Biology subjects

Przytycki, P. F.

Publications and source records attributed to Przytycki, P. F..

15 recordsLinked to original sources

bioRxiv2026

A Compact Regulatory Logic Governs Fibroblast Cell State Plasticity in Fibrosis

Fibroblast activation drives organ failure across human diseases, yet its regulatory basis remains largely undefined. Here, we repressed 280 conserved disease-activated enhancers using CRISPR droplet sequencing (CROP-seq) in ~550,000 fibroblasts, constructing a regulome of 15 gene programs governing the profibrotic state. Systematic TF perturbation converged on MEOX1 as a central regulator, and fibroblast-specific Meox1 deletion in mice improved cardiac function, reduced fibrosis, and rewired profibrotic fibroblasts toward quiescence in heart failure. Effector gene perturbation in human cardiac fibroblasts revealed distinct regulators of contractility, migration, and inflammatory signaling, and these programs were conserved across profibrotic states in human lung, liver, and skin. Simultaneous repression of just six enhancers using a multiplexed dCas12a-KRAB system collapsed the profibrotic network, suppressed migration and contraction, and rewired fibroblast paracrine signaling to attenuate macrophage inflammation. These findings define the regulatory logic of fibroblast plasticity and establish a new framework for reprogramming profibrotic cell states in disease.

genomics↗

bioRxiv2026

HyperCom: a hypergraph-based method to infer cell-resolved cell-cell communication

Cell-cell communication underpins intercellular responses allowing cells to dynamically change states across conditions; however current methods typically aggregate signals by cell type annotations which may introduce biases. We developed HyperCom, a hypergraph-based method to infer cell-resolved cell-cell communication from single cell transcriptomics data. HyperCom constructs a hypergraph from known ligand-receptor interactions and the expression of those ligands and receptors in individual cells and performs graph diffusion on the hypergraph to score cells on a continuous spectrum from sender to receiver for each LR pair. HyperCom then generates a cell-type agnostic ranking of LR pairs by communication either over the whole dataset or in a condition-aware manner. Validation of HyperCom on physical cell-to-cell contact, spatial, and multicondition datasets highlight its flexibility to capture subtle per cell communication information. HyperCom is available as a species-agnostic, scalable, and open source R package: https://github.com/pawelab/HyperCom.

bioinformatics↗

bioRxiv2026

Learned Immune Architectures of Durable Antibody Responses Across Vaccines

Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises from complex interplay between genetic, pathogen, and immunological factors. To date, most systems vaccinology studies have remained pathogen-specific, precluding the discovery of potential shared immune architectures underlying durable antibody responses. To address this gap, we leveraged transcriptomic data from 1,032 participants receiving influenza, hepatitis B, or yellow fever vaccines to develop an interpretable machine learning framework for comparative analysis across diverse vaccine platforms. Pathogen-specific models using Blood Transcriptional Module-based feature aggregation accurately predicted high antibody responders and consistently outperformed gene-level models. Distinct predictive immune architectures identified across vaccines were further resolved for dominant hierarchical immune programs using surrogate decision trees. This approach identified the dominant decision boundaries underlying each vaccine model, highlighting leukocyte migration and Th2 differentiation in Hepatitis B, CD4+ T cells, M2 macrophages, and c-MYC signaling in Influenza, and B-cell receptor signaling with B-cell developmental pathways in Yellow Fever. Cross-pathogen concordance analyses further identified four shared transcriptional modules, suggesting partially conserved immune architectures across diverse vaccines. Together, these findings provide new insights into the immune mechanistic underpinnings of durable vaccine responses across vaccines and provide an interpretable framework for comparative systems vaccinology that may guide the rational design of next-generation vaccines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/737303v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1646f50org.highwire.dtl.DTLVardef@c816eforg.highwire.dtl.DTLVardef@59b19eorg.highwire.dtl.DTLVardef@9c39cc_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Cis-TERN: a graph-based method to integrate single-cell chromatin accessibility with gene expression data to infer the pseudotime ordering of cells

