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Clinical Trial
. 2021 May 18:12:641886.
doi: 10.3389/fimmu.2021.641886. eCollection 2021.

The Chromatin Accessibility Landscape of Peripheral Blood Mononuclear Cells in Patients With Systemic Lupus Erythematosus at Single-Cell Resolution

Affiliations
Clinical Trial

The Chromatin Accessibility Landscape of Peripheral Blood Mononuclear Cells in Patients With Systemic Lupus Erythematosus at Single-Cell Resolution

Haiyan Yu et al. Front Immunol. .

Abstract

Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease, and various immune cells are involved in the initiation, progression, and regulation of SLE. Our goal was to reveal the chromatin accessibility landscape of peripheral blood mononuclear cells (PBMCs) in SLE patients at single-cell resolution and identify the transcription factors (TFs) that may drive abnormal immune responses.

Methods: The assay for transposase accessible chromatin in single-cell sequencing (scATAC-seq) method was applied to map the landscape of active regulatory DNA in immune cells from SLE patients at single-cell resolution, followed by clustering, peak annotation and motif analysis of PBMCs in SLE.

Results: Peripheral blood mononuclear cells were robustly clustered based on their types without using antibodies. We identified twenty patterns of TF activation that drive abnormal immune responses in SLE patients. Then, we observed ten genes that were highly associated with SLE pathogenesis by altering T cell activity. Finally, we found 12 key TFs regulating the above six genes (CD83, ELF4, ITPKB, RAB27A, RUNX3, and ZMIZ1) that may be related to SLE disease pathogenesis and were significantly enriched in SLE patients (p <0.05, FC >2). With qPCR experiments on CD83, ELF4, RUNX3, and ZMIZ1 in B cells, we observed a significant difference in the expression of genes (ELF4, RUNX3, and ZMIZ1), which were regulated by seven TFs (EWSR1-FLI1, MAF, MAFA, NFIB, NR2C2 (var. 2), TBX4, and TBX5). Meanwhile, the seven TFs showed highly accessible binding sites in SLE patients.

Conclusions: These results confirm the importance of using single-cell sequencing to uncover the real features of immune cells in SLE patients, reveal key TFs in SLE-PBMCs, and provide foundational insights relevant for epigenetic therapy.

