Biology subjects

Fertig, E. J.

Publications and source records attributed to Fertig, E. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Senescent cell networks link matrix remodeling and vascular dysfunction in human fibroids

Uterine fibroids (leiomyomas) are highly prevalent benign tumors defined by excessive extracellular matrix (ECM) deposition, altered vascular structure, and progressive tissue stiffening, yet the cellular programs that coordinate these features remain poorly understood. Cellular senescence has been implicated in fibroid biology, but whether senescence represents a uniform state or distinct, functionally specialized cell identities within fibroids is unknown. Here, we identify the distinct heterogeneous populations of senescent cells ("senotypes") present in human fibroids and characterize their role in shaping the fibroid microenvironment. Using single-cell RNA sequencing (scRNA-seq) integrated with a senescence gene signature and protein-level validation, we identify senescent cells (SnC) distributed across fibroblast, mural, and endothelial compartments, each exhibiting distinct transcriptional programs. SnC endothelial cells (ECs) are enriched in fibroids relative to matched myometrium and activate TEAD4-associated mechanosensing, angiogenic, and immune signaling pathways, despite being associated with impaired vessel maturation in situ. In parallel, SnC fibroblast and mural populations in fibroids upregulated SRF-associated cytoskeletal and ECM programs, accompanied by increased COL6A3 expression and collagen VI deposition, consistent with tissue stiffening. Ligand-receptor and spatial analyses reveal that these SnC populations function as interconnected signaling hubs, coordinating immune cell recruitment and stromal remodeling. Importantly, analysis of human fibroids treated with collagenase demonstrated a reduction in both ECM density and SnC burden, supporting a reinforcing relationship between matrix mechanics and senescence. Together, these findings establish senescence in fibroids as a heterogeneous, mechanically reinforced, and network-driven process that links vascular dysfunction, immune signaling, and fibrosis, highlighting distinct SnC states as potential translational targets for non-surgical therapies.

cell biology↗

bioRxiv2026

Distinct fibroblast and perivascular senotypes define spatial niches that regulate fibrosis

Fibrotic conditions contribute to significant global morbidity and mortality. Yet the underlying processes that orchestrate fibrosis remain poorly understood due to the cellular and spatial complexity of the stromal, immune, and vascular compartments that regulate fibrotic disease progression. Senescent cells (SnCs) have been implicated in fibrosis, but their roles are unclear, as evidence indicates that they serve both pathogenic and reparative functions. Here, we show that fibrosis-associated SnCs contain functionally divergent senotypes that are organized into distinct spatial niches. Using integrated single-cell and spatial transcriptomics analyses and hierarchical factorization in a murine fibrosis model, we identify fibroblast and perivascular SnC subpopulations that upregulate diverse programs related to extracellular matrix (ECM) production, immune signaling, and vascular remodeling. Fibroblast senotypes localize to discrete microenvironments with distinct tissue architectures, including niches associated with fibrotic signaling, immune activity, and cartilage development. Perivascular SnCs occupy interfaces between fibrotic signaling and immune-active niches and upregulate vascular and fibrotic remodeling pathways. Depletion of pericyte-lineage SnCs increases vascular maturation and fibrotic ECM deposition, providing mechanistic validation of the beneficial role these SnCs play in vascular remodeling and fibrosis modulation. In addition, using a new web-based infrastructure to query our senotype gene signatures in public datasets, we demonstrate that these senotypes are conserved across different murine and human fibrotic conditions. These findings establish senescence as a spatially organized regulator of fibrosis and identify perivascular senescence as a link between vascular remodeling and fibrotic outcomes.

bioengineering↗

bioRxiv2026

STAPLE: automating spatial transcriptomics analysis and AI interpretation

Spatial transcriptomics workflows often span separate tools for cell typing, neighborhoods, and cell-cell communication, yielding fragmented outputs that hinder scalability, interpretation, and reproducibility. STAPLE systematizes analyses across distinct methods into a modular framework, unifying data structures and cross-tool interoperability. End-to-end analyses are performed unassisted with a single invocation, fostering rigorous, reproducible spatial transcriptomics analysis. Its novel, AI-enabled reporting layer synthesizes quantitative results into summaries of biological findings, facilitating analysis interpretation.

bioinformatics↗

bioRxiv2026

Tumor-immune trajectory context connects static tissue architecture to clinical outcomes

