Biology subjects

McGinnis, C. S.

Publications and source records attributed to McGinnis, C. S..

18 recordsLinked to original sources

bioRxiv2026

Comparing bulk and single-cell methodologies and models to profile gene expression, chromatin accessibility and regulatory links in endothelial cells treated with TNFα

Genome-wide association studies (GWAS) have identified thousands of non-coding variants associated with complex traits and diseases. However, it remains challenging to pinpoint the causal genes that are regulated by associated genetic variants. Connecting causal non-coding variants with genes can rely on methods that identify direct physical interactions (e.g. chromosome conformation capture) or on probabilistic models that predict regulatory links. These statistical models take advantage of gene expression and chromatin accessibility profiles generated in cells and tissues by bulk or single-cell (sc) methodologies. Here, we tested whether using bulk or sc RNAseq/ATACseq data and corresponding predictive enhancer-to-gene models impact the prioritization of causal GWAS genes. Using non-treated and TNF-treated human endothelial cells in vitro as a well-controlled experimental system, we show that bulk and sc RNAseq/ATACseq profiles are similar and highlight the same biology (e.g. biological pathways). Despite these similarities, we show using GWAS results for coronary artery disease (CAD) and diastolic blood pressure that applying enhancer-to-gene models designed for bulk or sc methodologies can yield differences in terms of captured heritability, fine-mapped variants and linked genes. For instance, at one CAD locus, the bulk-based ABC model predicts a regulatory link with BCAR1, whereas the sc-based model scE2G prioritizes a different gene (CFDP1). On the same experimental model, our results indicate that choosing between a bulk or sc approach will influence regulatory link model predictions; this should be considered when planning functional experiments to characterize GWAS discoveries.

genomics↗

bioRxiv2026

FAK Inhibition Remodels the Metastatic ECM and Restores CD8+ T Cell Trafficking and Immunosurveillance

Metastatic breast cancer remains largely incurable, driven in part by immunosuppressive microenvironments that limit CD8+ T cell-mediated clearance. Using a murine pulmonary metastatic breast cancer model, we show that the focal adhesion kinase (FAK) inhibitor VS-4718 promotes a CD8+ T cell-dependent regression of metastatic lesions by reprograming the metastatic microenvironment. VS-4718 reduced immunosuppressive myeloid and regulatory T cells while increasing CD8+ T cell infiltration. Cellular and secreted proteome profiling revealed that VS-4718 downregulates ECM components such as laminin 5 and collagen VIII1, which we show impair CD8+ T cell migration and activity. In human breast cancer cohorts, elevated LAMA5/COL8A1 expression and a FAK-dependent ECM signature associate with poor outcome and prognostic for residual disease. Intravital imaging demonstrated that VS-4718 enhances CD8 T cell extravasation and induces T cell-tumor cell contacts necessary for cytotoxicity. Ex vivo lung slice cultures recapitulated these findings, showing enhanced T cell swarming, metastatic cluster shrinkage, and apoptosis. These findings reveal how FAK inhibition remodels the metastatic ECM to potentiate coordinated CD8+ T cell responses. VS-4718 might aid in clearing metastases in breast cancer patients through modulating both stromal and immune components. STATEMENT OF SIGNIFICANCEFocal adhesion kinase (FAK) inhibition remodels collagen- and laminin-rich extracellular matrix barriers and alleviates physical constraints that limit CD8 T cell access and activity in metastases. This enhances infiltration, migration, and tumor cell engagement, and synergizes with PD-1 blockade, supporting combined therapeutic strategies in metastatic breast cancer.

cancer biology↗

bioRxiv2025

Bispecific T cell engagers control solid tumors through clonal replacement and IL2-driven effector differentiation of CD8 T-cells

Bispecific T cell engagers (TCEs) often exhibit limited efficacy in solid tumors, in part due to immunosuppressive cues in the tumor microenvironment and low expression of targetable tumor antigens. Therapeutic strategies to improve TCE target sensitivity and enhance T cell effector functions therefore have significant translational potential. Here, we engineered TCEs that induce T cell activation in vitro against the low-abundance target antigens, TRP2/Kb and DLL3. Despite in vitro activity in these models, TCE monotherapy showed limited control of tumor growth in immunocompetent mice. Leveraging this in vivo model of TCE treatment failure, we discovered that co-treatment with TCE and a CD25-biased Interleukin-2 (IL2) rescues anti-tumor activity. Further, multimodal single-cell transcriptomic and immune repertoire analyses revealed that TCE-IL2 combination therapy controlled tumors by recruiting and activating new CD8+ T cells into the tumor microenvironment. These findings demonstrate that TCE-mediated anti-tumor responses function through a CD8+ T cell clonal replacement mechanism that can be augmented by cytokine therapy. One Sentence SummaryCombining TCE therapy with IL-2 enhances TCE efficacy in aggressive small-cell lung cancer and melanoma models with low target antigen density through T cell clonal replacement and CD8+ effector T cell differentiation.

