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Record W4390495577 · doi:10.1038/s42003-023-05687-0

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

2024· article· en· W4390495577 on OpenAlexafffund
Sarah Michaud, Helena Pětrošová, Nicholas J. Sinclair, Andrea L Kinnear, Angela Jackson, Jamie C. McGuire, Darryl B. Hardie, Pallab Bhowmick, Milan Ganguly, Ann M. Flenniken, Lauryl M. J. Nutter, Colin McKerlie, Derek Smith, Yassene Mohammed, David Schibli, Albert Sickmann, Christoph H. Borchers

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsJewish General HospitalToronto Centre for PhenogenomicsHospital for Sick ChildrenGenome British ColumbiaLunenfeld-Tanenbaum Research InstituteMcGill UniversityUniversity of Victoria
FundersNational Cancer InstituteWarren Y. Soper Charitable TrustFondation De Famille Alvin SegalJewish General HospitalGenome British ColumbiaOntario GenomicsGenome CanadaMcGill University
KeywordsBiologyComputational biologySelected reaction monitoringNodProteomicsMolecular biologyMass spectrometryChemistryGeneticsTandem mass spectrometryIn vivoChromatographyGene

Abstract

fetched live from OpenAlex

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations10
Published2024
Admission routes2
Has abstractyes

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