NBDC Research ID: hum0214.v9

 

SUMMARY

Aims: To elucidate the regulation of gene expression in each immune cell subset and its contribution to autoimmune diseases.

Methods:

JGAS000220 (JGAD000309, JGAD000310): Various immune cell subsets from 21 systemic sclerosis patients, 26 ANCA associated vasculitis, and 28 healthy controls were collected (Naive_B, SM_B, USM_B, DN_B, Plasmablast, Th1, Th2, Th17, Tfh, Naive_CD4, Mem_CD4, Fr._II_eTreg, Naive_CD8, Mem_CD8, mDC, pDC, CD16p_Mono, CD16n_Mono, NK, Neu) and total RNAs were extracted from each subset. RNA-seq was performed for each sample.

E-GEAD-397 / E-GEAD-398 / E-GEAD-420: Whole blood and 28 immune cell subsets from study population were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, Mem_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, LDG, Neu). Whole genome sequencing was performed with whole blood samples. RNA-seq was performed with each immune cell subset samples. After filtering and normalization of the gene expression data, eQTL analysis was performed in each immune cell type.

JGAS000296: 24 peripheral blood immune cell subsets from 50 systemic sclerosis patients and 48 healthy controls were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono, Int_Mono, NC_Mono, mDC, pDC). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

JGAS000220 (JGAD000371, JGAD000372, JGAD000373): 19 immune cell subsets from study population were collected (Naive_CD4, Mem_CD4, Fr._II_eTreg, Th1, Th2, Th17, Tfh, NK, Naive_CD8, Mem_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CD16n_Mono, CD16p_Mono, mDC, pDC). RNA-seq was performed with each immune cell subset sample. ATAC-seq of 15 immune cell subsets was also performed.

JGAS000486: Various peripheral blood immune cell subsets from 89 healthy volunteers and 136 systemic lupus erhythematosus (SLE) donors were collected (Naive_CD4, Mem_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, NK, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, Neu, LDG). 22 SLE patients were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

JGAS000598: Various peripheral blood immune cell subsets from 39 healthy volunteers and 50 rheumatoid (RA) donors were collected (CD16p_Mono, CL_Mono, DN_B, Fr_II_eTreg, mDC, Mem_CD4, Naive_B, Naive_CD4, Neu, NK, pDC, Plasmablast, SM_B, Tfh, Th1, Th17, Th2, USM_B). 15 RA patients were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

JGAS000485: Peripheral blood B cell subsets from study population were collected (Naive_B, USM_B, SM_B, DN_B, Plasmablast). RNA-seq was performed with each B cell subset samples. B cell receptor sequences were aligned.

JGAS000626: 9 immune cell subsets from study population were collected (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

JGAS000627: 27 immune cell subsets from study population were collected (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA, Naive_CD8, Naive_B, SM_B, USM_B, DN_B, Plasmablast, NK, CD16p_Mono, CL_Mono, Neu, mDC, pDC, TEMRA_CD8, CM_CD8, EM_CD8, NC_Mono, Int_Mono, LDG). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

JGAS000648: Peripheral blood, muscle tissue, and bronchial lavage fluid were collected from each subject. For peripheral blood, CD4T cells were collected and scRNA-seq was performed to quantify gene expression. For muscle tissue and bronchial lavage fluid, CD45-positive cells were collected, scRNA-seq was performed, gene expression was quantified, and CD4T cluster was selected.

Participants/Materials: Systemic Sclerosis, Systemic Lupus Erythematosus, Myositis, Mixed Connective Tissue Disease, Sjögren’s Syndrome, Rheumatoid Arthritis, Behçet’s Disease, Adult Onset Still’s Disease, ANCA-associated Vasculitis, Takayasu’s Arteritis, healthy individuals

URL: https://www.h.u-tokyo.ac.jp/english/centers-services/clinical-divisions/allergy-and-rheumatology/index.html

 

