Dataset Information

Automated sample preparation with SP3 for low-input clinical proteomics

ABSTRACT: High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples originating from a variety of sources, including frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format, performing unbiased protein purification and digestion delivering peptides that can be directly analyzed by LCMS. AutoSP3 eliminates hands-on time and minimizes the risk of error, and we show it reduces the protein quantification variability, and improves longitudinal performance and reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500-1000 proteins from 100-1000 cells (<100 ng protein). Furthermore, we added a LE220-plus focused- ultrasonicator (Covaris Ltd, UK) to our pipeline to include 96-well format lysis of fresh-frozen tissue and cells. Collectively, autoSP3 provides a generic, scalable, and cost-effective pipeline for routine and standardized proteomic sample processing that should enable reproducible proteomics in broad range of clinical and non-clinical applications.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Sus Scrofa Domesticus (domestic Pig) Mus Musculus (mouse)

TISSUE(S): Heart, Liver, Kidney, Hela Cell

DISEASE(S): Cervix Carcinoma,Cardiovascular System Disease,Disease Free

SUBMITTER: Torsten Mueller

LAB HEAD: Jeroen Krijgsveld

PROVIDER: PXD015840 | Pride | 2020-03-27

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20190911_QE1_TM_autoSP3_HeLa_1_20190916185152.raw	Raw
	20190911_QE1_TM_autoSP3_HeLa_2_20190916205221.raw	Raw
	20190911_QE1_TM_autoSP3_HeLa_3.raw	Raw
	20190911_QE1_TM_autoSP3_HeLa_4.raw	Raw
	20190911_QE1_TM_autoSP3_HeLa_4_re.raw	Raw

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Publications

Automated sample preparation with SP3 for low-input clinical proteomics.

Müller Torsten T Kalxdorf Mathias M Longuespée Rémi R Kazdal Daniel N DN Stenzinger Albrecht A Krijgsveld Jeroen J

Molecular systems biology 20200101 1

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directl ...[more]

PMID: 32129943

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Project description:Understanding the interplay of the proteome and the metabolome aids in understanding cellular phenotypes. To enable more robust inferences from such multi-omics analyses, combining proteomic and metabolomic datasets from the same sample provides major benefits by reducing technical variation between extracts during the pre-analytical phase, decreasing sample variation due to varying cellular content between aliquots, and limiting the required sample amount. We evaluated the advantages, practicality and feasibility of a single-sample workflow for combined proteome and metabolome analysis. In the workflow, termed MTBE-SP3, we combined a fully automated protein lysis and extraction protocol (autoSP3) with a semi-automated biphasic 75% EtOH/MTBE extraction for quantification of polar/non-polar metabolites. Additionally, we compared the resulting proteome of various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) between autoSP3 and MTBE-SP3. Our analysis revealed that the single-sample workflow provided similar results to those obtained from autoSP3 alone, with an 85-98% overlap of proteins detected across the different biological matrices. Additionally, it provides distinct advantages by decreasing (tissue) heterogeneity by retrieving metabolomics and proteomic data from the identical biological material, and limiting the total amount of required material. Lastly, we applied MTBE-SP3 to a lung adenocarcinoma cohort of 10 patients. Integrating the metabolic and proteomic alterations between tumour and non-tumour adjacent tissue yielded consistent data independent of the method used. This revealed mitochondrial dysfunction in tumor tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and confident parallel measurement of proteins and metabolites obtained from the same sample. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.