Pseudotime ordering methods computationally arrange individually sequenced cells along a continuous trajectory to infer their progression through biological processes. Chromatin accessibility at regulatory elements is pivotal for orchestrating the timing of gene expression during cellular differentiation. However, the sparsity inherent in single-cell data, along with the discrete grouping imposed by clustering-based approaches, often obscures subtle cell-state transitions and intermediate populations. Here, we introduce Cis-TERN (Cis-Temporally Encoded Regulation Network), a graph-based method that integrates single-cell measurements of gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq) to infer a pseudotime ordering of cells. Exploiting the fact that distal cis chromatin accessibility often precedes gene activation, Cis-TERN builds a directed bipartite graph between scRNA-seq and scATAC-seq cells with edge weights determined by either proximal or distal accessibility. Through network diffusion, Cis-TERN assigns continuous pseudotime scores to all cells. Cis-TERN infers cellular pseudotime without the need for prior biological knowledge, such as specifying a root cell type, thereby enabling unbiased reconstruction of differentiation from data alone. We show that our scores align with known lineages in healthy and tumor-derived hematopoietic cells. Additionally, Cis-TERN is able to trace individual cancer cells back to progenitor cell types. Our approach establishes a framework for leveraging chromatin accessibility and gene expression jointly to learn the timing of cell state progression.

bioinformatics↗

bioRxiv2024

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations

CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2s ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.

bioinformatics↗

bioRxiv2023

Hierarchical annotation of eQTLs enables identification of genes with cell-type divergent regulation

While context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell-type specific eQTLs are complicated by the nested nature of cell types. We present a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single cell chromatin accessibility data and no clustering of cells into discrete cell types. Using our model, we annotated bulk-derived eQTLs from the developing brain with high specificity to levels of a cell-type hierarchy. The increased annotation power provided by the hierarchical model allowed for sensitive detection of genes with multiple distinct non-coding elements regulating their expression in different cell types, which we validated in single-cell multiome data and reporter assays. Overall, we find that incorporating the hierarchical organization of cell types provides a powerful way to account for the relationships between cell types in complex tissues.

bioinformatics↗

bioRxiv2023

Massively parallel characterization of psychiatric disorder-associated and cell-type-specific regulatory elements in the developing human cortex

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and disease. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 sequences, including differentially accessible cell-type specific regions in the developing cortex and single-nucleotide variants associated with psychiatric disorders. In primary cells, we identified 46,802 active enhancer sequences and 164 disorder-associated variants that significantly alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning, we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development. One Sentence SummaryWe identify 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids.

genomics↗

bioRxiv2023

Chromatin Remodeling Drives Immune-Fibroblast Crosstalk in Heart Failure Pathogenesis

Chronic inflammation and tissue fibrosis are common stress responses that worsen organ function, yet the molecular mechanisms governing their crosstalk are poorly understood. In diseased organs, stress-induced changes in gene expression fuel maladaptive cell state transitions and pathological interaction between diverse cellular compartments. Although chronic fibroblast activation worsens dysfunction of lung, liver, kidney, and heart, and exacerbates many cancers, the stress-sensing mechanisms initiating the transcriptional activation of fibroblasts are not well understood. Here, we show that conditional deletion of the transcription co-activator Brd4 in Cx3cr1-positive myeloid cells ameliorates heart failure and is associated with a dramatic reduction in fibroblast activation. Analysis of single-cell chromatin accessibility and BRD4 occupancy in vivo in Cx3cr1-positive cells identified a large enhancer proximal to Interleukin-1 beta (Il1b), and a series of CRISPR deletions revealed the precise stress-dependent regulatory element that controlled expression of Il1b in disease. Secreted IL1B functioned non-cell autonomously to activate a p65/RELA-dependent enhancer near the transcription factor MEOX1, resulting in a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL1B neutralization prevented stress-induced expression of MEOX1, inhibited fibroblast activation, and improved cardiac function in heart failure. The elucidation of BRD4-dependent crosstalk between a specific immune cell subset and fibroblasts through IL1B provides new therapeutic strategies for heart disease and other disorders of chronic inflammation and maladaptive tissue remodeling.

molecular biology↗

bioRxiv2022

Three-dimensional genome re-wiring in loci with Human Accelerated Regions

Human Accelerated Regions (HARs) are conserved genomic loci that evolved at an accelerated rate in the human lineage and may underlie human-specific traits. We generated HARs and chimpanzee accelerated regions with the largest alignment of mammalian genomes to date. To facilitate exploration of accelerated evolution in other lineages, we implemented an open-source Nextflow pipeline that runs on any computing platform. Combining deep-learning with chromatin capture experiments in human and chimpanzee neural progenitor cells, we discovered a significant enrichment of HARs in topologically associating domains (TADs) containing human-specific genomic variants that change three-dimensional (3D) genome organization. Differential gene expression between humans and chimpanzees at these loci in multiple cell types suggests rewiring of regulatory interactions between HARs and neurodevelopmental genes. Thus, comparative genomics together with models of 3D genome folding revealed enhancer hijacking as an explanation for the rapid evolution of HARs. One-Sentence SummaryHuman-specific changes to 3D genome organization may have contributed to rapid evolution of mammalian-conserved loci in the human genome.