Dataset IDType of DataCriteriaRelease Date
JGAS000220 NGS (RNA-seq: Systemic sclerosis) Controlled-access (Type I) 2020/10/09
JGAS000220 NGS (RNA-seq: ANCA-associated Vasculitis) Controlled-access (Type I) 2021/03/05
E-GEAD-397 Read count data from RNA-seq Unrestricted-access 2021/04/28
E-GEAD-398 Conditional eQTL summary data (significant associations) Unrestricted-access 2021/04/28
E-GEAD-420 Nominal eQTL data (including non-significant associations) Unrestricted-access 2021/04/28
JGAS000296 NGS (RNA-seq) Controlled-access (Type I) 2022/01/21
JGAS000220 NGS (RNA-seq) Systemic Lupus Erythematosus Controlled-access (Type I) 2022/03/09
JGAS000220 NGS (ATAC-seq) Controlled-access (Type I) 2022/03/09
JGAS000486 NGS (RNA-seq) Controlled-access (Type I) 2022/08/25
JGAS000598 NGS (RNA-seq) Controlled-access (Type I) 2023/03/28
JGAS000485 B cell receptor repertoire clonotype data from B cell RNA-seq Controlled-access (Type I) 2023/07/06
JGAS000626 NGS (RNA-seq) Controlled-access (Type I) 2024/02/08
JGAS000627 NGS (RNA-seq) Controlled-access (Type I) 2024/02/08
JGAS000648 NGS (scRNA-seq) Controlled-access (Type I) 2024/02/08

*Release Note

*Data users need to apply an application for Using NBDC Human Data to reach the Controlled-access Data. Learn more

*When the research results including the data which were downloaded from NHA/DRA, are published or presented somewhere, the data user must refer the papers which are related to the data, or include in the acknowledgment. Learn more

 

MOLECULAR DATA

JGAS000220 (RNA-seq: Systemic sclerosis)

Participants/Materials

Systemic Sclerosis (ICD10: M340): 21 cases

13 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 19 immune cell subsets (Naive_B, SM_B, USM_B, DN_B, Plasmablast, Th1, Th2, Th17, Tfh, Naive_CD4, Mem_CD4, Fr._II_eTreg, Naive_CD8, Mem_CD8, mDC, pDC, CD16p_Mono, CD16n_Mono, NK)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (hg38)
QC Methods The adaptor sequences and 3’ low quality bases (Phred quality score < 20) were trimmed. Short reads (< 50bp) and reads containing many low quality bases (Phred quality score < 20 in > 20% of the bases) were removed. If the uniquely mapped rate was less than 80%, or the number of uniquely mapped reads was 5.00 x 106 reads, the sample was removed before further analysis. The correlation coefficient of the expression data between two samples belonging to the same cell subset and calculated the average of the correlation coefficient (Di). Samples for which Di was less than 0.9 were removed.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000309
Total Data Volume 125 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000220 (RNA-seq: ANCA-associated Vasculitis)

Participants/Materials

ANCA-associated Vasculitis (ICD10: M318): 26 cases

28 healthy controls (including above 13 individuals)

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 20 immune cell subsets (Naive_B, SM_B, USM_B, DN_B, Plasmablast, Th1, Th2, Th17, Tfh, Naive_CD4, Mem_CD4, Fr._II_eTreg, Naive_CD8, Mem_CD8, mDC, pDC, CD16p_Mono, CD16n_Mono, NK, Neu)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (hg38)
QC Methods The adaptor sequences and 3’ low quality bases (Phred quality score < 20) were trimmed. Short reads (< 50bp) and reads containing many low quality bases (Phred quality score < 20 in > 20% of the bases) were removed. If the uniquely mapped rate was less than 80%, or the number of uniquely mapped reads was 5.00 x 106 reads, the sample was removed before further analysis. The correlation coefficient of the expression data between two samples belonging to the same cell subset and calculated the average of the correlation coefficient (Di). Samples for which Di was less than 0.9 were removed.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000310
Total Data Volume 125 MB (count data, txt)
Comments (Policies) NBDC policy

 

E-GEAD-397 (RNA-seq)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 62 cases

