Literature DB >> 33352054

Fully Automated Sample Processing and Analysis Workflow for Low-Input Proteome Profiling.

Yiran Liang1, Hayden Acor1, Michaela A McCown2, Andikan J Nwosu1, Hannah Boekweg2, Nathaniel B Axtell1, Thy Truong1, Yongzheng Cong1, Samuel H Payne2, Ryan T Kelly1.   

Abstract

Recent advances in sample preparation and analysis have enabled direct profiling of protein expression in single mammalian cells and other trace samples. Several techniques to prepare and analyze low-input samples employ custom fluidics for nanoliter sample processing and manual sample injection onto a specialized separation column. While being effective, these highly specialized systems require significant expertise to fabricate and operate, which has greatly limited implementation in most proteomic laboratories. Here, we report a fully automated platform termed autoPOTS (automated preparation in one pot for trace samples) that uses only commercially available instrumentation for sample processing and analysis. An unmodified, low-cost commercial robotic pipetting platform was utilized for one-pot sample preparation. We used low-volume 384-well plates and periodically added water or buffer to the microwells to compensate for limited evaporation during sample incubation. Prepared samples were analyzed directly from the well plate with a commercial autosampler that was modified with a 10-port valve for compatibility with 30 μm i.d. nanoLC columns. We used autoPOTS to analyze 1-500 HeLa cells and observed only a moderate reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to our previously developed nanoPOTS platform. To evaluate clinical feasibility, we identified an average of 1095 protein groups from ∼130 sorted B or T lymphocytes. We anticipate that the straightforward implementation of autoPOTS will make it an attractive option for low-input and single-cell proteomics in many laboratories.

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Year:  2020        PMID: 33352054      PMCID: PMC8140400          DOI: 10.1021/acs.analchem.0c04240

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  43 in total

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Journal:  J Biol Chem       Date:  2002-12-08       Impact factor: 5.157

2.  Flow cytometry in the differential diagnosis of lymphocyte-rich thymoma from precursor T-cell acute lymphoblastic leukemia/lymphoblastic lymphoma.

Authors:  Shiyong Li; Jonathan Juco; Karen P Mann; Jeannine T Holden
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3.  Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets.

Authors:  Ying Zhu; Maowei Dou; Paul D Piehowski; Yiran Liang; Fangjun Wang; Rosalie K Chu; William B Chrisler; Jordan N Smith; Kaitlynn C Schwarz; Yufeng Shen; Anil K Shukla; Ronald J Moore; Richard D Smith; Wei-Jun Qian; Ryan T Kelly
Journal:  Mol Cell Proteomics       Date:  2018-06-24       Impact factor: 5.911

4.  Nanoliter-Scale Oil-Air-Droplet Chip-Based Single Cell Proteomic Analysis.

Authors:  Zi-Yi Li; Min Huang; Xiu-Kun Wang; Ying Zhu; Jin-Song Li; Catherine C L Wong; Qun Fang
Journal:  Anal Chem       Date:  2018-03-27       Impact factor: 6.986

5.  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

6.  Transfection of the CD20 cell surface molecule into ectopic cell types generates a Ca2+ conductance found constitutively in B lymphocytes.

Authors:  J K Bubien; L J Zhou; P D Bell; R A Frizzell; T F Tedder
Journal:  J Cell Biol       Date:  1993-06       Impact factor: 10.539

7.  Single-Cell Mass Spectrometry for Discovery Proteomics: Quantifying Translational Cell Heterogeneity in the 16-Cell Frog (Xenopus) Embryo.

Authors:  Camille Lombard-Banek; Sally A Moody; Peter Nemes
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8.  MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics.

Authors:  Andy T Kong; Felipe V Leprevost; Dmitry M Avtonomov; Dattatreya Mellacheruvu; Alexey I Nesvizhskii
Journal:  Nat Methods       Date:  2017-04-10       Impact factor: 28.547

9.  An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics.

Authors:  Chia-Feng Tsai; Rui Zhao; Sarah M Williams; Ronald J Moore; Kendall Schultz; William B Chrisler; Ljiljana Pasa-Tolic; Karin D Rodland; Richard D Smith; Tujin Shi; Ying Zhu; Tao Liu
Journal:  Mol Cell Proteomics       Date:  2020-03-03       Impact factor: 5.911

10.  Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant.

Authors:  Fengchao Yu; Sarah E Haynes; Guo Ci Teo; Dmitry M Avtonomov; Daniel A Polasky; Alexey I Nesvizhskii
Journal:  Mol Cell Proteomics       Date:  2020-07-02       Impact factor: 5.911

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  11 in total

1.  Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot.

Authors:  Yu Han; Cody T Thomas; Sara A Wennersten; Edward Lau; Maggie P Y Lam
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2.  Separation methods in single-cell proteomics: RPLC or CE?

Authors:  Kellye A Cupp-Sutton; Mulin Fang; Si Wu
Journal:  Int J Mass Spectrom       Date:  2022-08-17       Impact factor: 1.934

3.  Fe3+-NTA magnetic beads as an alternative to spin column-based phosphopeptide enrichment.

Authors:  Xinyue Liu; Valentina Rossio; Sanjukta Guha Thakurta; Amarjeet Flora; Leigh Foster; Ryan D Bomgarden; Steven P Gygi; Joao A Paulo
Journal:  J Proteomics       Date:  2022-03-21       Impact factor: 3.855

4.  Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry.

Authors:  Sofani Tafesse Gebreyesus; Asad Ali Siyal; Reta Birhanu Kitata; Eric Sheng-Wen Chen; Bayarmaa Enkhbayar; Takashi Angata; Kuo-I Lin; Yu-Ju Chen; Hsiung-Lin Tu
Journal:  Nat Commun       Date:  2022-01-10       Impact factor: 17.694

5.  To the proteome and beyond: advances in single-cell omics profiling for plant systems.

Authors:  Natalie M Clark; James Mitch Elmore; Justin W Walley
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6.  Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Authors:  Yan Chen; Nurgul Kaplan Lease; Jennifer W Gin; Tadeusz L Ogorzalek; Paul D Adams; Nathan J Hillson; Christopher J Petzold
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7.  Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation.

Authors:  Andreas-David Brunner; Marvin Thielert; Catherine Vasilopoulou; Constantin Ammar; Fabian Coscia; Andreas Mund; Ole B Hoerning; Nicolai Bache; Amalia Apalategui; Markus Lubeck; Sabrina Richter; David S Fischer; Oliver Raether; Melvin A Park; Florian Meier; Fabian J Theis; Matthias Mann
Journal:  Mol Syst Biol       Date:  2022-03       Impact factor: 11.429

Review 8.  The Opportunity of Proteomics to Advance the Understanding of Intra- and Extracellular Regulation of Malignant Hematopoiesis.

Authors:  Maria Jassinskaja; Jenny Hansson
Journal:  Front Cell Dev Biol       Date:  2022-03-08

9.  Toward a Multi-Omics-Based Single-Cell Environmental Chemistry and Toxicology.

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Journal:  Environ Sci Technol       Date:  2022-07-12       Impact factor: 11.357

10.  Facile One-Pot Nanoproteomics for Label-Free Proteome Profiling of 50-1000 Mammalian Cells.

Authors:  Kendall Martin; Tong Zhang; Tai-Tu Lin; Amber N Habowski; Rui Zhao; Chia-Feng Tsai; William B Chrisler; Ryan L Sontag; Daniel J Orton; Yong-Jie Lu; Karin D Rodland; Bin Yang; Tao Liu; Richard D Smith; Wei-Jun Qian; Marian L Waterman; H Steven Wiley; Tujin Shi
Journal:  J Proteome Res       Date:  2021-08-05       Impact factor: 4.466

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