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, 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 also benefits from the high level interactivity and rapidly growing user base of napari. Here we give an overview of napari-MM3 and test it against existing mother machine analysis software. By analyzing one dataset separately via multiple methods, we find that the distributions and correlations in several key physiological parameters are robust to the choice of analysis tool, though care must be taken when interpreting absolute spatial measurements such as cell size. By lowering the barrier to entry in image analysis - a key bottleneck in mother machine adoption - we aim to expand the user base of this powerful tool.

Light-sensitive proteins (opsins) are expressed in non-imaging tissues like the brain, dermis and reproductive organs of most animals. Such tissues have been shown to sense the intensity and spectrum of light over time. Functional links to circadian and reproductive rhythms have been speculated but remain uncertain. Here we use information theory to quantify the 'natural scene' for non-imaging opsins, i.e., spectral patterns in downwelling skylight. Our approach synthesizes measurements of natural downwelling spectra, atmospheric distortions, and weather, with the biophysical constraints of opsins and biochemical clocks, while minimizing assumptions about how organisms process such information. We find that tissues expressing multiple opsins could use twilight to extract significant information about lunar phase and time of day in many climates. In contrast, information in light intensity is far less robust to atmospheric perturbations. Thus our work quantifies circalunar and circadian regularities in the spectrum of downwelling radiance salient to non-imaging opsins.

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. In addition, the rapid adoption and widespread popularity of deep-learning methods by the scientific community raises an important question: to what extent can users trust the results generated by such “black box” methods? We explicitly demonstrate “What You Put Is What You Get” (WYPIWYG); i.e., the image analysis results can reflect the user bias encoded in the training dataset. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over a decade in our lab, we also provide useful information for those who want to implement mother-machine-based high-throughput imaging and image analysis methods in their research. This includes our guiding principles and best practices to ensure transparency and reproducible results.

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. In addition, the rapid adoption and widespread popularity of deep-learning methods by the scientific community raises an important question: to what extent can users trust the results generated by such “black box” methods? We explicitly demonstrate “What You Put Is What You Get” (WYPIWYG); i.e., the image analysis results can reflect the user bias encoded in the training dataset. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over a decade in our lab, we also provide useful information for those who want to implement mother-machine-based high-throughput imaging and image analysis methods in their research. This includes our guiding principles and best practices to ensure transparency and reproducible results.