IF-CRIB: A 3D-printable device to facilitate immunofluorescence experiments and its application in screening and characterizing cells expressing a degradable form of ERK2
Bibliographic record
Abstract
• The 3D-printable IF-CRIB simplifies washing and incubation steps in immunofluorescence on round coverslips; files included. • Improved rapid immunofluorescence protocol (IF-Express) designed for experiments with multiple experimental conditions. • Using IF-CRIB and IF-Express to screen and analyse knock-in cells expressing degradable ERK2-dTAG replacing endogenous ERK2. • Detailed methodology for generating ERK2-dTAG cells (PROTAC System) is provided with this work. Immunofluorescence-based detection of proteins in fixed cells is a powerful tool for research in cell and developmental biology. While a variety of immunofluorescence protocols exist, they can be time consuming or require expensive equipment which may not be accessible to all laboratories. A common challenge in these protocols is the numerous washing steps, particularly in experiments with numerous conditions. To address this, here we introduce the IF-CRIB device, a 3D-printable wash rack specifically designed for applications involving a high number of round coverslips with adherent cultured cells. We detail its design and the 3D printing process which can be easily used by any laboratory and we highlight that it facilitates the numerous washing steps. In addition, we present the IF-Express protocol, an optimized and effective method that enables fast and consistent immunofluorescence results. As an example of the utility of the IF-CRIB device and the IF-Express protocol, we describe their application in the screening and characterization of several NIH3T3 cell clones expressing a degradable form of ERK2 kinase (ERK2-dTAG) after treatment with the dTAG-13 compound. The generation of ERK2-dTAG clones involves a knock-in strategy. We provide a detailed methodology for clone selection, immunofluorescence screening, and characterization of ERK2-dTAG, including degradation kinetics, dose–response analysis, and nuclear translocation assays to assess ERK2-dTAG functionality. The IF-CRIB device and IF-Express protocol has been proven to be efficient for the obtention and characterization of ERK2dTAG-expressing clones thereby offering a powerful framework for studying ERK2 dynamics in cell biology and disease models.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".