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Supplementary Information for Single Cell Resolution Landscape of Equine Supplementary Information for Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse immune cell types including T-bet+ B cells Roosheel S. Patel, Joy E. Tomlinson, Thomas J. Divers, Gerlinde R. Van de Walle, Brad R. Rosenberg Corresponding Author: Brad R. Rosenberg E-mail: [email protected] Figures S1 to S6, Tables S1 Other supplementary materials for this manuscript include the following: Differential gene expression Datasets S1-5, 7-8 Dataset S1. Differentially expressed gene marker lists for major cell groups Dataset S2. Differentially expressed gene marker lists for monocyte and dendritic cell clusters Dataset S3. Differentially expressed gene marker lists for monocyte cell clusters Dataset S4. Differentially expressed gene marker lists for dendritic cell clusters Dataset S5. Differentially expressed gene marker lists for B cell clusters Dataset S7. Differentially expressed gene marker lists for CD3+ PRF1+ lymphocyte cell clusters Dataset S8. Differentially expressed gene marker lists for CD3+ PRF1- lymphocyte cell clusters Dataset S6. Genome annotation file specifying custom immunoglobulin gene entries; adapted from Wagner et al. [34] A Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Subject 7 2000 1500 **** **** **** **** **** **** **** 1000 Unique genes detected 500 0 ESAT workflow: + + + + + + + Median genes detected: 607 928 560 964 596 881 565 948 575 987 560 962 590 996 B chr6: 35293801 - 35321000 CD4 1000 800 600 400 Subject 1 200 0 1500 1000 500 Subject 2 0 800 600 400 Subject 3 200 0 1500 1000 500 Subject 4 0 2000 1500 Coverage 1000 (read count) 500 Subject 5 0 2000 1500 1000 500 Subject 6 0 1500 1000 500 Subject 7 0 Figure S1. Optimized scRNA-seq data processing workflow improves per cell gene detection. (A) Violin plot of number of unique genes detected per cell across each study subject (N = 7) using standard CellRanger (10X Genomics) workflow versus modified workflow incorporating ESAT. Box plot indicate median, 25th percentile and 75th percentile. Unpaired t-tests were conducted on a per subject basis, **** p < 1 x 10-15. (B) scRNA-Seq read mapping pattern at the CD4 locus, known to be abundantly expressed in equine PBMC, demonstrating majority of reads map directly down- stream of reference transcript annotation. A 3 ïve 4 11 17 3 25 + 23 2 ï 11 8 9 + ï 23 + 15 0 7 1 + 24 1 5 21 0 15 + 13 14 12 16 8 21 16 + 20 14 10 + ï 7 12 18 9 + 5 10 26 18 2 26 19 13 + 19 22 22 6 + + 20 17 25 HSC 24 6 4 B otalSeqB otalSeqB T T otalSeqB T ol ol r r ol r otalSeqB otalSeqB T T otalSeqB O T otalSeqB otalSeqB otalSeqB otalSeqB otalSeqB otalSeqB otalSeqB R T T T T T T T otalSeqB otalSeqB T T X4 XP3 O O CD14 CD4 CD45RA CD45 CD25 CD127 CD16 CD56 CD3 CD8a IGHD IGHM IGHG1 IGHG3 XBP1 JCHAIN IGHA1 CD14 MX1 IFITM3 FCGR3A CX3CR1 CLEC9A CADM1 FCER1A CD1C TCF4 LILRA4 NCAM1 NKG7 CCR7 TCF7 F IL2RA TRDC TRGC2 MKI67 PCNA S CD19 CD15 3 11 23 8 0 1 15 21 14 26 19 22 4 17 2 9 7 5 12 16 10 18 13 6 20 24 25 5 6 7 8 0 1 2 Figure S2. Human reference scRNA-seq clustering results and annotation. A 15000 10000 UMI/cell 5000 0 0 9 15 22 25 27 30 Cluster number (B cell major cell group) B Subject 1 Subject 2 Subject 3 9 14 10 Immunoglobulin isotype Subject 4 Subject 5 Subject 6 IGHM IGHG5 IGHG4 15 10 14 IGHG3 IGHG1 IGHE Subject 7 IGHA 12 Figure S3. B cell quality control metrics and antibody secreting cell immunoglobulin isotype usage. (A) Violin plot of UMI (tran- script) counts per cell across B cell clusters identified by unsuper- vised clustering. Cluster 25 was excluded from downstream analysis due to insufficient UMI counts. (B) Immunoglobulin isotype usage in antibody secreting cells (cluster 27) as determined by scRNA-Seq gene expression data. Center value indicates number of antibody secreting cells (cluster 27) detected by individual subject. A B Total PBMC Lin-PanIg+ B cells Lin-PanIg+ B cells Total PBMC Lin-PanIg+ B cells T-bet+ B cells T-bet+ 5.99 38.2 31.8 T-bet+ 20.4 1.17 35.0 Subject 1 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 11.4 41.0 14.7 11.1 34.4 44.0 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 9.96 26.9 