Lysosomal Ion Channels and Transporters: Recent Findings, Therapeutic Potential, and Technical Approaches
Bibliographic record
Abstract
In recent years, there has been a growing interest in lysosomal ion channels and transporters due to their critical role in maintaining lysosomal function and their involvement in a variety of diseases, particularly lysosomal storage diseases, cancer, and neurodegenerative disorders. Recent advancements in research techniques, including manual and automated patch clamp (APC) electrophysiology, solid-supported membrane-based electrophysiology (SSME), and fluorescence-based ion imaging, have further enhanced our ability to investigate lysosomal ion channels and transporters in both physiological and pathological conditions, spurring drug discovery efforts. Several pharmaceutical companies are now developing therapies aimed at modulating these channels and transporters to improve lysosomal function in disease. Small molecules targeting channels like transient receptor potential mucolipin (TRPML) 1 and TMEM175, as well as drugs modulating lysosomal pH, are currently in preclinical and clinical development. This review provides an overview of the role of lysosomal ion channels and transporters in health and disease, highlights the cutting-edge techniques used to study them, and discusses the therapeutic potential of targeting these channels and transporters in the treatment of various diseases. Furthermore, in addition to summarizing recent discoveries, we contribute novel functional data on cystinosin, TRPML1, and two-pore channel 2 (TPC2), utilizing both SSME and APC approaches.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".