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      SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

      1 , , 2 , 2 ,   2 , 3 ,
      Genome Biology
      BioMed Central

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          Some exciting biological questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level.

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          The online version of this article (10.1186/s13059-018-1547-5) contains supplementary material, which is available to authorized users.

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          Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.

          A novel instrument for real time analysis of individual biological cells or other microparticles is described. The instrument is based on inductively coupled plasma time-of-flight mass spectrometry and comprises a three-aperture plasma-vacuum interface, a dc quadrupole turning optics for decoupling ions from neutral components, an rf quadrupole ion guide discriminating against low-mass dominant plasma ions, a point-to-parallel focusing dc quadrupole doublet, an orthogonal acceleration reflectron analyzer, a discrete dynode fast ion detector, and an 8-bit 1 GHz digitizer. A high spectrum generation frequency of 76.8 kHz provides capability for collecting multiple spectra from each particle-induced transient ion cloud, typically of 200-300 micros duration. It is shown that the transients can be resolved and characterized individually at a peak frequency of 1100 particles per second. Design considerations and optimization data are presented. The figures of merit of the instrument are measured under standard inductively coupled plasma (ICP) operating conditions ( 900 for m/z = 159, the sensitivity with a standard sample introduction system of >1.4 x 10(8) ion counts per second per mg L(-1) of Tb and an abundance sensitivity of (6 x 10(-4))-(1.4 x 10(-3)) (trailing and leading masses, respectively) are shown. The mass range (m/z = 125-215) and abundance sensitivity are sufficient for elemental immunoassay with up to 60 distinct available elemental tags. When 500) can be used, which provides >2.4 x 10(8) cps per mg L(-1) of Tb, at (1.5 x 10(-3))-(5.0 x 10(-3)) abundance sensitivity. The real-time simultaneous detection of multiple isotopes from individual 1.8 microm polystyrene beads labeled with lanthanides is shown. A real time single cell 20 antigen expression assay of model cell lines and leukemia patient samples immuno-labeled with lanthanide-tagged antibodies is presented.
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            Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.

            Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.
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              Proteogenomics: concepts, applications and computational strategies.

              Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.

                Author and article information

                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                22 October 2018
                22 October 2018
                : 19
                : 161
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, MSPRL, FAS Division of Science, , Harvard University, ; Cambridge, MA 02138 USA
                [2 ]ISNI 0000 0001 2173 3359, GRID grid.261112.7, Department of Biology, , Northeastern University, ; Boston, MA 02115 USA
                [3 ]ISNI 0000 0001 2173 3359, GRID grid.261112.7, Department of Bioengineering, , Northeastern University, ; Boston, MA 02115 USA
                Author information
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                : 20 February 2018
                : 19 September 2018
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: 1DP2GM123497-01
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                © The Author(s) 2018



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