Single-cell transcriptome analysis identifies skin ...

Single-cell transcriptome analysis identifies skin-specific T-cell responses

in systemic sclerosis

Alyxzandria M. Gaydosik, Tracy Tabib, Robyn Domsic, Dinesh Khanna, Robert Lafyatis and

Patrizia Fuschiotti

Supplementary Materials

Supplemental Methods

Supplemental Figure 1-5

Supplemental Tables 1-4

BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any relianceSupplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis

doi: 10.1136/annrheumdis-2021-220209–8.:10 2021;Ann Rheum Dis, et al. Gaydosik AM

Supplemental Methods

Subjects and skin biopsies

Skin samples (3mm) were obtained from 32 patients with confirmed diagnosis of active diffuse cutaneous

SSc (dcSSc)[1-3] at the Scleroderma Clinic of the University of Pittsburgh Medical Center (UPMC) and

at the Scleroderma Program of the University of Michigan Medical School. Disease subtype and internal

organ involvement were assessed according to established criteria.[1, 3, 4] DcSSc patients present

rapidly progressive wide-spread fibrosis of the skin and early fibrosis of the lung and other internal

organs.[2, 3] Active disease in our study refers to patients with moderate to high skin scores, and/or early

in their disease process. Most of the patients, 21/27 (74%), had either a high skin score >25 and/or were

within 2 years of first non-Raynaud's disease manifestation. Only one patient had a disease duration

greater than five years. Twenty-seven patient samples were used for scRNA-seq and 5 for

immunofluorescence (IF) microscopy. Healthy control (HC) skin samples (n=13) were obtained from age

and sex-matched donors with no history of any connective tissue disease, recruited at the UPMC Arthritis

and Autoimmunity Center. Ten HC skin samples were used scRNAseq and 3 for IF microscopy. Research

protocols involving human subjects were approved by the Institutional Review Boards of the University

of Pittsburgh and University of Michigan Medical School. All participants gave their written informed

consent in accordance with the Declaration of Helsinki.

Single cell cDNA and library preparation.

Cell suspensions from skin were obtained as previously described.[5, 6] Briefly, skin biopsies were

minced and digested enzymatically (Whole Dissociation Skin Kit, Miltenyi Biotec) for 2 hours at 37°C and

further dispersed using the Miltenyi gentle MACS Octo Dissociator. A 3mm skin biopsy from each donor

yielded 870-3,179 cells from SSc and 1,035-3,145 cells from HC skin samples. Experimental procedures

followed established techniques[5, 6] using the Chromium Single Cell 3’ Library V2 kit (10x Genomics).

Cell suspensions were separated by the Chromium System (10X Genomics)[5-7] into mini-reaction

"partitions" or GEMs formed by oil micro-droplets, each containing a gel bead and a cell. A 1000-fold

excess of partitions compared to cells assured that most partitions/GEMs had only one cell per GEM.

Gel beads contained an oligonucleotide scaffold composed of an oligo-dT section for priming reverse

transcription, and barcodes for each cell (10X Genomics) and each transcript (unique molecular identifier,

UMI), as described.[8] 7,000 cells were loaded into the instrument to obtain data on ~4,000 cells with a

rate of ~3.1% of partitions showing more than one cell/partition. The following steps were all performed

using reagents and protocols developed by 10X Genomics. Following GEM formation, the emulsions

were transferred from the Chromium chip to a PCR cycler for cDNA synthesis. The emulsion was then

broken using a recovery agent, and following Dynabead and SPRI clean up, cDNAs were amplified by

PCR (C1000, Bio-Rad). cDNAs were sheared enzymatically into lengths of ~200bp. DNA fragment ends



were repaired, A-tailed, and adaptors ligated. The library was quantified using the KAPA Library

Quantification Kit (Illumina), and further characterized for cDNA length by bioanalyzer using a High

Sensitivity DNA kit.

RNA sequencing.

RNA-seq was performed on each sample by the University of Pittsburgh Genomics Research Core

(http://www.genetics.pitt.edu/our-services/rna-sequencing) using the NextSeq500 sequencing system

(Illumina). Genes detected (6,000 genes/cell) had a plateau at about 200,000 reads/cell (10X Genomics,

white paper). We obtained ~200 million reads/sample (NextSeq, Illumina) through the University of

Pittsburgh Genomics Core, Sequencing Facility.

Data Analysis.

