinformatics↗

bioRxiv2023

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

bioinformatics↗

bioRxiv2022

Thymic mesenchymal niche cells drive T cell immune regeneration

Thymic atrophy and the progressive immune decline that accompanies it is a major health problem, chronically with age and acutely with immune injury. No solution has been defined. Here we demonstrate that one of the three mesenchymal cell subsets identified by single-cell analysis of human and mouse thymic stroma is a critical niche component for T lymphopoiesis. The Postn+ subset is located perivascularly in the cortical-medullary junction, medulla and subcapsular regions. Cell depletion demonstrated that it recruits T competent cells to the thymus and initiates T lymphopoiesis in vivo. This subset distinctively expresses the chemokine Ccl19 necessary for niche functions. It markedly declines with age and in the acute setting of hematopoietic stem cell transplant conditioning. When isolated and adoptively transferred, these cells durably engrafted the atrophic thymus, recruited early T progenitors, increased T cell neogenesis, expanded TCR complexity and enhanced T cell response to vaccination. These data define a thymus lymphopoietic niche cell type that may be manipulated therapeutically to regenerate T lymphopoiesis.

immunology↗

bioRxiv2022

scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data

scFates provides an extensive toolset for analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into scanpy ecosystem for seamless analysis of trajectories from single cell data of various modalities (e.g. RNA, ATAC). Availability and implementationscFates is released as open-source software under the BSD 3-Clause "New" License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on Github at https://github.com/LouisFaure/scFates/ Supplementary informationA supplementary document is provided with a complete explanation of the underlying statistics, and two figures showing examples of analysis.

bioinformatics↗

bioRxiv2022

A new transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer in adults. When ccRCC is localized to the kidney, surgical resection or ablation of the tumor is often curative. However, in the metastatic setting, ccRCC remains a highly lethal disease. Here we take advantage of fresh patient samples that include treatment-naive primary tumor tissue, matched adjacent normal kidney tissue, as well as tumor samples collected from patients with bone metastases. Single-cell transcriptomic analysis of tumor cells from the primary tumors revealed a distinct transcriptional signature that was predictive of metastatic potential and patient survival. Analysis of supporting stromal cells within the tumor environment demonstrated vascular remodeling within the endothelial cells and a proliferative signature within the fibroblasts that was associated with poor survival. An in silico cell-to-cell interaction analysis highlighted the CXCL9/CXCL10-CXCR3 axis and the CD70-CD27 axis as potential therapeutic targets. Our findings provide biological insights into the interplay between tumor cells and the ccRCC microenvironment.

cancer biology↗

bioRxiv2022

Integrated single-cell and spatial transcriptomic analyses unravel the heterogeneity of the prostate tumor microenvironment

The treatment of primary prostate cancer delicately balances an active surveillance approach for low-risk disease with multimodal treatment including surgery, radiation therapy, and hormonal therapy for high-risk disease. Recurrence and development of metastatic disease remains a clinical problem, without a clear understanding of what drives immune escape and tumor progression. Here, we sought to comprehensively describe the tumor microenvironment of localized prostate cancer contrasting this with adjacent normal samples and healthy controls. We performed single-cell RNA sequencing and high-resolution spatial transcriptomic analysis. This revealed tumor context dependent changes in gene expression. Our data point towards an immune suppressive tumor microenvironment associated with suppressive myeloid populations and exhausted T-cells, in addition to high stromal angiogenic activity. We inferred cell-to-cell relationships at an unprecedented scale for ligand-receptor interactions within undissociated tissue sections. Our work provides a highly detailed and comprehensive resource of the prostate tumor microenvironment as well as tumor-stromal cell interactions. HighlightsO_LICharacterization of prostate cancer by combined scRNA-seq and spatial transcriptomic analysis C_LIO_LIPrimary prostate cancer establishes a suppressive immune microenvironment C_LIO_LIThe prostate tumor microenvironment exhibits a high angiogenic gene expression pattern C_LIO_LIA new computational analysis pipeline to deconvolute context-specific differential gene expression C_LI

cancer biology↗

bioRxiv2021

An atlas of healthy and injured cell states and niches in the human kidney

Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of [~]1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.

genomics↗

bioRxiv2020

Diversity of developing peripheral glia revealed by single cell RNA sequencing

The peripheral nervous system responds to a wide variety of sensory stimuli, a process that requires great neuronal diversity. These diverse peripheral sensory neurons are closely associated with glial cells that originate from the neural crest (NC). However, the molecular nature and origins of diversity among peripheral glia is not understood. Here we used single cell RNA sequencing to profile and compare developing and mature glia from somatosensory lumbar dorsal root ganglia (DRG) and auditory spiral ganglia (SG). We found that the glial precursors (GPs) differ in their transcriptional profile and prevalence in these two systems. Despite their unique features, somatosensory and auditory GPs undergo convergent differentiation to generate myelinating and non-myelinating Schwann cells that are molecularly uniform. By contrast, although satellite glia surround the neuronal cell bodies in both ganglia, we found that those in the SG express multiple myelination-associated genes, while DRG satellite cells express components that suppress myelination. Lastly, we identified a set of glial signature genes that are also expressed by placode-derived supporting cells, providing new insights into commonalities among glia across the nervous system. This comprehensive survey of gene expression in peripheral glia constitutes a valuable resource for understanding how glia acquire specialized functions and how their roles differ across sensory modalities.