36.2 10.8 2.47 T-bet+ 33.5 Subject 2 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 8.59 50.3 12.8 10.8 20.6 66.1 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 5.92 22.4 53.3 8.88 0.94 T-bet+ 60.7 Subject 3 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 15.7 60.7 11.0 20.4 13.1 77.0 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 3.80 30.1 35.5 17.1 2.16 T-bet+ 37.2 Subject 4 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 8.23 47.2 18.9 9.46 19.9 60.8 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 16.1 47.7 21.3 T-bet+ 25.8 2.17 24.2 Subject 5 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 11.7 30.2 5.99 14.1 25.5 46.5 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 10.8 24.5 35.5 2.23 46.7 T-bet+ 46.3 Subject 6 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 8.00 57.0 7.76 8.58 33.5 28.7 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM T-bet+ 4.65 11.2 41.2 5.01 70.8 T-bet+ 71.1 Subject 7 IgG1 T-bet T-bet CD23 Lin: CD3, CD14 Lin: CD3, CD14 13.4 77.7 6.45 15.4 21.1 32.7 PanIg CD11b CD21 PanIg Lin: CD3, CD14 IgM Lin-PanIg+ B cells T-bet+ B cells Figure S4. T-bet+ B cells identified by scRNA-Seq are detectable by flow cytometry in all subjects examined. Flow cytometry gating schemes for T-bet+ B cell characterization in equine PBMC across each study subject (N = 7). (A) T-bet+ B cell gating for flow cytometry panel including CD11b, CD21 and CD23 labeling. Labels above plots indicate + visualized gate. (B) T-bet B cell gating for flow cytometry panel including IgM and IgG1 surface immunoglobulin label- ing. Labels above plots indicate visualized gate. A B 4 CD4 CD8A 3 2 CD3D 1 0 5 4 14 20 17 4 3 2 CD3E CD8B 1 ENSECAG00000000775 0 5 4 14 20 17 4 3 2 Expression CD3G 1 0 lo hi 5 4 14 20 17 Cluster number (CD3+ PRF1+ lymphocyte major cell group) C 5 4 3 G00000000419 A 2 TRAC 1 0 ENSEC 5 4 14 20 17 5 4 3 G00000033316 A 2 TRBC1 1 0 ENSEC 5 4 14 20 17 5 4 3 G00000030258 A 2 TRBC2 1 0 ENSEC 5 4 14 20 17 Cluster number (CD3+ PRF1+ lymphocyte major cell group) Figure S5. Select gene expression patterns in CD3+ PRF1+ lymphocyte major cell group. (A) Expression of select CD3 transcripts by indicated clusters in CD3+PRF1+ lymphocyte major cell group. Values plotted as log normalized counts per cell. (B) Expression patterns of CD4, CD8A and ENSECAG00000000775 (CD8B) in cluster 5. Expression values are scaled independently for each plot, ranging from 2.5 to 97.5 percentile of gene expression across all plotted cells. (C) Expression of T cell receptor genes by indicated clusters in CD3+PRF1+ lymphocyte major cell group. Values plotted as log normalized counts per cell. A Total PBMC CD3-CD14+ CD3-CD14- CD3+CD14- B cells Non-classical NK cells monocyte Classical CD3 monocyte CD3 CD14 Pan-Ig CD14 CD16 CD16 CD16 B Total PBMC CD3+ T cells CD3+ T cells T CD8 T proliferating CD3+ T cells CD3 CD8 CD3 T CD4 Ki67 CD4 Ki67 Figure S6. Representative flow cytometry gating schemes for immunophenotyping of equine PBMC. PBMC single cell suspensions were labeled with fluorescent-conjugated antibodies and analyzed by flow cytometry as described in Materials and Methods. (A) Gating scheme for immunophenotyping panel to resolve monocyte populations, B cells, and NK cells. Labels above plots indicate visualized gate. (B) Gating scheme for immunophenotyping panel to resolve CD4+, CD8+, and proliferating T cell populations. Labels above plots indicate visualized gate. Target Antibody Clone Source Reference species Eq CD3-AF647 UC-F6G UC Davis, Dr. Stott Tomlinson et al., 2018, Blanchard- Channell et al. 1994; Lunn et al., 1998 Eq CD4-FITC CVS4 Bio Rad Antibodies Tomlinson et al., 2018; Lunn et al., 1991 Eq CD8-RPE CVS8 Bio Rad Antibodies Tomlinson et al., 2018; Lunn et al., 1991 Hu Ki67-PECy7 B56 BD Biosciences This study* Eq Pan B cells-RPE CVS36 Bio Rad Antibodies Tomlinson et al., 2018; Lunn et al., 1998 Eq CD16 1A2.D11 Cornell University, Dr. Antczak Noronha et al., 2012 Hu CD21-BV421 B-ly4 BD Biosciences Tomlinson et al., 2018; Ibrahim and Steinbach 2012 and 2007 Eq CD14-Sav 105 Cornell University, Dr. Wagner Kabithe et al., 2010 Eq CD14-AF647 105 Cornell University, Dr.
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