Updates need for CRI 2025

[evelyn-schmidt]

Trajectory

• update Cytotrace to version 2
• RNA velocity ???
  -- would have to preprocess the data
  -- find R base tool??
• Add diffusion map

Differential expression analysis (or GSEA or BOTH?)

• Pseudobulk -- is this apart of the seurat version that we have (5)?
• https://satijalab.org/seurat/articles/de_vignette
• maybe the pseudobulk wll just give us the pathways we want

BCR and TCR

• update to latest scRepertoire version (if that is what we want to do)
• fix this

[evelyn-schmidt]

Reviewing the TCR/BCR section

The previous year after the creation of this section we reviewed it and decided that screportoire might not be giving the expected or correct results. This was the last planned section of the single cell days so we did not get to it last year. Below, I will outline some problems that @ksinghal28 and I saw upon review.

clonalQuant(combined.TCR, 
            cloneCall="nt", 
            chain = "both", 
            scale = FALSE) +
  clonalQuant(combined.TCR, 
              cloneCall="aa", 
              chain = "both", 
              scale = FALSE) +
  clonalQuant(combined.TCR, 
              cloneCall="gene", 
              chain = "both", 
              scale = FALSE) +
  clonalQuant(combined.TCR, 
              cloneCall="strict", # gene and nucleotide 
              chain = "both", 
              scale = FALSE)

Why is Rep5 ICBdT so high (should be depleted CD4 Tcells- are they Cd8 Tcells??)

Clonal length distribution plots incorrect? (First distribution is probably TRA sequence+NA, NA+TRB sequence, etc,)

BCR

combined.BCR_default <- combineBCR(BCR.contigs, 
                           samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", 
                                       "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"), 
                           threshold = 0.85)

>> length(unique(combined.BCR_default[[1]]$CTaa))
[1] 270
>>  length(unique(combined.BCR_default[[1]]$CTstrict))
[1] 262

combined.BCR <- combineBCR(BCR.contigs, 
                                  samples = c("Rep1_ICB", "Rep1_ICBdT", "Rep3_ICB", 
                                              "Rep3_ICBdT", "Rep5_ICB", "Rep5_ICBdT"), 
                                  threshold = 0.05)

> length(unique(combined.BCR_normal[[1]]$CTaa))
[1] 270
>> length(unique(combined.BCR[[1]]$CTstrict))
[1] 248

Why doesn't the number of clones change with the threshold? This was the original question that made us wonder if scRepertoire is working the way we wanted. When we change the threshold argument from the default of 0.85 to a more permissive 0.05 (threshold is The normalized edit distance to consider -- The higher the number the more similarity of sequence will be used for clustering), we see no change in the clones listed when plotting by AA. However, when we use the strict category we do see a change. Does this make sense? Do we want to want to see the clones with alpha/beta listed as NA?

from the documentation:

"Unlike combineTCR(), combineBCR produces a column CTstrict of an index of nucleotide sequence and the corresponding V gene. This index automatically calculates the Levenshtein distance between sequences with the same V gene and will index sequences using a normalized Levenshtein distance with the same ID. After which, clone clusters are called using the igraph::components() function. Clones that are clustered across multiple sequences will then be labeled with "Cluster" in the CTstrict header."

clonalCompare(combined.BCR_default, 
              top.clones = 10, 
              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
              cloneCall="aa", 
              graph = "alluvial") +
  clonalCompare(combined.BCR, 
                top.clones = 10, 
                samples = c("Rep3_ICB", "Rep3_ICBdT"), 
                cloneCall="aa", 
                graph = "alluvial")

clonalCompare(combined.BCR_default, 
              top.clones = 10, 
              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
              cloneCall="strict", 
              graph = "alluvial") +
clonalCompare(combined.BCR, 
              top.clones = 10, 
              samples = c("Rep3_ICB", "Rep3_ICBdT"), 
              cloneCall="strict", 
              graph = "alluvial")

[evelyn-schmidt]

Recreate this analysis from Freshour et al. 2023

After cell typing was completed, we also explored the BCR and TCR sequencing results. This analysis revealed little evidence of clonotype expansion of BCRs or TCRs across any of the conditions or replicates. For the TCR analysis, we excluded samples containing less than 1,500 TCR+ cells after filtering. We found that the majority of samples with less than 1,500 cells did not appear to have had their naive T cell populations captured during sequencing, which could lead to a skewed appearance of clonal expansion among the T cell populations that were captured (Figures S2A and S2B). For the remaining samples, there did not appear to be any dominant clonotypes detected (Figure S3A). The most commonly observed clonotype in any sample was present in only 41 cells and the vast majority of clonotypes were present in only one cell (Figure S3B). While no samples had evidence of dominant clonotypes, several samples did show evidence of cells with modestly expanded clonotypes (i.e., clonotypes detected in more than one cell) clustering together on the tSNE projection (Figure S4A). These clonotypes were largely found to be expressed in CD8 effector T cells, which showed evidence of co-expression of some exhaustion markers (Figures S4B, S5A, and S5B). For BCR analysis, most samples (11 of 15) had less than 1,000 BCR+ cells that passed filtering (Figure S6A). For samples where more than 1,000 cells passed filtering, there was little evidence of clonal expansion (Figure S6B).