Chromium scRNA-seq data produced by the 10X Chromium Platform were processed to generate

sample-specific fastq files. Processed reads were then examined by quality metrics, mapped to a

reference human genome using RNA-seq aligner STAR and assigned to individual cells of origin

according to the cell specific barcodes, using the Cell Ranger pipeline (10X Genomics). To ensure that

PCR amplified transcripts were counted only once, only single UMIs were counted for gene expression

level [9]. In this way, cell x gene UMI counting matrices were generated for downstream analyses. Seurat,

an R package developed for single-cell analysis [10], was used to identify distinct cell populations and

visualize cell clusters in graphs as in [7]. Specifically, the UMI matrix was filtered such that only cells

expressing at least 200 genes were utilized in downstream analysis. Unwanted sources of variation were

regressed out of the data by constructing linear models to predict gene expression based on the number

of UMIs per cell as well as the percentage of mitochondrial gene content. Based on their average

expressions and dispersions, 4,451 highly variable genes were identified and principal component

analysis (PCA) was subsequently performed on the scaled data of the identified highly variable genes.

Statistically significant PCs were identified using a resampling test inspired by Jackstraw [11]. Cells were

clustered using Seurat [10] (Louvain clustering). The resultant clusters were then visualized using a t-

distributed stochastic neighbor embedding (t-SNE) projection plot [12]. For gene differential tests, we

used the “FindAllMarkers” package, which uses the Wilcoxon rank sum test to show differential genes

with a minimum percentage of cells of 25% per cluster.

Pseudo-temporal trajectory analysis

Pseudo-time analysis was performed using the Monocle 3.0 R package [13, 14]. Genes differentially

expressed across PhenoGraph-identified clusters were used as an input for the Monocle analysis. For

the heat map representation of pseudo-time genes, a time trace of each gene was taken using the



“plot_genes_in_pseudotime” function and dividing time into 100 equally sized bins. Time was measured

by selecting the longest path through the trajectory plot going from t = 0 to t = max.

Data availability

All scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus

database under accession number: GSE138669.

Multicolor immunohistochemistry

Single and dual antibody staining using tyramide signal amplification (ThermoFisher) were performed on

formalin-fixed, paraffin-embedded skin samples as previously described [15]. The antibodies employed

in these experiments are reported in Table S4. Confocal images were captured on an Olympus Fluoview

1000 confocal microscope using an oil immersion 100X objective

Bibliography

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(systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol. 1988 Feb; 15(2):202-

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2. LeRoy EC, Medsger TA, Jr. Criteria for the classification of early systemic sclerosis. J Rheumatol.

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3. Steen VD, Medsger TA, Jr. Severe organ involvement in systemic sclerosis with diffuse

scleroderma. Arthritis Rheum. 2000 Nov; 43(11):2437-2444.

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disease severity and prognosis. Clin Exp Rheumatol. 2003; 21(3 Suppl 29):S42-46.

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Fibroblast Populations in Human Skin. J Invest Dermatol. 2018 Apr; 138(4):802-810.

6. Gaydosik AM, Tabib T, Geskin LJ, Bayan CA, Conway JF, Lafyatis R, et al. Single-Cell Lymphocyte

Heterogeneity in Advanced Cutaneous T-cell Lymphoma Skin Tumors. Clin Cancer Res. 2019 Jul

15; 25(14):4443-4454.



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transcriptional profiling of single cells. Nat Commun. 2017 Jan 16; 8:14049.

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expression data. Nature biotechnology. 2015 May; 33(5):495-502.

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14. Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, et al. The single-cell transcriptional

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15. Gaydosik AM, Queen DS, Trager MH, Akilov OE, Geskin L, Fuschiotti P. Genome-wide

transcriptome analysis of the STAT6-regulated genes in advanced-stage cutaneous T-cell

lymphoma. Blood. 2020 May 21.

16. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate

integration of single-cell data with Harmony. Nature methods. 2019 Dec; 16(12):1289-1296.



