Biology subjects

Nazaret, A.

Publications and source records attributed to Nazaret, A..

5 recordsLinked to original sources

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

Spatiotemporal Single-Cell Analysis Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease

Allogeneic hematopoietic cell transplantation (alloHCT) is curative for various hematologic diseases but often leads to acute graft-versus-host disease (GVHD), a potentially life-threatening complication. We leverage GVHD as a uniquely tractable disease model to dissect complex T-cell-mediated pathology in 27 alloHCT recipients. We integrate pre-transplant identification of alloreactive T-cells with longitudinal tracking across blood and gut, using mixed lymphocyte reaction-based clonal "fingerprinting", TCR clonotyping, single-cell RNA/TCR sequencing, and spatial transcriptomics. Using DecompTCR, a novel computational tool for longitudinal TCR analysis, we uncover clonal expansion programs linked to GVHD severity and TCR features. Multi-omics profiling of gut biopsies reveals enrichment and clonal expansion of CD8 effector and ZNF683(Hobit) resident memory T-cells, cytolytic remodeling of regulatory and unconventional T-cells, and localization of CD8 effector T-cells near intestinal stem cells in crypt loss regions. This framework defines dynamic immune circuit rewiring and phenotypic plasticity with implications for biomarkers and therapies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/655962v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@178b77dorg.highwire.dtl.DTLVardef@569226org.highwire.dtl.DTLVardef@1951281org.highwire.dtl.DTLVardef@1f1e046_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPersistent expansion of diverse alloreactive T cell clones is a hallmark of severe GVHD C_LIO_LIDecompTCR reveals dynamic clonal expansion programs linked to GVHD severity and clinical outcome C_LIO_LICD8+ T cell clones exhibit phenotypic plasticity in vivo across intestinal tissue compartments in GVHD C_LIO_LIHigh-resolution spatial profiling shows CD8+ effector T cells localize near intestinal stem cell niches and drive epithelial injury in GVHD C_LI

systems biology↗

bioRxiv2023

Deep generative model deciphers derailed trajectories in acute myeloid leukemia

Biological insights often depend on comparing conditions such as disease and health, yet we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.

bioinformatics↗

bioRxiv2022

Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironment

Spatially-resolved gene expression profiling provides valuable insight into tissue organization and cell-cell crosstalk; however, spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for a rigorous interpretation of cell states and do not utilize associated histology images. Significant sample variation further complicates the integration of ST datasets, which is essential for identifying commonalities across tissues or altered cellular wiring in disease. Here, we present Starfysh, the first comprehensive computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. Starfysh uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference in characterizing known or novel tissue-specific cell states. Additionally, Starfysh improves the characterization of spatial dynamics in complex tissues by leveraging histology images and enables the comparison of niches as spatial "hubs" across tissues. Integrative analysis of primary estrogen receptor-positive (ER+) breast cancer, triple-negative breast cancer (TNBC), and metaplastic breast cancer (MBC) tumors using Starfysh led to the identification of heterogeneous patient- and disease-specific hubs as well as a shared stromal hub with varying spatial orientation. Our results show the ability to delineate the spatial co-evolution of tumor and immune cell states and their crosstalk underlying intratumoral heterogeneity in TNBC and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. Starfysh is publicly available (https://github.com/azizilab/starfysh).

bioinformatics↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