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Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.

Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.

Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.

Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/ zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Knowledge on the effects of perfluorohexane sulfonate (PFHxS) on ovarian function is limited. In the current study, we investigated the sensitivity of oocytes to PFHxS during in vitro maturation (IVM), including conse-quences on embryo development at the morphological, transcriptomic, and epigenomic levels. Bovine cumulus-oocyte complexes (COCs) were exposed to PFHxS during 22 h IVM. Following fertilisation, developmental competence was recorded until day 8 of culture. Two experiments were conducted: 1) exposure of COCs to 0.01 mu g mL(-1) -100 mu g mL(-1) PFHxS followed by confocal imaging to detect neutral lipids and nuclei, and 2) exposure of COCs to 0.1 mu g mL(-1) PFHxS followed by analysis of transcriptomic and DNA methylation changes in blastocysts. Decreased oocyte developmental competence was observed upon exposure to & nbsp;>= 40 mu g mL(-1) PFHxS and altered lipid distribution was observed in the blastocysts upon exposure to 1-10 mu g mL(-1) PFHxS (not observed at lower or higher concentrations). Transcriptomic data showed that genes affected by 0.1 mu g mL(-1) PFHxS were enriched for pathways related to increased synthesis and production of reactive oxygen species. Enrichment for peroxisome proliferator-activated receptor-gamma and oestrogen pathways was also observed. Genes linked to DNA methylation changes were enriched for similar pathways. In conclusion, exposure of the bovine oocyte to PFHxS during the narrow window of IVM affected subsequent embryonic development, as reflected by morphological and mo- lecular changes. This suggests that PFHxS interferes with the final nuclear and cytoplasmic maturation of the oocyte leading to decreased developmental competence to blastocyst stage.

Lymphangiogenesis, formation of lymphatic vessels from pre-existing vessels, is a dynamic process that requires cell migration. Regardless of location, migrating lymphatic endothelial cell (LEC) progenitors probe their surroundings to form the lymphatic network. Lymphatic-development regulation requires the transcription factor MAFB in different species. Zebrafish Mafba, expressed in LEC progenitors, is essential for their migration in the trunk. However, the transcriptional mechanism that orchestrates LEC migration in different lymphatic endothelial beds remains elusive. Here, we uncover topographically different requirements of the two paralogs, Mafba and Mafbb, for LEC migration. Both mafba and mafbb are necessary for facial lymphatic development, but mafbb is dispensable for trunk lymphatic development. On the molecular level, we demonstrate a regulatory network where Vegfc-Vegfd-SoxF-Mafba-Mafbb is essential in facial lymphangiogenesis. We identify that mafba and mafbb tune the directionality of LEC migration and vessel morphogenesis that is ultimately necessary for lymphatic function.

The objectives of this study were to evaluate the effect of perfluoroalkyl substances on early embryonic development and apoptosis in blastocysts using a porcine in vitro model. Porcine oocytes (N = 855) collected from abattoir ovaries were subjected to perfluorooctane sulfonic acid (PFOS) (0.1 mu g/ml) and perfluorohexane sulfonic acid (PFHxS) (40 mu g/ml) during in vitro maturation (IVM) for 45 h. The gametes were then fertilized and cultured in vitro, and developmental parameters were recorded. After 6 days of culture, resulting blastocysts (N = 146) were stained using a terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and imaged as stacks using confocal laser scanning microscopy. Proportion of apoptotic cells as well as total numbers of nuclei in each blastocyst were analyzed using objective image analysis. The experiment was run in 9 replicates, always with a control present. Effects on developmental parameters were analyzed using logistic regression, and effects on apoptosis and total numbers of nuclei were analyzed using linear regression. Higher cell count was associated with lower proportion of apoptotic cells, i.e., larger blastocysts contained less apoptotic cells. Upon PFAS exposure during IVM, PFHxS tended to result in higher blastocyst rates on day 5 post fertilization (p = 0.07) and on day 6 post fertilization (p = 0.05) as well as in higher apoptosis rates in blastocysts (p = 0.06). PFHxS resulted in higher total cell counts in blastocysts (p = 0.002). No effects attributable to the concentration of PFOS used here was seen. These findings add to the evidence that some perfluoroalkyl substances may affect female reproduction. More studies are needed to better understand potential implications for continued development as well as for human health.

A wide variety of anthropogenic chemicals is detected in humans and wildlife and the health effects of various chemical exposures are not well understood. Early life stages are generally the most susceptible to chemical disruption and developmental exposure can cause disease in adulthood, but the mechanistic understanding of such effects is poor. Within the EU project EDC-MixRisk, a chemical mixture (Mixture G) was identified in the Swedish pregnancy cohort SELMA by the inverse association between levels in women at around gestational week ten with birth weight of their children. This mixture was composed of mono-ethyl phthalate, mono-butyl phthalate, mono-benzyl phthalate, mono-ethylhexyl phthalate, mono-isononyl phthalate, triclosan, perfluorohexane sulfonate, perfluorooctanoic acid, and perfluorooctane sulfonate. In a series of experimental studies, we characterized effects of Mixture G on early development in zebrafish models. Here, we studied apoptosis and Wnt/β-catenin signaling which are two evolutionarily conserved signaling pathways of crucial importance during development. We determined effects on apoptosis by measuring TUNEL staining, caspase-3 activity, and acridine orange staining in wildtype zebrafish embryos, while Wnt/β-catenin signaling was assayed using a transgenic line expressing an EGFP reporter at β-catenin-regulated promoters. We found that Mixture G increased apoptosis, suppressed Wnt/β-catenin signaling in the caudal fin, and altered the shape of the caudal fin at water concentrations only 20–100 times higher than the geometric mean serum concentration in the human cohort. These findings call for awareness that pollutant mixtures like mixture G may interfere with a variety of developmental processes, possibly resulting in adverse health effects.

Creating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use inmany applications. Here, we present a study in which we used the output of a classical image analysis pipeline aslabels when training a convolutional neural network (CNN). This may not only reduce the time experts spendannotating images but it may also lead to an improvement of results when compared to the output from the classicalpipeline used in training. In our application, i.e., cell nuclei segmentation, we generated the annotations usingCellProfiler (a tool for developing classical image analysis pipelines for biomedical applications) and trained onthem a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a0.84 object-wise Jaccard index which was better than the classical method used for generating the annotations by0.02 and 0.34, respectively. Our experimental results show that in this application, not only such training is feasiblebut also that the deep learning segmentations are a clear improvement compared to the output from the classicalpipeline used for generating the annotations.

BACKGROUND: Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.

NEW METHOD: We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.

RESULTS: NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.

COMPARISON WITH EXISTING METHODS: The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In addition, NeuriteSegmentation was compared to NeuriteJ^®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.

CONCLUSION: The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.

Motivation: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.

Results: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.