open access publication

Article, 2024

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics

Plos One, ISSN 1932-6203, Volume 19, 5, 10.1371/journal.pone.0302853

Contributors

Moreno J. [1] Gluud L.L. 0000-0002-9462-4468 [2] [3] Galsgaard E.D. 0000-0002-0584-3803 [1] Hvid H. 0000-0002-3667-4141 [1] Mazzoni G. (Corresponding author) [1] Das V. 0000-0003-0614-0373 (Corresponding author) [1]

Affiliations

  1. [1] Novo Nordisk A/S
  2. [NORA names: Novo Nordisk; Private Research; Denmark; Europe, EU; Nordic; OECD];
  3. [2] Copenhagen University Hospital Hvidovre
  4. [NORA names: Capital Region of Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
  5. [3] University of Copenhagen
  6. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Background Chronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (LR) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts. Results The analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models. Conclusion Our study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.

Data Provider: Elsevier