scholarly journals Cryopreservation Preserves Cell-Type Composition and Gene Expression Profiles in Bone Marrow Aspirates From Multiple Myeloma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Duojiao Chen ◽  
Mohammad I. Abu Zaid ◽  
Jill L. Reiter ◽  
Magdalena Czader ◽  
Lin Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Type ◽  
Myeloma Cells ◽  
Freeze Thaw ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing reveals gene expression differences between individual cells and also identifies different cell populations that are present in the bulk starting material. To obtain an accurate assessment of patient samples, single-cell suspensions need to be generated as soon as possible once the tissue or sample has been collected. However, this requirement poses logistical challenges for experimental designs involving multiple samples from the same subject since these samples would ideally be processed at the same time to minimize technical variation in data analysis. Although cryopreservation has been shown to largely preserve the transcriptome, it is unclear whether the freeze-thaw process might alter gene expression profiles in a cell-type specific manner or whether changes in cell-type proportions might also occur. To address these questions in the context of multiple myeloma clinical studies, we performed single-cell RNA sequencing (scRNA-seq) to compare fresh and frozen cells isolated from bone marrow aspirates of six multiple myeloma patients, analyzing both myeloma cells (CD138+) and cells constituting the microenvironment (CD138−). We found that cryopreservation using 90% fetal calf serum and 10% dimethyl sulfoxide resulted in highly consistent gene expression profiles when comparing fresh and frozen samples from the same patient for both CD138+ myeloma cells (R ≥ 0.96) and for CD138– cells (R ≥ 0.9). We also demonstrate that CD138– cell-type proportions showed minimal alterations, which were mainly related to small differences in immune cell subtype sensitivity to the freeze-thaw procedures. Therefore, when processing fresh multiple myeloma samples is not feasible, cryopreservation is a useful option in single-cell profiling studies.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Multiple Myeloma Cells Alter Adipogenesis, Increase Senescence-Related and Inflammatory Gene Transcript Expression, and Alter Metabolism in Preadipocytes

2021 ◽  
Vol 10 ◽  
Author(s):  
Heather Fairfield ◽  
Samantha Costa ◽  
Carolyne Falank ◽  
Mariah Farrell ◽  
Connor S. Murphy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Lipid Accumulation ◽  
Expression Profiles ◽  
Adipogenic Differentiation ◽  
Gene Expression Profiles ◽  
Mouse Bone Marrow ◽  
Gene Transcript ◽  
Bone Marrow Mscs

Within the bone marrow microenvironment, mesenchymal stromal cells (MSCs) are an essential precursor to bone marrow adipocytes and osteoblasts. The balance between this progenitor pool and mature cells (adipocytes and osteoblasts) is often skewed by disease and aging. In multiple myeloma (MM), a cancer of the plasma cell that predominantly grows within the bone marrow, as well as other cancers, MSCs, preadipocytes, and adipocytes have been shown to directly support tumor cell survival and proliferation. Increasing evidence supports the idea that MM-associated MSCs are distinct from healthy MSCs, and their gene expression profiles may be predictive of myeloma patient outcomes. Here we directly investigate how MM cells affect the differentiation capacity and gene expression profiles of preadipocytes and bone marrow MSCs. Our studies reveal that MM.1S cells cause a marked decrease in lipid accumulation in differentiating 3T3-L1 cells. Also, MM.1S cells or MM.1S-conditioned media altered gene expression profiles of both 3T3-L1 and mouse bone marrow MSCs. 3T3-L1 cells exposed to MM.1S cells before adipogenic differentiation displayed gene expression changes leading to significantly altered pathways involved in steroid biosynthesis, the cell cycle, and metabolism (oxidative phosphorylation and glycolysis) after adipogenesis. MM.1S cells induced a marked increase in 3T3-L1 expression of MM-supportive genes including Il-6 and Cxcl12 (SDF1), which was confirmed in mouse MSCs by qRT-PCR, suggesting a forward-feedback mechanism. In vitro experiments revealed that indirect MM exposure prior to differentiation drives a senescent-like phenotype in differentiating MSCs, and this trend was confirmed in MM-associated MSCs compared to MSCs from normal donors. In direct co-culture, human mesenchymal stem cells (hMSCs) exposed to MM.1S, RPMI-8226, and OPM-2 prior to and during differentiation, exhibited different levels of lipid accumulation as well as secreted cytokines. Combined, our results suggest that MM cells can inhibit adipogenic differentiation while stimulating expression of the senescence associated secretory phenotype (SASP) and other pro-myeloma molecules. This study provides insight into a novel way in which MM cells manipulate their microenvironment by altering the expression of supportive cytokines and skewing the cellular diversity of the marrow.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals NEK2 Inhibition Enhances the Efficacy of PD-1/PD-L1 Blockade in Multiple Myeloma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2671-2671
Author(s):  
Yan Cheng ◽  
Fumou Sun ◽  
Huojun Cao ◽  
Dongzheng Gai ◽  
Bailu Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
B Cells ◽  
Cell Lines ◽  
Immune Checkpoint ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Myeloma Cells

