Dataset Information

Proteomic profiling of human iPSC-derived pancreatic endocrine cells for the identification of clinical biomarkers

ABSTRACT: Great progresses have been made for generating in vitro pluripotent stem cell pancreatic β-like cells. However, the maturation stage of the cells still requires in vivo maturation to recreate the environmental niche. A deeper understanding of the factors promoting maturation of the cells is of great interest for clinical applications. We performed label-free mass spectrometry-based proteomic analysis of samples from a longitudinal study of differentiation of human-induced pluripotent stem cells towards glucose-responsive insulin producing cells.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Stem Cell

SUBMITTER: Loïc Dayon

LAB HEAD: Marine Kraus

PROVIDER: PXD008267 | Pride | 2018-04-04

REPOSITORIES: Pride

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			Action	DRS
	File Name_ StemCells_InVitro_E36_D00_Rep2_02.raw (F005894).mzid_File_Name__StemCells_InVitro_E36_D00_Rep2_02.raw_(F005894).pride.mgf.gz	Mzid
	File Name_ StemCells_InVitro_E36_D00_Rep2_02.raw (F005894).pride.mztab.gz	Mztab
	File Name_ StemCells_InVitro_E36_D00_Rep2_03.raw (F006014).mzid_File_Name__StemCells_InVitro_E36_D00_Rep2_03.raw_(F006014).pride.mgf.gz	Mzid
	File Name_ StemCells_InVitro_E36_D00_Rep2_03.raw (F006014).pride.mztab.gz	Mztab
	File Name_ StemCells_InVitro_E36_D02_Rep2_02.raw (F005895).mzid_File_Name__StemCells_InVitro_E36_D02_Rep2_02.raw_(F005895).pride.mgf.gz	Mzid

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Publications

Insights into Islet Differentiation and Maturation through Proteomic Characterization of a Human iPSC-Derived Pancreatic Endocrine Model.

Haller Corinne C Chaskar Prasad P Piccand Julie J Cominetti Ornella O Macron Charlotte C Dayon Loïc L Kraus Marine R-C MR

Proteomics. Clinical applications 20180504 5

<h4>Purpose</h4>Great progresses have been made for generating in vitro pluripotent stem cell pancreatic β-like cells. However, the maturation stage of the cells still requires in vivo maturation to recreate the environmental niche. A deeper understanding of the factors promoting maturation of the cells is of great interest for clinical applications.<h4>Experimental design</h4>Label-free mass spectrometry based proteomic analysis is performed on samples from a longitudinal study of differentiati ...[more]

PMID: 29578310

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Project description:Being inspired by metabolomic data processing, we have developed a bioinformatic pipeline that optimizes the processing of mass spectral data obtained from isobaric Tandem Mass Tag (TMT) experiments. Our method focuses on the tandem mass spectral level by first quantifying and then identifying (QtI), while preserving unidentified spectra for further investigations. The raw datasets were previously generated [1, 2]. Two-proteome model experiments were considered where identical pools of human CSF or plasma samples were mixed with E. coli samples at different concentrations. E. coli protein extract was spiked in 400 µL of CSF at amounts of 0, 2, 3, 5, 6.25, and 7.5 µg. Such sets of 6 spiked CSF samples were prepared in triplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on two independent but identical LC MS/MS, for a total of 18 raw files [1]; this experiment is called “CSF-E.coli”. E. coli protein extract was spiked at 0, 2.5, 5, 6.25, 12.5, and 25 µg in 30 µL human plasma. Such sets of 6 spiked plasma samples were prepared in quadruplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on one LC MS/MS, for a total of 12 raw files [2]; this experiment is referred as “Plasma-E.coli”.The so-called “96samples-CSF” experiment consists of 16 replicate TMT sixplex experiments measuring identical CSF samples from the pool described above [2], analyzed in triplicates on one LCMS/MS for a total of 48 raw files. The so-called “96samples-plasma” experiment consists of 16 replicate TMT sixplex experiments measuring identical plasma samples from the pool described before, analyzed in triplicates on one LC MS/MS for a total of 48 raw files [1]. References: [1] Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. & Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13, 3837-3845 (2014). [2] Núñez Galindo, A., Kussmann, M. & Dayon, L. Proteomics of Cerebrospinal Fluid: Throughput and Robustness Using a Scalable Automated Analysis Pipeline for Biomarker Discovery. Anal. Chem. 87, 10755-10761 (2015).