Biology subjects

Gogate, A. A.

Publications and source records attributed to Gogate, A. A..

4 recordsLinked to original sources

bioRxiv2026

SpatialTRACE predicts anatomical axes and regions in spatial transcriptomics and microscopy

Spatial transcriptomics measures gene expression in tissue sections. However, interpretation requires anatomical maps that link gene expression and cellular composition to tissue structure. Annotating entire sections often requires extensive manual labor. We developed SpatialTRACE (Tissue Region and Axis Coordinate Estimation), consisting of graph- and image-based models, to extend annotations of a few structures into tissue-wide maps of anatomical axes and regions. SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict anatomical coordinates or region membership throughout spatial transcriptomic datasets. In held-out mouse small-intestine sections, it predicted crypt-villus and epithelial-distance axis coordinates using as few as 10 annotated training villi and identified Peyer's patches from region annotations. SpatialTRACE-Image predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone. This multiscale vision transformer learns from coordinate and region predictions generated by SpatialTRACE-Graph. We applied SpatialTRACE-Image to immunofluorescence images to map the anatomical distribution of antigen-specific P14 CD8 T cells responding to acute systemic infection with lymphocytic choriomeningitis virus (LCMV) Armstrong in the small intestine. Compared with the vehicle-treated section, a section treated with a retinoic acid receptor inhibitor contained fewer P14 CD8 T cells overall, with a smaller fraction in the upper-villus lamina propria and a relative enrichment in the muscularis. Overall, the SpatialTRACE models reduce repeated manual annotation and provide a route to extend anatomical maps learned from spatial transcriptomics to DAPI-containing microscopy data.

bioinformatics↗

bioRxiv2022

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

Cataloging the diverse cellular architecture of the primate brain is crucial for understanding cognition, behavior and disease in humans. Here, we generated a brain-wide single-cell multimodal molecular atlas of the rhesus macaque brain. Altogether, we profiled 2.58M transcriptomes and 1.59M epigenomes from single nuclei sampled from 30 regions across the adult brain. Cell composition differed extensively across the brain, revealing cellular signatures of region-specific functions. We also identified 1.19M candidate regulatory elements, many novel, allowing us to explore the landscape of cis-regulatory grammar and neurological disease risk in a cell-type-specific manner. Together, this multi-omic atlas provides an open resource for investigating the evolution of the human brain and identifying novel targets for disease interventions.

neuroscience↗

bioRxiv2022

Human iPSC Derived Enamel Organoid Guided by Single-Cell Atlas of Human Tooth Development

Tooth enamel secreted by ameloblasts is the hardest material in the human body, acting as a shield protecting the teeth. However, the enamel is gradually damaged or partially lost in over 90% of adults and cannot be regenerated due to a lack of ameloblasts in erupted teeth. Here we use sci-RNA-seq to establish a spatiotemporal single-cell atlas for the developing human tooth and identify regulatory mechanisms controlling the differentiation process of human ameloblasts. We reveal key signaling pathways involved between the support cells and ameloblasts during fetal development and recapitulate those findings in a novel human ameloblast in vitro differentiation from iPSCs. We furthermore develop a mineralizing enamel organ-like 3D organoid system. These studies pave the way for future regenerative dentistry and therapies toward genetic diseases affecting enamel formation.

developmental biology↗

bioRxiv2022

Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex. Sex dependent alterations include pathways such as TGF{beta} signaling and metabolic shifts by sex, evident in both transcriptional alterations and differing presence of transcription factor (TF) motifs in accessible chromatin. Age was associated with changes such as immune activation-related transcriptional and chromatin accessibility differences, as well as changes in the relative proportion of cardiomyocytes, neurons, and perivascular cells. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs.

genomics↗