Biology subjects

Awayan, K.

Publications and source records attributed to Awayan, K..

5 recordsLinked to original sources

bioRxiv2026

gene2fish: a multi-gene spatial expression browser for in situ hybridization data across zebrafish development

Single-cell RNA-sequencing (scRNA-seq) atlases of zebrafish development have catalogued marker genes for hundreds of cell types, but transcriptomic data alone cannot reveal their expression patterns in the embryo. The systematically annotated Thisse high-throughput whole-mount in situ hybridization (ISH) collection at ZFIN provides spatial expression data across twelve developmental stages, yet the ZFIN web interface is organized around individual genes, making side-by-side cross-gene comparison laborious. We present gene2fish, a web application that displays ISH images from the Thisse dataset, linked directly to their ZFIN records, in a gene-by-developmental-stage grid. With gene2fish, researchers can quickly explore and validate the spatial expression of marker gene lists, and find the genes annotated to specific anatomical structures across developmental time. The application is part of Zebrahub and is freely available at https://zebrahub.sf.czbiohub.org/gene2fish.

developmental biology↗

bioRxiv2024

inTRACKtive - A Web-Based Tool for Interactive Cell Tracking Visualization

We introduce inTRACKtive, an innovative web-based tool for interactive visualization and sharing of large 3D cell tracking datasets, eliminating the need for software installations or data downloads. Built with modern web technologies, inTRACKtive enables researchers to explore cell-tracking results from terabyte-scale microscopy data, conduct virtual fate-mapping experiments, and share these results via simple hyperlinks. The platform powers the Virtual Embryo Zoo, an online resource showcasing cell-tracking datasets from state-of-the-art light-sheet embryonic microscopy of six model organisms. inTRACKtives open-source code allows users to visualize their own data or host customized viewer instances. By providing easy access to complex tracking datasets, inTRACKtive offers a versatile, interactive, collaborative tool for developmental biology.

developmental biology↗

bioRxiv2024

Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis

During embryonic development, gene regulatory networks (GRNs) drive molecular differentiation of cell types. However, the temporal dynamics of these networks remain poorly understood. Here, we present Zebrahub-Multiome, a single-cell multiomic atlas that captures chromatin accessibility and gene expression from 94,562 cells across six stages of zebrafish embryogenesis (10-24 hours post-fertilization), capturing key developmental stages from the end of gastrulation to the onset of organogenesis. By measuring regulatory element activity alongside transcriptional output from the same cells, we identify 640,000 cis-regulatory elements organized into 402 hierarchically structured modules corresponding to specific developmental pathways. Early embryonic stages employ broadly shared regulatory programs that progressively fragment into lineage-specific modules. Timeresolved gene regulatory network inference reveals that transcription factors undergo functional transitions - from multilineage regulators to specialized, lineage-committed factors. These quantitative measurements reveal the regulatory network rewiring that drives cell fate specification. Our interactive web portal (zebrahub.org/epigenomics) enables exploration of gene dynamics, regulatory networks, and perturbation predictions, providing a quantitative framework for understanding vertebrate developmental regulation.

developmental biology↗

bioRxiv2023

Zebrahub - Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors

Elucidating the developmental processes of organisms requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate mapping experiments. To demonstrate the versatility of our multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

developmental biology↗

bioRxiv2022

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, directly mapping single cell identities to spatial positions. MERFISH produces an intrinsically different data type than scRNA-seq and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas as well as from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression and can robustly identify cell types without the need for computational integration with scRNA-seq reference atlases.

bioinformatics↗