cancer biology↗

bioRxiv2025

Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC

Large-scale scATAC-seq experiments are challenging because of their costs, lengthy protocols, and confounding batch effects. Several sample multiplexing technologies aim to address these challenges, but do not remove batch effects introduced when performing transposition reactions in parallel. We demonstrate that sample-to-sample variability in nuclei-to-Tn5 ratios is a major cause of batch effects and develop MULTI-ATAC, a multiplexing method that pools samples prior to transposition, as a solution. MULTI-ATAC provides high accuracy in sample classification and doublet detection while eliminating batch effects associated with variable nucleus-to-Tn5 ratio. We illustrate the power of MULTI-ATAC by performing a 96-plex multiomic drug assay targeting epigenetic remodelers in a model of primary immune cell activation, uncovering tens of thousands of drug-responsive chromatin regions, cell-type specific effects, and potent differences between matched inhibitors and degraders. MULTI-ATAC therefore enables batch-free and scalable scATAC-seq workflows, providing deeper insights into complex biological processes and potential therapeutic targets.

molecular biology↗

bioRxiv2024

Mapping enhancer-gene regulatory interactions from single-cell data

Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here, we introduce a new family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell ATAC-seq or multiomic RNA and ATAC-seq data and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped eQTLs, and GWAS variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across multiple cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte counts. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of diverse human cell types.

genetics↗

bioRxiv2023

Epithelial zonation along the mouse and human small intestine defines five discrete metabolic domains

A key aspect of nutrient absorption is the exquisite division of labor across the length of the small intestine, with individual classes of micronutrients taken up at different positions. For millennia, the small intestine was thought to comprise three segments with indefinite borders: the duodenum, jejunum, and ileum. By examining fine-scale longitudinal segmentation of the mouse and human small intestines, we identified transcriptional signatures and upstream regulatory factors that define five domains of nutrient absorption, distinct from the three traditional sections. Spatially restricted expression programs were most prominent in nutrient-absorbing enterocytes but initially arose in intestinal stem cells residing in three regional populations. While a core signature was maintained across mice and humans with different diets and environments, domain properties were influenced by dietary changes. We established the functions of Ppar- and Cdx1 in patterning lipid metabolism in distal domains and generated a predictive model of additional transcription factors that direct domain identity. Molecular domain identity can be detected with machine learning, representing the first systematic method to computationally identify specific intestinal regions in mice. These findings provide a foundational framework for the identity and control of longitudinal zonation of absorption along the proximal:distal small intestinal axis.

genomics↗

bioRxiv2023

Patterning and folding of intestinal villi by active mesenchymal dewetting

Tissue folding generates structural motifs critical to organ function. In the intestine, bending of a flat epithelium into a periodic pattern of folds gives rise to villi, the numerous finger-like protrusions that are essential for nutrient absorption. However, the molecular and mechanical mechanisms driving the initiation and morphogenesis of villi remain a matter of debate. Here, we identify an active mechanical mechanism that simultaneously patterns and folds intestinal villi. We find that PDGFRA+ subepithelial mesenchymal cells generate myosin II-dependent forces sufficient to produce patterned curvature in neighboring tissue interfaces. At the cell-level, this occurs through a process dependent upon matrix metalloproteinase-mediated tissue fluidization and altered cell-ECM adhesion. By combining computational models with in vivo experiments, we reveal these cellular features manifest at the tissue-level as differences in interfacial tensions that promote mesenchymal aggregation and interface bending through a process analogous to the active de-wetting of a thin liquid film.

developmental biology↗

bioRxiv2023

The temporal progression of immune remodeling during metastasis.

