Single-cell time series analysis reveals the dynamics of HSPC response to inflammation.
Brigitte J BoumanYasmin DemerdashShubhankar SoodFlorian GrünschlägerFranziska PilzAbdul R ItaniAndrea KuckValérie Marot-LassauzaieSimon HaasLaleh HaghverdiMarieke Alida Gertruda EssersPublished in: Life science alliance (2023)
Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.
Keyphrases
- single cell
- rna seq
- oxidative stress
- inflammatory response
- dendritic cells
- high throughput
- immune response
- liver failure
- bone marrow
- machine learning
- genome wide
- combination therapy
- induced apoptosis
- respiratory failure
- copy number
- intensive care unit
- transcription factor
- quality improvement
- antibiotic resistance genes
- bioinformatics analysis
- genome wide identification