Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Fluctuating conditions and diverse stresses are typical in natural environments. In response, cells mount complex responses across multiple scales, including adjusting their shape to withstand stress. In enterobacteria, the Rcs phosphorelay is activated by cell envelope damage and by changes to periplasmic dimensions and cell width. Here, we investigated the physiological and morphological consequences of Rcs activation in Escherichia coli in the absence of stresses, using an inducible version of RcsF that mislocalizes to the inner membrane, RcsFIM. Expression of RcsFIM immediately reduced cellular growth rate and the added length per cell cycle in a manner that was directly dependent on induction levels, but independent of Rcs-induced capsule production. At the same time, cells increased intracellular concentration of the cell division protein FtsZ, and decreased the distance between division rings in filamentous cells. Depletion of the Rcs negative regulator IgaA phenocopied RcsFIM induction, indicating that IgaA is essential due to growth inhibition in its absence. However, A22 treatment did not affect growth rate or FtsZ intracellular concentration, despite activating the Rcs system. These findings suggest that the effect of Rcs activation on FtsZ levels is mediated indirectly through growth-rate changes, and highlight feedbacks among the Rcs stress response, growth dynamics, and cell-size control.

Organoids are powerful experimental models for studying the ontogeny and progression of diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, organoids in bulk culture physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or microenvironment variability. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can be retrieved from their microwells for sequencing and molecular profiling. Coupled with a deep-learning image processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity.