TCRpheno applied to T cell fate

Overview

tcrpheno is an R package that applies a logistic regression model to the amino acid sequences of T-cell receptor complementarity-determining regions (CDRs) 1, 2, and 3. This model produces phenotype scores associated with specific T cell fates, providing insights into the potential functional trajectory of T cells based on their TCR sequences.

More information on individual phenotypes:

The tcrpheno package calculates four distinct scores, each linked to a potential T cell phenotype

TCRinnate: Higher scores suggest a greater likelihood of the T cell adopting an innate-like, PLZF-high phenotype, characteristic of mucosal-associated invariant T (MAIT) cells or invariant natural killer T (iNKT). This score is strongly influenced by features in CDR2α and specific TRAV gene usage.
TCR.8: Higher scores indicate a predisposition towards a CD8+ T cell fate over a CD4+ fate. TCRs with high TCR.8 scores tend to have a depletion of positive charge in the mid-region of their CDR3 loops.
TCRreg: Higher scores point to an increased probability of the T cell becoming a regulatory T cell (Treg), encompassing both CD4+ and CD8+ Treg populations. This is associated with increased hydrophobicity in CDR3β and CDR3α residues.
TCRmem: Higher scores suggest a T cell is more likely to differentiate into a memory cell rather than remaining naive. This score reflects a general propensity for T-cell activation and is influenced by features in both CDR3α and CDR3β. Notably, higher TCRmem scores correlate with increased T-cell activation even among T cells recognizing the same antigen and correspond to the strength of positive selection in the thymus.

Citation

If using tcrpheno, please cite the article: Lagattuta, K. et al. The T cell receptor sequence influences the likelihood of T cell memory formation. Cell Reports. 2025 Jan 28;44(1)

Installation

# Ensure 'remotes' is installed: install.packages('remotes')
remotes::install_github("kalaga27/tcrpheno")

Loading Data

his vignette uses example data provided by the scRepertoire package to demonstrate the workflow. For more details on scRepertoire’s example data and loading mechanisms, refer here. For the sake of comparison, we will also filter out any cell without clonal information.

# Load and normalize RNA
scRep_example <- readRDS("scRep_example_full.rds") %>%
    NormalizeData(verbose = F)

# Adding Clonal Information
scRep_example <- combineExpression(combined.TCR, scRep_example, cloneCall = "aa", group.by = "sample",
    proportion = TRUE)

# Filtering for single-cells with TCRs
scRep_example <- subset(scRep_example, cells = colnames(scRep_example)[!is.na(scRep_example$CTaa)])

Exporting Clonal Information for tcrpheno

The tcrpheno package requires TCR data in a specific format. The exportClones() function from scRepertoire can now output data directly in this tcrpheno format. This format includes separate columns for TRA V gene, TRA J gene, TRA CDR3 sequence, TRB V gene, TRB J gene, and TRB CDR3 sequence, along with a cell identifier.

exported_clones <- exportClones(scRep_example, write.file = FALSE, format = "tcrpheno")
exported_clones <- na.omit(exported_clones)
head(exported_clones)