Myositis (ICD10: M339, M332): 65 cases

Systemic Sclerosis (ICD10: M340): 67 cases

Mixed Connective Tissue Disease (ICD10: M351): 19 cases

Sjögren’s Syndrome (ICD10: M350): 18 cases

Rheumatoid Arthritis (ICD10: M0690): 25 cases

Behçet’s Disease (ICD10: M352): 23 cases

Adult Onset Still’s Disease (ICD10: M0610): 18 cases

ANCA-associated Vasculitis (ICD10: M318: 26 cases

Takayasu’s Arteritis (ICD10: M314): 16 cases

92 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 28 immune cell subsets (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, Mem_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, LDG, Neu)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (GRCh38)
QC Methods From sequenced reads, adaptor sequences were trimmed using cutadapt (v1.16). In addition, 3′- ends with low-quality bases (Phred quality score < 20) were trimmed using the fastx-toolkit (v0.0.14). Reads containing more than 20% low-quality bases were removed. Subsequently, reads were aligned against the GRCh38 reference sequence using STAR (v2.5.3) in two-pass mode with Gencode version 27 annotations. We excluded samples with uniquely mapped read rates < 90% (with the exception of < 70% for plasmablasts and <85% for the other B cell subsets) or unique read counts < 6 × 10^6. Expression was quantified using HTSeq (v 0.11.2.). For QC of the expression data, in each cell population, we filtered low count genes (< 10 in > 90% of samples), normalized between samples with a trimmed mean of M values (TMM) implemented in edgeR software, converted to log-transformed count per million (CPM), removed batch effects using ComBat software and computed inter-sample Spearman’s correlations of expression levels between each sample and the remaining samples from the same cell subset. We excluded samples with mean correlation coefficients less than 0.9.
Gene Number 53344
Genomic Expression Archive ID E-GEAD-397
Total Data Volume 1.3 GB (clinical data and count data of autosomal genes, txt)
Comments (Policies) NBDC policy

 

E-GEAD-398 / E-GEAD-420 (eQTL)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 62 cases

Myositis (ICD10: M339, M332): 65 cases

Systemic Sclerosis (ICD10: M340): 67 cases

Mixed Connective Tissue Disease (ICD10: M351): 19 cases

Sjögren’s Syndrome (ICD10: M350): 18 cases

Rheumatoid Arthritis (ICD10: M0690): 24 cases

Behçet’s Disease (ICD10: M352): 23 cases

Adult Onset Still’s Disease (ICD10: M0610): 18 cases

ANCA-associated Vasculitis (ICD10: M318: 25 cases

Takayasu’s Arteritis (ICD10: M314): 16 cases

79 healthy controls

Targets eQTL
Target Loci for Capture Methods -
Platform Illumina [HiSeq X Ten]
Library Source DNAs extracted from whole blood
Cell Lines -
Library Construction (kit name) TruSeq DNA PCR-Free Library prep kit
Fragmentation Methods TruSeq DNA PCR-Free Library prep kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 151 bp
Mapping Methods BWA-MEM(GRCh38)
QC Methods WGS data processing was performed based on the standardized best-practice method proposed by GATK (v 4.0.6.0). Samples with genotyping call rates < 99% were removed. We used BEAGLE (v 5.1) to impute missing genotypes. Variants with call rate < 85%, HWE P-value < 1.0 x 10-6 or minor allele frequency < 1% were excluded.
Gene Number

E-GEAD-398: 19441

E-GEAD-420: 22381

Detection method of eQTL Genes expressed at low levels (< 5 count in more than 80% samples or < 0.5 CPM in more than 80% samples) were filtered out in each cell subset. The residual autosomal expression data were normalized between samples with TMM, converted to CPM and then normalized across samples using an inverse normal transform. A Probabilistic Estimation of Expression Residuals (PEER) method was applied to normalized expression data to infer hidden covariates. The top 2 genetic principal components, sample collection phase, clinical diagnosis, sex and latent factors were utilized as covariates for eQTL analysis. Mem CD8s, which were collected in Phase1 and divided into CM CD8 and EM CD8 in Phase2, were analyzed jointly with EM CD8 for eQTL analysis because the majority of the Mem CD8 population consisted of EM CD8. For each cell subset conditional eQTL analysis, we used a QTLtools permutation pass with 10,000 permutations to obtain gene-level nominal P value thresholds corresponding to FDR < 0.05. We subsequently performed forward-backward stepwise regression eQTL analysis with a QTLtools conditional pass. For nominal eQTL analysis, we used a QTLtools nominal pass and tested for the association of the variants located within 1Mbp from the TSS of the genes.
Genomic Expression Archive ID

E-GEAD-398

E-GEAD-420 (2021/6/9: Added columns for REF/ALT allele. Slope indicates the effect size of alternative alleles.)

