Single-cell sequencing and proteomics have been critical for the study of human disease. However, highly multiplexed microscopy has revolutionized spatial biology by measuring cell expression from ~50 proteins while maintaining spatial locations of cells. This presents unique computational challenges; acquiring manual annotations across so many image channels is challenging, therefore supervised learning methods for classification are undesirable. To overcome this limitation we have developed a decision-tree classifier for the multiclass annotation of renal cells that is analogous to well-established flow cytometry-based cell analyses. We demonstrate this method of cell annotation in a dataset of 54 kidney biopsies from patients with three different pathologies: 25 with lupus nephritis, 23 with renal allograft rejection, and six with non-autoimmune conditions. Biopsies were iteratively stained and imaged using the PhenoCycler protocol to acquire high-resolution, full-section images with a 43-marker panel. Nucleus segmentation was performed using Cellpose2.0 and whole cell segmentation was approximated by dilating the nucleus masks. In our decision tree, cells are sequentially sorted into marker-negative and marker-positive populations using their mean fluorescence intensity (MFI). A multi-Otsu threshold, in conjunction with manual spot checking, is used for determining the optimal MFI threshold for each branching of the decision tree. Marker order is based upon well-established, hierarchical expression of immunological cell markers created in consultation with expert immunologists. We have further developed another algorithm to probe microtubule organizing center polarization, an important immunologic behavior. Ultimately, we were able to assign biologically-defined cell classes to 1.59 million of 2.19 million cells captured in tissue.
Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus, with up to 30% of LN patients progressing to end-stage kidney disease within ten years of diagnosis. Spatial relationships between specific types of immune cells and kidney structures hold valuable information clinically and biologically. Thus, we develop a modular computational pipeline to analyze the spatially resolved molecular features from high-plex immunofluorescence imaging data. Here, we present three modules of the pipeline, with the goal of achieving multiclass segmentation of renal cells and structures.
SignificanceManual annotations are necessary for training supervised learning algorithms for object detection and instance segmentation. These manual annotations are difficult to acquire, noisy, and inconsistent across readers.AimThe goal of this work is to describe and demonstrate multireader generalizations of the Jaccard and Sørensen indices for object detection and instance segmentation.ApproachThe multireader Jaccard and Sørensen indices are described in terms of “calls,” “objects,” and number of readers. These generalizations reduce to the equations defined by confusion matrix variables in the two-reader case. In a test set of 50 cell microscopy images, we use these generalizations to assess reader variability and compare the performance of an object detection network (Yolov5) and an instance segmentation algorithm (Cellpose2.0) with a group of five human readers using the Mann–Whitney U-test with Bonferroni correction for multiplicity.ResultsThe multireader generalizations were statistically different from the mean of pairwise comparisons of readers (p < 0.0001). Further, these multireader generalizations informed when a reader was performing differently than the group. Finally, these generalizations show that Yolov5 and Cellpose2.0 performed similarly to the pool of human readers. The lower bound of the one-sided 90% confidence interval for the difference in the multireader Jaccard index between the pool of human readers and the pool of human readers plus an algorithm were −0.019 and −0.016 for Yolov5 and Cellpose2.0, respectively.ConclusionsMultireader generalizations of the Jaccard and Sørensen indices provide metrics for characterizing the agreement of an arbitrary number of readers on object detection and instance segmentation tasks.
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