Multiplexed tissue imaging (MTI) has revealed recurrent tumor microenvironment (TME) architectures with prognostic value, yet these measurements are inherently static, obscuring dynamic changes in the TME that govern therapeutic response. Here, we introduce a trajectory-centric framework that reconstructs continuous TME dynamics by integrating agent-based mathematical modeling and simulation with state space analysis. This approach yields a mechanistically constrained reference landscape built entirely from in silico simulation, and onto which static patient biospecimens can be projected and mapped onto simulated TME trajectories. Systematic simulation of tumor-immune interactions in triple-negative breast cancer identifies six metastable TME states connected by transition pathways spanning immune control to immune escape. Mapping MTI data from two independent patient cohorts, including longitudinal samples from a randomized immunotherapy trial, validates this landscape by positioning individual biospecimens along inferred TME trajectories rather than in static states. We show that treatment-phase TME states, but not pre-treatment configurations, robustly predict immunotherapy response, and identical terminal states can arise from distinct trajectory histories corresponding to immune failure or resolved inflammation. Thus, this framework enables mechanistic simulations to define a reference dynamical landscape that serves as a coordinate system for interpreting static clinical spatial data, providing a principled basis for evaluating consistency, predictiveness, and clinical relevance across independent patient cohorts. Altogether, this study advances spatial tumor profiling from static state classification of human tissues to dynamic trajectory inference, establishing a quantitative framework for trajectory-informed, state-guided, and temporally adaptive immunotherapy strategies.

cancer biology↗

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2026

Immune checkpoint inhibitors amplify type 2 immune mediated repair bypro-regenerative scaffolds

Extracellular matrix (ECM) scaffolds induce type 2 immunity to promote repair. Here, we show that immune cells recruited to ECM-treated murine muscle injuries and clinical soft tissue defects express immune checkpoints. Specifically, TH2 cells and regulatory T cells (Tregs) increase LAG3 expression, while macrophages express PDL2. TCR analysis and a triple-reporter strain for interleukin (IL)-13 and Treg fate-mapping suggest that Tregs in ECM-treated wounds transition into TH2-like exTregs that express LAG3. Immune checkpoint inhibition (ICI) significantly stimulated type 2 immunity in ECM-treated wounds, including increased TH2 cells, Treg transition to TH2-like exTregs, and pro-regenerative macrophages. Moreover, ICI enhanced muscle repair and reduced fibrosis in ECM-treated wounds. Collectively, these findings show Treg/TH2 plasticity in wound healing and introduce a novel ICI application to enhance immune-mediated regeneration.

bioengineering↗

bioRxiv2026

Reconstructing clone-resolved transcriptional programs from bulk tumor sequencing

Tumor clones acquire distinct transcriptional programs as they evolve, but bulk RNA-seq averages over clonal mixtures and obscures the lineage-specific biology that DNA-sequencing reveals. We present PICTographPlus, the first method to infer clone-resolved transcriptional programs by integrating bulk DNA-derived clonal phylogenies and proportions with bulk RNA-seq alone, without single-cell data. Benchmarked against experimentally measured ground truth, scDNA/scRNA co-profiled cells from a wellDR-seq cancer dataset, across 320 pseudo-bulk replicates spanning four tumor purities and four sample counts and evaluated under seven regularization models, PICTographPlus recovers clone-level expression at mean Pearson r [≥] 0.92 and localizes pathway gains and losses to correct evolutionary branches (median F1 0.31-0.40, well above a no-deconvolution baseline). Applied to multi-region NSCLC, pancreatic precursor lesions, and rapid-autopsy PDAC, it localizes metabolic reprogramming, precursor-to-invasive transitions, and organ-adapted metastatic states to specific clonal branches. PICTographPlus turns standard bulk assays into clone-resolved transcriptional maps, enabling retrospective analyses where single-cell profiling is impractical.

genomics↗

bioRxiv2026

Regulatory T cells clonally expand and contribute to stromal cell function in fibrotic response to synthetic implants

Fibrosis plays a key role in both chronic disease progression and failure of synthetic biomaterial implants. However, the contribution of adaptive immunity to fibrotic development remains incompletely understood, particularly for regulatory T cells (Tregs). Here, we used single-cell multiomic profiling, integrating transcriptomics with T cell receptor (TCR) sequencing, to map Treg heterogeneity and clonal dynamics in a synthetic material-induced model of fibrosis. We uncovered progressive Treg clonal expansion accompanied by TCR activation signatures and an increasingly immunosuppressive phenotype along a continuous transcriptional trajectory. These Tregs suppressed immune responses and influenced extracellular matrix and vascular gene expression. Cell-cell communication inference predicted Treg-driven activation of pro-fibrotic and vasculogenic transcriptional programs in fibroblasts and endothelial cells, including Sox-family transcription factors. Functional Treg depletion increased inflammation and significantly reduced neovascularization. Together, these findings identify Treg functions in the fibro-vascular niche through stromal cell modulation, highlighting immune-stromal interactions as an important axis in fibrosis.

immunology↗

bioRxiv2025

CycleGRN: Inferring Gene Regulatory Networks from Cyclic Flow Dynamics in Single-Cell RNA-seq