Total Data Volume

E-GEAD-398: 3.9 GB (conditional eQTL summary data [FDR<0.05], txt)

E-GEAD-420: 38 GB (nominal eQTL data [full], txt)

Comments (Policies) NBDC policy

 

JGAS000296 (RNA-seq)

Participants/Materials

Systemic Sclerosis (ICD10: M340): 50 cases

48 healthy controls

(including same cases in E-GEAD-397)

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 24 immune cell subsets (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono, Int_Mono, NC_Mono, mDC, pDC)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (hg38)
QC Methods The adaptor sequences and 3’ low quality bases (Phred quality score < 20) were trimmed. Short reads (< 50bp) and reads containing many low quality bases (Phred quality score < 20 in > 20% of the bases) were removed. If the uniquely mapped rate was less than 80%, or the number of uniquely mapped reads was 5.00 x 106 reads, the sample was removed before further analysis. The correlation coefficient of the expression data between two samples belonging to the same cell subset and calculated the average of the correlation coefficient (Di). Samples for which Di was less than 0.9 were removed.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000309
Total Data Volume 172.8 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000220 (RNA-seq: Systemic Lupus Erythematosus)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 107 cases

92 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 19 immune cell subsets (Naive_CD4, Mem_CD4, Fr._II_eTreg, Th1, Th2, Th17, Tfh, NK, Naive_CD8, Mem_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CD16n_Mono, CD16p_Mono, mDC, pDC)
Cell Lines -
Library Construction (kit name) Smart-Seq v2 for the test cohort and SMART-seq v4 Ultra Low Input RNA Kit for the validation cohort
Fragmentation Methods Smart-Seq v2 for the test cohort and SMART-seq v4 Ultra Low Input RNA Kit for the validation cohort
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers)

76 bp (test cohort)

100 bp (validation cohort)

Mapping Methods STAR (GRCh38)
QC Methods FASTQ files were aligned to the human genome (GRCh38; GenBank assembly GCA_000001405.18) using STAR (v2.5). HTSeq-count (v0.6.1) was used to generate gene counts. In the quality-control analysis, low-quality bases (Phred quality score < 20) were trimmed using the fastx-toolkit (v0.0.14). As the level of mitochondrial transcription is an indicator of cell stress, we applied a cutoff percentage of mitochondrial gene transcripts of < 8%. For detecting outlier samples, Spearman’s correlation for each subset was calculated, and samples with an average r2 < 0.8 were omitted as outliers.
Gene Number

26354 (test cohort)

26485 (validation cohort)

Japanese Genotype-phenotype Archive Dataset ID

JGAD000371

JGAD000372

Total Data Volume 250 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000220 (ATAC-seq)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 8 cases

8 healthy controls

Targets ATAC-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source DNAs extracted from 15 immune cell subsets (Naive_B, SM_B, USM_B, DN_B, Plasmablast, Th1, Th2, Th17, Tfh, Naive_CD4, Mem_CD4, Naive_CD8, CD16p_Mono, CD16n_Mono, NK)
Cell Lines -
Library Construction (kit name) Fast-ATAC-seq protocol
Fragmentation Methods Fast-ATAC-seq protocol
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 102 bp
Japanese Genotype-phenotype Archive Dataset ID JGAD000373
Total Data Volume 27 GB (tdf, bed)
Comments (Policies) NBDC policy

 

JGAS000486 (RNA-seq: Systemic Lupus Erythematosus)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 136 cases

(22 SLE patients were analyzed longitudinally, total 159 samples)

89 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500, NovaSeq 6000]
Library Source Total RNAs extracted from 27 immune cell subsets (Naive_CD4, Mem_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, NK, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, Neu, LDG)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp (HiSeq 2500), 150bp (NovaSeq 6000)
Mapping Methods STAR (GRCh38)
QC Methods Adaptor sequences were trimmed using Cutadapt and reads containing low-quality bases (Phred quality score < 20 in > 20% of the bases) were removed. Reads were aligned to the human genome within the UCSC Genome Browser (GRCh38) using STAR, and expression was counted with HTSeq. Samples with uniquely mapped read rates < 80% or unique read counts < 5 × 106 were excluded. We calculated Spearman’s correlations of the expressions between two samples from the same cell type and then removed the samples with mean correlation coefficients < 0.9. In addition, samples with the concordance rates between RNA-seq-based genotype and WGS-based genotype at the heterozygous loci < 0.9 were excluded.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000603
Total Data Volume 478.3 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000598 (RNA-seq: Rheumatoid Arthritis)

Participants/Materials

Rheumatoid Arthritis (ICD10: M0690): 50 cases

(15 RA patients were analyzed longitudinally)

