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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.

Pluripotent stem cell-derived committed neural precursors are an important source of cells to treat neurodegenerative diseases including spinal cord injury. There remains an urgency to identify markers for monitoring of neural progenitor specificity, estimation of neural fate and follow-up correlation with therapeutic effect in preclinical studies using animal disease models. Cell surface capture technology was used to uncover the cell surface exposed N-glycoproteome of neural precursor cells upon neuronal differentiation as well as post-mitotic mature hNT neurons. The data presented depict an extensive study of surfaceome during neuronal differentiation, confirming glycosylation at a particular predicted site of many of the identified proteins. Quantitative changes detected in cell surface protein levels reveal a set of proteins that highlight the complexity of the neuronal differentiation process. Several of these proteins including the cell adhesion molecules ICAM1, CHL1, and astrotactin1 as well as LAMP1 were validated by SRM. Combination of immunofluorescence staining of ICAM1 and flow cytometry indicated a possible direction for future scrutiny of such proteins as targets for enrichment of the neuronal subpopulation from mixed cultures after differentiation of neural precursor cells. These surface proteins hold an important key for development of safe strategies in cell-replacement therapies of neuronal disorders. Biological significance Neural stem and/or precursor cells have a great potential for cell-replacement therapies of neuronal diseases. Availability of well characterised and expandable neural cell lineage specific populations is critical for addressing such a challenge. In our study we identified and relatively quantified several hundred surface N-glycoproteins in the course of neuronal differentiation. We further confirmed the abundant changes for several cell adhesion proteins by SRM and outlined a strategy for utilisation of such N-glycoproteins in antibody based cell sorting. The comprehensive dataset presented here demonstrates the molecular background of neuronal differentiation highly useful for development of new plasma membrane markers to identify and select neuronal subpopulation from mixed neural cell cultures.

Neurodegenerative diseases are devastating disorders and the demands on their treatment are set to rise in connection with higher disease incidence. Knowledge of the spatiotemporal profile of cellular protein expression during neural differentiation and definition of a set of markers highly specific for targeted neural populations is a key challenge. Intracellular proteins may be utilized as a readout for follow-up transplantation and cell surface proteins may facilitate isolation of the cell subpopulations, while secreted proteins could help unravel intercellular communication and immunomodulation. This review summarizes the potential of proteomics in revealing molecular mechanisms underlying neural differentiation of stem cells and presents novel candidate proteins of neural subpopulations, where understanding of their functionality may accelerate transition to cell replacement therapies.

Drug resistance is the major obstacle to successful cancer therapy. Our study focuses on resistance to Aurora kinase inhibitors tested as anti-cancer drugs in clinical trials. We have used 2D electrophoresis in the pH ranges of 4-7 and 6-11 followed by protein identification using MALDI-TOF/TOF to compare the protein composition of HCT116 colon cancer cells either sensitive to CYC116 and ZM447439 inhibitors or resistant toward these drugs. The analysis also included p53(+/+) and p53(-/-) phenotypes of HCT116 cells. Our findings demonstrate that platelet-activating factor acetylhydrolase and GTP-binding nuclear protein Ran contribute to the development of resistance to ZM447439 only where resistance is related to p53. On the other hand, serine hydroxymethyltransferase was found to promote the tumor growth in cells resistant to CYC116 without the influence of p53. Computer modeling of interaction networks highlighted a direct link of the p53-independent mechanism of resistance to CYC116 with autophagy. Importantly, serine hydroxymethyltransferase, serpin B5, and calretinin represent the target proteins that may help overcome resistance in combination therapies. In addition, serpin B5 and calretinin appear to be candidate biomarkers that may be accessible in patients for monitoring of cancer therapy with ease.

A comprehensive proteome map of T-lymphoblastic leukemia cells and its alterations after daunorubicin, doxorubicin and mitoxantrone treatments was monitored and evaluated either by paired comparison with relevant untreated control and using multivariate classification of treated and untreated samples. With the main focus on early time intervals when the influence of apoptosis is minimized, we found significantly different levels of proteins, which corresponded to 1%–2% of the total amount of protein spots detected. According to Gene Ontology classification of biological processes, the highest representation of identified proteins for all three drugs belong to metabolic processes of proteins and nucleic acids and cellular processes, mainly cytoskeleton organisation and ubiquitin-proteasome pathway. Importantly, we observed significant proportion of changes in proteins involved in the generation of precursor metabolites and energy typical for daunorubicin, transport proteins participating in response to doxorubicin and a group of proteins of immune system characterising response to mitoxantrone. Both a paired comparison and the multivariate evaluation of quantitative data revealed daunorubicin as a distinct member of the group of anthracycline/anthracenedione drugs. A combination of identified drug specific protein changes, which may help to explain anti-cancer activity, together with the benefit of blocking activation of adaptive cancer pathways, presents important approaches to improving treatment outcomes in cancer.