Biology subjects

Ergen, C.

Publications and source records attributed to Ergen, C..

13 recordsLinked to original sources

bioRxiv2026

immgenT: A Comprehensive Reference of Convergent T-cell States in the Mouse

The immgenT collaborative project generated a comprehensive molecular atlas of T cells spanning virtually all mouse organs and disease states, profiling ~800,000 cells from 750 samples with RNA, 128-plex surface protein, and {beta}TCR sequence. Applying a deep generative model to joint RNA and protein data defined the landscape of T-cell states organized into eight lineages and 107 robust clusters, integrating similar cells from different contexts, and resolving prior nomenclatures. Analysis of effector molecules, transcription factors and modules showed that both immunological functions and regulatory programs are shared across cell states. This framework provides a stable, reusable reference, demonstrated by computationally integrating 16 external datasets from diverse biological contexts. A set of public web tools supports browsing of these data and mapping of any dataset onto the immgenT framework. These results propose a molecular classification of T cells organized around a set of shared states reused across immunological contexts.

immunology↗

bioRxiv2026

Mitigating Bias in Spatial Transcriptomic Pipelines via Human Feedback

Biological discovery from experimental data, particularly large-scale assays, requires extensive preprocessing, during which raw outputs (e.g., images, sequences) are processed into structured forms that are more amenable to analysis. While statistical methods for such processed data are at the core of computational biology, the problem of coping with uncertainties introduced during preprocessing is a significant and underexplored issue. We address this issue in the context of differential expression analysis in spatial transcriptomics, which depends on a series of preprocessing steps, including demarcation of cell regions (segmentation), quantification of gene expression in cells, and cell-type annotation. We introduce Corrected Spatial Differential Expression (CSDE), a method that builds on Prediction-Powered Inference to leverage a small set of expert-validated data points (cells) to account for uncertainty due to preprocessing errors. Using two case studies, we demonstrate that CSDE produces more reliable and calibrated estimates of differential expression compared to the prevalent approach that neglects the impact of preprocessing. CSDE incorporates an efficient workflow to generate the required expert-annotated data, and is available as open-source at https://github.com/YosefLab/CSDE.

bioinformatics↗

bioRxiv2025

AMICI: Attention Mechanism Interpretation of Cell-cell Interactions

Spatial transcriptomic data enable study of cell-cell communication, yet current analysis tools often fail to provide dynamic, interpretable estimates of interactions and their spatial range across tissue. We present AMICI, an interpretable attention framework that jointly estimates interaction length scales, adaptively resolves sender-receiver subpopulations, and links communication to downstream gene programs. AMICI recovers ground-truth interactions in semi-synthetic data, uncovers gene programs linked to cell communication in the mouse cortex, and reveals length-scale-dependent tumor-immune signaling that reinforces estrogen receptor (ER) programs in breast cancer.

bioinformatics↗

bioRxiv2025

CytoVI: Deep generative modeling of antibody-based single cell technologies

Due to their robustness, dynamic range and scalability, antibody-based single cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become an irreplaceable part of routine clinics and a powerful tool for basic research. However, their analysis is complicated by measurement noise and bias, differences between batches, technology platforms, and restricted antibody panels. This results in a limited capacity to accumulate knowledge across technologies, studies, experimental batches, or across different antibody panels. Here, we present CytoVI - a probabilistic generative model designed to address these challenges and enable statistically rigorous and integrative analysis for antibody-based single cell technologies. We show that CytoVI outperforms existing computational methods and effectively handles a variety of integration scenarios. CytoVI enables key functionalities such as generating informative cell embeddings, imputing missing measurements, differential protein expression testing, and automated annotation of cells. We applied CytoVI to generate an integrated B cell maturation atlas across 350 proteins from a set of smaller antibody panels measured by conventional mass cytometry, and identified proteins associated with immunoglobulin class-switching in healthy humans. Using a cohort of B cell non-Hodgkin lymphoma patients measured by flow cytometry, CytoVI uncovered T cell states that are associated with disease. Finally, we show that CytoVI is a robust probabilistic framework for the analysis of standard diagnostic flow cytometry antibody panels, enabling the automated detection of tumor populations and diagnoses of incoming patient samples. CytoVI facilitates accurate and automated analysis in both preclinical and clinical settings and is available as open-source software at scvi-tools.org.

bioinformatics↗

bioRxiv2025

High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer

Charting the spatiotemporal dynamics of cell fate determination in development and disease is a long-standing objective in biology. Here we present the design, development, and extensive validation of PEtracer, a prime editing-based, evolving lineage tracing technology compatible with both single-cell sequencing and multimodal imaging methodologies to jointly profile cell state and lineage in dissociated cells or while preserving cellular context in tissues with high spatial resolution. Using PEtracer coupled with MERFISH spatial transcriptomic profiling in a syngeneic mouse model of tumor metastasis, we reconstruct the growth of individually-seeded tumors in vivo and uncover distinct modules of cell-intrinsic and cell-extrinsic factors that coordinate tumor growth. More generally, PEtracer enables systematic characterization of cell state and lineage relationships in intact tissues over biologically-relevant temporal and spatial scales.

molecular biology↗

bioRxiv2025

scVIVA: a probabilistic framework for representation of cells and their environments in spatial transcriptomics