39 healthy controls

(including same cases in E-GEAD-397)

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 18 immune cell subsets (CD16p_Mono, CL_Mono, DN_B, Fr_II_eTreg, mDC, Mem_CD4, Naive_B, Naive_CD4, Neu, NK, pDC, Plasmablast, SM_B, Tfh, Th1, Th17, Th2, USM_B)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (hg38)
QC Methods The adaptor sequences and 3’ low quality bases (Phred quality score < 20) were trimmed. Short reads (< 50bp) and reads containing many low quality bases (Phred quality score < 20 in > 20% of the bases) were removed. If the uniquely mapped rate was less than 80%, or the number of uniquely mapped reads was 5.00 x 106 reads, the sample was removed before further analysis. The correlation coefficient of the expression data between two samples belonging to the same cell subset and calculated the average of the correlation coefficient (Di). Samples for which Di was less than the mean – 2SD were removed.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000727
Total Data Volume 113.9 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000485 (BCR repertoire analysis)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 136 cases (159 samples)

Systemic Sclerosis (ICD10: M340): 90 cases (90 samples)

Myositis (ICD10: M339, M332): 85 cases (85 samples)

Mixed Connective Tissue Disease (ICD10: M351): 21 cases (21 samples)

Sjögren’s Syndrome (ICD10: M350): 18 cases (18 samples)

Rheumatoid Arthritis (ICD10: M0690): 27 cases (27 samples)

Behçet’s Disease (ICD10: M352): 23 cases (23 samples)

Adult Onset Still’s Disease (ICD10: M0610): 18 cases (18 samples)

ANCA-associated Vasculitis (ICD10: M318: 24 cases (24 samples)

Takayasu’s Arteritis (ICD10: M314): 16 cases (16 samples)

13 healthy controls (13 samples)

Targets RNA-seq / BCR repertoire analysis
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 5 B cell subsets (Naive_B, USM_B, SM_B, DN_B, Plasmablast)
Cell Lines -
Library Construction (kit name) SMART-Seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-Seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods MiXCR
QC Methods The adaptor sequences and 3’ low quality bases (Phred quality score < 20) were trimmed. Short reads (< 50bp) and reads containing many low quality bases (Phred quality score < 20 in > 20% of the bases) were removed.Samples with more than 500 unique CDR-H3 sequences were used for the analysis.
BCR repertoire analysis Methods (software) MiXCR
Japanese Genotype-phenotype Archive Dataset ID JGAD000602
Total Data Volume 12.9 GB (B cell receptor repertoire clonotype data, txt)
Comments (Policies) NBDC policy

 

JGAS000626 (RNA-seq)

Participants/Materials

Rheumatoid Arthritis (ICD10: M0690): 19 cases

Systemic Lupus Erythematosus (ICD10: M329): 62 cases

Idiopathic Inflammatory Myopathy (ICD10: M339, M332): 40 cases

64 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500]
Library Source Total RNAs extracted from 9 immune cell subsets (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp
Mapping Methods STAR (GRCh38)
QC Methods From sequenced reads, adaptor sequences were trimmed using cutadapt (v1.16). In addition, 3′- ends with low-quality bases (Phred quality score < 20) were trimmed using the fastx-toolkit (v0.0.14). Reads containing more than 20% low-quality bases were removed. Subsequently, reads were aligned against the GRCh38 reference sequence using STAR (v2.5.3) in two-pass mode with Gencode version 27 annotations. We excluded samples with uniquely mapped read rates < 90% (with the exception of < 70% for plasmablasts and <85% for the other B cell subsets) or unique read counts < 6 × 10^6. Expression was quantified using HTSeq (v 0.11.2.). For QC of the expression data, in each cell population, we filtered low count genes (< 10 in > 90% of samples), normalized between samples with a trimmed mean of M values (TMM) implemented in edgeR software, converted to log-transformed count per million (CPM), removed batch effects using ComBat software and computed inter-sample Spearman’s correlations of expression levels between each sample and the remaining samples from the same cell subset. We excluded samples with mean correlation coefficients less than 0.9.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000755
Total Data Volume 36.3 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000627 (RNA-seq)

