Image_1_Uncovering a novel role of focal adhesion and interferon-gamma in cellular rejection of kidney allografts at single cell resolution.jpeg

BackgroundKidney transplant recipients are currently treated with nonspecific immunosuppressants that cause severe systemic side effects. Current immunosuppressants were developed based on their effect on T-cell activation rather than the underlying mechanisms driving alloimmune responses. Thus, understanding the role of the intragraft microenvironment will help us identify more directed therapies with lower side effects.MethodsTo understand the role of the alloimmune response and the intragraft microenvironment in cellular rejection progression, we conducted a Single nucleus RNA sequencing (snRNA-seq) on one human non-rejecting kidney allograft sample, one borderline sample, and T-cell mediated rejection (TCMR) sample (Banff IIa). We studied the differential gene expression and enriched pathways in different conditions, in addition to ligand-receptor (L-R) interactions.ResultsPathway analysis of T-cells in borderline sample showed enrichment for allograft rejection pathway, suggesting that the borderline sample reflects an early rejection. Hence, this allows for studying the early stages of cellular rejection. Moreover, we showed that focal adhesion (FA), IFNg pathways, and endomucin (EMCN) were significantly upregulated in endothelial cell clusters (ECs) of borderline compared to ECs TCMR. Furthermore, we found that pericytes in TCMR seem to favor endothelial permeability compared to borderline. Similarly, T-cells interaction with ECs in borderline differs from TCMR by involving DAMPS-TLRs interactions.ConclusionOur data revealed novel roles of T-cells, ECs, and pericytes in cellular rejection progression, providing new clues on the pathophysiology of allograft rejection.</p

Distance trees of expression profiles.

<p>We constructed neighbor-joining trees based on the correlation between expression values (FPKM>1.0) between samples, with 1 minus Spearman's rho defining the distance. Colors denote library construction methods (poly-A: blue, DSN: red). We divided transcribed loci into (a) protein coding genes with RNA-Seq support, either annotated by EnsEMBL in dog or EnsEMBL in the human orthologous regions. Replicates cluster together, so do the library constructions methods poly-A and DSN, as well as related tissues, such as heart and muscle; (b) antisense transcripts, that overlap at least one exon of a protein coding gene, as defined in (a). With the exception of testis, poly-A and DSN separate the samples, with both the poly-A and DSN sub-trees maintaining closer relationships between the related tissues heart and muscle; (c) spliced intergenic loci, excluding sequences that have coding potential. Similar to protein coding genes, the poly-A and DSN group by tissue first, with the exception of kidney DSN; and (d) intergenic and uncharacterized single-exon transcript loci. In this set, DSN and poly-A are, similar to antisense loci, the most dominant factor when grouping samples.</p

Location of lincRNAs and single-exon intergenic non-coding transcripts.

<p>(a) We show the mapping of lincRNAs, broken down by sample across chromosome 1. (b) Histogram of distance from intergenic transcripts to the next known transcribed element in both the 5′ (left) and 3′ (right) direction. The average distance is around 150,000 nucleotides, suggesting that these loci are not closely associated with known genes.</p

Transcribed loci per tissue and library preparation.

<p>N/A, due to poor alignment performance, this library was excluded from subsequent analyses.</p

Comparison of canFam2.0 and canFam3.1.

<p>Comparison of canFam2.0 and canFam3.1.</p