Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published August 22, 2018 | Submitted
Report Open

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Abstract

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing- and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their surrounding environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states.

Additional Information

The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv preprint first posted online Mar. 2, 2018. Code Availability: Code is deposited at https://bitbucket.org/qzhu/smfish-hmrf. Data Availability: Expression data, spatial coordinates, SVM prediction results and HMRF segmentation results are deposited at https://bitbucket.org/qzhu/smfish-hmrf. Animal research was conducted in compliance with all relevant ethical regulations and other institutional requirements.

Attached Files

Submitted - 275156.full.pdf

Files

275156.full.pdf
Files (3.7 MB)
Name Size Download all
md5:c6b0dedea5e30f738187ec16116c897e
3.7 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023