Reference-based comparison of adaptive immune receptor repertoires

Abstract

B- and T-cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters (e.g., clonal diversity, germline usage). Here, we introduce immuneREF: a quantitative multi-dimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. immuneREF is implemented in an R package and was validated based on detection sensitivity of immune repertoires with known similarities and dissimilarities. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2400 datasets from individuals with varying immune states (healthy, [autoimmune] disease and infection [Covid-19], immune cell population). Importantly we discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF implements population-wide analysis of immune repertoire similarity and thus enables the study of the adaptive immune response across health and disease states.

CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching

“…Due to flexible specification of summary statistics and output, CompAIRR is easily integrated with any tool capable of reading in either (i) a pairwise distance matrix containing cross-AIRR matches, (ii) a matrix showing individual AIR presence in one or more AIRRs or (iii) an AIRR-compliant TSV file containing (approximately) matching AIRs between AIRRs. This allows accelerating a variety of analyses where AIRR comparison is a core computational component, including AIRR similarity ( Weber et al , 2022 ) and clustering ( Rempała and Seweryn, 2013 ; Shugay et al , 2015 ), phylogenetic clustering ( Hoehn et al , 2022 ), graph analysis ( Madi et al , 2017 ; Miho et al , 2019 ; Pogorelyy et al , 2019 ) and immune state classification ( Emerson et al , 2017 ).…”

“…CompAIRR (1.3.1) was benchmarked against VDJtools (1.2.1) ( Shugay et al , 2015 ), immunarch (0.6.5) ( Nazarov et al , 2019 ) and immuneREF (0.5.0) ( Weber et al , 2022 ) by calculating the pairwise AIRR overlap of datasets ranging from 10 to 10 4 AIRRs. Each AIRR consisted of 10 5 amino acid AIR sequences generated using OLGA (1.2.2) ( Sethna et al , 2019 ) with the default human IgH CDR3 model.…”

“…We here present CompAIRR, a tool that allows to compute AIRR intersections up to 1000-fold faster than current implementations ( Nazarov et al , 2019 ; Shugay et al , 2015 ; Weber et al , 2022 ). In contrast to existing tools, CompAIRR supports both exact and approximate sequence matching between AIRs when determining AIRR overlap.…”

Multi-omic data integration study of immune system alterations in the development of minimal hepatic encephalopathy in patients with liver cirrhosis

“…This is in line with recent results on larger cohorts. Specifically, our findings suggest that for autoimmune diseases, the immune signal is very weak if not isolated by cell type for example 295 . (2) Another reason could be the low sample number.…”

“…(3) It may also be that the proposed features (repertoire overlap, repertoire diversity, network analysis, k-mer, and comparison with existing databases) do not capture the full biological heterogeneity of TCR repertoires. However, we have recently shown that these features cover a large part of immune repertoire diversity 295 . Our pipeline can be applied to any bulk RNA-seq dataset obtained from a sample containing T cells, thanks to the Nextflow implementation.…”