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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. 

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 engineeredEscherichia coliover 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.

 

ABSTRACT The rotavirus polymerase VP1 mediates all stages of viral RNA synthesis within the confines of subviral particles and while associated with the core shell protein VP2. Transcription (positive-strand RNA [+RNA] synthesis) by VP1 occurs within double-layered particles (DLPs), while genome replication (double-stranded RNA [dsRNA] synthesis) by VP1 occurs within assembly intermediates. VP2 is critical for VP1 enzymatic activity; yet, the mechanism by which the core shell protein triggers polymerase function remains poorly understood. Structural analyses of transcriptionally competent DLPs show that VP1 is located beneath the VP2 core shell and sits slightly off-center from each of the icosahedral 5-fold axes. In this position, the polymerase is contacted by the core shell at 5 distinct surface-exposed sites, comprising VP1 residues 264 to 267, 547 to 550, 614 to 620, 968 to 980, and 1022 to 1025. Here, we sought to test the functional significance of these VP2 contact sites on VP1 with regard to polymerase activity. We engineered 19 recombinant VP1 (rVP1) proteins that contained single- or multipoint alanine mutations within each individual contact site and assayed them for the capacity to synthesize dsRNA in vitro in the presence of rVP2. Three rVP1 mutants (E265A/L267A, R614A, and D971A/S978A/I980A) exhibited diminished in vitro dsRNA synthesis. Despite their loss-of-function phenotypes, the mutants did not show major structural changes in silico, and they maintained their overall capacity to bind rVP2 in vitro via their nonmutated contact sites. These results move us toward a mechanistic understanding of rotavirus replication and identify precise VP2-binding sites on the polymerase surface that are critical for its enzymatic activation. IMPORTANCE Rotaviruses are important pathogens that cause severe gastroenteritis in the young of many animals. The viral polymerase VP1 mediates all stages of viral RNA synthesis, and it requires the core shell protein VP2 for its enzymatic activity. Yet, there are several gaps in knowledge about how VP2 engages and activates VP1. Here, we probed the functional significance of 5 distinct VP2 contact sites on VP1 that were revealed through previous structural studies. Specifically, we engineered alanine amino acid substitutions within each of the 5 VP1 regions and assayed the mutant polymerases for the capacity to synthesize RNA in the presence of VP2 in a test tube. Our results identified residues within 3 of the VP2 contact sites that are critical for robust polymerase activity. These results are important because they enhance the understanding of a key step of the rotavirus replication cycle. 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.