Fluorescence-Quenched Substrates for Live Cell Imaging of Human Glucocerebrosidase Activity
Bibliographic record
Abstract
Deficiency of the lysosomal glycoside hydrolase glucocerebrosidase (GCase) leads to abnormal accumulation of glucosyl ceramide in lysosomes and the development of the lysosomal storage disease known as Gaucher's disease. More recently, mutations in the GBA1 gene that encodes GCase have been uncovered as a major genetic risk factor for Parkinson's disease (PD). Current therapeutic strategies to increase GCase activity in lysosomes involve enzyme replacement therapy (ERT) and molecular chaperone therapy. One challenge associated with developing and optimizing these therapies is the difficulty in determining levels of GCase activity present within the lysosomes of live cells. Indeed, visualizing the activity of endogenous levels of any glycoside hydrolases, including GCase, has proven problematic within live mammalian cells. Here we describe the successful modular design and synthesis of fluorescence-quenched substrates for GCase. The selection of a suitable fluorophore and quencher pair permits the generation of substrates that allow convenient time-dependent monitoring of endogenous GCase activity within cells as well as localization of activity within lysosomes. These efficiently quenched (∼99.9%) fluorescent substrates also permit assessment of GCase inhibition in live cells by either confocal microscopy or high content imaging. Such substrates should enable improved understanding of GCase in situ as well the optimization of small-molecule chaperones for this enzyme. These findings also suggest routes to generate fluorescence-quenched substrates for other mammalian glycoside hydrolases for use in live cell imaging.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".