Additional file 1 of B cell receptor repertoire analysis in primary Sjogren’s syndrome salivary glands identifies repertoire features associated with clinical activity
Creators: name: Chang, Ling
name: Zheng, Zihan
name: Zhou, Yiwen
name: Liu, Kun
name: Li, Yinong
name: Zhong, Bing
name: Zhao, Zihua
name: Chen, Chengshun
name: Qian, Can
name: Ni, Qingshan
name: Zou, Qinghua
name: Wu, Yuzhang
name: Li, Jingyi
name: Zou, Liyun
Publisher: name: figshare
Year: 2024
Subjects: subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 1: Figure S1. Gene expression differences between B cell populations in labial and peripheral blood samples. A) Heatmap of the top 50 differentially expressed genes marking each of the three cell types we annotated. B-D) Top differentially expressed genes among naïve B cells (B), memory B cells (C), and plasma cells (D) between peripheral blood and SG samples. DEGs calculated using Wilcoxon test, with adjusted p values &lt; 0.05. Genes expressed in fewer than 20% of cells of at least one group were excluded. Figure S2. BCR clustering based on IgH CDR3 AA sequence. A) Network visualization as in Fig. 2A, annotated according to patient origin. B) A) Network visualization as in Fig. 2A, annotated according to heavy chain isotype usage. Figure S3. BCR clustering and associated isotypes using strict clustering of paired IgH and IgL/IgK chains. Network visualization of BCR repertoire sequences clustering by paired CDR3 amino acid sequence; sequences with Levenshtein distance between 1 and 3 are connected by edges. A) Nodes annotated according to patient origin. B) Nodes colored according to heavy chain isotype. C) Nodes colored according to cell type. Figure S4. Plasma cell differentiation in pSS SGs does not form a continuous trajectory. A) Trajectory analysis of all labial gland B cells using slingshot infers a lineage connecting naïve, memory, and plasma states. B) Stream plot of the distribution of each cell type in each bin of the pseudotime trajectory. C) Pyramid heatmap of the number of differentially expressed genes from bin-to-bin along the pseudotime trajectory. A large number of DEGs can be seen in the step that would cross from memory cell to plasma cell, causing an inferred break in the trajectory. D) Relative step change across each pseudotime step shows a break in the trajectory corresponding to the plasma cell crossover point. Since currently available trajectory inference algorithms implicitly presume continuity, a lack of continuity in a real-world dataset will likely lead to the trajectory inference being error-prone and oftentimes nonsensical. Figure S5. LASSO selection of key pathway features. To identify genes associated with pseudotime progression from each of the four pathways noted, we used LASSO for feature selection based on the scaled gene expression of each feature. A-D) Mean-squared error plots for each pathway. E-H) Correlation coefficients of each model with respect to pseudotime progression. While the model is unsurprisingly noisy given the technical dropout in single cells, correlation values are at least better than 0.6 for these four pathways. Figure S6. Gene expression change with respect to pseudotime in selected pathways. A-D) Scaled gene expression patterns over the course of trajectory progression of LASSO-selected features identified in each of the four pathways selected. Figure S7. pSS patient glandular BCR counts is only weakly associated with disease activity metrics. A) Correlation between total log-normalized IgH read counts and ESSDAI in labial (top) and parotid (bottom) samples. Non-pSS samples were excluded. Correlations given as Pearson’s R. B) Correlation as in (A) of log-normalized IgK light chain counts and ESSDAI. C) Correlation as in (A) of log-normalized IgL light chain counts and ESSDAI. Figure S8. pSS patient BCR diversity is not clearly associated with disease activity metrics. A) Correlation between count-adjusted Shannon entropy of IgH CDR3 AA sequences and ESSDAI in labial (top) and parotid (bottom) samples. Non-pSS samples were excluded. Correlations given as Pearson’s R. B) Correlation as in (A) of count-adjusted Shannon entropy of IgK light chain CDR3 AA and ESSDAI. C) Correlation as in (A) of count-adjusted Shannon entropy of IgL light chain CDR3 AA and ESSDAI. Figure S9. BCR isotype usage rates in salivary glands. Bar plots of isotype usage rates for 6 other Ig isotypes compared between pSS patients and controls for both labial and parotid glands (see also Fig. 4C, D). One other isotype, IgE, went essentially undetected and was excluded from analysis. Figure S10. BCR repertoire sharing between parotid and labial glands. A-C) Bar plot of repertoire similarity of IgH (A), IgK (B), and IgL (C) CDR3 AA sequences between labial and parotid glands of pSS patients and controls, calculated using Morisita-Horn similarity (MHS). Only IgK shows a significant difference between the two groups, with pSS samples being more dissimilar compared with controls. D-F) Differences in adjusted Shannon entropy between labial and parotid gland samples of pSS patients and controls for IgH (D), IgK (E), and IgL (F) CDR3 AA sequences. p values calculated using Student’s two-tailed t-test.