Tissue gene expression profiling is performed on homogenates or on populations of isolated single cells to resolve molecular states of different cell types. In both approaches, histological context is lost. We have developed an in situ sequencing method for parallel targeted analysis of short RNA fragments in morphologically preserved cells and tissue. We demonstrate in situ sequencing of point mutations and multiplexed gene expression profiling in human breast cancer tissue sections.

2020. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncology 21 (2) , pp. 222-232.

SummaryWe present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two-as well as three-dimensional images.

SUMMARY The opportunistic pathogen Pseudomonas aeruginosa causes serious human infections, but effective treatments and the mechanisms mediating pathogenesis remain elusive. Caenorhabditis elegans shares innate immune pathways with humans, making it invaluable to investigate infection. To determine how P. aeruginosa disrupts host biology, we studied how P. aeruginosa kills C. elegans in a liquid-based pathogenesis model. We found that P. aeruginosa-mediated killing does not require quorum-sensing pathways or host colonization. A chemical genetic screen revealed that iron chelators alleviate P. aeruginosa-mediated killing. Consistent with a role for iron in P. aeruginosa pathogenesis, the bacterial siderophore pyoverdin was required for virulence and was sufficient to induce a hypoxic response and death in the absence of bacteria. Loss of the C. elegans hypoxia-inducing factor HIF-1, which regulates iron homeostasis, exacerbated P. aeruginosa pathogenesis, further linking hypoxia and killing. As pyoverdin is indispensable for virulence in mice, pyoverdin-mediated hypoxia is likely relevant in human pathogenesis.

Improved methods are needed for in situ characterization of post-translational modifications in cell lines and tissues. For example, it is desirable to monitor the phosphorylation status of individual receptor tyrosine kinases in samples from human tumors treated with inhibitors to evaluate therapeutic responses. Unfortunately the leading methods for observing the dynamics of tissue post-translational modifications in situ, immunohistochemistry and immunofluorescence, exhibit limited sensitivity and selectivity. Proximity ligation assay is a novel method that offers improved selectivity through the requirement of dual recognition and increased sensitivity by including DNA amplification as a component of detection of the target molecule. Here we therefore established a generalized in situ proximity ligation assay to investigate phosphorylation of platelet-derived growth factor receptor ␤ (PDGFR␤) in cells stimulated with platelet-derived growth factor BB. Antibodies specific for immunoglobulins from different species, modified by attachment of DNA strands, were used as secondary proximity probes together with a pair of primary antibodies from the corresponding species. Dual recognition of receptors and phosphorylated sites by the primary antibodies in combination with the secondary proximity probes was used to generate circular DNA strands; this was followed by signal amplification by replicating the DNA circles via rolling circle amplification. We detected tyrosine phosphorylated PDGFR␤ in human embryonic kidney cells stably overexpressing human influenza hemagglutinin-tagged human PDGFR␤ in porcine aortic endothelial cells transfected with the ␤-receptor, but not in cells transfected with the ␣-receptor, and also in immortalized human foreskin fibroblasts, BJ hTert, endogenously expressing the PDGFR␤. We furthermore visualized tyrosine phosphorylated PDGFR␤ in tissue sections from fresh frozen human scar tissue undergoing wound healing. The method should be of great value to study signal transduction, screen for effects of pharmacological agents, and enhance the diagnostic potential in histopathology. Molecular & Cellular Proteomics 6:1500 -1509, 2007.

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease.

The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality.

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.