Cell identity revealed by precise cell cycle state mapping links data modalities

Alahmari, S.; Schultz, A.; Albrecht, J.; Tagal, V.; Siddiqui, Z.; Prabhakaran, S.; El Naqa, I.; Anderson, A.; Heiser, L.; Andor, N.

doi:10.1101/2024.09.04.610488

bioRxiv · 10.1101/2024.09.04.610488

Cell identity revealed by precise cell cycle state mapping links data modalities

Alahmari, S.·Schultz, A.·Albrecht, J.·Tagal, V.·Siddiqui, Z.·Prabhakaran, S.·El Naqa, I.·Anderson, A.·Heiser, L.·Andor, N.

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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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Alahmari, S., Schultz, A., Albrecht, J., Tagal, V., Siddiqui, Z., Prabhakaran, S., El Naqa, I., Anderson, A., Heiser, L., Andor, N.. 2024-09-08. Cell identity revealed by precise cell cycle state mapping links data modalities. https://doi.org/10.1101/2024.09.04.610488

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