Design and mechanistic insight into ultrafast calcium indicators for monitoring intracellular calcium dynamics
Bibliographic record
Abstract
Abstract Calmodulin-based genetically encoded fluorescent calcium indicators (GCaMP-s) are powerful tools of imaging calcium dynamics from cells to freely moving animals. High affinity indicators with slow kinetics however distort the temporal profile of calcium transients. Here we report the development of reduced affinity ultrafast variants of GCaMP6s and GCaMP6f. We hypothesized that GCaMP-s have a common kinetic mechanism with a rate-limiting process in the interaction of the RS20 peptide and calcium-calmodulin. Therefore we targeted specific residues in the binding interface by rational design generating improved indicators with GCaMP6fu displaying fluorescence rise and decay times (t1/2) of 1 and 3 ms (37 °C) in vitro, 9 and 22-fold faster than GCaMP6f respectively. In HEK293T cells, GCaMP6fu revealed a 4-fold faster decay of ATP-evoked intracellular calcium transients than GCaMP6f. Stimulation of hippocampal CA1 pyramidal neurons with five action potentials fired at 100 Hz resulted in a single dendritic calcium transient with a 2-fold faster rise and 7-fold faster decay time (t1/2 of 40 ms) than GCaMP6f, indicating that tracking high frequency action potentials may be limited by calcium dynamics. We propose that the design strategy used for generating GCaMP6fu is applicable for the acceleration of the response kinetics of GCaMP-type calcium indicators.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".