DOI: 10.5061/dryad.2fqz612xd
title: Data for: Tools and methods for high-throughput single-cell imaging with the mother machine

description: 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
 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.
description: The dataset consists of time-lapse images of <em>E.
 coli </em>cells grown in the microfluidic mother machine device.
 Images were obtained on an inverted microscope (Nikon Ti-E) with Perfect
 Focus 3 (PFS3), 100x oil immersion objective (PH3, numerical aperture =
 1.45), and Obis laser 488LX(Coherent Inc., CA) as a fluorescence light
 source, and an Andor NEO sCMOS (Andor Technology) camera. The laser power
 was 18 mW. The exposure time was 200 ms for phase contrast imaging and 50
 ms for fluorescence.
description: # Data for: Tools and methods for high-throughput single-cell imaging with
 the mother machine ### Overview This dataset consists of unprocessed
 time-lapse imaging data of *E. coli* cells grown in the "mother
 machine" microfluidic device. ### Imaging conditions `Imaging
 interval` 150 seconds `Resolution` 0.065 uM / pixel Images were obtained
 on an inverted microscope (Nikon Ti-E) with Perfect Focus 3 (PFS3), 100x
 oil immersion objective (PH3, numerical aperture = 1.45), and Obis laser
 488LX(Coherent Inc., CA) as a fluorescence light source, and an Andor NEO
 sCMOS (Andor Technology) camera. The laser power was 18 mW. The exposure
 time was 200 ms for phase contrast imaging and 50 ms for fluorescence. ###
 Experimental conditions `Strain` *E. coli* MG1655 F- λ- rph-1 DnaN-Ypet
 (kanamycin resistant) `Growth medium` MOPS 0.4% glycerol + 11 amino acids.
 See reference for detailed media recipe. `Temperature` 37C ###
 Publications associated with this dataset **Original publication** Fangwei
 Si, Guillaume Le Treut, John T. Sauls, Stephen Vadia, Petra Anne Levin,
 Suckjoon Jun. Mechanistic Origin of Cell-Size Control and Homeostasis in
 Bacteria. Current Biology. Volume 29, Issue 11, 2019, Pages 1760-1770.e7.
 ISSN 0960-9822. https://doi.org/10.1016/j.cub.2019.04.062. **Secondary
 publication** The dataset was reanalyzed in: Ryan Thiermann, Michael
 Sandler, Gursharan Ahir, John T. Sauls, Jeremy W. Schroeder, Steven D.
 Brown, Guillaume Le Treut, Fangwei Si, Dongyang Li, Jue D. Wang, Suckjoon
 Jun. 2023. Tools and methods for high-throughput single-cell imaging with
 the mother machine. eLife12:RP88463. https://doi.org/10.7554/eLife.88463.2
 Processed data from this reference is available at the GitHub link in the
 Related Works section.