Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

TracX improves the accuracy of single-cell tracking by using a fingerprinting approach to measure the similarity between cells in two consecutive images. The approach is applicable across modalities and can enables biological discovery. Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (Trac(X)) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

Automated summary

TracX improves the accuracy of single-cell tracking using a fingerprinting approach to measure cell similarity across consecutive images. It is applicable across different cell types and image modalities, aiding in accurate cell observation and biological discovery in single-cell biology.