Modeling the potential of kidney disease with an integrated organoid omics map

Proteome of kidney organoids evolves with duration in culture. a) Volcano plot of proteomic differential expression analysis (log2 fold change of label-free quantification intensity comparing D29 with D21). Proteins are represented by dots and the black line illustrates the significance cut-off (two sided t test, FDR < 0.05 and s0 = 0.1). Red colored proteins meet the significance threshold. Examples of strongly regulated proteins are labeled. Blue colored and labeled dots represent proteins highlighted in (b). b) Immunofluorescence imaging of sectioned kidney organoids showing expression of (top panel) podocyte markers nephrin (NPHS1) and synaptopodin (SYNPO), and (bottom panel) cell structure and differentiation markers smooth muscle actin (ACTA2) and platelet-derived growth factor receptor alpha (PDGFRA), plus nuclear marker DAPI, n = 3, representative images shown, scale bar: 50 µm. c) Heatmap (maximum distance) of normalized protein expression (mean subtracted label-free quantification values (average of three replicates)) during organoid differentiation. The zoomed-in region expands on the clusters that undergo marked changes during differentiation. Five clusters (of size >25 proteins) were distinguished and are indicated with colored rectangles. These five clusters are shown in (d). d) GO enrichment analysis of unbiased clustering (clusters >25 proteins) of proteins during organoid differentiation (D21–D29) (Fisher’s exact test, FDR < 0.05). Credit: Nature Communications, doi: 10.1038/s41467-023-39740-7

Kidney organoids provide a promising platform in vitro to model the mechanisms of kidney disease, however, they are limited by an existing lack of knowledge of their inherent functional protein expression. In a new report in Nature Communications, Martiz Lassé and a team of scientists in medicine and kidney health, in Denmark, Germany, and the U.S., defined the organoid proteome and their transcriptome trajectories during culture, and upon exposure to the tumor necrosis factor alpha (TNFα); a cytokine stressor.

While older organoids increasingly deposited an extracellular matrix and expressed decreased glomerular proteins, the team focused on highly aggregated expressions of TNFα associated protein expression in human tubular and organoid kidney cell populations to emphasize the scope of the models to advance the development of relevant biomarkers. The results provide crucial evidence of the functional relevance of the kidney organoid model to recreate human kidney disease in the lab.

The organoid model system

Organoids form an integral in vitro model system to explore disease development. Kidney organoids have modeled kidney cancer, glomerular diseases, and polycystic kidney disease. These platforms also offer a promising screening tool for therapeutics, to model viral infection and organ cryopreservation in combination with organ-on-a-chip microfluidics.

The concept can improve the understanding of genetic kidney diseases, interrogate kidney development and molecular mechanisms underlying pathologies. Kidney organoids can be generated from human pluripotent stem cells to recapitulate the elements of pathology that are lacking in two-dimensional tissue culture models and in animal models.

Modeling the potential of kidney disease with an integrated organoid omics map
Integrated expression analysis of proteome-transcriptome trajectories over organoid culture duration. a) Scatterplot of RNA copy number (D21, D25, D29) and protein copy number with light blue color indicating high data point density and green for low density. Associated two-dimensional UniProt-keyword enrichment with basic functional terms, including “Differentiation,” “Glycolysis,” “Mitochondrion,” “TCA-cycle’ and “Protein-biosynthesis’ are highlighted in red. b) Barplots of basic functional terms from 2D UniProt-keyword enrichment (from a). c) Uniform Manifold Approximation and Projection (UMAP) representation of combined datasets from single cell transcriptomes of D25 untreated plus D24 TNFα-treated and vehicle control (VC) distinguished 14 cell type clusters. Visualization was carried out using Cell x Gene software; the contribution to the total cell number by each sample and cell type cluster is shown on the right. d) The summed protein expression direction of corresponding transcript markers which were used to define the 14 cell clusters. Proteins were classified as overexpressed (FDR < 0.05 and log2 fold-change >0), under-expressed (FDR < 0.05 and log2 fold-change <0) or not differentially expressed (FDR ≥ 0.05) between D21 and D29. e) Heatmap k-means clustering of bulk RNA and bulk protein of transcript cell-type markers for early glomerular epithelial 1 (EGE1), maturing podocyte (Podo), proximal tubular (PT) and stromal cells. The top 30 differentially expressed proteins D29 versus D21 (FDR < 0.01) were plotted. Credit: Nature Communications, doi: 10.1038/s41467-023-39740-7

During the experiments, Lassé and colleagues at first described the evolution of the organoid proteome and transcriptome as a function of cell culture duration, to identify organoid cell types with changing protein expression. The team compared the expression of podocyte-specific proteins in organoids with the expression of native glomeruli and cultured podocyte cells.

The team analyzed the reaction of organoids to TNFα to provide disease-relevant biomarkers for precision diagnostics. Kidney organoid characterization allowed the platform’s expansion for drug discovery, while assessing cellular biology underlying kidney disease to discover potential therapies. The outcomes provide a rich resource to the research community to promote kidney disease modeling, biomarker discovery, and therapeutic screening.

The duration of cell culture and the changing kidney organoid protein expression levels

After three weeks of kidney organoid culture, Lassé and colleagues performed proteomic and transcriptional profiling. The scientists identified and quantified 5,403 proteins using principal component analysis, the results highlighted key podocyte markers/indicators of glomerular differentiation such as nephrin and synaptopodin to decrease with time, to highlight a loss of podocytes or cell populations with time.

