Lombard-Banek C et al. (2017), New-generation mass spectrometry expands the to...

XB-ART-53022

Genesis 2017 Jan 01;551-2:. doi: 10.1002/dvg.23012.

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New-generation mass spectrometry expands the toolbox of cell and developmental biology.

Lombard-Banek C , Portero EP , Onjiko RM , Nemes P .

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Systems cell biology understanding of development requires characterization of all the molecules produced in the biological system. Decades of research and new-generation sequencing provided functional information on key genes and transcripts. However, there is less information available on how differential gene expression translates into the domains of functionally important proteins, peptides, and metabolites, and how changes in these molecules impact development. Mass spectrometry (MS) is the current technology of choice for the detection and quantification of large numbers of proteins and metabolites, because it requires no use of antibodies, functional probes, or a priori knowledge of molecules produced in the system. This review focuses on recent technologies that have improved MS sensitivity for proteins and metabolites and enabled new functionalities to assess their temporal and spatial changes during vertebrate embryonic development. This review highlights case studies, in which new-generation MS tools have enabled the study of hundreds-to-thousands of proteins and metabolites in tissues, cell populations, and single cells in model systems of vertebrate development, particularly the frog (Xenopus), zebrafish, and mouse. New-generation MS expands the toolbox of cell and developmental studies, raising exciting potentials to advance basic and translational research in the life sciences.

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	Figure 1. General MS strategy to detect proteins (left column) and small molecules (right column) in Xenopus laevis embryos. Proteins and metabolites are extracted, processed, separated, and identified. Quantitative approaches are used to determine relative or absolute levels of hundreds-to-thousands of these molecules. (Figures adapted with permission from references (Lombard-Banek et al., 2016b; Onjiko et al., 2015; Onjiko et al., 2016b))
	Figure 2. Quantification of protein production in the developing Xenopus laevis embryo by MS. (a) Comparison of gene transcription and translation in the developing embryo using Next-Generation Sequencing and liquid chromatography MS. (b) Liquid chromatography MS detection of proteins in cells isolated from the embryo at different developmental stages. (c) Our single-cell capillary electrophoresis MS approach enabled the discovery characterization of protein differences between identified single cells in the 16-cell embryo (top panel). This technology found proteomic differences between neural-fated D11, epidermal-fated V11, and hindgut-fated V21 cells (bottom left panel). The analysis also revealed detectable proteomic heterogeneity between D11 cells that were isolated from different embryos (bottom right panel). Scale bar = 200 Âµm (embryo), 1.25 mm (vial). (Figures adapted with permission from references (Lombard-Banek et al., 2016c; Lombard-Banek et al., 2016d; Peshkin et al., 2015; Sun et al., 2016))
	Figure 3. General strategy of mass spectrometry (MS) imaging. Focused projectiles (e.g., laser light, ions, charged particles or droplets) are used to desorb proteins and metabolites pixel by pixel on a sectioned sample (e.g., tadpole shown), usually by scanning the tissue section in three dimensions with an X-Y-Z translation stage. These molecules are ionized and detected by a mass spectrometer. The spatial distribution of the detected signal is reconstructed by mapping the ion signal abundance to the pre-recorded X-Y-Z coordinates. (Figure adapted with permission from reference (Goto-Inoue et al., 2016))
	Figure 4. Uncovering metabolic processes during early development of the Xenopus laevis embryo. (a) By quantifying 48 different metabolites, LC-MS uncovered metabolomic remodeling during early embryonic development between stages 0 and 9. (b) Our single-cell analysis pipeline uncovered metabolic differences between single embryonic cells (blastomeres) in the 16-cell embryo (top panel). Metabolite network reconstruction based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) (bottom left panel). Single-cell CE-ESI-MS revealed different metabolic activities between D11 and V11 cells; acetylcholine and methionine were enriched in the V11 blastomere. Tracking cell fates by micro-injection of mRNA encoding the green fluorescent protein (GFP) (bottom right panels). D11 blastomeres, which normally give rise to neural tissues (see â€œControlâ€), alter their cell fate upon microinjection of histidine and threonine in a concentration endogenous to V11 cells (see â€œAltered Cell Fateâ€). Scaleâ€‰=â€‰275 Âµm. (Figures adapted with permission from references (Onjiko et al., 2015; Vastag et al., 2011))
	Figure 5. Spatial mapping of metabolites in embryos and tadpoles with scalable imaging resolution. (a) Desorption electrospray ionization (DESI) MS for profiling lipid signatures at different developmental stages of the mouse embryo. (b) MALDI MS imaging of metabolites with tissue-specific accumulation in the Xenopus tropicalis tadpole (imaging resolution: 75 Âµm). (c) Nanostructure-initiator MS (NIMS) imaging of glycerophosphatidylcholine in the vertebra and the heart of the developing mouse embryo (imaging resolution: âˆ¼150 nm). (d) Time-of-flight secondary ion MS imaging (ToF-SIMS) of glycerophosphocholine/sphingomyelin fragment (m/z 125.0), sphingomyelin fragment (m/z 265.2), and monoacylglycerol fragment (m/z 313.3) in the animal side of the 2-, 8-, and 32-cell Xenopus laevis embryos. (Figures adapted with permission from references (Ferreira et al., 2012b; Goto-Inoue et al., 2016; Northen et al., 2007; Tian et al., 2014))

References [+] :

Aebersold, Western blots versus selected reaction monitoring assays: time to turn the tables? 2013, Pubmed