Literature DB >> 34597050

Driving Single Cell Proteomics Forward with Innovation.

Nikolai Slavov1,2.   

Abstract

Current single-cell mass spectrometry (MS) methods can quantify thousands of peptides per single cell while detecting peptide-like features that may support the quantification of 10-fold more peptides. This 10-fold gain might be attained by innovations in data acquisition and interpretation even while using existing instrumentation. This perspective discusses possible directions for such innovations with the aim to stimulate community efforts for increasing the coverage and quantitative accuracy of single proteomics while simultaneously decreasing missing data. Parallel improvements in instrumentation, sample preparation, and peptide separation will afford additional gains. Together, these synergistic routes for innovation project a rapid growth in the capabilities of MS based single-cell protein analysis. These gains will directly empower applications of single-cell proteomics to biomedical research.

Entities:  

Keywords:  data acquisition; data interpretation; peptide identity propagation; single-cell proteomics; ultrasensitive proteomics

Mesh:

Substances:

Year:  2021        PMID: 34597050      PMCID: PMC8571067          DOI: 10.1021/acs.jproteome.1c00639

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  21 in total

1.  A platform for accurate mass and time analyses of mass spectrometry data.

Authors:  Damon May; Matt Fitzgibbon; Yan Liu; Ted Holzman; Jimmy Eng; C J Kemp; Jeff Whiteaker; Amanda Paulovich; Martin McIntosh
Journal:  J Proteome Res       Date:  2007-06-09       Impact factor: 4.466

Review 2.  Transformative Opportunities for Single-Cell Proteomics.

Authors:  Harrison Specht; Nikolai Slavov
Journal:  J Proteome Res       Date:  2018-07-19       Impact factor: 4.466

3.  Improved Single-Cell Proteome Coverage Using Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry.

Authors:  Yongzheng Cong; Yiran Liang; Khatereh Motamedchaboki; Romain Huguet; Thy Truong; Rui Zhao; Yufeng Shen; Daniel Lopez-Ferrer; Ying Zhu; Ryan T Kelly
Journal:  Anal Chem       Date:  2020-01-21       Impact factor: 6.986

4.  Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers.

Authors:  Harrison Specht; Nikolai Slavov
Journal:  J Proteome Res       Date:  2020-11-14       Impact factor: 4.466

5.  IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs.

Authors:  Fengchao Yu; Sarah E Haynes; Alexey I Nesvizhskii
Journal:  Mol Cell Proteomics       Date:  2021-04-02       Impact factor: 5.911

6.  A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides.

Authors:  Joel M Chick; Deepak Kolippakkam; David P Nusinow; Bo Zhai; Ramin Rad; Edward L Huttlin; Steven P Gygi
Journal:  Nat Biotechnol       Date:  2015-06-15       Impact factor: 54.908

7.  DART-ID increases single-cell proteome coverage.

Authors:  Albert Tian Chen; Alexander Franks; Nikolai Slavov
Journal:  PLoS Comput Biol       Date:  2019-07-01       Impact factor: 4.475

8.  Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2.

Authors:  Harrison Specht; Edward Emmott; Aleksandra A Petelski; R Gray Huffman; David H Perlman; Marco Serra; Peter Kharchenko; Antonius Koller; Nikolai Slavov
Journal:  Genome Biol       Date:  2021-01-27       Impact factor: 13.583

9.  Quantitative single-cell proteomics as a tool to characterize cellular hierarchies.

Authors:  Erwin M Schoof; Benjamin Furtwängler; Nil Üresin; Nicolas Rapin; Simonas Savickas; Coline Gentil; Eric Lechman; Ulrich Auf dem Keller; John E Dick; Bo T Porse
Journal:  Nat Commun       Date:  2021-06-07       Impact factor: 14.919

10.  IceR improves proteome coverage and data completeness in global and single-cell proteomics.

Authors:  Mathias Kalxdorf; Torsten Müller; Oliver Stegle; Jeroen Krijgsveld
Journal:  Nat Commun       Date:  2021-08-09       Impact factor: 14.919

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  5 in total

1.  Increasing the throughput of sensitive proteomics by plexDIA.

Authors:  Jason Derks; Andrew Leduc; Georg Wallmann; R Gray Huffman; Matthew Willetts; Saad Khan; Harrison Specht; Markus Ralser; Vadim Demichev; Nikolai Slavov
Journal:  Nat Biotechnol       Date:  2022-07-14       Impact factor: 68.164

2.  Counting protein molecules for single-cell proteomics.

Authors:  Nikolai Slavov
Journal:  Cell       Date:  2022-01-20       Impact factor: 41.582

3.  Learning from natural variation across the proteomes of single cells.

Authors:  Nikolai Slavov
Journal:  PLoS Biol       Date:  2022-01-05       Impact factor: 8.029

Review 4.  Application of nanomaterials in proteomics-driven precision medicine.

Authors:  Yong Zhang; Haonan Yang; Yanbao Yu; Ying Zhang
Journal:  Theranostics       Date:  2022-03-06       Impact factor: 11.600

5.  Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity.

Authors:  Lakmini Senavirathna; Cheng Ma; Ru Chen; Sheng Pan
Journal:  Cells       Date:  2022-08-07       Impact factor: 7.666

  5 in total

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