Digital Microfluidics for Immunoprecipitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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