Spatial transcriptomics provides a significant advance over studies of dissociated cells in that it reveals the environment in which cells reside, thus opening the way for a more complete description of their state and function. However, most current methods for embedding and discovery of cell states rely only on the cells own gene expression profile, thus raising the need for ways to account for the neighboring cells as well. Here, we introduce scVIVA, a deep generative model that leverages both cell-intrinsic and neighboring gene expression profiles to output stochastic embeddings of cell states as well as normalized gene expression profiles. We demonstrate that scVIVA produces informative fine-grained partitions of cells that reflect both their internal state and the surrounding tissue and that its generative model facilitates the testing of hypotheses of differential expression between tissue niches. We leverage these properties of scVIVA to uncover a spatially-restricted tumor-promoting endothelial population in breast cancer and niche-associated T cell states that are shared across multiple cancers. scVIVA is available as open source software within scvi-tools.org.

bioinformatics↗

bioRxiv2025

ResolVI - addressing noise and bias in spatial transcriptomics

Technologies for estimating RNA expression at high throughput, in intact tissue slices, and with high spatial resolution (spatial transcriptomics; ST) shed new light on how cells communicate and tissues function. A fundamental step common to all ST protocols is quantification, namely segmenting the plane into regions, each approximating a cell, and then collating the molecules inside each region to estimate the cellular expression profile. Despite many advances in this area, a persisting problem is that of wrong assignment of molecules to cells, which limits most current applications to the level of a priori defined cell subsets and complicates the discovery of novel cell states. Here, we develop resolVI, a model that operates downstream of any segmentation algorithm to generate a probabilistic representation, correcting for misassignment of molecules, as well as for batch effects and other nuisance factors. We demonstrate that resolVI improves our ability to distinguish between cell states, to identify subtle expression changes in space, and to perform integrated analysis across datasets. ResolVI is available as open source software within scvi-tools.

bioinformatics↗

bioRxiv2024

Scvi-hub: an actionable repository for model-driven single cell analysis

The accumulation of single-cell omics datasets in the public domain has opened new opportunities for reusing and leveraging the vast amount of information they contain. Such uses, however, are complicated by the need for complex and resource-consuming procedures for data transfer, normalization and integration that must be addressed prior to any analysis. Here we present scvi-hub: a platform for efficiently sharing and accessing single-cell omics datasets using pre-trained probabilistic models. We demonstrate that scvi-hub allows immediate access to a slew of fundamental tasks like visualization, imputation, annotation, outlier detection, and deconvolution of new (query) datasets, using state of the art algorithms and with a requirement for storage and compute resources that is much lower compared to standard approaches. We also show that the pre-trained models enable efficient analysis and new discoveries with existing references, including large atlases such as the CZ CELLxGENE Discover Census. Scvi-hub is built within the scvi-tools open source environment and integrated into scverse. It provides powerful and readily available tools for utilizing a large collection of already-loaded datasets while also enabling easy inclusion of new datasets, thus putting the power of atlas-level analysis at the fingertips of a broad community of users.

bioinformatics↗

bioRxiv2024

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age

The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.

immunology↗

bioRxiv2023

Calibrated Identification of Feature Dependencies in Single-cell Multiomics

Data-driven identification of functional relationships between cellular properties is an exciting promise of single-cell genomics, especially given the increasing prevalence of assays for multiomic and spatial transcriptomic analysis. Major challenges include dealing with technical factors that might introduce or obscure dependencies between measurements, handling complex generative processes that require nonlinear modeling, and correctly assessing the statistical significance of discoveries. VI-VS (Variational Inference for Variable Selection) is a comprehensive framework designed to strike a balance between robustness and interpretability. VI-VS employs nonlinear generative models to identify conditionally dependent features, all while maintaining control over false discovery rates. These conditional dependencies are more stringent and more likely to represent genuine causal relationships. VI-VS is openly available at https://github.com/YosefLab/VIVS, offering a no-compromise solution for identifying relevant feature relationships in multiomic data, advancing our understanding of molecular biology.

bioinformatics↗

bioRxiv2023

Consensus prediction of cell type labels with popV

Cell-type classification is a crucial step in single-cell analysis. To facilitate this, several methods have been proposed for the task of transferring a cell-type label from an annotated reference atlas to unannotated query data sets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV, https://github.com/YosefLab/popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides effective uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

bioinformatics↗

bioRxiv2023

Emergence and suppressive function of Tr1 cells in glomerulonephritis

T regulatory type 1 (Tr1) cells, which are defined by their regulatory function, lack of Foxp3, high expression of IL-10, CD49b, and LAG3, are known to be able to suppress Th1 and Th17 in the intestine. Th1 and Th17 cells are also the main drivers of crescentic glomerulonephritis, the most severe form of renal autoimmune disease. However, whether Tr1 cells emerge in renal inflammation and moreover, whether they exhibit regulatory function during glomerulonephritis has not been thoroughly investigated yet. To address these questions, we used a mouse model of experimental crescentic glomerulonephritis and double Foxp3mRFP IL-10eGFP reporter mice. We found that Foxp3neg IL-10-producing CD4+ T cells infiltrate the kidneys during glomerulonephritis progression. Using single-cell RNA- sequencing, we could show that these cells express the core transcriptional factors characteristic of Tr1 cells. In line with this, Tr1 cells showed a strong suppressive activity ex vivo and were protective in experimental crescentic glomerulonephritis in vivo. Finally, we could also identify Tr1 cells in the kidneys of patients with anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis and define their transcriptional profile. Tr1 cells are currently used in several immune-mediated inflammatory diseases, e.g. as T- cell therapy. Thus, our study provides proof of concept for Tr1 cell-based therapies in experimental glomerulonephritis.

immunology↗

bioRxiv2022

DiSCERN - Deep Single Cell Expression ReconstructioN for improved cell clustering and cell subtype and state detection

Single cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. Here we present DISCERN, a novel deep generative network that reconstructs missing single cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We used DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilized T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 81% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single cell sequencing workflows and readily adapted to enhance various other biomedical data types.

bioinformatics↗