Longitudinal Single-Cell Imaging of Engineered Strains with Stimulated Raman Scattering to Characterize Heterogeneity in Fatty Acid Production.

Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single-cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells. This method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme-dependent manner. By pairing time-lapse and SRS imaging, the relationship between growth and production at the single-cell level are examined. The results demonstrate that cell-to-cell production heterogeneity is pervasive and provides a means to link single-cell and population-level production.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

The motility regulator flhDC drives intracellular accumulation and tumor colonization of Salmonella.

BACKGROUND: Salmonella have potential as anticancer therapeutic because of their innate tumor specificity. In clinical studies, this specificity has been hampered by heterogeneous responses. Understanding the mechanisms that control tumor colonization would enable the design of more robust therapeutic strains. Two mechanisms that could affect tumor colonization are intracellular accumulation and intratumoral motility. Both of these mechanisms have elements that are controlled by the master motility regulator flhDC. We hypothesized that 1) overexpressing flhDC in Salmonella increases intracellular bacterial accumulation in tumor cell masses, and 2) intracellular accumulation of Salmonella drives tumor colonization in vitro. METHODS: To test these hypotheses, we transformed Salmonella with genetic circuits that induce flhDC and express green fluorescent protein after intracellular invasion. The genetically modified Salmonella was perfused into an in vitro tumor-on-a-chip device. Time-lapse fluorescence microscopy was used to quantify intracellular and colonization dynamics within tumor masses. A mathematical model was used to determine how these mechanisms are related to each other. RESULTS: Overexpression of flhDC increased intracellular accumulation and tumor colonization 2.5 and 5 times more than control Salmonella, respectively (P < 0.05). Non-motile Salmonella accumulated in cancer cells 26 times less than controls (P < 0.001). Minimally invasive, ΔsipB, Salmonella colonized tumor masses 2.5 times less than controls (P < 0.05). When flhDC was selectively induced after penetration into tumor masses, Salmonella both accumulated intracellularly and colonized tumor masses 2 times more than controls (P < 0.05). Mathematical modeling of tumor colonization dynamics demonstrated that intracellular accumulation increased retention of Salmonella in tumors by effectively causing the bacteria to bind to cancer cells and preventing leakage out of the tumors. These results demonstrated that increasing intracellular bacterial density increased overall tumor colonization and that flhDC could be used to control both. CONCLUSIONS: This study demonstrates a mechanistic link between motility, intracellular accumulation and tumor colonization. Based on our results, we envision that therapeutic strains of Salmonella could use inducible flhDC to drive tumor colonization. More intratumoral bacteria would enable delivery of higher therapeutic payloads into tumors and would improve treatment efficacy.