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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) - that is, 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.

Abstract Sulfur mustard (SM) is a highly toxic chemical warfare agent. Exposure to SM results in various pathologies including skin lesions with subsequent impaired wound healing. To date, there are no effective treatments available. Here we discover a SM-triggered pathomechanism involving miR-497-5p and its target survivin which contributes to keratinocyte dysfunction. Transcriptome analysis using RNA-seq in normal human epidermal keratinocytes (NHEK) revealed that SM evoked differential expression of 1896 mRNAs and 25 miRNAs with many of these RNAs known to be involved in keratinocyte function and wound healing. We demonstrated that keratinocyte differentiation and proliferation were efficiently regulated by miRNAs induced in skin cells after exposure to SM. The inhibition of miR-497-5p counteracted SM-induced premature differentiation and stimulated proliferation of NHEK. In addition, we showed that microneedle-mediated transdermal application of lipid-nanoparticles containing miR-497-5p inhibitor restored survivin biosynthesis and cellular functionality upon exposure to SM using human skin biopsies. Our findings expand the current understanding of SM-associated molecular toxicology in keratinocytes and highlight miR-497-5p as feasible clinical target for specific skin therapy in SM-exposed patients and beyond.

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. 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) – that is, 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.

Discourses can be stronger than facts and, therefore, harder to dismantle. For example, the extinction of the Selk’nam is a fallacy that has remained for more than a century since it was decreed by the scientific community itself, thus making their descendants invisible. Combining this invisibility with that of peatlands - the original habitat of the Selk’nam - this project seeks to dismantle these discourses by reusing and resignifying the same nineteenth-century visualization devices that erroneously helped decree a race’s extinction.

Intoxication by organophosphorus (OP) poisons, like nerve agents and pesticides, is characterized by the life-threatening inhibition of acetylcholinesterase (AChE) caused by covalent reaction with the serine residue of the active site of the enzyme (phosphylation). Similar reactions occur with butyrylcholinesterase (BChE) and serum albumin present in blood as dissolved proteins. For forensic purposes, products (adducts) with the latter proteins are highly valuable long-lived biomarkers of exposure to OP agents that are accessible by diverse mass spectrometric procedures. In addition, the evidence of poison incorporation might also succeed by the detection of remaining traces of the agent itself, but more likely its hydrolysis and/or enzymatic degradation products. These relatively short-lived molecules are distributed in blood and tissue, and excreted via urine. This review presents the mass spectrometry-based methods targeting the different groups of biomarkers in biological samples, which are already internationally accepted by the Organisation for the Prohibition of Chemical Weapons (OPCW), introduces novel approaches in the field of biomedical verification, and outlines the strict quality criteria that must be fulfilled for unambiguous forensic analysis.

Motor neurons (MNs) derived from human-induced pluripotent stem cells (hiPSC) hold great potential for the treatment of various motor neurodegenerative diseases as transplantations with a low-risk of rejection are made possible. There are many hiPSC differentiation protocols that pursue to imitate the multistep process of motor neurogenesis in vivo. However, these often apply viral vectors, feeder cells, or antibiotics to generate hiPSC and MNs, limiting their translational potential. In this study, a virus-, feeder-, and antibiotic-free method was used for reprogramming hiPSC, which were maintained in culture medium produced under clinical good manufacturing practice. Differentiation into MNs was performed with standardized, chemically defined, and antibiotic-free culture media. The identity of hiPSC, neuronal progenitors, and mature MNs was continuously verified by the detection of specific markers at the genetic and protein level via qRT-PCR, flow cytometry, Western Blot, and immunofluorescence. MNX1- and ChAT-positive motoneuronal progenitor cells were formed after neural induction via dual-SMAD inhibition and expansion. For maturation, an approach aiming to directly mature these progenitors was compared to an approach that included an additional differentiation step for further specification. Although both approaches generated mature MNs expressing characteristic postmitotic markers, the direct maturation approach appeared to be more efficient. These results provide new insights into the suitability of two standardized differentiation approaches for generating mature MNs, which might pave the way for future clinical applications.

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) - that is, 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. [ABSTRACT FROM AUTHOR]