Supplemental Figure 1. Grouping of SSc and control skin populations. Transcriptomes of 74,607

cells from 10 HC (20,073) and 27 SSc (54,534 cells) skin biopsies clustered using Seurat [10]. (A) Two-

dimensional t-SNE shows dimensional reduction of reads from single cells, revealing grouping in each

SSc sample compared to all healthy control (HC) skin samples. Cells from each subject are indicated by

different colors. (B) All samples are combined. (C) Distinct gene expression signatures are represented

by the clustering of known markers for multiple cell types [6] and visualized using t-SNE. Clusters

belonging to each cell type are color coded. (D) Cell types in skin cell suspensions were identified by cell-

specific marker as previously described [6], including AIF1 - macrophages; VWF - endothelial cells;

TPSAB1 - mast cells; SCGB1B2P - secretory (glandular) cells; RGS5 - pericytes; PMEL - melanocytes;

MS4A1 - B cells; KRT1 - keratinocytes; DES - smooth muscle cells; COL1A1 – fibroblasts; CD3D - T

lymphocytes; and CD1C - dendritic cells. Intensity of purple color indicates the normalized level of gene

expression. Cell-type specific clusters are indicated by an arrow. t-SNE, t-distributed stochastic neighbor

embedding.



Supplemental Figure 2. Batch correlation by Harmony.[16] (A) UMAP of all cell types using Harmony

to verify there are no batch effects as all samples overlap per cell type in this UMAP as we have shown

in Seurat’s clustering. (B) UMAP of all samples IDs of T cells to verify clustering has no batch effects as

no individual samples create own clusters. (C) UMAP of T cells using Harmony to verify clustering is

without batch effects. Unique clusters of SSc cells still visible with corrections. (D) UMAP shows similar

clustering as Seurat in separating out unique T-cell clusters.



Supplemental Figure 3. Identification of resident and recirculating T cells in HC and SSc skin.

Expression dynamics of skin-residency (A) or T-cell activation (B) markers along the pseudotime of SSc

and HC T cells by scatter plots with regression curves.



Supplemental Figure 4. Two-dimensional t-SNE shows dimensional reduction of reads from single cells,

revealing grouping in all HC (left) compared to SSc (right) skin samples. Expression of CD3 (A) or

CXCL13 (B) is shown. (C-D) Representative examples of CXCL13+ T cells expressing IL-4 or IL-21

measured by intracellular staining and flow cytometry in blood (HC n=3, dcSSc n=5) are reported. Freshly

isolated PBMCs were stimulated for 6 hours with PMA and ionomycin in the presence of brefeldin A.

Cells were gated on lymphocyte scatter and CD3 and CD4 positivity. (E) The percentage of circulating

CD4+CXCL13+ cells in HCs and SSc patients is shown. Statistics by Student's T test. (F) Dot plot showing

the proportion of cells and the scaled average gene expression of selected DE genes by SSc and HC

Tregs. Immunofluorescence microscopy shows co-expression of CXCL13, CXCR5 and BCL6 in dcSSc

skin samples (n=5). A representative experiment is shown at 1000X (G); and positive controls staining

for BCL6 (tonsils) and CCR5 (tonsils), 1000X (H).



Supplemental Figure 5. Unsupervised clustering of microarray data by Affymetrix with analysis by the

Transcriptome Analysis Console software (Affymetrix). A hierarchical relationship is suggested between

CXCL13 up-regulation (red arrowhead at right) and a T- and B-cell gene expression signature.



Supplementary Table 1. Demographic and clinical features of patients whose samples were used in scRNAseq.

Subject Sex Age at Biopsy (years)

Race Diagnosis Disease Duration (years)

