Biology subjects

Setty, M.

Publications and source records attributed to Setty, M..

18 recordsLinked to original sources

bioRxiv2026

Dysregulation of epithelial differentiation architecture underlies field cancerization in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) has exceptionally high recurrence rates, exceeding 50%, contributing to mortality and morbidity. This recurrence rate is proposed to be due to field cancerization, a phenomenon where tumor-adjacent "field" tissue that appears histologically normal harbors molecular changes that prime it for subsequent tumor growth. Current models attribute field cancerization to shared driver mutations between tumors and adjacent at-risk tissue, but this assumption has never been directly tested. To comprehensively characterize this phenomenon, we profiled patient-matched tumor, normal-adjacent field (NAF), contralateral normal, and blood samples from HPV-negative HNSCC patients using whole genome sequencing, targeted driver gene sequencing, single-nucleus multiome profiling, spatial transcriptomics and functional proliferation assays. While histologically normal NAF tissues did harbor expanded mutant clones, these mutations rarely overlapped with paired tumors and were not shared across patients, suggesting molecular features beyond mutations are altered in NAF. Single-cell multiomics revealed that NAF epithelium loses its normal organizational hierarchy: basal stem cell markers persist in differentiated cells, while terminal differentiation programs fail to activate, with reconfiguration of both gene regulatory networks and cell-cell interactions. Proliferating cells appeared in tissue layers normally restricted to post-mitotic cells, a pattern typically seen only in overt dysplasia. Within this disrupted hierarchy, a distinct epithelial state arose in NAF that expressed both normal epithelial and tumor-associated markers, which we localized to the parabasal layers using immunofluorescence and spatial transcriptomics. Together, our findings demonstrate that field cancerization operates through epithelial hierarchy disorganization that disrupts differentiation, generates cells with tumor-like features, and may create a permissive environment for the expansion of cancer-associated mutations, all within tissue that appears histologically normal.

cancer biology↗

bioRxiv2026

AnnZarro: scalable, interactive exploration of cell-by-cell and gene-by-gene relationships in single-cell data

Cell-by-cell and gene-by-gene relationships, such as kernels and correlations, shape most single-cell analyses but are rarely inspected directly due to a lack of visualization tools that display them. AnnZarro is a point-and-click browser viewer that shows these matrices linked to an AnnData object's cells x genes data. Using AnnZarro, experimental biologists can explore data without code and computational biologists can probe the relationships behind their analyses. One focused cell and one focused gene drive every panel. Clicking a cell paints its row of any cell-by-cell matrix over an embedding or spatial coordinates; clicking a gene paints its row of any gene-by-gene matrix over a gene plot such as a volcano. The focus also links genes to the cells where they change, and cells to the genes that distinguish them. AnnZarro reads only the matrix rows and columns each panel displays from zarr stores and hence users can explore matrices far larger than memory on a laptop or a shared server. Large datasets open on a reproducible subset, and users step through the remaining cells in parts. On a laptop, a 100,000-cell part of Tahoe-100M opens in 4 s and stays interactive. With the subset switched off, AnnZarro draws all 95.6 million cells in one plot in about 5 to 7 s. This tutorial prepares a dataset with Kompot differential abundance and expression, a diffusion kernel and gene correlations, then explores it in six worked examples.

bioinformatics↗

bioRxiv2026

Human tissue-resident CD8 T cells contribute to trophoblast homeostasis in health and during acute inflammation

Previous studies have highlighted that some T cell subsets in tissues can provide signals to support tissue cell homeostasis and differentiation. If and how T cell-tissue cell signaling is altered in healthy compared to inflamed tissues is poorly understood. Here, we address if communication between human T cells and tissue cells changes from steady state to an acutely inflamed state in the human placenta. We used single cell analysis strategies to examine invasive cytotrophoblasts (iCTBs) and immune cells isolated from third trimester healthy and acutely inflamed human placentas. We performed cell communication analysis to predict cell-cell communication networks, and found evidence that iCTBs provided signals to support the recruitment of T cells, as well as the formation of tissue-resident memory CD8 T cells (Trm). In exchange, Trm provide signals to support iCTB homeostasis. During acute inflammation, iCTBs and macrophages underwent profound transcriptional changes, while most T cell subsets only underwent limited transcriptional changes. This was not due to T cell exhaustion or tolerance, as T cells were functionally intact. Cell communication analysis and validation at the protein level provide evidence that T cells can maintain their homeostatic support to iCTBs during acute inflammation.

