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

Cell morphology is one of the most described phenotypes in biology, yet systematic quantification and classification of morphology remains limited. Here, the authors present a computational approach for cell morphometry and multi-modal analysis based on concepts from metric geometry. 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.

Automated summary

The authors present a computational approach for cell morphometry and multi-modal analysis, which is based on concepts from metric geometry. They demonstrate the utility of this approach in integrating cell morphology data into single-cell omics analyses. The approach involves building cell morphology latent spaces using metric geometry, which facilitate the integration of single-cell morphological data and inference of relations with other data.