Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

Abstract

Multi-modal single-cell technologies capable of simultaneously assaying gene expression and surface phenotype across large numbers of immune cells have described extensive heterogeneity within these complex populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX) profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph analysis or "CoNGA"---that identifies correlations between GEX profile and TCR sequence through statistical analysis of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a potential previously undescribed lineage of "natural lymphocytes"; a striking association between usage of a specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between TCR sequence and cellular state in single-cell applications..

Two types of human TCR differentially regulate reactivity to self and non-self antigens

“…We filtered out barcodes associated with nonunique cells or did not have a resolved CDR3 amino acid or nucleotide sequence. As previously reported (Schattgen et al, 2021), we found substantial non-specific binding in donor 1 and excluded this sample.…”

SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8 T cells

“…To correct erroneous or missing dextramer assignments for individual cells within a clone we assign each T cell a specificity of the majority of cells from this clone. To measure the distance between TCR α/β clonotypes and plot logos for dominant motifs we used the TCRdist algorithm implementation and plotting functions from conga python package 58 . Sequence similarity network analysis and visualizations were performed with the igraph R package 59 and gephi software 60 .…”

Convergent clonal selection of donor- and recipient-derived CMV-specific T cells in hematopoietic stem cell transplant patients

“…TCR sequence matching was performed using the TCRdist algorithm as implemented in the Clonotype Neighbor Graph Analysis (CoNGA) python package's find_significant_tcrdist_matches function (https://github. com/phbradley/conga) (48). In this approach, the TCRdist score for a match is compared to a background distribution of TCRdist scores for the same TCRs matched to random TCR sequences generated using a probabilistic model of the V(D)J recombination process.…”