Skin Score

Renal Crisis

PAH ILD ANA Immunosuppressant

medication at time of biopsy

% CD3+ CXCL13+

cells*

HC- 1 Male 63 White HC NA NA NA NA NA NA NA 0


HC- 3 Female 66 White HC NA NA NA NA NA NA NA 0

HC- 4 Female 23 Asian HC NA NA NA NA NA NA NA 0.5






HC-10 Male 61 African American

HC NA NA NA NA NA NA NA 0

SSc- 1 Female 35 white SSc 6.48 37 no no no scl-70 MTX, CellCept 0

SSc- 2 Female 55 white SSc 4.74 17 no no no scl-70 CellCept, Plaquenil 0

SSc- 3 Male 64 white SSc 2.67 34 no no ND positive CellCept, Plaquenil 1.3

SSc- 4 Female 61 white SSc 2.26 26 no no no ACA CellCept 0

SSc- 5 Male 63 ND SSc 1.21 34 no no ND positive None 1.8

SSc- 6 Male 49 white SSc 1.08 17 no no ND pol3 D-Pen, CellCept 0.9

SSc- 7 Male 60 white SSc 1.5 27 no no ND pol3 CellCept 0

SSc- 8 Female 40 white SSc 0.48 25 no no no scl-70 MTX 0

SSc- 9 Female 55 African American

SSc 2.52 21 no no yes scl-70 None 0

SSc-10 Female - non child bearing

59 white SSc 1.16 28 no no no positive None 2.3


69 white SSc 1.2 32 no no yes pol3 None 3.4

SSc-12 Male 41 white SSc 1.5 12 no no no positive None 0

SSc-13 Female 46 white SSc 1.1 11 no no yes positive None 2.6



SSc-14 Female 22 white SSc 4.75 16 no no ND not done None 3


58 white SSc 3 16 no no yes positive None 2

SSc-16 Female 41 white SSc 3.03 12 no no NO scl-70 MTX 2

SSc-17 Female 64 white SSc 2.25 43 no no yes scl-70 CellCept 0.5

SSc-18 Male 69 white SSc 0.83 21 no no yes pol3 None 3.3


40 white SSc 2.16 24 no no no RNP None 0

SSc-20 Male 44 white SSc 3.1 19 no no ND pol3 None 0

SSc-21 Female 34 white SSc 2.8 34 no no no pol3 None 0.4

SSc-22 Male 58 white SSc 1.1 21 no no yes pol3 None 8.5


70 white SSc 2.8 18 no no yes pol3/scl-70

None 0

SSc-24 Male 60 African American

SSc 1.6 25 no no yes scl-70 None 4


66 white SSc 3.75 30 no no no pol3 None 1.1

SSc-26 Female 37 white SSc 3.75 40 no no no scl-70 None 3.2

SSc-27 Male 50 white SSc 0.3 23 no no yes pol3 None 5

* percentage of total T-cell count Abbreviations: NA = Not Applicable, ND = No Data, MTX = Methotrexate



Supplemental Table 2. Counts of CD3+ cells contributing to each cluster from the different SSc and HC samples (Figure 1C).

HC SSc Total T cells

Cluster 0 261 1040 1301

Cluster 1 208 539 747

Cluster 2 152 571 723

Cluster 3 141 354 495

Cluster 4 48 100 148

Cluster 5 40 107 147

Cluster 6 6 75 81

Cluster 7 2 47 49

Cluster 8 9 29 38

Total 867 2862



Supplementary Table 3. List of genes expressed by modules 9 and 16.

Module 9 Gene list Module 16 Gene list

Cluster 3 Avg. Exp.

Cluster 7 Avg. Exp.

Cluster 3 Avg. Exp.

Cluster 7 Avg. Exp.

