GFPnovo2, a brighter GFP variant for in vivo labeling in C. elegans
Bibliographic record
Abstract
Green fluorescent protein (GFP) is one of the most common fluorophores used to label cells and proteins in C. elegans. Previous artificial protein evolution strategy has generated a variant of eGFP, GFPnovo2, which is 3.3 times brighter than the original eGFP in the DT40 cell line (Arakawa et al, 2008). GFPnovo2 carries four mutations from the original eGFP (Y145F, V163A, S202T, L221V), and has the same excitation/emission wavelengths as the original eGFP. Here we compared the brightness of GFPnovo2, which was generated by introducing above mentioned mutations in the pSM (eGFP_unc-54 3’ utr: kind gift from Cori Bargmann) vector (Figure 1A), with that of original eGFP in the C. elegans nervous system. Under the fluorescent dissection scope, all four GFPnovo2 lines with high transmission rate (over 80%) we examined had brighter fluorescent signal at all developmental stages (late embryo to adult) than five eGFP lines with the similar high transmission rate. As axons and dendrites are thin, it is often difficult to visualize when the copy number of the transgene is low. Indeed, we observed fewer neurites when we labeled neurons with the low dose of eGFP (1ng/μl) expressed under the pan-neuronal promoter, Prab-3 (Figure 1B, top). In contrast, GFPnovo2 was considerably brighter than eGFP and nicely labeled neurites when injected at the same concentration (1ng/μl) (Figure 1B, bottom). As a result, the average signal intensities of the dorsal nerve cord, which contains only neurites, was significantly and consistently brighter in the animals expressing GFPnovo2 than those expressing eGFP (Figure 1C). The variation among the animals expressing GFPnovo2 is likely due to the mosaic nature of the extra-chromosomal array. Nevertheless, all GFPnovo2 animals had a brighter signal than eGFP animals. Similarly, we were able to label the entire axon of DA9 neuron with GFPnovo2 expressed under the DA9 specific promoter, itr-1 (Chen et al., 2018), including the axonal tip which was not easy to detect with eGFP (unpublished). We did not notice detectable photobleaching while examining animals expressing GFPnovo2 under the fluorescent compound microscope or the confocal microscope, suggesting that GFPnovo2 is at least as stable as eGFP.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".