Participants/Materials

Systemic Lupus Erythematosus (ICD10: M329): 136 cases

89 healthy controls

Targets RNA-seq
Target Loci for Capture Methods -
Platform Illumina [HiSeq 2500, NovaSeq 6000]
Library Source Total RNAs extracted from 27 immune cell subsets (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA, Naive_CD8, Naive_B, SM_B, USM_B, DN_B, Plasmablast, NK, CD16p_Mono, CL_Mono, Neu, mDC, pDC, TEMRA_CD8, CM_CD8, EM_CD8, NC_Mono, Int_Mono, LDG)
Cell Lines -
Library Construction (kit name) SMART-seq v4 Ultra Low Input RNA Kit
Fragmentation Methods SMART-seq v4 Ultra Low Input RNA Kit
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 100 bp (HiSeq 2500), 150 bp (NovaSeq 6000)
Mapping Methods STAR (GRCh38)
QC Methods Adaptor sequences were trimmed using Cutadapt and reads containing low-quality bases (Phred quality score < 20 in > 20% of the bases) were removed. Reads were aligned to the human genome within the UCSC Genome Browser (GRCh38) using STAR, and expression was counted with HTSeq. Samples with uniquely mapped read rates < 80% or unique read counts < 5 × 106 were excluded. We calculated Spearman’s correlations of the expressions between two samples from the same cell type and then removed the samples with mean correlation coefficients < 0.9. In addition, samples with the concordance rates between RNA-seq-based genotype and WGS-based genotype at the heterozygous loci < 0.9 were excluded.
Gene Number 26353
Japanese Genotype-phenotype Archive Dataset ID JGAD000756
Total Data Volume 129.4 MB (count data, txt)
Comments (Policies) NBDC policy

 

JGAS000648 (scRNA-seq)

Participants/Materials

Idiopathic Inflammatory Myopathy (ICD10: M339, M332): 6 cases

     muscle biopsy: 3 cases, 3 samples

     bronchoalveolar lavage fluid: 3 cases, 3 samples

1 healthy control

     peripheral blood: 1 sample

Targets scRNA-seq
Target Loci for Capture Methods -
Platform Illumina [NovaSeq 6000]
Library Source Total RNAs extracted from CD4T cells for peripheral blood or CD45-positive cells for muscle tissue and bronchial lavage fluid
Cell Lines -
Library Construction (kit name) Chromium Next GEM Single Cell 5’ Reagent Kit v2 Dual Index
Fragmentation Methods Chromium Next GEM Single Cell 5’ Reagent Kit v2 Dual Index
Spot Type Paired-end
Read Length (without Barcodes, Adaptors, Primers, and Linkers) 150 bp
Mapping Methods Cell Ranger (GRCh38)
QC Methods For gene detection, a range of 500-3,500 genes from a peripheral blood sample, 1,500-5,500 genes from muscle biopsy samples, 1,000-5,500 genes from bronchoalveolar lavage fluid samples, and less than 5% of cells from mitochondrial genes were analyzed.
Gene Number

peripheral blood: 17,441 genes

muscle biopsy: 18,184 genes

bronchoalveolar lavage fluid: 20,314 genes

Japanese Genotype-phenotype Archive Dataset ID JGAD000778
Total Data Volume 40.2 MB (count data, txt)
Comments (Policies) NBDC policy

 

DATA PROVIDER

Principal Investigator: Keishi Fujio

Affiliation: Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo

Project / Group Name: Immune cell multi-omics analysis of immune-mediated diseases

URL: https://www.h.u-tokyo.ac.jp/english/centers-services/clinical-divisions/allergy-and-rheumatology/index.html

Funds / Grants (Research Project Number):

NameTitleProject Number
Collaborative research fund with Chugai Pharmaceutical Co., Ltd. - -
Practical Research Project for Rare / Intractable Diseases, Japan Agency for Medical Research and Development (AMED) Identification of therapeutic targets and development of intervention strategy for systemic lupus erythematosus based on the comprehensive analysis of genome and transcriptome. JP17ek0109103
Platform Program for Promotion of Genome Medicine, Japan Agency for Medical Research and Development (AMED) Construction of stratification and prognosis prediction models from immune-mediated disease genomic information using immune cell eQTL data JP21tm0424221
Moonshot Research and Development Program, Japan Agency for Medical Research and Development (AMED) Quantum and neuron modulation technologies to suppress tissue-specific disease-related microinflammation JP21zf0127004
Practical Research Project for Allergic Diseases and Immunology, Japan Agency for Medical Research and Development (AMED) Integrative multi-omics analysis of autoimmune diseases based on single cell RNA-sequencing of inflammatory organs JP22ek0410074
Advanced Research and Development Programs for Medical Innovation , Japan Agency for Medical Research and Development (AMED-CREST) Study of T cell subsets associated with immune memory for both cytotoxic and adaptive immune responses in autoimmune diseases JP23gm1810005
JSPS Grant-in-Aid for Scientific Research (B) Single-cell multiome profiling and functional analysis of human age-associated T cells in autoimmune diseases JP22H03110

