FETCH enables fluorescent labeling of membrane proteins in vivo with spatiotemporal control in <i>Drosophila</i>
Bibliographic record
Abstract
Fluorescent labeling approaches are crucial for elucidating protein function and dynamics. While robust methods to monitor gene transcription are widespread, the visualization of proteins in vivo is more elusive. To meet this challenge, we developed Fluorescent Endogenous Tagging with a Covalent Hook (FETCH) to label cell surface proteins (CSPs) in vivo through a stable covalent bond mediated by the DogTag-DogCatcher peptide partner system. FETCH leverages a spontaneous covalent isopeptide bond that forms between the 23-amino acid DogTag and the 15-kDa DogCatcher. Unlike most tags that work best at protein termini, DogTag functions well in protein loops, expanding the range of sites that can be targeted in proteins. In FETCH, DogTag is introduced into extracellular loops of CSPs through genome engineering, enabling covalent bond formation with a genetically encoded DogCatcher-GFP fusion protein that can be secreted from a variety of cell types in intact animals. To identify optimal DogTag insertions into CSPs, we describe a flow cytometry–based platform for rapidly screening candidates in vitro. We demonstrate the ability to tag and visualize three members of the immunoglobulin superfamily (IgSF) in vivo: the transmembrane protein mCD8 and two GPI-anchored proteins belonging to the DIP-Dpr interactome that interact biophysically to facilitate neuronal target recognition at Drosophila neuromuscular and brain synapses. FETCH enables precise temporal and spatial control to visualize tagged proteins in vivo, features that are adaptable to a multitude of applications for modifying any cell surface protein.
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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) | 0.000 | 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) | 0.002 | 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".