evolutionary biology↗

bioRxiv2021

CellWalkR: An R Package for integrating single-cell and bulk data to resolve regulatory elements

While single-cell open chromatin (scATAC-seq) data allows for the identification of cell type-specific regulatory regions, it is much sparser than bulk data. CellWalkR is an R package that performs an integration of external labeling and bulk epigenetic data with scATAC-seq using a network-based random walk model to help overcome this sparsity. Outputs include cell type labels for individual cells and regulatory regions. Availability and implementationCellWalkR is freely available as an R package under a GNU GPL-2.0 License, and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette for analyzing example data.

bioinformatics↗

bioRxiv2020

Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis

MotivationUnsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter, but then only report one. ResultsWe developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, coexpression, biological processes, and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, to provide novel insight into cell populations. Availability and implementationUpon request

bioinformatics↗

bioRxiv2020

A Transcriptional Switch Governing Fibroblast Plasticity Underlies Reversibility of Chronic Heart Disease

In diseased organs, stress-activated signaling cascades alter chromatin, triggering broad shifts in transcription and cell state that exacerbate pathology. Fibroblast activation is a common stress response that worsens lung, liver, kidney and heart disease, yet its mechanistic basis remains poorly understood1,2. Pharmacologic inhibition of the BET family of transcriptional coactivators alleviates cardiac dysfunction and associated fibrosis, providing a tool to mechanistically interrogate maladaptive fibroblast states and modulate their plasticity as a potential therapeutic approach3-8. Here, we leverage dynamic single cell transcriptomic and epigenomic interrogation of heart tissue with and without BET inhibition to reveal a reversible transcriptional switch underlying stress-induced fibroblast activation. Transcriptomes of resident cardiac fibroblasts demonstrated robust and rapid toggling between the quiescent fibroblast and activated myofibroblast state in a manner that directly correlated with BET inhibitor exposure and cardiac function. Correlation of single cell chromatin accessibility with cardiac function revealed a novel set of reversibly accessible DNA elements that correlated with disease severity. Among the most dynamic elements was an enhancer regulating the transcription factor MEOX1, which was specifically expressed in activated myofibroblasts, occupied putative regulatory elements of a broad fibrotic gene program, and was required for TGF{beta}-induced myofibroblast activation. CRISPR interference of the most dynamic cis-element within the enhancer, marked by nascent transcription, prevented TGF{beta}-induced activation of Meox1. These findings identify MEOX1 as a central regulator of stress-induced myofibroblast activation associated with cardiac dysfunction. The plasticity and specificity of the BET-dependent regulation of MEOX1 in endogenous tissue fibroblasts provides new trans- and cis- targets for treating fibrotic disease.

molecular biology↗

bioRxiv2019

Single cell epigenomic atlas of the developing human brain and organoids

Dynamic changes in chromatin accessibility coincide with important aspects of neuronal differentiation, such as fate specification and arealization and confer cell type-specific associations to neurodevelopmental disorders. However, studies of the epigenomic landscape of the developing human brain have yet to be performed at single-cell resolution. Here, we profiled chromatin accessibility of >75,000 cells from eight distinct areas of developing human forebrain using single cell ATAC-seq (scATACseq). We identified thousands of loci that undergo extensive cell type-specific changes in accessibility during corticogenesis. Chromatin state profiling also reveals novel distinctions between neural progenitor cells from different cortical areas not seen in transcriptomic profiles and suggests a role for retinoic acid signaling in cortical arealization. Comparison of the cell type-specific chromatin landscape of cerebral organoids to primary developing cortex found that organoids establish broad cell type-specific enhancer accessibility patterns similar to the developing cortex, but lack many putative regulatory elements identified in homologous primary cell types. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

developmental biology↗

bioRxiv2019

Semi-supervised identification of cell populations in single-cell ATAC-seq

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 CellWalkers 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.

bioinformatics↗

bioRxiv2019

Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated By Cis Noncoding Mutations

Identifying cancer-relevant mutations in noncoding regions is extremely challenging due to the large numbers of such mutations, their low levels of recurrence, and the general difficulty in interpreting their impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individuals cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods readily detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. Overall, our results suggest that differential ASE is a promising means for discovering gene dysregulation within an individual due to cis noncoding mutations.

bioinformatics↗