
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.04.610488
preprint
1
Article
Cell identity revealed by precise cell cycle state mapping links data modalities
Alahmari Saeed http://orcid.org/0000-0001-7344-9448

Schultz Andrew
Albrecht Jordan
Tagal Vural
Siddiqui Zaid
Prabhakaran Sandhya
El Naqa Issam
Anderson Alexander
Heiser Laura
Andor Noemi
08 9 2024
2024.09.04.610488https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.09.04.610488
nihpp-2024.09.04.610488.pdf
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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pmc
