CAJAL enables analysis and integration of single-cell morphological data using metric geometry

Kiya W. Govek, Patrick Nicodemus, Yuxuan Lin, Jake Crawford, Artur B. Saturnino, Hannah Cui, Kristi Zoga, Michael P. Hart and Pablo G. Camara ()
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Kiya W. Govek: University of Pennsylvania
Patrick Nicodemus: University of Pennsylvania
Yuxuan Lin: University of Pennsylvania
Jake Crawford: University of Pennsylvania
Artur B. Saturnino: University of Pennsylvania
Hannah Cui: University of Pennsylvania
Kristi Zoga: University of Pennsylvania
Michael P. Hart: University of Pennsylvania
Pablo G. Camara: University of Pennsylvania

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic data. We demonstrate the utility of CAJAL with several morphological datasets of neurons and glia and identify genes associated with neuronal plasticity in C. elegans. Our approach provides an effective strategy for integrating cell morphology data into single-cell omics analyses.

Date: 2023
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DOI: 10.1038/s41467-023-39424-2

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