Abstract Introduction The development of new treatments for high-risk multiple myeloma (HRMM) are needed. The PD-1/PD-L1 axis is one of the chief inhibitory immune checkpoints in antitumor immunity. Despite the success of PD-1 (PDCD1) / PD-L1 (CD274) blockade in some neoplasms, use of it as a monotherapy has failed to improve outcome in RRMM. We have previously demonstrated that the cell-cycle-regulated serine-threonine kinase, NEK2 is elevated in HRMM and that inhibition of NEK2 can overcome drug-resistance and prolong survival of xenografted MM cells. Here, we aimed to investigate the possible role of NEK2 in regulating the immune checkpoint response in MM and development of possible anti-PD1/PDL1 combination therapies. Methods Gene expression profiles and pathway enrichment analyses were conducted on oligonucleotide microarray gene expression profiles from over 1000 primary MM samples to evaluate the correlation of NEK2 and immune checkpoint expression levels. To elucidate the underlying mechanism, we used Nek2 -/- mice crossed with EμMyc mice to generate B cell tumor mouse model with NEK2 deficiency. RNA-sequencing analyses of premalignant B cells was compared between EμMyc/Nek2 WT and EμMyc/Nek2 -/- mice. The hub molecular regulators in the NEK2 correlated pathways were further determined by western blot using NEK2 overexpressing and knockdown cell lines and then verified by co-immunoprecipitation with a NEK2 antibody. Lastly, to establish its clinic significance, the efficacy of INH1 (small compound NEK2 inhibitor), (D)-PPA 1 (peptide-based PD-1/PD-L1 interaction inhibitor) or a PD-L1 (monoclonal antibody) was tested in bone marrow BM mononuclear cells from primary MM patients in-vitro as well as in MM xenografts. Tumor burden and T cell immune responses were monitored by M-spike and mass cytometry. Results Gene expression profiles demonstrated that CD274 expression was significantly higher in the non-proliferative hyperdiploid (HY) subtype of MM, representing between 25-35% of all MM. NEK2 was negatively correlated with CD274 gene expression across all 7 MM subtypes. Gene set enrichment analysis showed that the IFN-γ signaling pathway, which can induce CD274 expression, was significantly enriched in the HY subtype as well as premalignant B cells from EμMyc/Nek2 -/- mice. Elevated expression of EZH2, a histone methyltransferase gene, is also highly correlated wirth NEK2 levels in primary MM. We found that NEK2 inhibition increases CD274 expression as well as reduced EZH2 expression and H3K27me3 levels in MM cell lines. In contrarst, myeloma cells overexpressing NEK2 showed increased expression and activity of EZH2 and H3K27me3 levels. Thus, NEK2 appears to regulate CD274/PD-L1 expression through EZH2-mediated histone methylation. Next we demonstrated that NEK2 and EZH2 directly interact and that overexpression of NEK2 leads to increased methylation of the CD274/PD-L1 gene. We treated BM mononuclear cells from primary MM with PD-1/PD-L1 inhibitor with and without a NEK2 inhibitor. The combination was most effective at eliminating CD138 + myeloma cells while having no effects on T, B and myeloid cell populations. Conclusion Our study showed that expression of CD274/PD-L1 is suppressed in primary HRMM and that CD274/PD-L1 expression is negatively regulated by NEK2 via EZH2-mediated methylation. Inhibition of NEK2 sensitizes myeloma cells to PD-1/PD-L1 blockade, showing either a synergistic or an additive effect in MM cell cytotoxicity. Disclosures No relevant conflicts of interest to declare.

