A versatile fluorescence-quenched substrate for quantitative measurement of glucocerebrosidase activity within live cells
Bibliographic record
Abstract
Loss of activity of the lysosomal glycosidase β-glucocerebrosidase (GCase) causes the lysosomal storage disease Gaucher disease (GD) and has emerged as the greatest genetic risk factor for the development of both Parkinson disease (PD) and dementia with Lewy bodies. There is significant interest into how GCase dysfunction contributes to these diseases, however, progress toward a full understanding is complicated by presence of endogenous cellular factors that influence lysosomal GCase activity. Indeed, such factors are thought to contribute to the high degree of variable penetrance of GBA mutations among patients. Robust methods to quantitatively measure GCase activity within lysosomes are therefore needed to advance research in this area, as well as to develop clinical assays to monitor disease progression and assess GCase-directed therapeutics. Here, we report a selective fluorescence-quenched substrate, LysoFQ-GBA, which enables measuring endogenous levels of lysosomal GCase activity within living cells. LysoFQ-GBA is a sensitive tool for studying chemical or genetic perturbations of GCase activity using either fluorescence microscopy or flow cytometry. We validate the quantitative nature of measurements made with LysoFQ-GBA using various cell types and demonstrate that it accurately reports on both target engagement by GCase inhibitors and the GBA allele status of cells. Furthermore, through comparisons of GD, PD, and control patient-derived tissues, we show there is a close correlation in the lysosomal GCase activity within monocytes, neuronal progenitor cells, and neurons. Accordingly, analysis of clinical blood samples using LysoFQ-GBA may provide a surrogate marker of lysosomal GCase activity in neuronal tissue.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".