Biology subjects

Sauls, J. T.

Publications and source records attributed to Sauls, J. T..

5 recordsLinked to original sources

bioRxiv2023

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

systems biology↗

bioRxiv2019

Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

systems biology↗

bioRxiv2019

Gram-positive and Gram-negative Bacteria Share Common Principles to Coordinate Growth and the Cell Cycle at the Single-cell Level

Bacillus subtilis and Escherichia coli are evolutionarily divergent model organisms that have elucidated fundamental differences between Gram-positive and Gram-negative bacteria, respectively. Despite their differences in cell cycle control at the molecular level, both organisms follow the same phenomenological principle for cell size homeostasis known as the adder. We thus asked to what extent B. subtilis and E. coli share common physiological principles in coordinating growth and the cell cycle. To answer this question, we measured physiological parameters of B. subtilis under various steady-state growth conditions with and without translation inhibition at both population and single-cell level. These experiments revealed core shared physiological principles between B. subtilis and E. coli. Specifically, we show that both organisms maintain an invariant cell size per replication origin at initiation, with and without growth inhibition, and even during nutrient shifts at the single-cell level. Furthermore, both organisms also inherit the same \"hierarchy\" of physiological parameters ranked by their coefficient of variation. Based on these findings, we suggest that the basic coordination principles between growth and the cell cycle in bacteria may have been established in the very early stages of evolution.

systems biology↗

bioRxiv2018

Mechanistic origin of cell-size control and homeostasis in bacteria

Evolutionarily divergent bacteria share a common phenomenological strategy for cell-size homeostasis under steady-state conditions. In the presence of inherent physiological stochasticity, cells following this \"adder\" principle gradually return to their steady-state size by adding a constant volume between birth and division regardless of their size at birth. However, the mechanism of the adder has been unknown despite intense efforts. In this work, we show that the adder is a direct consequence of two general processes in biology: (1) threshold -- accumulation of initiators and precursors required for cell division to a respective fixed number, and (2) balanced biosynthesis -- maintenance of their production proportional to volume growth. This mechanism is naturally robust to static growth inhibition, but also allows us to \"reprogram\" cell-size homeostasis in a quantitatively predictive manner in both Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. By generating dynamic oscillations in the concentration of the division protein FtsZ, we were able to oscillate cell size at division and systematically break the adder. In contrast, periodic induction of replication initiator protein DnaA caused oscillations in cell size at initiation, but did not alter division size or the adder. Finally, we were able to restore the adder phenotype in slow growing E. coli, the only known steady-state growth condition wherein E. coli significantly deviates from the adder, by repressing active degradation of division proteins. Together these results show that division and replication are independently controlled, and that division processes exclusively drive cell-size homeostasis in bacteria.

systems biology↗

bioRxiv2016

Deconstructing cell size control into physiological modules in Escherichia coli

It is generally assumed that the allocation and synthesis of total cellular resources in microorganisms are uniquely determined by the growth conditions. Adaptation to a new physiological state leads to a change in cell size via reallocation of cellular resources. However, it has not been understood how cell size is coordinated with biosynthesis and robustly adapts to physiological states. We show that cell size in Escherichia coli can be predicted for any steady-state condition by projecting all biosynthesis into three measurable variables representing replication initiation, replication-division cycle, and the global biosynthesis rate. These variables can be decoupled by selectively controlling their respective core biosynthesis using CRISPR interference and antibiotics, verifying our predictions that different physiological states can result in the same cell size. We performed extensive growth inhibition experiments, and discovered that cell size at replication initiation per origin, namely the initiation mass or \"unit cell,\" is remarkably invariant under perturbations targeting transcription, translation, ribosome content, replication kinetics, fatty acid and cell-wall synthesis, cell division, and cell shape. Based on this invariance and balanced resource allocation, we explain why the total cell size is the sum of all unit cells. These results provide an overarching framework with quantitative predictive power over cell size in bacteria.

microbiology↗