Though entire tissue sections are stained, out of necessity, analysis is performed only for a small number of sub-samples called regions of interest (ROIs). More commonly, IF analyses require time-consuming scoring or counting procedures or semi-automated thresholding approaches. There are relatively few techniques 7, 8, 9 available to extract spatial data from stained kidney tissue that can study 10 4 cells or more per sample, as is routinely achieved with single cell approaches. In contrast, IF staining of tissue sections is straightforward, can confirm cell-level (or even sub-cellular) changes in expression, can measure spatial relationships between cells, and is accessible to any laboratory. While spatial transcriptomic techniques may address some of these shortcomings, these techniques are expensive and require highly specialized equipment and training. Understanding the spatial organization of cells is critical to understand the pathogenesis of AKI and recovery, but this information cannot be measured directly in single cell -omics approaches. Recently, transcriptomic analyses of single cells have revealed a wide range of cell states after kidney injury 2, 3, 4, 5, consistent with the observation that recovery from AKI can be inconsistent and heterogenous 6.
This technique has the unique ability to simultaneously capture both expression levels of multiple proteins and the spatial relationships between the cells and tissues expressing them. Immunofluorescence (IF) imaging is a widely used technique to study AKI in human biopsies and in animal models. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.Īcute kidney injury (AKI) is a common clinical problem associated with significant mortality and cost 1. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density.
We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise.
ROI IMAGEJ MANUAL
Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Recovery from acute kidney injury can vary widely in patients and in animal models.
0 kommentar(er)
