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TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

ABSTRACT: TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

PROVIDER: PRJNA1198939 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR31734505_1.fastq.gz	Fastqsanger.gz
	SRR31734505_2.fastq.gz	Fastqsanger.gz
	SRR31734506_1.fastq.gz	Fastqsanger.gz
	SRR31734506_2.fastq.gz	Fastqsanger.gz
	SRR31734507_1.fastq.gz	Fastqsanger.gz

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Project description:Mass spectrometry-based omics technologies are of increasing relevance for drug discovery. In order to map drug treatments to biological pathways for mode of action (MoA) deconvolution, they rely on the determination of multiple testing corrected significant molecular events. The main contributors to statistical significance are the effect size and the variation of a given molecule during the drug treatment. While the former is largely determined by the biological system and the impact of the drug, the latter reflects the robustness of the experimental workflow. The impact of sample preparation and instrumentation on variation are well characterized, but the impact of cell culture conditions on variation of omics readouts is less explored, specifically related to subculture conditions during cell expansion. Here, we provide evidence that memory effects from subculture directly influence the variation of multilevel omics readouts, including oxylipins and fatty acids, proteins and phosphorylated sites, and phenotypic parameters, e.g. cell motility. When comparing drug treatments after homogeneous and heterogeneous subculture, the coefficients of variation (CVs) of controls increased from 1% to 3% for oxylipins and fatty acids, 11% to 23% for proteomics, 24% to 53% for phosphoproteomics and 6% to 14% for cell motility, respectively. The number of identifications remained comparable and the fold-change distributions were also largely similar after both subculture conditions. Therefore, the decrease in CVs seems to be the main contributor to the increased number of significant molecular events in drug treatments after homogeneous subculture. This in turn increased information content in the obtained perturbation network, based on proteomics and phosphoproteomics data, from 58 to 321 causal conjectures. Perturbation networks after homogeneous and heterogeneous subculture allowed for delineating the established MoA of arsenic trioxide in detail, including enhanced kinase signaling and reduced MYC-related gene expression, induction of heat shock proteins and cell cycle disruption. However, only the perturbation network after homogeneous subculture enabled the discovery of unprecedented drug effects, including down-regulation and quadruple dephosphorylation of the transcription factor RB1, among others. In summary, memory effects from subculture have broad relevance for MoA testing in drug discovery since this impact clearly manifests in supernatants, whole cell lysates and phenotypic properties and its control enables a considerably enhanced MoA deconvolution, facilitating discovery efforts.

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TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

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