scholarly journals G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Weimiao Wu ◽  
Qile Dai ◽  
Yunqing Liu ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

Genome-Wide Profiling of Endothelial Progenitor Cells in Multiple Myeloma: Overlaps with Myeloma Tumor Cells and Common Cancer Gene-Pathways.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 394-394
Author(s):  
Marc J. Braunstein ◽  
Daniel R. Carrasco ◽  
Fabien Campagne ◽  
Piali Mukherjee ◽  
Kumar Sukhdeo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Biomarkers ◽  
Genome Wide

Abstract Background: In multiple myeloma (MM), bone-marrow-derived endothelial progenitor cells (EPCs) contribute to tumor neoangiogenesis, and their levels covary with tumor mass and prognosis. Recent X-chromosome inactivation studies showed that EPCs are clonally restricted in MM. In addition, high-resolution array comparative genomic hybridization (aCGH) found that the genomes of EPCs and MM cells display similar chromosomal gains and losses in the same patient. In this study, we performed an integrative analysis of EPCs and tumor cells by genome-wide expression profiling, and applied a bioinformatics approach that leverages gene expression data from cancer datasets to mine MM gene pathways common to multiple tumor tissues and likely involved in MM pathogenesis. Methods: Confluent EPCs (>98% vWF/CD133/KDR+ and CD38−) were outgrown from 22 untreated MM patients’ bone marrow aspirates by adherence to laminin. The fractions enriched for tumor cells were >50% CD38+. For gene expression profiling, total RNA from EPCs, MM cells, and control HUVECs were hybridized to cDNA microarrays, and comparisons were made by analysis of variance. Results: Two sets of EPC gene profiles were of particular interest. The first contained genes that differ significantly between EPCs and HUVEC, but not between EPCs and tumor (Profile 1). We hypothesize that this profile is a consequence of the clonal identity previously reported between EPCs and tumor, and that a subset of these genes is largely responsible for MM progression. The second set of important EPC genes are differentially regulated compared both to HUVECs and to tumor cells (Profile 2). These genes may represent the profile of EPCs that are clonally diverse from tumor cells but nevertheless display common gene expression patterns with other cancers. Profile 2 genes may also represent genes that confer a predisposition to clonal transformation of EPCs. When genes in Profile 1 and Profile 2 were overlapped with published lists of cancer biomarkers, significant similarities (P<.05) were apparent. The largest overlaps were observed with the HM200 gene list, a list composed of 200 genes most consistently differentially expressed in human/mouse cancers (Campagne and Skrabanek, BMC Bioinformatics 2006). More than 80% of genes in either EPC profile have not been previously characterized in MM, but have been identified as cancer biomarkers in other cancer studies. These genes will be presented and discussed in the context of MM. Current studies are aimed at integrating Profile 1 and Profile 2 genes in each patient with chromosomal copy number abnormalities (CNAs) found in EPCs, and also with clinical stage and disease severity, in order to elucidate the pathogenic information that the profiles hold. Conclusions: The genomes of EPCs display ranges of overlap with tumor cells in MM, evidenced by gene expression profiles with varying similarity to those found in MM tumor cells. More importantly, MM EPC gene expression profiles, in contrast to normal endothelial cells, contain cancer biomarker genes in tumors not yet associated with MM. Results strongly support the concept that EPCs are an integral part of the neoplastic process in MM.