Tumor metastasis requires systemic remodeling of distant organ microenvironments which impacts immune cell phenotypes, population structure, and intercellular communication networks. However, our understanding of immune phenotypic dynamics in the metastatic niche remains incomplete. Here, we longitudinally assayed lung immune cell gene expression profiles in mice bearing PyMT-driven metastatic breast tumors from the onset of primary tumorigenesis, through formation of the pre-metastatic niche, to the final stages of metastatic outgrowth. Computational analysis of these data revealed an ordered series of immunological changes that correspond to metastatic progression. Specifically, we uncovered a TLR-NF{kappa}B myeloid inflammatory program which correlates with pre-metastatic niche formation and mirrors described signatures of CD14+ activated MDSCs in the primary tumor. Moreover, we observed that cytotoxic NK cell proportions increased over time which illustrates how the PyMT lung metastatic niche is both inflammatory and immunosuppressive. Finally, we predicted metastasis-associated immune intercellular signaling interactions involving Igf1 and Ccl6 which may organize the metastatic niche. In summary, this work identifies novel immunological signatures of metastasis and discovers new details about established mechanisms that drive metastatic progression. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/539153v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1333376org.highwire.dtl.DTLVardef@77c937org.highwire.dtl.DTLVardef@3b78b1org.highwire.dtl.DTLVardef@70d497_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefMcGinnis et al. report a longitudinal scRNA-seq atlas of lung immune cells in mice bearing PyMT-driven metastatic breast tumors and identify immune cell transcriptional states, shifts in population structure, and rewiring of cell-cell signaling networks which correlate with metastatic progression. HighlightsO_LILongitudinal scRNA-seq reveals distinct stages of immune remodeling before, during, and after metastatic colonization in the lungs of PyMT mice. C_LIO_LITLR-NF{kappa}B inflammation correlates with pre-metastatic niche formation and involves both tissue-resident and bone marrow-derived myeloid cell populations. C_LIO_LIInflammatory lung myeloid cells mirror activated primary tumor MDSCs, suggesting that primary tumor-derived cues induce Cd14 expression and TLR-NF{kappa}B inflammation in the lung. C_LIO_LILymphocytes contribute to the inflammatory and immunosuppressive lung metastatic microenvironment, highlighted by enrichment of cytotoxic NK cells in the lung over time. C_LIO_LICell-cell signaling network modeling predicts cell type-specific Ccl6 regulation and IGF1-IGF1R signaling between neutrophils and interstitial macrophages. C_LI

immunology↗

bioRxiv2023

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN constructs probabilistic models of regulatory interactions between genes or gene-expression programs to fit the cell state distributions under different perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdown perturbations. D-SPIN also dissects gene-level drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs acting on distinct regulators induce novel cell states through additive recruitment of gene expression programs. The D-SPIN model further predicts cell state distributions under drug dosage combinations beyond the training data. D-SPIN provides a computational framework for constructing interpretable models of gene regulatory networks to reveal principles of cellular information processing and physiological control.

bioinformatics↗

bioRxiv2022

Dissecting the contributions of tumor heterogeneity on metastasis at single-cell resolution

Metastasis is the leading cause of cancer-related deaths, but metastasis research is challenged by limited access to patient material and a lack of experimental models that appropriately recapitulate tumor heterogeneity. Here, we analyzed single-cell transcriptomes of matched primary tumor and metastasis from patient-derived xenograft models of breast cancer, demonstrating that primary tumor and metastatic cells show profound transcriptional differences across heterogeneous tumors. While primary tumor cells upregulated several metabolic genes, metastatic cells displayed a motility phenotype in micrometastatic lesions and increased stress response signaling during metastatic progression. Additionally, we identified gene signatures that are associated with the metastatic potential and correlated with patient outcomes. Poorly metastatic primary tumors showed increased immune-regulatory control that may prevent metastasis, whereas highly metastatic primary tumors upregulated markers of epithelial-mesenchymal transition (EMT). We found that intra-tumor heterogeneity is dominated by epithelial-mesenchymal plasticity (EMP) which presented as a dynamic continuum with intermediate cell states that were characterized by novel, specific markers. These intermediate EMP markers correlated with worse patient outcomes and could serve as potential new therapeutic targets to block metastatic development.

cancer biology↗

bioRxiv2021

Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth, expansion to neighboring and distal tissues, and therapeutic resistance. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma which enabled us to track tumor evolution from single transformed cells to metastatic tumors at unprecedented resolution. We found that loss of the initial, stable alveolar-type2-like state was accompanied by transient increase in plasticity. This was followed by adoption of distinct fitness-associated transcriptional programs which enable rapid expansion and ultimately clonal sweep of rare, stable subclones capable of metastasizing to distant sites. Finally, we showed that tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates tumor progression by creating novel evolutionary paths. Overall, our study elucidates the hierarchical nature of tumor evolution, and more broadly enables the in-depth study of tumor progression.

cancer biology↗

bioRxiv2021

Single Cell Enhancer Activity Maps Neuronal Lineages in Embryonic Mouse Basal Ganglia