immunology↗

bioRxiv2026

Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions

Cell-state transitions during differentiation and disease involve coordinated changes across gene expression and chromatin accessibility, but these modalities do not change in lockstep. For example, regulatory elements can be primed before their target genes are expressed or remain accessible after expression ceases. This desynchronization between changes in gene expression and chromatin accessibility can manifest at the level of cell states. Understanding the drivers of this desynchronization can give insights into the molecular mechanisms underlying cell-state progression. Here we introduce Echo, a statistical framework that identifies desynchronized cell states and the associated genes and regulatory elements from paired single-cell RNA and ATAC data. Echo States estimates cell-state density independently for each modality and compares them to determine which states are better resolved in RNA or ATAC. Echo Features then predicts feature values over each state space to identify the genes, regulatory loci, and transcription-factor motifs driving this desynchronization. Applying Echo to the developing human fetal retina, we find that desynchronization is pervasive across every major cell population. Expression of cell-cycle genes resolves multipotent progenitors in gene expression but not chromatin accessibility, while fate priming resolves cycling neurogenic precursors in chromatin accessibility before gene expression. By combining desynchronized states and features along the cone trajectory, we reconstructed the regulatory logic of cone fate specification from multipotent progenitors, revealing a tight coupling between multipotency exit, cell cycle and lineage specification. Applying Echo to human hematopoiesis, we identified that the balance between stem-cell quiescence and differentiation is resolved more strongly in chromatin accessibility than in gene expression. Our results establish desynchronization as a pervasive, structured feature of differentiating systems, and Echo as a framework for characterizing the interplay between gene expression and chromatin accessibility during cell-state transitions.

bioinformatics↗

bioRxiv2026

SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiation

Identifying the precise transcriptomic states at which cells commit to a lineage (branchpoints) and the temporal lag in which chromatin accessibility foreshadows gene expression (epigenetic priming) remain fundamental challenges in developmental biology. While current methods for single-cell sequencing data effectively capture developmental flow, they often lack a principled mechanism for delineating the discrete boundaries, a crucial aspect required to map the molecular logic of lineage commitment. We present SCOPE (Semi-supervised Conformal Prediction), a framework that transforms high-dimensional single-cell measurements into rigorous, discrete prediction sets of all plausible future fates. By formalizing fate uncertainty via conformal inference, SCOPE localizes the precise biological windows during which multipotent progenitors specify their fate. In multi-omic data, SCOPE uncovers epigenetic priming and identifies its driving transcription factors by detecting regimes where chromatin-derived prediction sets resolve toward terminal fates significantly before their transcriptomic counterparts. We apply SCOPE across simulations, lineage-traced mouse hematopoiesis, multiple human hematopoietic datasets, and human retinogenesis to demonstrate its broad applicability and ability to recapitulate known fate specification drivers. Ultimately, SCOPE provides a statistically grounded foundation for localizing fate decisions across biological replicates and modalities, offering a robust tool for identifying the onset of lineage specification in complex developmental systems.

cell biology↗

bioRxiv2025

Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution

Single-cell studies are frequently designed to compare across conditions such as health and disease. However, existing computational approaches typically rely on grouping cells into discrete populations before making comparisons, which can limit resolution for detecting state-dependent changes. Here, we introduce Kompot, a statistical framework for comparative analysis of multi-condition single-cell data. Kompot quantifies both differential abundance, capturing how cells redistribute across the phenotypic space, and differential expression, identifying condition-specific transcriptional changes that may be localized, heterogeneous, or oppositely regulated across states. By modeling cell density and gene expression as continuous functions over a shared cell-state representation, Kompot enables single-cell resolution inference with principled uncertainty estimates, without requiring predefined clusters or cell types. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. We demonstrate the utility of Kompot in disease settings by identifying cell-state and gene expression changes associated with improved efficacy of combinatorial immunotherapy in melanoma. Additionally, Kompot enables multi-sample comparative analysis by accounting for sample-to-sample heterogeneity. By capturing both global and cell-state-specific effects of perturbation, the Kompot framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes.

