Biology subjects

Yosef, N.

Publications and source records attributed to Yosef, N..

At least 19 recordsLinked to original sources

bioRxiv2026

NovaClone: A Network-Based Algorithm for Clonal and Subclonal Genotyping of Barcoded Transgene Integrations

Single-cell lineage tracing technologies are providing new and powerful ways to interrogate the evolution and divergence of cell populations in cancer, development, and other contexts. A key initial step in any such analysis is the grouping of cells into clonal populations, based on clone-level marks. Unfortunately, clone calling is prone to technical effects due to sequencing errors, missing data, multiplets, background noise, and accidental sharing of clonal barcodes between unrelated clones (homoplasy). We present NovaClone, a principled algorithm for hierarchical clone calling that is broadly applicable to all current tracing technologies, including both static barcoding and the more recent evolving tracers. We benchmark NovaClone on simulated and real data to show that it outperforms the current solutions in terms of both quality and speed, thereby helping to mitigate one of the most prevalent problems with single-cell lineage tracing. To complement NovaClone, we introduce a suite of algorithm-agnostic quality control metrics to evaluate clone calls when ground truth is not available. NovaClone and the associated QCs are available through the open source Python package nova-clone.

genomics↗

bioRxiv2026

Comprehensive Lineage Tracing Maps the Landscape of Cell Fate Decisions in Mouse Embryogenesis

A comprehensive cell fate map of mammalian embryogenesis has remained out of reach due to the scale, cellular diversity, and non-deterministic nature of development in utero. Here, we use PEtracer to continuously install heritable genetic marks as cells divide, reconstructing lineage trees that resolve [~]75% of cell divisions across >1.5 million cells from 16 mouse embryos collected at half-day intervals from E7.5-E10.0. We pair these trees with deep transcriptional profiling to chart the landscape of cell fate decisions during gastrulation and early organogenesis. Using these data, we quantify cell fate biases, restriction timing, progenitor pool sizes, and lineage relationships across the embryo, revealing strikingly reproducible lineage architecture across replicate embryos despite the regulative flexibility of mammalian development. We further show how lineage, spatial position, and signaling jointly determine fate outcomes and timing, with their relative influence varying by tissue. This dataset provides a quantitative framework for understanding cell fate specification and a lineage-resolved reference for generating and contextualizing developmental hypotheses at organismal scale.

developmental biology↗

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

Metabolic zonation and characterization of tissue slices with spatial transcriptomics

The exchanges of small molecules between cells and their environments are essential for the formation of functioning tissues. To study them at scale, we developed Harreman (Basque for "receive and give"), an algorithm for identifying metabolic crosstalk from spatially resolved transcriptomics of intact tissues. Unlike previous methods, which primarily focus on the secretion or reception of protein signals, Harreman reconstructs molecular metabolic crosstalk based on the co-localized expression of metabolite transporters. By utilizing a series of increasingly detailed models for testing spatial correlation, Harreman provides insight at multiple levels: a) coarse partition of the tissue into regions sharing metabolic characteristics; b) identification of metabolic exchanges within each region; and c) inference of the cell subsets involved in those exchanges. Harreman identified a sodium/calcium exchange at the tumor boundary in human lung metastases of human renal cancers, and associated it with nearby pro-inflammatory macrophages. In the mouse model of DSS-induced colitis, Harreman identified vitamin A and lysophosphatidylcholine transport at the interface of the epithelial monolayer as major signals associated with regeneration. Harreman is available at https://github.com/YosefLab/Harreman.

bioinformatics↗

bioRxiv2025

An open CAR-T single-cell atlas to enable in-depth characterization and rational engineering of CAR-T products

BRIEF ABSTRACTWe built a CAR-T cell functional atlas from over one million cells across 13 studies, integrating data from patients and healthy donors. The atlas captures 11 phenotypes, links infusion product composition with clinical response, and reveals sex- and age-dependent effects, metabolic signatures, and rare ICANS-associated cells. This open-access resource provides a foundation to understand CAR-T cell function and guide the design of next-generation therapies.

bioinformatics↗

bioRxiv2025

Preselection CD4+CD8+ thymocytes modulate TCR responsiveness following TCRβ selection

Modulation of TCR sensitivity during positive selection is critical to avoid negative selection and direct thymocytes into their appropriate lineage. Thymocytes just prior to positive selection (preselection) are highly responsive to low affinity self-ligands and are also actively rearranging their TCR locus as they await a positive selection signal. Preselection DP thymocytes were thought to be relatively homogeneous and TCR modulation during this stage had not been previously described. Here we provide evidence for progressive gene expression changes within the preselection DP thymocyte population that correlates with a gradual loss of TCR responsiveness and a defect in upregulating TCR target genes associated with the CD4 fate. We relate these observations to the link between positive selection and T cell lineage commitment.

immunology↗

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

Aging-Associated Microbiota Drives Treg Dysfunction via TNF Signaling

Aging is associated with a chronic, low-grade inflammatory state referred to as inflammaging, which contributes to impaired immune regulation and increased susceptibility to disease. While regulatory T (Treg) cells are key mediators of immune homeostasis, their role in the context of age-related inflammation remains poorly understood. Here we demonstrate that age-related changes in the microbiota promote impaired Treg cell function, resulting in the differentiation of inflammatory T cells. In agreement, we find that aged germ-free (GF) mice exhibited a more balanced immune profile, where the Treg cells are functional and pro-inflammatory mediators are reduced, suggesting that microbial exposure is essential for the establishment of inflammaging. Furthermore, we show that the use of old microbiota in young animals was sufficient to induce pro-inflammatory T cell responses and impaired mucosal Treg cell proliferation, while young microbiota restored Treg cell function in old animals. Mechanistically, we show that exposure to aged microbiota was associated with sustained TNF signaling, elevated oxidative stress, DNA damage, and increased expression of senescence markers such as {gamma}H2AX and p16 in Treg cells. These findings uncover a microbiota-TNF-dependent mechanism by which age-associated microbial dysbiosis drives Treg cell dysfunction and promotes immune aging, highlighting the therapeutic potential of microbiota-targeted strategies to restore immune homeostasis in the elderly.

