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High Sensitivity Top‐down Proteomics: Coomassie for In‐gel Proteoform Detection Rivals MS‐based Peptide Detection

2018· article· en· W3175205794 on OpenAlexaff
Nour Noaman, Prabhodh S. Abbineni, M. K. Withers, Jens R. Coorssen

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsBrock University
Fundersnot available
KeywordsProteomeProteomicsMass spectrometryCoomassie Brilliant BlueComputational biologyGel electrophoresisChemistryHuman proteome projectChromatographyShotgun proteomicsShotgunQuantitative proteomicsDetection limitGeneBiochemistryBiologyStainingGenetics

Abstract

fetched live from OpenAlex

Several thousands of gene and splice variants, as well as post‐translational modifications, deepen the immense complexity of the human proteome. As these protein species or proteoforms define biological mechanisms in health and disease their direct analysis is critical, necessitating top‐down assessments in which the complete amino acid sequence and all modifications remain intact. Two‐dimensional gel electrophoresis (2DE) coupled with downstream mass spectrometry (MS) remains the most thorough and practical approach to Top‐down Discovery Proteomics, with resolving power to routinely analyze complex mixtures of intact proteoforms [1]. Increasing the quality and quantity of 2DE‐derived data largely relies on developing, optimising, and rigorously characterising methods for in‐gel total protein detection, providing quantitative, sensitive detection over a wide dynamic range while remaining practical [2]. Modified colloidal Coomassie Brilliant Blue (cCBB) staining meets these criteria [3, 4]; cCBB provides (sub)femtomole detection sensitivity for SDS‐PAGE and 2DE‐resolved intact proteoforms using either near‐infrared fluorescence detection (nIRFD) or densitometry [5]. This thus rivals the detection sensitivity of peptides by MS in ‘bottom‐up’ (i.e. shotgun) approaches to proteomics, which appear to provide larger protein catalogues but at the expense of critical data concerning proteoforms. Our ongoing work addresses a number of experimental variables which impact detection sensitivity in standard 1 mm‐thick polyacrylamide gels, providing a more realistic baseline measure of cCBB detection sensitivity and enhancing the overall practicality of 2DE for comprehensive proteome analysis. Here we showcase our most recent findings, including preliminary findings of further improvements to cCBB detection sensitivity when using ultra‐thin polyacrylamide gels. The novel ultra‐thin method utilised has been developed from minimally modified commercial equipment for vertical mini‐format gel electrophoresis and is thus accessible, affordable, and simple to implement in laboratories equipped for standard SDSPAGE and 2DE. The ultra‐thin gels do not require backing supports for handling, and potentially are compatible with commercial immobilised pH gradient strips routinely used for first dimension isoelectric focussing, further adding to the value and practicality of the method. Ultra‐thin 2DE may thus enable further mining of 2DE‐resolved proteomes, including the application of high resolution gel‐based proteomics in situations of limited sample quantity (e.g. biopsies, and perhaps single cells). Support or Funding Information NN and PSA received scholarship support from the WSU Molecular Medicine Research Group and the WSU School of Medicine, respectively. JRC notes the support of an anonymous private family foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.266
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
Admission routes1
Has abstractyes

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