Lupus nephritis (LuN) is an inflammatory kidney disease characterized by the infiltration of immune cells into the kidney, including T-cells, B-cells, and dendritic cells. Here, we combine high-dimensional immunofluorescence microscopy with computer vision to identify and segment multiple populations of cells. A U-Net was trained to segment CD4+ T-cells in high-resolution LuN biopsy images and subsequently used to make CD4+ T-cell predictions on a test-set from a lower-resolution, high-dimensional LuN dataset. This produced higher precision, but lower recall and intersection over union for cells in the low-resolution dataset. Further application of U-Nets to immune cell segmentation will be discussed.
Several disease states, including cancer and autoimmunity, are characterized by the infiltration of large populations of immune cells into organ tissue. The degree and composition of these invading cells have been correlated with patient outcomes, suggesting that the intercellular interactions occurring in inflamed tissue play a role in pathology. Immunofluorescence staining paired with confocal microscopy produce detailed visualizations of these interactions. Applying computer vision and machine learning methods to the resulting images allows for robust quantification of immune infiltrates. We are developing an analytical pipeline to assess the immune environments of two distinct disease states: lupus nephritis and triple-negative breast cancer (TNBC). Biopsies of inflamed kidney tissue (lupus) and tumors (TNBC) were stained and imaged for panels of 20 markers using a strip-reprobe technique. This set of markers interrogates populations of T-cells, B-cells, and antigen presenting cells. To detect T cells, we first trained a U-Net to segment CD3+CD4+ T-cells in images of lupus biopsies and achieved an object-level precision of 0.855 and recall of 0.607 on an independent test set. We then evaluated the generalizability of this network to CD3+CD8+ T cells in lupus nephritis and CD3+CD4+ T cells in TNBC, and the extent to which fine-tuning the network improved performance for these cell types. We found that recall increased moderately with finetuning, while precision did not. Further work will focus on developing robust methods of segmenting a larger variety of T cell markers in both tissue contexts with high fidelity.
Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions.
Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells.
Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance.
Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, p ≪ 0.0001).
Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.
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