immunology↗

bioRxiv2025

Can you trust your reconstructed lineage tree? A homoplasy-based approach for irreversible evolution

Phylogeny inference is a fundamental problem in computational biology, with many proposed algorithms. Emerging techniques that couple single-cell genomics with Cas9-based genome editing open the way for in-depth analysis of cell phylogenies that underlie processes of clonal expansion, selection and diversification, from embryogenesis to cancer. A key distinguishing feature of cell lineage analysis with these techniques is the non-modifiability of Cas9-induced mutations, which motivates revisiting questions in phylogenetics. In this work, we ask one such fundamental question: is it possible to assess the reliability of an inferred lineage tree, even though we do not know its underlying ground truth? We present a homoplasy-based approach for this question that leverages the non-modifiability property. We show via simulations that under a broad range of settings, our method can effectively distinguish accurate reconstructions out of a pool of candidate solutions. Importantly, our homoplasy-based score is substantially more powerful than the commonly used parsimony score - a result that we back by both empirical and theoretical analysis. The computation of the homoplasy score is simple and scalable, thus opening the way for more rigorous analysis of cell lineages.

evolutionary biology↗

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

Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis

Tumour progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumours can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumour progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumour expansion, plasticity, and metastasis co-evolve with microenvironmental remodelling in a Kras;p53-driven mouse model of lung adenocarcinoma. We find that rapid subclonal expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with the emergence of pro-metastatic cancer cell states. Furthermore, metastases arise from spatially-confined subclones of primary tumours and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, we present a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumour progression.

cancer biology↗

bioRxiv2024

Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining

CRISPR-Cas9 based lineage tracing technologies have enabled the reconstruction of single-cell phylogenies from transcriptional readouts. However, developing tree-reconstruction algorithms with theoretical guarantees in this setting is challenging. In this work, we derive a reconstruction algorithm with theoretical guarantees using Neighbor-Joining (NJ) on distances that are moment-matched to estimate the true tree distances. We develop a series of tools to analyze this algorithm and prove its theoretical guarantees. When the parameters of the data generating process are known and there is no missing data, our results align with established results from common evolutionary models, such as Cavender-Farris-Neyman and Jukes-Cantor. However, to account for the realistic case where the parameters of the data generating process are not known and there is missing data, we develop new theory that shows for the first time that it is still possible to obtain reconstruction guarantees in the CRISPR-Cas9 case and in other models of evolution. Empirically, we show on both simulated lineage tracing data and on real data from a mouse model of lung cancer the improved performance of our method as compared to the traditional use of NJ.

bioinformatics↗

bioRxiv2024

The glycolytic reaction PGAM unexpectedly restrains Th17 pathogenicity and Th17-dependent autoimmunity

Glucose metabolism is a critical regulator of T cell function, largely thought to support their activation and effector differentiation. Here, we investigate the relevance of individual glycolytic reactions in determining the pathogenicity of T helper 17 (Th17) cells using single-cell RNA-seq and Compass, an algorithm we previously developed for estimating metabolic flux from single-cell transcriptomes. Surprisingly, Compass predicted that the metabolic shunt between 3-phosphoglycerate (3PG) and 2-phosphoglycerate (2PG) is inversely correlated with pathogenicity in these cells, whereas both its upstream and downstream reactions were positively correlated. Perturbation of phosphoglycerate mutase (PGAM), an enzyme required for 3PG to 2PG conversion, resulted in an increase in protein expression of IL2, IL17, and TNFa, as well as induction of a pathogenic gene expression program. Consistent with PGAM playing a pro-regulatory role, inhibiting PGAM in Th17 cells resulted in exacerbated autoimmune responses in the adoptive transfer model of experimental autoimmune encephalomyelitis (EAE). Finally, we further investigated the effects of modulating glucose concentration on Th17 cells in culture. Th17 cells differentiated under high- and low-glucose conditions substantially differed in their metabolic and effector transcriptomic programs, both central to Th17 function. Importantly, the PGAM-dependent gene module marks the least pathogenic state of Th17 cells irrespective of glucose concentration. Overall, our study identifies PGAM, contrary to other glycolytic enzymes, as a negative regulator of Th17 pathogenicity.

immunology↗

bioRxiv2024

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

BackgroundIncreasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases. ResultsWe collected epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we constructed networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks served as the base for a rich series of analyses, through which we demonstrated their temporal dynamics and enrichment for various disease-associated variants. We applied the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrated methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. ConclusionsOur findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes. This includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

genomics↗

bioRxiv2024

A heterogenous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We introduce SCHEMATIC, which integrates multiplex single-cell chemical transcriptomics with deep-generative classification to resolve chemotype-specific and shared programs and apply it to to define the molecular response of glioblastoma to EGFRis. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. We find that induction of an adaptive transcriptional program is associated with persistence of surviving cells after EGFR inhibition, and that concurrent EGFR/PI3K inhibition attenuates this program. We also find that pro-immunogenic expression changes associated with a subset of tyrphostin-family EGFR inhibitors are accompanied by enhanced antigen-specific cytotoxic T-cell killing in vitro. Our study provides a framework that considers each agents unique and often unknown poly-pharmacology to prioritize compounds pre-clinically that induce favorable molecular responses.

genomics↗

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↗