Monzó et al., Dietary restriction mitigates the age-associated decline in mouse B cell receptor repertoire diversity, Cell Reports (2023), https://doi.org/10.1016/j.celrep.2023.112722
Immunosenescence is the decline in adaptive immune function with age that increases vulnerability to infection and participates in the feedback cycle that decreases health in old age.
In this study we have 45 mice corresponding to 8 biological conditions. We performed BCR-Seq based on the work by Turchainova et al 2017 and prepared libraries from frozen spleen and ileum.
General processing steps in analysis_BCR_DR/scripts/CMD_SLURM/ call into CMD_FOF:
Unzip fastq files
Filter by quality 20
Map PCR2 primers (internal barcodes) to identify which reads correspond to each mouse
Pair Forward and Reverse reads with the internal barcode information
Split reads into fastqs corresponding to each mouse
Extract UMI sequence from begining of Forward read
Write the UMI information on the Reverse read
Parse read headers and include information of file of origin
Concatenate Forward and Reverse reads of the same mouse that come from different origins
Build consensus from reads sharing the same UMI
Pair consensus from forward and reverse
Assemble forward and reverse into a merged full read
Map PCR1 primers to identify the isotype of each molecule
Transform to fasta
Parse the isotype table
Assign V-D-J genes
Generate ChangeO database for analysis
Extract only productive V-D-J rearrangements
Use machine learning and hierarchical clustering to identify the threshold that splits each sample into clone and non-clone molecules
Identify novel alleles and reassign genotypes including this information
Using genotypes and threshold, define which reads correspond to each clone
Create final germlines for analysis
We calculate and analyze many different parameters, gross and individually per isotype (and organ), and then all together we evaluate them in the context of the pathologies observed in the mice in study.
[SPL|ILE]_parser_tigger_all.R = Find novel alleles
[SPL|ILE]_novelAlleles_plot.R = Plotting alleles identified during germline anotation for validation
[SPL|ILE]_parser_novelAlleles_all.R = Plot all novel alleles and genotypes
explore_general.ipynb = Quality control, compare clonal assignment methods and thresholds, number of new alleles per sample
hill_diversity_[SPL|ILE]_studio.Rmd = Estimate alpha diversity and clonal abundance
[SPL|ILE]_Alpha_diversity.ipynb = Plotting Hill, Alpha boxplots, Kruskal with Mann Whitney, 2-way ANOVA
isotypes_alpha_calculate.R = Estimate alpha diversity per isotype
Isotype_alpha_[SPL|ILE].ipynb = Plotting Hill, Alpha boxplots, 2-way ANOVA and linear regression through age, Kruskal with Mann Whitney, all by isotypes
rdi_beta_[SPL|ILE].Rmd = Use RDI to calculate beta diversity, whole and by isotype. PCOA and dendogram
RDI_plot_analysis.ipynb = Plot boxplots, 2-way ANOVA and linear regression through age, Kruskall with Mann Whitney
RDI_isotypes.ipynb = Plot boxplots, 2-way ANOVA and linear regression through age, Anova between diets. By isotype
Clonal_Abundance.ipynb = Plot log abundance curves, extract p20 (sum of frequencies from all clones with rank below or equal to 20), plot boxplots, Kruskall with Mann Whitney, 2-way ANOVA (no age effect, therefore no linear regression)
ClonalAbundance_isotypes_calc.R = Calculate clonal abundance per isotype
ClonalAbundance_isotypes.ipynb = Plot log abundance curves, extract p20, boxplots, Kruskall with Mann Whitney, 2-way ANOVA (no age effect, therefore no linear regression)
stacked_isotypes.ipynb = Calculate frequency of isotypes, boxplots, stacked plots, Kruskall with Mann Whitney, 2-way ANOVA (no differences by age, so no linear regression), all by isotype
mutational_load_quantification.R = Calculate mutational load for IMGT_V for both synonymous and non-synonymous
SHM_study.ipynb = Stats for SHM calculated in R, 2-way ANOVA, Kruskall with Mann Whitney, boxplots, linear regression. Also for isotypes
NaiveClassSwitch.ipynb = Find Class switch using Isotype and SHM status, linear regression, 2-way ANOVA, Kruskall with Mann Whitney
CDR3_length.ipynb = Plot gaussian curves by biorep, compare mu and sigma between groups using Kruskall with Mann Whitney, use KW to compare the full curves, compare ratio of number of significantly different biological replicates comparisons between ages and diets using linear regression and fisher tests
CDR3_length-isotypes.ipynb = Plot gaussian curves by biorep, compare mu and sigma between groups using Kruskall with Mann whithey, KW to compare full curves, ratio using linear regression and fishers, for all isotypes
VJ_geneUsage.Rmd = Calculate gene usage for tissues and isotypes
VJusage.ipynb = Plot heatmaps for biorep and means, 2-way ANOVA
VJusage-isotypes.ipynb = Plot heatmaps for biorep and means, Kruskall with Mann Whitney, linear regression, 2-way ANOVA for all isotypes
Selection_pressure.R = testing
merging_param_patholo.ipynb = Formats F2 tumor load, SPL alpha diversity (richness, shannon, simpson), clonal abundance, SHM frequency (synonymous, non-synonymous, and mutational status 1 mutated, more than 0.01 mutation frequency, and 0 non mutated), isotypes relative frequency, class switch status, F2 pathology missing many other things to calculate like RDI, isotype specific data, VJ usage etc
pathology_alpha_stats.Rmd = GLME of alpha diversity to pathology burden and presence/absence
tumour_alpha_stats.Rmd = GLME of alpha diversity to tumor burden and presence/absence
AlphaSPL_pathology.ipynb = Boxplots of alpha diversity with specific pathologies/tumors to visualize association
mixOmics_igseqmicrobiome.Rmd = Diablo package to merge microbiome and spleen igseq clones, splsda, PC, auroc, circos, variables contributions, PC contributions
mixomics_ilesplmicro.Rmd = Mixomics also including ileum igseq, comparing clones with spleen and microbiome
morbidity_BCRparam.ipynb = Calculating morbidity score, OLS to p20, mufreq, mutstatus, relisotypes, naiveperc, CSperc, alpha; using rsquared for variance explained, spearman to know positive and negative correlation This plots and calculations should be repeated with all the parameters to predict morbidity score