##                      cell     TCRA_cdr3aa TCRA_vgene TCRA_jgene
## 1 P17B_AAAGCAACAGACAAAT-1  CALFTSGNTGKLIF     TRAV16     TRAJ37
## 2 P17B_AACCATGAGGCATGTG-1   CAVEDPRDYKLSF      TRAV2     TRAJ20
## 4 P17B_AAGCCGCCAATCTACG-1 CALSEARETGNQFYF     TRAV19     TRAJ49
## 5 P17B_AAGCCGCCATCCCACT-1   CAASINNNARLMF   TRAV13-1     TRAJ31
## 6 P17B_AAGCCGCGTCACCTAA-1  CAVQAGDSWGKLQF     TRAV20     TRAJ24
## 7 P17B_AAGGAGCGTTCTCATT-1  CATAPRDSWGKLQF     TRAV17     TRAJ24
##                                     TCRA_cdr3nt      TCRB_cdr3aa TCRB_vgene
## 1    TGTGCTCTCTTTACCTCTGGCAACACAGGCAAACTAATCTTT    CAIKGTGNGEQYF   TRBV10-3
## 2       TGTGCTGTGGAGGATCCTCGGGACTACAAGCTCAGCTTT CASSLGGAGGGYEQYF   TRBV11-2
## 4 TGTGCTCTGAGTGAGGCGAGGGAAACCGGTAACCAGTTCTATTTT  CASSQDADSFYEQYF    TRBV4-1
## 5       TGTGCAGCAAGTATAAATAACAATGCCAGACTCATGTTT    CASSPVRTDTQYF    TRBV5-4
## 6    TGTGCTGTGCAGGCCGGTGACAGCTGGGGGAAATTGCAGTTT   CASSLDGGSDTQYF   TRBV11-3
## 7    TGTGCTACGGCCCCCAGGGACAGCTGGGGGAAATTGCAGTTT  CASSLYDTNTGELFF    TRBV5-1
##   TCRB_jgene                                      TCRB_cdr3nt
## 1    TRBJ2-7          TGTGCCATCAAGGGGACAGGGAATGGTGAGCAGTACTTC
## 2    TRBJ2-7 TGTGCCAGCAGCTTGGGGGGCGCGGGTGGGGGCTACGAGCAGTACTTC
## 4    TRBJ2-7    TGCGCCAGCAGCCAAGATGCGGACAGCTTCTACGAGCAGTACTTC
## 5    TRBJ2-3          TGTGCCAGCAGCCCCGTTCGAACAGATACGCAGTATTTT
## 6    TRBJ2-3       TGTGCCAGCAGCTTAGACGGGGGCTCAGATACGCAGTATTTT
## 7    TRBJ2-2    TGCGCCAGCAGCTTGTACGACACAAACACCGGGGAGCTGTTTTTT

Generating Phenotype Scores

With the TCR data correctly formatted, we can use the score_tcrs() function from the tcrpheno package to calculate the phenotype scores.

tcrpheno.results <- score_tcrs(exported_clones, "ab")

## [1] "adding CDR1 and CDR2 based on V gene..."
## [1] "identifying amino acids at each position..."
## [1] "converting amino acids into Atchley factors..."
## [1] 18670
## [1] 18670
## [1] "adding interactions between adjacent residues..."
## [1] "TCRs featurized!"
## [1] "scoring TCRs..."
## [1] "all done!"

head(tcrpheno.results)

##                         TCR.innate     TCR.CD8    TCR.reg    TCR.mem
## P17B_AAAGCAACAGACAAAT-1 -0.0675532 -1.19523036  0.4049552 -0.1555845
## P17B_AACCATGAGGCATGTG-1  0.6501125  0.02883556 -1.1217330 -0.1431962
## P17B_AAGCCGCCAATCTACG-1  0.2681321  3.05374879 -0.8430967 -0.1919169
## P17B_AAGCCGCCATCCCACT-1 -0.1228840 -0.97613780  0.4996984 -1.2836692
## P17B_AAGCCGCGTCACCTAA-1 -0.6160124  0.27602943 -1.0244453  0.5860345
## P17B_AAGGAGCGTTCTCATT-1 -0.1305800 -0.06879752  0.3131880 -0.2055134

Adding to Single Cell Object

To visualize and analyze these TCR-derived phenotype scores in conjunction with gene expression data, we add them to the metadata of our Seurat object.

scRep_example <- AddMetaData(scRep_example, tcrpheno.results)

Visualizing Predictions

Now that the phenotype scores are part of the Seurat object, we can visualize them on dimensionality reduction plots, such as UMAPs. This helps to see if cells with particular TCR-derived phenotype scores cluster together or co-localize with known cell populations.

tcrpheno.plots <- FeaturePlot(scRep_example, features = c("TCR.mem", "TCR.reg", "TCR.CD8", "TCR.innate"))

lapply(tcrpheno.plots, function(x) {
    x + scale_color_viridis(option = "B")
}) -> tcrpheno.plots

wrap_plots(tcrpheno.plots)

Comparing With Gene Expression

A key aspect of integrating TCR phenotype scores is to compare them with the expression of known marker genes associated with different T cell states. This can help validate or provide biological context to the tcrpheno predictions.

RNA.plots <- FeaturePlot(scRep_example, features = c("CD4", "CD8A", "FOXP3", "KLRB1"), combine = FALSE)

lapply(RNA.plots, function(x) {
    x + scale_color_viridis(option = "B")
}) -> RNA.plots

wrap_plots(RNA.plots)

This concludes the vignette on applying tcrpheno to predict T cell fate from TCR sequences and integrating these predictions with single-cell RNA sequencing data.

Compiled: July 17, 2025