Single-cell transcriptomics dissects hematopoietic cell destruction and T-cell engagement in aplastic anemia.
Caiying ZhuYu LianChenchen WangPeng WuXuan LiYan GaoSibin FanLanlan AiLiwei FangHong PanTao ChengJun ShiCaiying ZhuPublished in: Blood (2021)
Aplastic anemia (AA) is a T cell-mediated autoimmune disorder of the hematopoietic system manifested by severe depletion of the hematopoietic stem and progenitor cells (HSPCs). Nonetheless, our understanding of the complex relationship between HSPCs and T cells is still obscure, mainly limited by techniques and the sparsity of HSPCs in the context of bone marrow failure. Here we performed single-cell transcriptome analysis of residual HSPCs and T cells to identify the molecular players from patients with AA. We observed that residual HSPCs in AA exhibited lineage-specific alterations in gene expression and transcriptional regulatory networks, indicating a selective disruption of distinct lineage-committed progenitor pools. In particular, HSPCs displayed frequently altered alternative splicing events and skewed patterns of polyadenylation in transcripts related to DNA damage and repair, suggesting a likely role in AA progression to myelodysplastic syndromes. We further identified cell type-specific ligand-receptor interactions as potential mediators for ongoing HSPCs destruction by T cells. By tracking patients after immunosuppressive therapy (IST), we showed that hematopoiesis remission was incomplete accompanied by IST insensitive interactions between HSPCs and T cells as well as sustained abnormal transcription state. These data collectively constitute the transcriptomic landscape of disrupted hematopoiesis in AA at single-cell resolution, providing new insights into the molecular interactions of engaged T cells with residual HSPCs and render novel therapeutic opportunities for AA.
Keyphrases
- single cell
- rna seq
- bone marrow
- gene expression
- high throughput
- dna damage
- chronic kidney disease
- end stage renal disease
- transcription factor
- dna methylation
- oxidative stress
- multiple sclerosis
- ejection fraction
- social media
- acute lymphoblastic leukemia
- stem cells
- mesenchymal stem cells
- machine learning
- early onset
- drug induced
- disease activity
- artificial intelligence
- risk assessment
- data analysis
- patient reported outcomes
- ulcerative colitis
- resting state
- heat shock