bioinformatics↗

bioRxiv2023

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellons efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

bioinformatics↗

bioRxiv2023

Single-cell analysis of bidirectional reprogramming between early embryonic states reveals mechanisms of differential lineage plasticities

Two distinct fates, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common progenitor cells, the inner cell mass (ICM), in mammalian embryos. To study how these sister identities are forged, we leveraged embryonic (ES) and eXtraembryonic ENdoderm (XEN) stem cells - in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses uncovered distinct rates, efficiencies and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions. While GATA4-mediated ES-to-iXEN conversion was rapid and nearly deterministic, OCT4, KLF4 and SOX2-induced XEN-to-iPS reprogramming progressed with diminished efficiency and kinetics. The dominant PrE transcriptional program, safeguarded by Gata4, and globally elevated chromatin accessibility of EPI underscored the differential plasticities of the two states. Mapping in vitro trajectories to embryos revealed reprogramming in either direction tracked along, and toggled between, EPI and PrE in vivo states without transitioning through the ICM.

developmental biology↗

bioRxiv2023

Molecular heterogeneity of C. elegans glia across sexes

A comprehensive description of nervous system function, and sex dimorphism within, is incomplete without clear assessment of the diversity of its component cell types, neurons and glia. C. elegans has an invariant nervous system with the first mapped connectome of a multi- cellular organism and single-cell atlas of component neurons. Here we present single nuclear RNA-seq evaluation of glia across the entire adult C. elegans nervous system, including both sexes. Machine learning models enabled us to identify both sex-shared and sex-specific glia and glial subclasses. We have identified and validated molecular markers in silico and in vivo for these molecular subcategories. Comparative analytics also reveals previously unappreciated molecular heterogeneity in anatomically identical glia between and within sexes, indicating consequent functional heterogeneity. Furthermore, our datasets reveal that while adult C. elegans glia express neuropeptide genes, they lack the canonical unc-31/CAPS-dependent dense core vesicle release machinery. Thus, glia employ alternate neuromodulator processing mechanisms. Overall, this molecular atlas, available at www.wormglia.org, reveals rich insights into heterogeneity and sex dimorphism in glia across the entire nervous system of an adult animal.

neuroscience↗

bioRxiv2022

SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here, we present single-cell aggregation of cell-states (SEACells), an algorithm for identifying metacells; overcoming the sparsity of single-cell data, while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying accurate, compact, and well-separated metacells in both RNA and ATAC modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and measure gene accessibility in each metacell. Metacell-level analysis scales to large datasets and are particularly well suited for patient cohorts, including facilitation of data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation, and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a COVID-19 patient cohort.

bioinformatics↗

bioRxiv2022

Defining cellular population dynamics at single cell resolution during prostate cancer progression

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigated prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in vivo. We observe a dramatic expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is in part androgen responsive. Androgen independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

cancer biology↗

bioRxiv2021

Simultaneous CUT&Tag profiling of the accessible and silenced regulome in single cells

Cleavage Under Targets & Tagmentation (CUT&Tag) is an antibody-directed transposase tethering strategy for in situ chromatin profiling in small samples and single cells. We describe a modified CUT&Tag protocol using a mixture of an antibody to the initiation form of RNA Polymerase II (Pol2 Serine-5 phosphate) and an antibody to repressive Polycomb domains (H3K27me3) followed by computational signal deconvolution to produce high-resolution maps of both the active and repressive regulomes in single cells. The ability to seamlessly map active promoters, enhancers and repressive regulatory elements using a single workflow provides a complete regulome profiling strategy suitable for high-throughput single-cell platforms.

genomics↗

bioRxiv2021

Dynamic structural cell responses in the thymus to acute injury, regeneration, and age

