3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single Cell–ICP–ToF-MS for the Multiplexed Determination of Proteins: Evaluation of the Cellular Stress Response

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          An automated and straightforward detection and data treatment strategy for the determination of the protein relative concentration in individual human cells by single cell–inductively coupled plasma–time-of-flight mass spectrometry (sc-ICP-ToF-MS) is proposed. Metal nanocluster (NC)-labeled specific antibodies for the target proteins were employed, and ruthenium red (RR) staining, which binds to the cells surface, was used to determine the number of cell events as well as to evaluate the relative volume of the cells. As a proof of concept, the expression of hepcidin, metallothionein-2, and ferroportin employing specific antibodies labeled with IrNCs, PtNCs, and AuNCs, respectively, was investigated by sc-ICP-ToF-MS in human ARPE-19 cells. Taking into account that ARPE-19 cells are spherical in suspension and RR binds to the surface of the cells, the Ru intensity was related to the cell volume (i.e., the cell volume is directly proportional to (Ru intensity) 3/2), making it possible to determine not only the mass of the target proteins in each individual cell but also the relative concentration. The proposed approach is of particular interest in comparing cell cultures subjected to different supplementations. ARPE-19 cell cultures under two stress conditions were compared: a hyperglycemic model and an oxidative stress model. The comparison of the control with treated cells shows not only the mass of analyzed species but also the relative changes in the cell volume and concentration of target proteins, clearly allowing the identification of subpopulations under the respective treatment.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell sequencing-based technologies will revolutionize whole-organism science.

          The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

            Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms.

              Individual mammalian cells exhibit large variability in cellular volume, even with the same absolute DNA content, and so must compensate for differences in DNA concentration in order to maintain constant concentration of gene expression products. Using single-molecule counting and computational image analysis, we show that transcript abundance correlates with cellular volume at the single-cell level due to increased global transcription in larger cells. Cell fusion experiments establish that increased cellular content itself can directly increase transcription. Quantitative analysis shows that this mechanism measures the ratio of cellular volume to DNA content, most likely through sequestration of a transcriptional factor to DNA. Analysis of transcriptional bursts reveals a separate mechanism for gene dosage compensation after DNA replication that enables proper transcriptional output during early and late S phase. Our results provide a framework for quantitatively understanding the relationships among DNA content, cell size, and gene expression variability in single cells.
                Bookmark

                Author and article information

                Journal
                Anal Chem
                Anal Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                11 August 2023
                05 September 2023
                : 95
                : 35
                : 13322-13329
                Affiliations
                []Department of Physical and Analytical Chemistry, University of Oviedo , Julián Clavería 8, 33006 Oviedo, Spain
                []Division 1.1 − Inorganic Trace Analysis, Federal Institute for Materials Research and Testing (BAM) , Richard-Willstätter-Str. 11, 12489 Berlin, Germany
                [§ ]Elemental Scientific, Inc. , 7277 World Communications Drive, Omaha, Nebraska 68122, United States
                []Instituto de Productos Lácteos de Asturias, Consejo Superior de Investigaciones Científicas (IPLA-CSIC) , 33300 Villaviciosa, Spain
                Author notes
                Author information
                https://orcid.org/0000-0002-7421-6504
                https://orcid.org/0000-0002-2592-1442
                https://orcid.org/0000-0002-8636-0765
                Article
                10.1021/acs.analchem.3c02558
                10483461
                37566513
                a3769fee-82e3-4cc5-a138-c60f89e63f35
                © 2023 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 13 June 2023
                : 03 August 2023
                Funding
                Funded by: Gobierno del Principado de Asturias, doi 10.13039/100011941;
                Award ID: AYUD/2021/51289-FICYT
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: PID2019-107838RB-I00
                Funded by: Ministerio de Educación, Cultura y Deporte, doi 10.13039/501100003176;
                Award ID: FPU19/00556
                Categories
                Article
                Custom metadata
                ac3c02558
                ac3c02558

                Analytical chemistry
                Analytical chemistry

                Comments

                Comment on this article