McGrady, M. | bioRxiv Science

bioRxiv2023

Characterizing tissue structures from spatial omics with spatial cellular graph partition

Spatial transcriptomic and proteomic measurements enable high-dimensional characterization of tissues. However, understanding organizations of cells at different spatial scales and extracting tissue structures of interest remain challenging tasks that require extensive human annotations. To address this need for consistent identification of tissue structures, in this work, we present a novel annotation method Spatial Cellular Graph Partitioning (SCGP) that allows unsupervised identification of tissue structures that reflect the anatomical and functional units of human tissues. We further present a reference-query extension pipeline SCGP-Extension that enables the generalization of existing reference tissue structures to previously unseen samples. Our experiments demonstrate reliable and robust partitionings of both spatial transcriptomics and proteomics datasets encompassing different tissue types and profiling techniques. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease and skin disorder, underscoring its potential in facilitating spatial analysis and driving new discoveries.

bioinformatics↗

bioRxiv2023

Spatial proteomics of human diabetic kidney disease, from health to class III

Aims/HypothesisDiabetic kidney disease (DKD) remains a significant cause of morbidity and mortality in people with diabetes. Though animal models have taught us much about the molecular mechanisms of DKD, translating these findings to human disease requires greater knowledge of the molecular changes caused by diabetes in human kidneys. Establishing this knowledge base requires building carefully curated, reliable, and complete repositories of human kidney tissue, as well as tissue proteomics platforms capable of simultaneous, spatially resolved examination of multiple proteins. MethodsWe used the multiplexed immunofluorescence platform CO-Detection by indexing (CODEX) to image and analyze the expression of 21 proteins in 23 tissue sections from 12 individuals with diabetes and healthy kidneys (DM, 5 individuals), DKD classes IIA, and IIB (2 individuals per class), IIA-B intermediate (2 individuals), and III (one individual). ResultsAnalysis of the 21-plex immunofluorescence images revealed 18 cellular clusters, corresponding to 10 known kidney compartments and cell types, including proximal tubules, distal nephron, podocytes, glomerular endothelial and peritubular capillaries, blood vessels, including endothelial cells and vascular smooth muscle cells, macrophages, cells of the myeloid lineage, broad CD45+ inflammatory cells and the basement membrane. DKD progression was associated with co-localized increase in collagen IV deposition and infiltration of inflammatory cells, as well as loss of native proteins of each nephron segment at variable rates. Compartment-specific cellular changes corroborated this general theme, with compartment-specific variations. Cell type frequency and cell-to-cell adjacency highlighted (statistically) significant increase in inflammatory cells and their adjacency to tubular and SMA+ cells in DKD kidneys. Finally, DKD progression was marked by substantial regional variability within single tissue sections, as well as variability across patients within the same DKD class. The sizable intra-personal variability in DKD severity impacts pathologic classifications, and the attendant clinical decisions, which are usually based on small tissue biopsies. Conclusions/InterpretationsHigh-plex immunofluorescence images revealed changes in protein expression corresponding to differences in cellular phenotypic composition and microenvironment structure with DKD progression. This initial dataset demonstrates the combined power of curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools in revealing pathophysiology of human DKD.

molecular biology↗