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Abstract: TH-OR50

Single Nuclei RNA-Sequencing Identifies Distinct Immune Profiles in Interstitial Fibrosis and Tubular Atrophy Human Allografts Following 15 Months Post-Transplantation

Session Information

Category: Transplantation

  • 2001 Transplantation: Basic

Authors

  • Rousselle, Thomas, University of Maryland Baltimore, Baltimore, Maryland, United States
  • McDaniels, Jennifer M., University of Maryland Baltimore, Baltimore, Maryland, United States
  • Shetty, Amol C., University of Maryland Baltimore, Baltimore, Maryland, United States
  • Bardhi, Elissa, University of Maryland Baltimore, Baltimore, Maryland, United States
  • Kuscu, Cem, The University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee, United States
  • Kuscu, Canan, The University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee, United States
  • Talwar, Manish, The University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee, United States
  • Muthukumar, Thangamani, Cornell University, Ithaca, New York, United States
  • Eason, James D., The University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee, United States
  • Maluf, Daniel G., University of Maryland Baltimore, Baltimore, Maryland, United States
  • Mas, Valeria R., University of Maryland Baltimore, Baltimore, Maryland, United States
Background

The cellular immune response associated with interstitial fibrosis and tubular atrophy (IFTA) following kidney transplantation is unclear. This study utilized snRNAseq to uncover the immune landscape between normal and IFTA human allografts after 15-months post-transplantation (PT). Immune profiling is validated using imaging mass cytometry (IMC).

Methods

snRNAseq was performed for normal (n=3) and IFTA (n=5) human kidney allografts. Differences in ECM production and immune cell distribution led to the division of two IFTA groups: low vs high ECM (L-ECM vs H-ECM). Ligand receptor analysis was performed using the LRdb package in R and an interaction score was computed. IMC detected immune cells and spatial distribution. IMC images were processed in QuPath using Ir192 nuclei stain to detect cells. Antibody expression per cell was quantified by an artificial neural network and significant values (p≤0.05) used to annotate cellular identity.

Results

snRNAseq revealed 12 distinct immune subclusters. Higher proportions of monocytes (MO1/MO2), dendritic, and mast cells were detected in L-ECM whereas B- and T-cells were more abundant in H-ECM. Congruent with snRNAseq results, B- and T-cells were largely detected in H-ECM. However, memory T-cells (CD3+CD4+CD45RO+) were more abundant in L-ECM than H-ECM. In addition, IMC revealed a distinct subpopulation of double negative T-cells (CD3+CD4-CD8-) that was not previously explored in snRNAseq. Significant proportions of macrophages (CD68+) were found to be increased in L- and H-ECM, indicating its key role in pathogenesis.

Conclusion

Defining the immune cell landscape will uncover novel immune to kidney cell interactions to target fibrogenesis and improve long-term outcomes.

Funding

  • NIDDK Support