Data for: 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
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.