Enhancers integrate transcription factor signaling pathways that drive cell fate specification in the developing brain. We used single cell RNA-sequencing (scRNA-seq) to capture enhancer activity at single cell resolution and delineate specification of cells labeled by enhancers in mouse medial, lateral, and caudal ganglionic eminences (MGE, LGE, and CGE) at embryonic day (E)11.5. We combine enhancer-based reporter labeling with single-cell transcriptional readout to characterize enhancer activity and define cell populations in vivo. Seven enhancers had diverse activities in specific progenitor and neuronal populations within the GEs. We then applied enhancer-based labeling, scRNA-seq, and analysis of in situ hybridization (ISH) data to distinguish subtypes of MGE-derived GABAergic and cholinergic projection neurons and interneurons. This work demonstrates how the power of scRNA-seq can be extended by enhancer-based labelling and leveraging ISH data and reveals novel lineage specification paths underlying patterning of developing mouse brain.

neuroscience↗

bioRxiv2020

Human microglia upregulate cytokine signatures and accelerate maturation of neural networks

Microglia are the resident macrophages of the brain that emerge in early development and play vital role disease states, as well as in normal development. Many fundamental questions about microglia diversity and function during human brain development remain unanswered, as we currently lack cellular-resolution datasets focusing on microglia in developing primary tissue, or experimental strategies for interrogating their function. Here, we report an integrative analysis of microglia throughout human brain development, which reveals molecular signatures of stepwise maturation, as well as human-specific cytokine-associated subtype that emerges around the onset of neurogenesis. To demonstrate the utility of this atlas, we have compared microglia across several culture models, including cultured primary microglia, pluripotent stem cell-derived microglia. We identify gene expression signatures differentially recruited and attenuated across experimental models, which will accelerate functional characterization of microglia across perturbations, species, and disease conditions. Finally, we identify a role for human microglia in development of synchronized network activity using a xenotransplantation model of human microglia into cerebral organoids.

developmental biology↗

bioRxiv2020

No detectable alloreactive transcriptional responses during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurement of transcript counts in individual cells. However, high assay costs limit the study of large numbers of samples. Sample multiplexing technologies such as antibody hashing and MULTI-seq use sample-specific sequence tags to enable individual samples (e.g., different patients) to be sequenced in a pooled format before downstream computational demultiplexing. Critically, no study to date has evaluated whether the mixing of samples from different donors in this manner results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self immune antigens). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Here, we compared the expression profiles of peripheral blood mononuclear cells (PBMCs) from a single donor with and without pooling with PBMCs isolated from other donors with different blood types. We find that there was no evidence of alloreactivity in the multiplexed samples following three distinct multiplexing workflows (antibody hashing, MULTI-seq, and in silico genotyping using souporcell). Moreover, we identified biases amongst antibody hashing sample classification results in this particular experimental system, as well as gene expression signatures linked to PBMC preparation method (e.g., Ficoll-Paque density gradient centrifugation with or without apheresis using Trima filtration).

genomics↗

bioRxiv2020

ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

Spatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ZipSeq, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

genomics↗

bioRxiv2018

Mapping the complex paracrine response to hormones in the human breast at single-cell resolution

The human breast undergoes lifelong remodeling in response to estrogen and progesterone, but hormone exposure also increases breast cancer risk. Here, we use single-cell analysis to identify distinct mechanisms through which breast composition and cell state affect hormone signaling. We show that prior pregnancy reduces the transcriptional response of hormone-responsive (HR+) epithelial cells, whereas high body mass index (BMI) reduces overall HR+ cell proportions. These distinct changes both impact neighboring cells by effectively reducing the magnitude of paracrine signals originating from HR+ cells. Because pregnancy and high BMI are known to protect against hormone-dependent breast cancer in premenopausal women, our findings directly link breast cancer risk with person-to-person heterogeneity in hormone responsiveness. More broadly, our findings illustrate how cell proportions and cell state can collectively impact cell communities through the action of cell-to-cell signaling networks.

systems biology↗

bioRxiv2018

MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

We describe MULTI-seq: A rapid, modular, and universal scRNA-seq sample multiplexing strategy using lipid-tagged indices. MULTI-seq reagents can barcode any cell type from any species with an accessible plasma membrane. The method is compatible with enzymatic tissue dissociation, and also preserves viability and endogenous gene expression patterns. We leverage these features to multiplex the analysis of multiple solid tissues comprising human and mouse cells isolated from patient-derived xenograft mouse models. We also utilize MULTI-seqs modular design to perform a 96-plex perturbation experiment with human mammary epithelial cells. MULTI-seq also enables robust doublet identification, which improves data quality and increases scRNA-seq cell throughput by minimizing the negative effects of Poisson loading. We anticipate that the sample throughput and reagent savings enabled by MULTI-seq will expand the purview of scRNA-seq and democratize the application of these technologies within the scientific community.

genomics↗

bioRxiv2018

DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors

Single-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool - DoubletFinder - that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.

bioinformatics↗