22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona

      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

          Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

          Abstract

          Large-scale, unbiased proteomics studies of biological samples like plasma are constrained by the complexity of the proteome. Herein, the authors develop a highly parallel protein quantitation platform leveraging multi nanoparticle protein coronas for deep proteome sampling and biomarker discovery.

          Related collections

          Most cited references76

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

          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

              Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
                Bookmark

                Author and article information

                Contributors
                jblume@seer.bio
                vivekf@mit.edu
                ofarokhzad@bwh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 July 2020
                22 July 2020
                2020
                : 11
                : 3662
                Affiliations
                [1 ]Seer, Inc., Redwood City, CA 94065 USA
                [2 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA 02142 USA
                [3 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, David H. Koch Institute for Integrative Cancer Research, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [4 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Sloan School and Operations Research Center, Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Center for Nanomedicine and Department of Anesthesiology, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                Author information
                http://orcid.org/0000-0003-3026-953X
                http://orcid.org/0000-0003-2933-8809
                http://orcid.org/0000-0003-4255-0492
                http://orcid.org/0000-0002-5856-9246
                http://orcid.org/0000-0003-2009-270X
                Article
                17033
                10.1038/s41467-020-17033-7
                7376165
                32699280
                d9c018e4-191d-4fcc-a94b-ddc4c2a0010a
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 December 2019
                : 2 June 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                blood proteins,mass spectrometry,proteomics,non-small-cell lung cancer,nanoparticles
                Uncategorized
                blood proteins, mass spectrometry, proteomics, non-small-cell lung cancer, nanoparticles

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content271

                Cited by121

                Most referenced authors4,254