The thymus is essential for establishing adaptive immunity yet undergoes age-related atrophy leading to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age. Focusing on non-hematopoietic stromal cells, and using single-cell and spatial transcriptomics, lineage-tracing, and advanced imaging, we discovered two atypical thymic epithelial cell (TEC) states that emerged with age. Age-associated (aa)TECs formed atypical high-density epithelial clusters that were devoid of thymocytes, an accretion of non-functional thymic tissue that worsened with age and exhibited features of partial epithelial-to-mesenchymal transition (EMT). In silico interaction analysis revealed that aaTEC emergence drew tonic signals from other TEC populations at baseline, acting as a sink for TEC growth factors. Following damage, aaTEC expanded substantially, further perturbing trophic pathways, and correlating with defective regeneration of the involuted thymus. These findings define a unique feature of thymic involution linked to immune aging.

immunology↗

bioRxiv2021

Reversal of lineage plasticity in RB1/TP53-deleted prostate cancer through FGFR and Janus kinase inhibition

The inherent plasticity of tumor cells provides a mechanism of resistance to many molecularly targeted therapies, exemplified by adeno-to-neuroendocrine lineage transitions seen in prostate and lung cancer. Here we investigate the root cause of this lineage plasticity in a primary murine prostate organoid model that mirrors the lineage transition seen in patients. These cells lose luminal identity within weeks following deletion of Trp53 and Rb1, ultimately acquiring an Ar-negative, Syp+ phenotype after orthotopic in vivo transplantation. Single-cell transcriptomic analysis revealed progressive mixing of luminal-basal lineage features after tumor suppressor gene deletion, accompanied by activation of Jak/Stat and Fgfr pathway signaling and interferon-a and -g gene expression programs prior to any morphologic changes. Genetic or pharmacologic inhibition of Jak1/2 in combination with FGFR blockade restored luminal differentiation and sensitivity to antiandrogen therapy in models with residual AR expression. Collectively, we show lineage plasticity initiates quickly as a largely cell-autonomous process and, through newly developed computational approaches, identify a pharmacological strategy that restores lineage identity using clinical grade inhibitors.

cancer biology↗

bioRxiv2020

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

bioinformatics↗

bioRxiv2018

Charting the emergent organotypic landscape of the mammalian gut endoderm at single-cell resolution

To comprehensively delineate the ontogeny of an organ system, we generated 112,217 singlecell transcriptomes representing all endoderm populations within the mouse embryo until midgestation. We employed graph-based approaches to model differentiating cells for spatio-temporal characterization of developmental trajectories. Our analysis reveals the detailed architecture of the emergence of the first (primitive or extra-embryonic) endodermal population and pluripotent epiblast. We uncover an unappreciated relationship between descendants of these lineages, before the onset of gastrulation, suggesting that mixing of extra-embryonic and embryonic endoderm cells occurs more than once during mammalian development. We map the trajectories of endoderm cells as they acquire embryonic versus extra-embryonic fates, and their spatial convergence within the gut endoderm; revealing them to be globally similar but retaining aspects of their lineage history. We observe the regionalized localization of cells along the forming gut tube, reflecting their extra-embryonic or embryonic origin, and their coordinate patterning into organ-specific territories along the anterior-posterior axis.

developmental biology↗

bioRxiv2018

Palantir characterizes cell fate continuities in human hematopoiesis

Recent studies using single cell RNA-seq (scRNA-seq) data derived from differentiating systems have raised fundamental questions regarding the discrete vs continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells, which treats cell-fate as a probabilistic process, and leverages entropy to measure the changing nature of cell plasticity along the differentiation trajectory. Palantir generates a high resolution pseudotime ordering of cells, and assigns each cell state with its probability to differentiate into each terminal state. We apply Palantir to human bone marrow scRNA-seq data and detect key landmarks of hematopoietic differentiation. Palantirs resolution enables identification of key transcription factors driving lineage fate choices, as these TFs closely track when cells lose plasticity. We demonstrate that Palantir is generalizable to diverse tissue types and well-suited to resolve less studied differentiating systems.

systems biology↗

bioRxiv2017

Single-Cell Immune Map of Breast Carcinoma Reveals Diverse Phenotypic States Driven by the Tumor Microenvironment

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.

immunology↗