neuroscience↗

bioRxiv2020

Selective vulnerability of supragranular layer neurons in schizophrenia

Schizophrenia is one of the most wide-spread mental brain disorders with complex and largely unknown etiology. To characterize the impact of schizophrenia at a cellular level, we performed single nucleus RNA sequencing of >190,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls (7 vs 11, respectively). In addition, to correlate data with cortical anatomy, >100,000 neurons were analyzed topographically by immunohistochemistry in an extended cohort of cases with schizophrenia and controls (10 vs 10). Compositional analysis of RNA sequencing data revealed reduction in relative abundance across all families of GABAergic neurons and a concomitant increase in principal neurons, which was most pronounced for supragranular subtypes (layers 2-3). Moreover, supragranular subtypes of GABAergic interneurons showed most dramatic transcriptomic changes. These results were substantiated by histological analysis, which revealed a reduction in the density of calretinin, calbindin and parvalbumin GABAergic interneurons particularly in layer 2. Common effect of schizophrenia on supragranular neuronal networks was underlined by downregulation of protein processing genes and upregulation of neuronal development/plasticity genes across supragranular subtypes of principal neurons and GABAergic interneurons. In situ hybridization and spatial transcriptomics further confirmed supragranular layer neuron vulnerability, revealing complexity of schizophrenia-affected cortical circuits. These point towards general network impairment within supragranular layers being a core substrate associated with schizophrenia symptomatology.

neuroscience↗

bioRxiv2020

Bayesian segmentation of spatially resolved transcriptomics data

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for Bayesian Segmentation of Spatial Transcriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.

bioinformatics↗

bioRxiv2020

Malignant Schwann cell precursors mediate intratumoral plasticity in human neuroblastoma

Neuroblastoma is a heterogeneous embryonal malignancy and the most deadly tumor of childhood, although a minor subset may show spontaneous differentiation. It arises from the multipotent neural crest lineage during development. Some of this multipotency is retained in neuroblastoma, which can give rise to both adrenergic and mesenchymal tumor cells. The mechanisms enabling such dual fates are unknown, but likely help neuroblastoma to evade existing therapies. To understand neuroblastoma plasticity, we analyzed patient tumors using single-cell transcriptomics. In addition to the heterogeneous adrenergic and mesenchymal populations, we identify a subpopulation of malignant cells resembling Schwann cell precursors (SCPs). This SCP-like population connects the adrenergic and mesenchymal compartments through transitions structurally reminiscent of the SCP cell-fate decision fork that occurs during normal development. While the directionality of such transitions in neuroblastoma remains to be established, this finding expands the potential reservoirs of malignant cells, and suggests intratumoral plasticity mechanisms relevant for therapeutic resistance and relapse.

cancer biology↗

bioRxiv2020

Distinct evolutionary paths in chronic lymphocytic leukemia during resistance to graft-versus-leukemia

Resistance to the graft-versus-leukemia (GvL) effect remains the major barrier to successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) for aggressive hematologic malignancies. The basis of GvL resistance for advanced lymphoid malignancies remains incompletely understood. We hypothesized that for patients with chronic lymphocytic leukemia (CLL) treated with allo-HSCT, leukemic cell-intrinsic features shape GvL outcomes by directing the evolutionary trajectories of CLL cells. Integrated genetic, transcriptomic and epigenetic analyses of CLL cells from 10 patients revealed that the clinical kinetics of post- HSCT relapse are shaped by distinct molecular dynamics and suggest that the selection pressures of the GvL bottleneck are unlike those imposed by chemotherapy. No selective advantage for HLA loss was observed, even when present in pre-transplant subpopulations. Regardless of post-transplant relapse kinetics, gain of stem cell modules was a common signature associated with leukemia relapse. These data elucidate the biological pathways that underlie GvL resistance and post-transplant relapse. One Sentence SummaryWe find that the clinical kinetics of chronic lymphocytic leukemia relapse after stem cell transplant are underwritten by distinct genetic and epigenetic evolutionary trajectories and suggest that the selection pressures of the post-transplant, immunologic bottleneck are unlike those imposed by chemotherapy.

cancer biology↗

bioRxiv2018

Wiring together large single-cell RNA-seq sample collections

Single-cell RNA-seq methods are being increasingly applied in complex study designs, which involve measurements of many samples, commonly spanning multiple individuals, conditions, or tissue compartments. Joint analysis of such extensive, and often heterogeneous, sample collections requires a way of identifying and tracking recurrent cell subpopulations across the entire collection. Here we describe a flexible approach, called Conos (Clustering On Network Of Samples), that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph can then be used to propagate information between samples and to identify cell communities that show consistent grouping across broad subsets of the collected samples. Conos results enable investigators to balance between resolution and breadth of the detected subpopulations. In this way, it is possible to focus on the fine-grained clusters appearing within more similar subsets of samples, or analyze coarser clusters spanning broader sets of samples in the collection. Such multi-resolution joint clustering provides an important basis for downstream analysis and interpretation of sizeable multi-sample single-cell studies and atlas-scale collections.

bioinformatics↗