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Immune cells transcriptome

ABSTRACT: Immune cells transcriptome

PROVIDER: PRJNA659489 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12518513.fastq.gz	Fastqsanger.gz
	SRR12518514.fastq.gz	Fastqsanger.gz
	SRR12518515.fastq.gz	Fastqsanger.gz
	SRR12518516.fastq.gz	Fastqsanger.gz
	SRR12518517.fastq.gz	Fastqsanger.gz

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Project description:Objectives: To perform long-read transcriptome and proteome profiling of pathogen-stimulated peripheral blood mononuclear cells (PBMCs) from healthy donors. We aim to discover new transcripts and protein isoforms expressed during immune responses to diverse pathogens. Methods: PBMCs were exposed to four microbial stimuli for 24 hours: the TLR4 ligand lipopolysaccharide (LPS), the TLR3 ligand Poly(I:C), heat-inactivated Staphylococcus aureus, Candida albicans, and RPMI medium as negative controls. Long-read sequencing (PacBio) of one donor and secretome proteomics and short-read sequencing of five donors were performed. IsoQuant was used for transcriptome construction, Metamorpheus/FlashLFQ for proteome analysis, and Illumina short-read 3’-end mRNA sequencing for transcript quantification. Results: Long-read transcriptome profiling reveals the expression of novel sequences and isoform switching induced upon pathogen stimulation, including transcripts that are difficult to detect using traditional short-read sequencing. We observe widespread loss of intron retention as a common result of all pathogen stimulations. We highlight novel transcripts of NFKB1 and CASP1 that may indicate novel immunological mechanisms. In general, RNA expression differences did not result in differences in the amounts of secreted proteins. Interindividual differences in the proteome were larger than the differences between stimulated and unstimulated PBMCs. Clustering analysis of secreted proteins revealed a correlation between chemokine (receptor) expression on the RNA and protein levels in C. albicans- and Poly(I:C)-stimulated PBMCs. Conclusion: Isoform aware long-read sequencing of pathogen-stimulated immune cells highlights the potential of these methods to identify novel transcripts, revealing a more complex transcriptome landscape than previously appreciated.

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