Proteomic Analysis of Human Neural Stem Cell Differentiation by SWATH-MS

Jazyk angličtina Země Spojené státy americké Médium print

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid35579839

The unique properties of stem cells to self-renew and differentiate hold great promise in disease modelling and regenerative medicine. However, more information about basic stem cell biology and thorough characterization of available stem cell lines is needed. This is especially essential to ensure safety before any possible clinical use of stem cells or partially committed cell lines. As proteins are the key effector molecules in the cell, the proteomic characterization of cell lines, cell compartments or cell secretome and microenvironment is highly beneficial to answer above mentioned questions. Nowadays, method of choice for large-scale discovery-based proteomic analysis is mass spectrometry (MS) with data-independent acquisition (DIA). DIA is a robust, highly reproducible, high-throughput quantitative MS approach that enables relative quantification of thousands of proteins in one sample. In the current protocol, we describe a specific variant of DIA known as SWATH-MS for characterization of neural stem cell differentiation. The protocol covers the whole process from cell culture, sample preparation for MS analysis, the SWATH-MS data acquisition on TTOF 5600, the complete SWATH-MS data processing and quality control using Skyline software and the basic statistical analysis in R and MSstats package. The protocol for SWATH-MS data acquisition and analysis can be easily adapted to other samples amenable to MS-based proteomics.

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Thomson JA, Itskovitz-Eldor J, Shapiro SS et al (1998) Embryonic stem cell lines derived from human blastocysts. Science 282:1145–1147 PubMed DOI

Barker RA, de Beaufort I (2013) Scientific and ethical issues related to stem cell research and interventions in neurodegenerative disorders of the brain. Prog Neurobiol 110:63–73 PubMed DOI

Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676 PubMed DOI

Goldman SA (2016) Stem and progenitor cell-based therapy of the central nervous system: hopes, hype, and wishful thinking. Cell Stem Cell 18:174–188 PubMed DOI PMC

Zizkova M, Sucha R, Tyleckova J et al (2015) Proteome-wide analysis of neural stem cell differentiation to facilitate transition to cell replacement therapies. Expert Rev Proteomics 12:83–95 PubMed DOI

Boese AC, Hamblin MH, Lee J-P (2020) Neural stem cell therapy for neurovascular injury in Alzheimer’s disease. Exp Neurol 324:113112 PubMed DOI

Choi K-A, Hong S (2017) Induced neural stem cells as a means of treatment in Huntington’s disease. Expert Opin Biol Ther 17:1333–1343 PubMed

Fan Y, Winanto, Ng S-Y (2020) Replacing what’s lost: a new era of stem cell therapy for Parkinson’s disease. Transl Neurodegener 9:2 PubMed DOI PMC

Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207 PubMed DOI

Sucha R, Kubickova M, Cervenka J et al (2021) Targeted mass spectrometry for monitoring of neural differentiation. Biol Open 10:bio058727 PubMed DOI PMC

Gillet LC, Navarro P, Tate S et al (2012) Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6):O111.016717 PubMed DOI PMC

Ludwig C, Gillet L, Rosenberger G et al (2018) Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14:e8126 PubMed DOI PMC

Tsou C-C, Avtonomov D, Larsen B et al (2015) DIA-umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12:258–264, 7 p following 264 PubMed DOI PMC

Meyer JG, Schilling B (2017) Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 14:419–429 PubMed DOI PMC

MacLean B, Tomazela DM, Shulman N et al (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968 PubMed DOI PMC

Choi M, Chang C-Y, Clough T et al (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30:2524–2526 PubMed DOI

Červenka J, Tylečková J, Kupcová Skalníková H et al (2021) Proteomic characterization of human neural stem cells and their secretome during in vitro differentiation. Front Cell Neurosci 14:612560 PubMed DOI PMC

R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

Wiśniewski JR (2016) Quantitative evaluation of filter aided sample preparation (FASP) and multienzyme digestion FASP protocols. Anal Chem 88:5438–5443 PubMed DOI

Röst HL, Rosenberger G, Navarro P et al (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223 PubMed DOI

Röst HL, Aebersold R, Schubert OT (2017) Automated SWATH data analysis using targeted extraction of ion chromatograms. In: Comai L, Katz JE, Mallick P (eds) Proteomics. Springer, New York, pp 289–307 DOI

Holewinski RJ, Parker SJ, Matlock AD et al (2016) Methods for SWATH™: data independent acquisition on TripleTOF mass spectrometers. Methods Mol Biol 1410:265–279 PubMed DOI

Li Y, Zhong C-Q, Xu X et al (2015) Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files. Nat Methods 12:1105–1106 PubMed DOI

Sinitcyn P, Hamzeiy H, Salinas Soto F et al (2021) MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 39:1–11 DOI

Egertson JD, MacLean B, Johnson R et al (2015) Multiplexed peptide analysis using data independent acquisition and skyline. Nat Protoc 10:887–903 PubMed DOI PMC

Kelstrup CD, Bekker-Jensen DB, Arrey TN et al (2018) Performance evaluation of the Q exactive HF-X for shotgun proteomics. J Proteome Res 17:727–738 PubMed DOI

Koopmans F, Ho JTC, Smit AB et al (2018) Comparative analyses of data independent acquisition mass spectrometric approaches: DIA, WiSIM-DIA, and untargeted DIA. Proteomics 18:1700304 PubMed DOI PMC

Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47 PubMed DOI PMC

Smyth G, Hu Y, Ritchie M et al (2020) limma: linear models for microarray data, bioconductor version: release (3.10)

Bates D, Mächler M, Bolker B et al (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48 DOI

Bates D, Maechler M, Bolker B et al (2018) lme4: linear mixed-effects models using “Eigen” and S4

RStudio Team (2021) RStudio: Integrated Development Environment for R. RStudio, Inc., Boston, MA

Chiva C, Olivella R, Borràs E et al (2018) QCloud: a cloud-based quality control system for mass spectrometry-based proteomics laboratories. PLoS One 13:e0189209 PubMed DOI PMC

Rosenberger G, Koh CC, Guo T et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:140031 PubMed DOI PMC

Reiter L, Rinner O, Picotti P et al (2011) mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 8:430–435 PubMed DOI

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