Digital Microfluidics for Immunoprecipitation
Bibliographic record
Abstract
Immunoprecipitation (IP) is a common method for isolating a targeted protein from a complex sample such as blood, serum, or cell lysate. In particular, IP is often used as the primary means of target purification for the analysis by mass spectrometry of novel biologically derived pharmaceuticals, with particular utility for the identification of molecules bound to a protein target. Unfortunately, IP is a labor-intensive technique, is difficult to perform in parallel, and has limited options for automation. Furthermore, the technique is typically limited to large sample volumes, making the application of IP cleanup to precious samples nearly impossible. In recognition of these challenges, we introduce a method for performing microscale IP using magnetic particles and digital microfluidics (DMF-IP). The new method allows for 80% recovery of model proteins from approximately microliter volumes of serum in a sample-to-answer run time of approximately 25 min. Uniquely, analytes are eluted from these small samples in a format compatible with direct analysis by mass spectrometry. To extend the technique to be useful for large samples, we also developed a macro-to-microscale interface called preconcentration using liquid intake by paper (P-CLIP). This technique allows for efficient analysis of samples >100× larger than are typically processed on microfluidic devices. As described herein, DMF-IP and P-CLIP-DMF-IP are rapid, automated, and multiplexed methods that have the potential to reduce the time and effort required for IP sample preparations with applications in the fields of pharmacy, biomarker discovery, and protein biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".