Cell Identity Revealed by Precise Cell Cycle State Mapping Links Data Modalities

Overview

Journal bioRxiv

Date 2024 Sep 16

PMID 39282313

Authors

Saeed Alahmari

Andrew Schultz

Jordan Albrecht

Vural Tagal

Zaid Siddiqui

Sandhya Prabhakaran

Issam El Naqa

Alexander Anderson

Laura Heiser

Noemi Andor

Affiliations

Soon will be listed here.

Abstract

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

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