TNFRSF18 6.666 5.407 SDF4 0.822 0.888

TNFRSF4 4.381 4.306 CASP9 0.071 0.087

TNFRSF9 3.597 2.692 LCK 2.664 2.225

PARK7 4.675 3.579 JAK1 1.669 0.993

MIIP 0.498 0.389 SH3GLB1 0.974 0.967

ID3 3.360 1.241 SARS 0.659 1.020

GADD45A 2.003 1.348 RAP1A 3.058 2.429

GBP2 2.107 1.044 PTPN22 0.948 0.697

GBP5 0.975 0.044 CD2 4.513 5.583

RHOC 1.496 1.684 THEM5 0.009 0.000

CTSS 0.988 0.624 PBXIP1 2.062 1.030

PYHIN1 0.659 0.476 IFI16 2.011 2.178

F5 0.262 0.089 UCHL5 0.079 0.256

SELP 0.009 0.000 B3GALT2 0.072 0.040

MIR181A1HG 0.423 0.214 GNG4 0.000 0.623

DUSP10 1.296 0.374 GPR137B 0.277 0.351

EXO1 0.042 0.000 LAPTM4A 2.999 1.956

PPM1G 1.441 1.872 REL 5.630 10.349

UGP2 3.914 0.569 STARD7 0.676 0.600

PELI1 0.736 0.178 LIMS1 0.396 1.589

LINC01943 1.385 1.084 BCL2L11 0.364 0.180

CYTOR 2.727 2.153 GORASP2 0.206 0.388

AC133644.2 0.856 0.179 CHN1 0.464 1.838

IL1R1 0.181 0.045 ZDBF2 0.144 0.268

MIR4435-2HG 1.218 0.725 BHLHE40-AS1 0.004 0.179

AC017002.3 0.353 0.180 GLB1 0.469 0.208

TANK 2.429 1.689 CXCR6 3.274 0.398

SPATS2L 0.493 0.461 PRKAR2A 0.300 0.262

CFLAR 1.065 1.044 PPP4R2 0.830 0.663

ABI2 0.478 0.130 ZBED2 0.342 2.530

CTLA4 2.764 1.532 CD200 0.040 2.158

ICOS 3.235 2.500 BTLA 0.106 0.853

IKZF2 0.639 0.089 CDV3 0.587 0.662

WNT10A 0.764 0.404 FAM43A 0.267 0.934

LMCD1 0.334 0.039 TMEM175 0.153 0.240

JAGN1 0.297 0.482 IGFBP7 1.674 2.157

SLC25A38 0.661 0.301 CXCL13 0.004 15.293

CCR3 0.004 0.000 FAM173B 0.012 0.046

ZNF80 0.221 0.000 PAM 0.105 0.253

TIGIT 5.528 4.960 CDO1 0.000 0.134

SLC41A3 0.238 0.225 GRAMD2B 0.158 0.211

ATP2C1 0.143 0.080 UBE2D2 3.001 2.002

NEK11 0.027 0.000 TNIP1 0.990 0.356

ZBTB38 0.511 0.348 PTTG1 2.894 2.004

LINC00885 0.013 0.000 MIR3142HG 0.251 0.406

WDR53 0.090 0.000 MGAT1 0.672 0.449

CCNG2 1.019 0.877 HLA-A 43.591 36.996

TNIP3 0.141 0.172 HLA-E 13.080 11.985

HPGD 1.117 0.000 HLA-C 27.807 26.274

ARL15 0.048 0.130 HLA-B 38.955 36.606

IQGAP2 0.496 0.272 BAK1 0.570 0.618

GLRX 3.179 0.803 ASF1A 0.999 0.361

PPP2R2B 0.299 0.043 STX11 0.996 1.556

CD74 11.037 6.410 FGFR1OP 0.149 0.094

FBXW11 0.123 0.045 TBP 0.039 0.000

HLA-F 1.217 1.345 RAC1 3.898 3.824

HLA-DRA 2.299 1.585 ITGB8 0.039 0.180

HLA-DRB1 6.066 2.998 CREB3L2 0.154 0.044

HLA-DQA1 1.327 0.945 TRBC2 7.481 5.390

HLA-DQB1 2.754 1.393 SAT1 13.041 9.593

HLA-DMA 0.479 0.125 LINC01281 0.080 0.177



HLA-DPA1 1.668 1.059 CHST7 0.317 0.043

HLA-DPB1 1.915 1.330 IL2RG 2.641 1.755

ETV7 0.204 0.173 ITM2A 2.462 9.878

CUL9 0.358 0.132 SH3BGRL 2.066 1.729

DNPH1 1.319 1.056 ZCCHC18 0.031 0.090

AKIRIN2 1.383 0.689 STAG2 0.351 0.438

PNISR 2.614 4.072 DUSP4 7.439 12.585

PRDM1 1.516 0.798 FABP5 1.156 4.544

ZC3H12D 0.530 0.511 GEM 1.689 8.267

IPCEF1 0.499 0.176 RNF19A 0.886 3.832

ICA1 0.699 0.862 SLA 1.298 2.312

GARS 0.274 0.172 LY6E-DT 0.000 0.090

RHBDD2 1.088 0.706 NDUFB6 0.719 1.455

PHTF2 0.977 0.501 SIT1 2.174 1.500

NAMPT 6.118 3.949 NINJ1 1.284 1.512

CPA5 0.079 0.118 TRAF1 1.252 1.581

TRBC1 7.146 4.322 NPDC1 0.260 0.433

RAB9A 1.150 0.832 NAP1L4 0.724 1.951

SMS 1.284 2.898 ARNTL 0.283 0.046

GK 0.792 0.925 CD82 0.781 1.876