 

PUBLICATIONS

TitleDOIDataset ID
1 Integrated bulk and single-cell RNA-sequencing identified disease-relevant monocytes and a gene network module underlying systemic sclerosis doi: 10.1016/j.jaut.2020.102547

JGAD000309

E-GEAD-344

2 Identifying the most influential gene expression profile in distinguishing ANCA-associated vasculitis from healthy controls doi: 10.1016/j.jaut.2021.102617 JGAD000310
3 Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases doi: 10.1016/j.cell.2021.03.056

E-GEAD-397

E-GEAD-398

E-GEAD-420

4 Dysregulation of the gene signature of effector regulatory T cells in the early phase of systemic sclerosis doi: 10.1093/rheumatology/keac031 JGAD000406
5 Immune cell multiomics analysis reveals contribution of oxidative phosphorylation to B-cell functions and organ damage of lupus doi: 10.1136/annrheumdis-2021-221464

JGAD000371

JGAD000372

JGAD000373

6 Distinct transcriptome architectures underlying lupus establishment and exacerbation doi: 10.1016/j.cell.2022.07.021 JGAD000603
7 Immunomics analysis of rheumatoid arthritis identified precursor dendritic cells as a key cell subset of treatment resistance doi: 10.1136/ard-2022-223645

JGAD000727

JGAD000731

8 Multimodal repertoire analysis unveils B cell biology in immune-mediated diseases doi: 10.1136/ard-2023-224421 JGAD000602
9 Age-associated CD4+ T cells with B cell-promoting functions are regulated by ZEB2 in autoimmunity doi: 10.1126/sciimmunol.adk1643

JGAD000755

JGAD000756

JGAD000778

 

USRES (Controlled-access Data)

Principal InvestigatorAffiliationCountry/RegionResearch TitleData in Use (Dataset ID)Period of Data Use
Jiucun Wang Department of anthropology and human genetics, Fudan University China The role and mechanism study of glycosyltransferase-B3GNT2 in regulating macrophages of Ankylosing Spondylitis JGAD000309,
JGAD000603
2023/03/07-2024/03/01
Jacob D Jaffe Odyssey Therapeutics United States of America Investigation of immune cell transition states in autoimmune disease JGAD000309,
JGAD000371,
JGAD000372,
JGAD000603
2023/03/19-2023/10/02
Yoshito Takeda Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University Japan Pathophysiology and diagnostic development with a focus on extracellular vesicles in respiratory and immunological diseases JGAD000310,
JGAD000371,
JGAD000372,
JGAD000373
2023/04/10-2025/03/31
Ana Rita Grosso Universidade Nova de Lisboa - NOVA School of Science and Technology Portugal Assessing transcriptional dyregulation of repetitive elements and monoallelic-expressed genes in lupus JGAD000603 2023/04/25-2026/11/30
Yong-Fei WANG The Chinese University of Hong Kong Hong Kong Investigating the Molecular Mechanisms of Systemic Lupus Erythematosus Using Functional Genomics Data JGAD000371,
JGAD000372,
JGAD000373,
JGAD000603
2023/06/21-2025/05/22
Timothy Vyse Molecular and Medical genetics, King's College London United Kingdom of Great Britain and Northern Ireland Sequencing Based Genetic Analysis of Systemic Lupus JGAD000603 2023/08/25-2025/07/28
Shimpei Kubota Institute for Genetic Medicine, Hokkaido University Japan Gene expression and IL-6 amplifying circuit activators in rheumatoid arthritis and systemic lupus erythematosus JGAD000309,
JGAD000310,
JGAD000371,
JGAD000372,
JGAD000373,
JGAD000406,
JGAD000602,
JGAD000603,
JGAD000727
2024/02/26-2025/03/31
Hironao Suzuki Pharmacology Department, Drug Research Center, Kaken Pharmaceutical co., LTD. Japan Bioinformatics analysis of immune cells from SLE patients JGAD000603 2024/05/09-2025/03/31