Development and Characterization of Calcium Sensors for In vivo Neuronal Activity Imaging
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Bibliographic record
Abstract
Genetically-Encoded Fluorescent Calcium Indicators for Optical Imaging (GECOs), that modulate their fluorescence intensity in response to changes in calcium ion concentration, are powerful tools for the investigation of cell biology. These fluorescent indicators are vital when it comes to cellular imaging because they allow the non-invasive study of cells, tissues, and sub-cellular structures at a detail that was previously not possible. The focus of this project is engineering a new GECO that exhibits favourable characteristics, such as increased brightness and a higher fold change. To develop this new GECO, we started from mNeonGreen, the brightest monomeric fluorescent protein currently available. An initial prototype construct was made using rational design, following the precedent of the GCaMP series of indicators. This construct was further improved using directed protein evolution with colony-based screening of libraries of randomly generated variants. We observe the brightness of promising new variants and perform tests to see how new mutations have affected the brightness and the fold change of the variant. After many rounds of screening, our latest variant of mNeonGreen-GECO exhibits a Ca2+-dependent change of eight. When compared to the original construct, this indicator appears significantly brighter with higher contrast between Ca2+-bound and Ca2+-free states. Directed evolution is ongoing and we expect to produce a fluorescent indicator that will be used for in vivo imaging of intracellular Ca2+ dynamics. We will further image its calcium dynamics directly in Zebrafish and compare our variants with mNeonGreen fluorescent protein and the GCaMP series. Using mNeonGreen, we hope to create a calcium indicator that researchers can use to investigate the physiological activity of organs, understand the signaling patterns within tissues, and use to study the various disease states which may lead to the development of new therapies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it