Oscillatory processes such as the cell cycle play critical roles in cell fate determination and disease development, yet existing gene regulatory network (GRN) inference methods often fail to account for their dynamic nature. We propose CycleGRN, a novel framework that treats cell cycle gene expression observations as an invariant measure of a stochastic differential equation and learns from data a dynamical system that fits cycling biological processes. Using a directed graph constructed along the inferred flow field in the cell space, we estimate Lie derivatives for all genes, enabling velocity inference beyond the cell cycle subspace. To quantify regulatory interactions, we introduce a time-lagged correlation operator between any pair of genes supported on the flow-aligned directed graph, which respects the intrinsic geometry of the data manifold and allows temporal ordering consistent with the underlying oscillatory process. The method requires only raw gene expression data at single-cell resolution and a list of cycle genes, without temporal binning or splicing dynamics. We evaluate our method on four synthetic datasets generated from mechanistic models with known network structures with oscillatory subnetworks, and on a mouse retinal progenitor single-cell RNA-seq dataset spanning three cell types and a knockout condition. Across all settings, our method consistently ranks among the top-performing approaches and demonstrates strong recovery of oscillatory and directional interactions.

bioinformatics↗

bioRxiv2025

Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy

AbstractComputational models are increasingly used to predict treatment response and optimize cancer therapy strategies. Among these, quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker identification. Coupling QSP with an agent-based model yields a spatial QSP (spQSP) platform that captures tissue-level spatial organization of the tumor microenvironment (TME). However, parameterizing such models to represent tumor biology remains an open problem. In this study, we developed a calibration framework using the Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) approach to calibrate the spQSP model with a combination of clinical and spatial molecular data, reflecting the TME characteristics of human tumors. This calibration framework matches tumor architectures between spQSP model predictions and patient spatial molecular data by fitting statistical summaries of cellular neighborhoods. We demonstrate that model calibration using CODEX data from untreated HCC patients enables prediction of TME spatial molecular states in an independent cohort receiving immune-checkpoint inhibitor (ICI) and tyrosine kinase inhibitor (TKI) combination therapy. Finally, we identify spatial and non-spatial pretreatment biomarkers and assess their predictive power for therapeutic response. This workflow demonstrates how integrating spatial-omics with multiscale mechanistic models enables quantitative calibration, biological insight, and in silico biomarker discovery, providing a framework for personalized cancer therapy across tumor types. SignificanceDigital twins and computational models are increasingly used to simulate disease and guide therapy, but they often struggle to capture the immense complexity driving the spatiotemporal evolution of the TME. The challenge is compounded by clinical sample limitations, which typically provide measurements from only static snapshots of the TME for parameter estimation. We demonstrate how mechanistic modeling frameworks can overcome this limitation by enabling inference of spatiotemporal model parameters from static spatial data - an intractable task for purely data-driven approaches. Ultimately, our work presents a workflow that integrates spatial-omics with multiscale mechanistic models, enabling quantitative calibration, deeper biological insight, and in silico biomarker discovery.

bioinformatics↗

bioRxiv2025

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

bioinformatics↗

bioRxiv2025

γδ17 T cell-stromal networks modulate matrix composition and vascularity in foreign body response

Immune-stromal crosstalk governs tissue fibrosis, which is marked by dysregulated extracellular matrix (ECM) production and aberrant vasculature. Here, we investigate how {gamma}{delta} T cell interactions with stromal cells shape fibrosis in the foreign body response. During the acute reaction, type-1 ({gamma}{delta}IFN{gamma}) and type-17 ({gamma}{delta}17) effector subsets accumulated at the implant. While {gamma}{delta}IFN{gamma} decreased as fibrosis progressed, activated {gamma}{delta}17 persisted as dominant interleukin-17 producers. The {gamma}{delta}17 increased with aging and high-fat diet, both factors associated with chronic inflammation and fibrosis. Co-culture with {gamma}{delta}17 stimulated fibroblast expression of collagen genes and intercellular communication inference linked {gamma}{delta} T cell ligands to activation of ECM remodeling and vascular development programs in fibroblasts and endothelial cells. Finally, genetic deletion of {gamma}{delta} T cells altered expression of ECM components and increased vessel size within the fibrotic matrix. Altogether, our findings implicate {gamma}{delta} T cells in regulating stromal behavior to modulate composition and vascularity of fibrotic tissues.

immunology↗

bioRxiv2025

PIVOT: an open-source tool for multi-omic spatial data registration

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOTs strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

bioinformatics↗

bioRxiv2025

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. This new functionality includes compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems.