GK-AS1 0.077 0.132 MS4A6A 0.013 1.823

GPR82 0.332 0.183 CCND1 0.032 0.580

PCSK1N 0.461 0.045 SESN3 0.283 1.976

PIM2 1.283 1.001 BIRC3 10.962 13.901

FOXP3 0.904 0.043 SIDT2 0.067 0.045

MAGEH1 0.581 1.226 GRAMD1B 0.030 0.133

BEX3 1.004 0.405 SRPRA 0.403 0.416

RAB11FIP1 1.396 0.925 GATA3 2.139 0.992

NSD3 2.672 1.282 OPTN 1.087 0.906

TOX 0.385 0.440 PSAP 1.798 1.381

ZC2HC1A 0.655 0.126 GLUD1 1.610 0.853

IL7 0.049 0.045 NFKB2 0.835 0.824

HTATIP2 1.328 0.360 CPM 0.196 0.810

PGM2L1 0.780 1.684 ATP2B1 1.382 0.865

CTSC 4.121 0.783 CRADD 0.277 0.237

CASP1 0.864 0.603 ACACB 0.004 0.043

CARD16 3.982 2.144 POP5 0.249 0.421

CARD17 0.049 0.000 LHFPL6 0.023 0.805

LAYN 1.823 0.040 RAP2A 0.031 0.093

SNX19 0.020 0.000 TRAC 12.438 7.840

IL2RA 2.560 0.311 LRP10 0.670 0.621

HACD1 0.303 0.131 MBIP 0.396 0.089

STAM 0.761 0.661 CNIH1 0.815 2.395

SGMS1 0.442 0.421 FAM71D 0.018 0.043

RTKN2 1.263 0.233 RASGRP1 0.647 0.270

ENTPD1 0.846 0.221 ARPP19 1.483 0.937

FANK1 0.508 0.038 C15orf65 0.004 0.083

CD27 3.404 1.098 NMB 0.323 2.791

GPR19 0.044 0.000 ZNF267 0.552 0.356

APOLD1 1.233 0.702 C16orf87 0.419 0.340

PCED1B 0.462 0.378 CCL22 0.018 0.038

COPZ1 0.417 0.180 CMTM3 0.767 0.395

IKZF4 0.223 0.201 CYB5B 0.673 0.309

APAF1 0.083 0.000 MPRIP-AS1 0.028 0.043

ANKS1B 0.170 0.178 ALDH3A1 0.013 0.126

PMCH 0.278 0.046 EPOP 0.172 0.305

EPSTI1 0.300 0.367 IGFBP4 0.153 0.861

TBC1D4 1.580 0.616 STAT5A 0.331 0.654

MYCBP2 0.774 1.029 SEC14L1 0.309 0.243

ANKRD10 1.147 0.954 SEPT9 1.388 1.370

LINC01588 0.326 0.216 CCDC40 0.004 0.045

AKAP5 0.183 0.230 GAPLINC 0.004 0.089

RAD51B 0.039 0.079 PQLC1 0.054 0.170

BATF 5.128 2.673 SOX12 0.009 0.045

SLC12A6 0.148 0.000 CPNE1 0.604 0.581

ZC3H7A 0.344 0.212 TOX2 0.085 0.586

LINC02195 0.656 0.000 YWHAB 4.130 2.801

CPNE2 0.389 0.043 TSHZ2 0.772 3.377



NEURL4 0.018 0.000 NCLN 0.027 0.127

AC005224.3 0.105 0.086 SIRT6 0.117 0.222

TNFRSF13B 0.113 0.000 EBI3 0.016 0.350

STAT3 2.346 3.480 GRAMD1A 0.630 1.170

MEOX1 0.136 0.045 IGFLR1 0.177 0.273

HLF 0.262 0.000 RELB 0.610 0.789

CD79B 0.600 0.341 PVALB 0.000 0.282

TTC39C 1.504 0.219 BTG3 1.953 1.868

PMAIP1 4.344 3.434 MRPS6 2.807 4.181

BCL2L1 0.332 0.441

NCOA5 0.080 0.000

CIRBP 7.217 5.583

CCDC159 0.117 0.043

DNASE2 0.212 0.043

IL27RA 0.660 0.875

ZBTB32 0.288 0.000

CALM3 1.792 1.912

PTGIR 0.346 0.045

GNG8 0.135 0.176

LAIR2 0.783 0.394

C22orf39 0.317 0.214

EWSR1 0.922 1.153

PIK3IP1 1.212 1.147

PARVB 0.180 0.271

PIM3 3.923 3.935

TYMP 3.543 2.170

U62317.2 0.066 0.036

MIR155HG 0.513 2.577

LINC00649 0.885 0.470



Supplementary Table 4. Antibodies used in multi-color immunofluorescent microscopy.

Antibody Company Clone Species

CD3 THERMO Polyclonal Rabbit

CD4 ABCAM EPR15861 Rabbit

CXCL13 ABCAM Polyclonal Rabbit

ICOS ABCAM EPR19518 Rabbit

CTLA4 ABCAM CD3-12 Rat

TIGIT R&D Polyclonal Goat

IL21 R&D 718916 Mouse

IFNg ABCAM JES10-5A2 Rat

CD19 ABCAM Polyclonal Rabbit

CD20 NOVUS Polyclonal Rabbit

CXCR5 LS BIO Polyclonal Rabbit

BCL6 LS BIO Polyclonal Rabbit



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