Mechanically resolved imaging of bacteria using expansion microscopy.

Imaging dense and diverse microbial communities has broad applications in basic microbiology and medicine, but remains a grand challenge due to the fact that many species adopt similar morphologies. While prior studies have relied on techniques involving spectral labeling, we have developed an expansion microscopy method (µExM) in which bacterial cells are physically expanded prior to imaging. We find that expansion patterns depend on the structural and mechanical properties of the cell wall, which vary across species and conditions. We use this phenomenon as a quantitative and sensitive phenotypic imaging contrast orthogonal to spectral separation to resolve bacterial cells of different species or in distinct physiological states. Focusing on host-microbe interactions that are difficult to quantify through fluorescence alone, we demonstrate the ability of µExM to distinguish species through an in vitro defined community of human gut commensals and in vivo imaging of a model gut microbiota, and to sensitively detect cell-envelope damage caused by antibiotics or previously unrecognized cell-to-cell phenotypic heterogeneity among pathogenic bacteria as they infect macrophages.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

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.

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level.

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.