Keywords: marker; peripheral blood mononuclear cells; single-cell chromatin accessible assay; systemic lupus erythematosus; transcription factor.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Cell-type-specific clustering of human PBMCs according to scATAC-seq. (A) Schematic showing the process of isolating PBMCs for scATAC-seq; (B) Histogram of the distribution of fragment lengths in reads from the SLE_PBMC and NC_PBMC groups; (C) Histograms showing enrichment of fragments at TSSs; (D) tSNE plot of cellular populations in the SLE_PBMC and NC_PBMC groups; (E) tSNE plot of canonical cell markers used to label clusters, color-coded for expression levels (gray to red); (F) tSNE plot of cell type-specific TF motifs (left), color-coded for expression levels (gray to red), and Venn diagram showing the distribution of 15 TF motifs with significantly differences (p <0.05) between different clusters (right). PBMCs, peripheral blood mononuclear cells; scATAC-seq, assaying transposase-accessible chromatin in single-cell sequencing; SLE_PBMC, PBMCs from patients with systemic lupus erythematosus (SLE); NC_PBMC, PBMCs from healthy controls; NK cells, natural killer cells; DCs, dendritic cells; TF, transcription factor; TSSs, transcription start sites. The p-values were calculated with Loupe Cell Browser 3.1.1 through the difference analysis feature and adjusted using the Benjamini–Hochberg correction for multiple tests.
Figure 2
Figure 2
Epigenomic analysis of human PBMCs. (A) Cell ratios in each cell type for comparison between the SLE_PBMC and NC_PBMC libraries; (B) Number of different peaks in each cell type for comparison between the SLE_PBMC and NC_PBMC libraries (p <0.05); (C) Number of different TF motifs in each cell type for comparison between the SLE_PBMC and NC_PBMC libraries (p <0.05); (D) tSNE plot of B cells, DCs, monocytes, NK cells and T cells, color-coded by their associated subcluster; (E) Cell ratios in each subcluster for comparison between the SLE_PBMC and NC_PBMC libraries; (F) Number of different peaks in each subcluster for comparison between the SLE_PBMC and NC_PBMC libraries (p <0.05); (G) Number of different TF motifs in each subcluster for comparison between the SLE_PBMC and NC_PBMC libraries (p <0.05); PBMCs, peripheral blood mononuclear cells; SLE_PBMC, PBMCs from patients with systemic lupus erythematosus (SLE); NC_PBMC, PBMCs from healthy controls; NK cells, natural killer cells. The p-values were calculated with Loupe Cell Browser 3.1.1 through the difference analysis feature and adjusted using the Benjamini–Hochberg correction for multiple tests.
Figure 3
Figure 3
Functional analysis of significantly differential peaks between the SLE_PBMC and NC_PBMC libraries (p <0.05). (A) GO analysis of 16 differential genes between the SLE_PBMC and NC_PBMC libraries in the unknown group; (B) GO analysis of 447 differential genes between the SLE_PBMC and NC_PBMC libraries in B cells; (C) GO analysis of 917 differential genes between the SLE_PBMC and NC_PBMC libraries in subcluster 4 of NK cells (NK-4); (D) GO analysis of 11 differential genes between the SLE_PBMC and NC_PBMC libraries in subcluster 1 of T cells (T-1); (E) GO analysis revealing the 10 most significant pathways in subcluster 3 of B cells (B-3); (F) GO analysis revealing the 10 most significant pathways in subcluster 2 of DCs (DC-2); (G) GO analysis revealing the 10 most significant pathways in subcluster 3 of monocytes (Monocyte-3); (H) Venn-diagram showing distribution of genes corresponding to T cell activation in (E–G); (I) Example locus near BCL11B with differential accessibility across B cell subclusters and monocyte subpopulations; (J) Venn diagram showing the distribution of 52 observed enriched TF motifs between the SLE_PBMC and NC_PBMC libraries. GO, Gene Ontology; SLE_PBMC, PBMCs from patients with systemic lupus erythematosus (SLE); NC_PBMC, PBMCs from healthy controls; NK cells, natural killer cells; DCs, dendritic cells; TF, transcription factor. The p-values were calculated with Loupe Cell Browser 3.1.1 through the difference analysis feature and adjusted using the Benjamini–Hochberg correction for multiple tests.
Figure 4
Figure 4
Identifying key TF regulators and their regulatory networks involved in the T cell activation pathway. (A) Venn diagram showing the distribution of enriched TF regulators in B-3, Monocyte-3, and DC-2 between the SLE_PBMC and NC_PBMC libraries (p <0.05, FC >2) and the 157 TF regulators involved in regulating the target genes (BCL11B, CCR7, CD83, ELF4, ITPKB, NCK2, NKAP, RAB27A, RUNX3, and ZMIZ1) to activate T cells; (B) Position weight matrices (PWMs) for the shared TFs in (A); (C) Example locus near RAB27A, RUNX3, ZMIZ1, ITPKB, CD83 and ELF4 with differential accessibility between the SLE_PBMC and NC_PBMC libraries across DC-2 and B-3. SLE_PBMC, PBMCs from patients with systemic lupus erythematosus (SLE); NC_PBMC, PBMCs from healthy controls; NK cells, natural killer cells; DCs, dendritic cells. The p-values in this manuscript were calculated with Loupe Cell Browser 3.1.1 through the difference analysis feature and adjusted using the Benjamini–Hochberg correction for multiple tests.

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References

    1. Moore PM, Lisak RP. Systemic Lupus Erythematosus: Immunopathogenesis of Neurologic Dysfunction. Springer Semin Immunopathol (1995) 17:43–60. 10.1007/BF00194099 - DOI - PubMed
    1. Deng Y, Tsao BP. Genetic Susceptibility to Systemic Lupus Erythematosus in the Genomic Era. Nat Rev Rheumatol (2010) 6:683–92. 10.1038/nrrheum.2010.176 - DOI - PMC - PubMed
    1. Teruel M, Chamberlain C, Alarcón-Riquelme ME. Omics Studies: Their Use in Diagnosis and Reclassification of SLE and Other Systemic Autoimmune Diseases. Rheumatol (Oxford) (2017) 56:i78–87. 10.1093/rheumatology/kew339 - DOI - PubMed
    1. Hui-Yuen JS, Zhu L, Wong LP, Jiang K, Chen Y, Liu T, et al. . Chromatin Landscapes and Genetic Risk in Systemic Lupus. Arthritis Res Ther (2016) 18:281. 10.1186/s13075-016-1169-9 - DOI - PMC - PubMed
    1. Chen L, Morris DL, Vyse TJ. Genetic Advances in Systemic Lupus Erythematosus: An Update. Curr Opin Rheumatol (2017) 29:423–33. 10.1097/BOR.0000000000000411 - DOI - PubMed

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