Effect of Imids on Gene Expression Profiles of Fresh Human Myeloma Cells

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 5013-5013
Author(s):  
Ines Tagoug ◽  
Adriana Plesa ◽  
Julie Vendrell ◽  
Charles Dumontet
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Family Member ◽  
Magnetic Beads ◽  
Expression Profiles ◽  
Calf Serum ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Myeloma Cells ◽  
Human Myeloma Cells

Abstract Abstract 5013 Immunomodulatory drugs represent a major therapeutic advance in the treatment of patients with multiple myeloma. While these agents appear to exert various effects on the microenvironment, including effect on immune cells and angiogenesis, a direct effect on the tumor cells themselves is also likely. To describe and compare the effect of the three clinically available agents (thalidomide, lenalidomide, pomalidomide) we analyzed the gene expression profiles of fresh human myeloma cells exposed to thalidomide, lenalidomide or pomalidomide, using high density DNA arrays. Fresh human myeloma samples were obtained from bone marrow aspirates of patients with myeloma, and myeloma cells were immunopurified using anti CD138 magnetic beads. Purified myeloma cells (1.106 cells/ml) were incubated for 24 hours in RPMI 1640 medium supplemented with 10% fetal calf serum under each of the four following conditions: 1) DMSO; 2) thalidomide 40 microM; 3) lenalidomide 1 microM; 4) pomalidomide 100 nM. These levels are achievable in the plasma of MM pts. Pangenomic array experiments were performed usingWhole Human Genome 4 × 44K Agilent one-color microarrays. Data were normalized using the quantile normalization method. Samples were analysed for differentially expressed genes, taking into account both the level of significance and the fold-change. Ten evaluable samples were processed. Exposure to thalidomide, lenalidomide and pomalidomide induced differential expression of 36, 50 and 75 genes, respectively, in comparison to DMSO-exposed controls, the total list including 101 genes. Twelve of these were found to be differentially expressed after exposure to all of the three agents, including trophoblast glycoprotein, WAS protein family member 1, dickkopf homolog 1, pentraxin-related gene, CD28, interleukin 12B, tissue factor pathway inhibitor 2, phospholipase A2, dehydrogenase/reductase (SDR family) member 9, hypothetical LOC145788 and betacellulin. These commonly altered genes could be mechanistically involved in themultiple activities of these agents in multiple myeloma or may represent epiphenoma mechanistically unrelated to drug-induced cell death. Genes differentially expressed between the treatment with each of these agents could be indicative of the different and non-overlapping actions these agents have in multiple myeloma. An example of this is the recent demonstration that pomalidomide is clinically active in lenalidomide refractory patients. These results suggest that exposure to IMIDs induce various intracellular signalization pathways in myeloma cells which might be involved in the cytotoxic activity of these compounds. Disclosures: Dumontet: Celgene: Research Funding.

scholarly journals Impact of sequencing depth and read length on single cell RNA sequencing data: lessons from T cells

2017 ◽  
Author(s):  
Simone Rizzetto ◽  
Auda A. Eltahla ◽  
Peijie Lin ◽  
Rowena Bull ◽  
Andrew R. Lloyd ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Depth ◽  
Read Length ◽  
Single Cell Rna Sequencing

ABSTRACTSingle cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

scholarly journals SC2disease: a manually curated database of single-cell transcriptome for human diseases

2020 ◽  
Vol 49 (D1) ◽  
pp. D1413-D1419 ◽  
Author(s):  
Tianyi Zhao ◽  
Shuxuan Lyu ◽  
Guilin Lu ◽  
Liran Juan ◽  
Xi Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cellular Level ◽  
Human Diseases ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

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