Human Peripheral Blood Neutrophil Isolation for Interrogating the Parkinson's Associated LRRK2 Kinase Pathway by Assessing Rab10 Phosphorylation
Bibliographic record
Abstract
The leucine rich repeat kinase 2 (LRRK2) is the most frequently mutated gene in hereditary Parkinson' disease (PD) and all pathogenic LRRK2 mutations result in hyperactivation of its kinase function. Here, we describe an easy and robust assay to quantify LRRK2 kinase pathway activity in human peripheral blood neutrophils by measuring LRRK2-controlled phosphorylation of one of its physiological substrates, Rab10 at threonine 73. The immunoblotting analysis described requires a fully selective and phosphospecific antibody that recognizes the Rab10 Thr73 epitope phosphorylated by LRRK2, such as the MJFF-pRab10 rabbit monoclonal antibody. It uses human peripheral blood neutrophils, because peripheral blood is easily accessible and neutrophils are an abundant and homogenous constituent. Importantly, neutrophils express relatively high levels of both LRRK2 and Rab10. A potential drawback of neutrophils is their high intrinsic serine protease activity, which necessitates the use of very potent protease inhibitors such as the organophosphorus neurotoxin diisopropylfluorophosphate (DIFP) as part of the lysis buffer. Nevertheless, neutrophils are a valuable resource for research into LRRK2 kinase pathway activity in vivo and should be considered for inclusion into PD biorepository collections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".