They observed the increased expression of structural proteins such as smooth muscle actin and key regulatory differentiation proteins platelet-derived growth factor receptor alpha, to highlight an increased production of the extracellular matrix. The scientists validated these observations with immunofluorescence. They similarly noted higher expression of collagen type I alpha chain and fibronectin type 1 to confirm the patterns of increased extracellular matrix protein production with increased duration of cell culture.

Modeling the potential of kidney disease with an integrated organoid omics map
Organoid proteome organization shows similarities with human kidneys. a Venn diagram of the organoid proteome compared with microdissected single glomeruli and tubules proteomes. Four compartments indicate proteins identified in (i) single glomeruli only, (ii) both single glomeruli and organoids, (iii) single tubules only, and (iv) both single tubules and organoids. b Over-representation plots corresponding to Venn diagram compartments in (a) for Gene Ontology (GO) terms related to biological process (BP), molecular function (MF), cellular component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Fisher’s exact test, adjusted p-value < 0.05). The x-axis corresponds to the combined score from EnrichR analysis. c Venn diagram (top) with associated over-representation plot (bottom) of terms uniquely mapping to the organoid proteome but not to cultured podocyte proteome. d Protein copy number based on intensity based absolute quantification (IBAQ) of podocyte markers expressed in organoids (n = 3) compared to human tissue (n = 2), mouse tissue (n = 3) and cultured human podocytes (undifferentiated and differentiated, both n = 3). Human (H); Mouse (M). Data are represented as mean ± SEM. Source data are provided as a Source Data file. Credit: Nature Communications, doi: 10.1038/s41467-023-39740-7

The experiments

The scientists explored organoid proteome-transcriptome integration to uncover the cellular origins of proteins. The results suggested the largest proteome changes to occur in the stromal cell population, while the early glomeruli epithelial cells and maturing podocytes were less represented with longer culture duration, in agreement with the decreased cell-specific protein expression analysis too. The scientists compared the organoid proteome organization with mature human kidney tissue, which showed similarities with native human kidneys to indicate how such kidney organoids faithfully represented most key proteins in native kidneys.

The role of TNFα in organoid culture

The team showed the response of organoids to TNFα by monitoring the expression of proinflammatory proteins. Notably, TNFα treated kidney organoids expressed key transcripts, including chemokine ligand 2, chemoattractant protein 1, and the tissue inhibitor of metalloproteinases. Lassé and colleagues reproduced TNFα induced gene expression to show a complex biological response of treated organoids, compared to the human podocyte model. The team demonstrated proteome alterations in TNFα induced organoids and revealed kidney cell-specific pathological mechanisms.

The TNF-dependent proteins also showed higher expression on a genetic level in various diseased kidney tissue, when compared to living donor tissue. While at the cellular level, the organoid TNF signature focused on gene activity, the comparative tissue TNF signature was more complex. Using literature-based networks, the scientists explored the unified interplay of the networks to identify sub-groups of individuals in disease cohorts with diverse pathomechanisms benefiting from a variety of targeted therapies.

Modeling the potential of kidney disease with an integrated organoid omics map
Omics-expanded organoid modeling potential of kidney disease. a) Number of proteins associated with monogenic kidney disease expressed in kidney organoids. b) Heatmap of protein expression of genes associated with nephrotic syndrome expressed during organoid differentiation, D21 to D29 (visualization using Kidney Disease Explorer c) Protein–protein interaction networks created with STRING database (V.11.0) and Cytoscape (V 3.8.2), demonstrating differences in extracellular matrix protein expression in aging (upper left) and TNFα-treated (upper right) organoids. As comparisons, corresponding networks of human FSGS tissue vs. control (lower left) as well as puromycin aminonucleoside (PAN)-treated rat kidney tissue vs. control (lower right) are illustrated. d) Schematic of how organoids can contribute to our understanding of kidney disease through the integration of pre-clinical models with human data to drive better outcomes for individuals with kidney disease. d) created using BioRender. Source data are provided as a Source Data file. Credit: Nature Communications, doi: 10.1038/s41467-023-39740-7


In this way, Martiz Lassé and colleagues showed how kidney organoids generated from human pluripotent stem cells have significant scope to advance the understanding of kidney development and disease. To successfully implement this concept as a model system, the scientists had to understand the complex structures and their relevance to human kidney disease. The experimental setup additionally provided insights to the pathobiology of TNFα associated kidney disease.

The study outcomes support the use of organoids to model complex human kidney diseases. By integrating the omics data derived from organoid disease models and other preclinical models, the team can drive better kidney outcomes for patients. The organoids can also capture critical elements of complex and heterogenous responses of the human kidney disease spectrum.

The omics map highlighted the capacity to trigger inflammation and fibrosis in organoids, even in the absence of immune cells to open the door to model more complex diseases with capacity to personalize disease modeling as well as therapeutic screening.

More information:
Moritz Lassé et al, An integrated organoid omics map extends modeling potential of kidney disease, Nature Communications (2023). DOI: 10.1038/s41467-023-39740-7

The Organoid Cell Atlas, Nature Biotechnology (2020). DOI: 10.1038/s41587-020-00762-x.

© 2023 Science X Network

Modeling the potential of kidney disease with an integrated organoid omics map (2023, September 4)
retrieved 5 September 2023

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.