Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry
Nikhil Singh, … , Gilbert W. Moeckel, Lloyd G. Cantley
Nikhil Singh, … , Gilbert W. Moeckel, Lloyd G. Cantley
Published June 20, 2019
Citation Information: JCI Insight. 2019;4(12):e129477. https://doi.org/10.1172/jci.insight.129477.
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Research Article Nephrology

Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry

Abstract

An incomplete understanding of the biology of the human kidney, including the relative abundances of and interactions between intrinsic and immune cells, has long constrained the development of therapies for kidney disease. The small amount of tissue obtained by renal biopsy has previously limited the ability to use patient samples for discovery purposes. Imaging mass cytometry (IMC) is an ideal technology for quantitative interrogation of scarce samples, permitting concurrent analysis of more than 40 markers on a single tissue section. Using a validated panel of metal-conjugated antibodies designed to confer unique signatures on the structural and infiltrating cells comprising the human kidney, we performed simultaneous multiplexed imaging with IMC in 23 channels on 16 histopathologically normal human samples. We devised a machine-learning pipeline (Kidney-MAPPS) to perform single-cell segmentation, phenotyping, and quantification, thus creating a spatially preserved quantitative atlas of the normal human kidney. These data define selected baseline renal cell types, respective numbers, organization, and variability. We demonstrate the utility of IMC coupled to Kidney-MAPPS to qualitatively and quantitatively distinguish individual cell types and reveal expected as well as potentially novel abnormalities in diseased versus normal tissue. Our studies define a critical baseline data set for future quantitative analysis of human kidney disease.

Authors

Nikhil Singh, Zachary M. Avigan, Judith A. Kliegel, Brian M. Shuch, Ruth R. Montgomery, Gilbert W. Moeckel, Lloyd G. Cantley

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Figure 5

Single-cell segmentation, characterization, and quantification of a representative region imaged with IMC.

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Single-cell segmentation, characterization, and quantification of a repr...
(A and B) Merged, pseudocolored images showing the entirety (A) and inset (B) of a region of a nephrectomy sample imaged by IMC, with selected markers depicted as indicated. (C–F) Probability images generated from a machine-learning algorithm applied to processed multiplexed images, in order to define pixels with the highest probability of belonging to nuclei (C), endothelial cells (D), tubular cells (E), and stromal cells, mesangium, and podocytes (F). These probability images were used as the basis to generate a single-cell segmentation mask. (G) Single-cell segmentation mask, with borders defined for each cell, and colors depicting tubular, endothelial, and all other cell types. (H and I) The segmentation mask in G was used as the basis for analysis of raw data from this IMC experiment. Based on raw data from each of the 23 input channels, each individual cell was clustered by expression by HistoCAT and manually assigned a phenotype. Resultant pseudocolored images are depicted. (H) Depicts cells assigned to tubular phenotypes, as well as vascular cells, immune cells, stromal cells, podocytes, and endothelial cells. (I) Shows specific tubular subtypes, as indicated. Colors were selected to approximate those used in the raw data in B. (J) Two-dimensional t-distributed stochastic neighbor embedding (t-SNE) projection showing the clusters and relative abundances of cells, their phenotypic relationship to each other, as well as their manually assigned phenotypic classes, with colors indicated on the associated legend. Four separate immune clusters were manually grouped together as one in this projection (cyan blue). (K) Quantification of the ratio of cells counted versus those calculated by the above machine-learning methodology for 3 selected regions. Bars indicate mean ± standard error of the mean. (L) Comparison of cells positive for the indicated markers detected either by manual counting (circles) or using the automated methodology (triangles). Mean values are shown by bars. n = 3. Statistical comparisons were made using Wilcoxon’s matched-pairs signed-ranked test, with P values indicated. Tub, tubular; Endothel., endothelial; Vasc, vascular; Strom., stromal; Podo., podocyte; PT, proximal tubule; tALH, thick ascending limb of the loop of Henle; DCT, distal convoluted tubule; CT/CD, connecting tubule and collecting duct; AQP1, aquaporin-1; AQP2, aquaporin-2; CALB, calbindin; CK7, cytokeratin-7; MEG, megalin; aSMA, α-smooth muscle actin; THP, Tamm-Horsfall protein; NES, nestin. Scale bars: 600 μm (A) and 150 μm (B).