bioinformatics↗

bioRxiv2025

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

bioRxiv2025

Automated Quality Control of Time-Course Imaging from 3D in vitro cultures

Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. Existing computational methods struggle to address this issue. We present a new algorithm to evaluate data integrity and rectify mislabeling in longitudinal imaging of 3D cell culture. Our algorithm integrates permutation-based optimization with Procrustes analysis. By using X and Y coordinates of images, it accurately reorders, matches, and aligns object positions across time points, correcting for rotation, translation, and small movements. Validation with simulated data confirmed its accuracy and robustness. Applied to longitudinal imaging of tumor spheroids, our algorithm revealed frequent displacement amongst the spheroids between time points and corrected many mislabeled images. This computationally efficient and adaptable method needs no experimental adjustments and presents a readily accessible solution for data quality control. MotivationThree-dimensional (3D) in vitro models, such as tumor organoids and spheroids embedded in an extracellular matrix, are increasingly vital for studying normal and disease biology, including drug responses.1-3 A key advantage of these models is that imaging platforms can perform continuous longitudinal imaging to track phenotypic changes. However, common issues in 3D techniques, such as matrix shifts during experimental setup or image capture, can introduce technical artifacts that affect downstream analyses. Currently, no automated analytical approaches exist for assessing or correcting technical artifacts. Here, we introduce a robust, automated algorithm for assessing the quality of time-course image data and, in some cases, correcting object mislabeling to enable accurate tracking of individual spheroids over time. This approach relies only on image metadata, requiring no experimental modifications. It offers a readily implementable solution for improving data integrity and reproducibility and enhancing the reliability of longitudinal 3D cell culture studies.

bioengineering↗

bioRxiv2025

Antigen-presenting cancer-associated fibroblasts in murine pancreatic tumors differentially control regulatory T cell phenotype and function via CXCL9 and CCL22

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a complex tumor microenvironment (TME) including stromal cells that influence resistance to therapy. Recent studies have revealed that stromal cancer-associated fibroblasts (CAFs) are heterogeneous in origin, gene expression, and function. Antigen-presenting CAFs (apCAFs), are defined by major histocompatibility complex (MHC)-II expression and can activate effector CD4+ T cells that have the potential to contribute to the anti-cancer immune response, but also can induce regulatory T cell (Treg) differentiation. Whether apCAFs promote or restrain the antitumor response remains uncertain. Using tumor clones of the KPC murine PDAC model differing in sensitivity to immune checkpoint blockade (ICB), we found that immunosensitive (sKPC) tumors were characterized by higher immune cell and apCAF infiltration than resistant (rKPC) tumors. IMC analysis showed proximity of apCAFs and CD4+ T cells in both sKPC and rKPC tumors implicating interaction within the TME. apCAF-depleted sKPC tumor-bearing mice had diminished sensitivity to ICB. apCAFs from both sKPC and rKPC tumors activated tumor-infiltrating CD4+ T cells and induced Treg differentiation. However, transcriptomic analysis showed that Tregs induced by apCAFs were overexpressed for immunosuppressive genes in rKPCs relative to sKPCs, and that this is associated with differential chemokine signaling from apCAFs depending on tumor origin. Together these data implicate apCAFs as important mediators of the antitumor immune response, modulation of which could facilitate the development of more effective anti-tumor immune based approaches for PDAC patients.

cancer biology↗

bioRxiv2025

Cancer associated fibroblasts drive epithelial to mesenchymal transition and classical to basal change in pancreatic ductal adenocarcinoma cells with loss of IL-8 expression

Pancreatic ductal adenocarcinoma (PDAC) carries an extremely poor prognosis, in part resulting from cellular heterogeneity that supports overall tumorigenicity. Cancer associated fibroblasts (CAF) are key determinants of PDAC biology and response to systemic therapy. While CAF subtypes have been defined, the effects of patient-specific CAF heterogeneity and plasticity on tumor cell behavior remain unclear. Here, multi-omics was used to characterize the tumor microenvironment (TME) in tumors from patients undergoing curative-intent surgery for PDAC. In these same patients, matched tumor organoid and CAF lines were established to functionally validate the impact of CAFs on the tumor cells. CAFs were found to drive epithelial-mesenchymal transition (EMT) and a switch in tumor cell classificiaton from classical to basal subtype. Furthermore, we identified CAF-specific interleukin 8 (IL-8) as an important modulator of tumor cell subtype. Finally, we defined neighborhood relationships between tumor cell and T cell subsets. Statement of SignificanceThis multidimensional analysis highlights the diverse role CAFs have in influencing other cell types in the TME, including epithelial-derived tumor and infiltrating immune cells. Our methods provide a platform for evaluating emerging therapeutic approaches and for studying mechanisms that dictate tumor behavior in a manner that reflects patient-specific heterogeneity.

cancer biology↗