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Record W2091825484 · doi:10.3791/51887

Immunodetection of Outer Membrane Proteins by Flow Cytometry of Isolated Mitochondria

2014· article· en· W2091825484 on OpenAlexafffund
Sarah Pickles, Nathalie Arbour, Christine Vande Velde

Bibliographic record

VenueJournal of Visualized Experiments · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchALS Society of Canada
KeywordsMitochondrionFlow cytometryBiologyOrganelleCell biologyStainingMolecular biologyBiochemistry

Abstract

fetched live from OpenAlex

Methods to detect and monitor mitochondrial outer membrane protein components in animal tissues are vital to study mitochondrial physiology and pathophysiology. This protocol describes a technique where mitochondria isolated from rodent tissue are immunolabeled and analyzed by flow cytometry. Mitochondria are isolated from rodent spinal cords and subjected to a rapid enrichment step so as to remove myelin, a major contaminant of mitochondrial fractions prepared from nervous tissue. Isolated mitochondria are then labeled with an antibody of choice and a fluorescently conjugated secondary antibody. Analysis by flow cytometry verifies the relative purity of mitochondrial preparations by staining with a mitochondrial specific dye, followed by detection and quantification of immunolabeled protein. This technique is rapid, quantifiable and high-throughput, allowing for the analysis of hundreds of thousands of mitochondria per sample. It is applicable to assess novel proteins at the mitochondrial surface under normal physiological conditions as well as the proteins that may become mislocalized to this organelle during pathology. Importantly, this method can be coupled to fluorescent indicator dyes to report on certain activities of mitochondrial subpopulations and is feasible for mitochondria from the central nervous system (brain and spinal cord) as well as liver.

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 score0.106
Threshold uncertainty score0.509

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 head0.011
GPT teacher head0.339
Teacher spread0.328 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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