SPR-MS: from identifying adsorbed molecules to image tissues
Bibliographic record
Abstract
Surface plasmon resonance (SPR) sensors have become valuable analytical sensors for biomolecule detection. While SPR is heralded with high sensitivity, label-free and real-time detection, nonspecific adsorption and detection of ultralow concentrations remain issues. Nonspecific adsorption can be minimized using adequate surface chemistry. For example, we have employed peptide monolayers to reduce nonspecific adsorption of crude serum or cell lysate. It is important to uncover the nature of molecules nonspecifically adsorbing to surfaces in these biofluids, to further improve understanding of the nonspecific adsorption processes. Mass spectrometry (MS) provides a complementary tool to SPR to identify biomolecule adsorbed to surface. Trypsic digestion of the proteins adsorbed to surfaces led to identification of characteristic peptides from the proteins involved in nonspecific adsorption. Nonspecific adsorption in crude cell lysate results mainly from lipids, as confirmed with SPR and MS but proteins were observed on some surfaces. In another application of SPR and MS, imaging SPR can be used in combination to imaging MS to image tissue sections. Thin sections of mouse liver were inserted in the fluidic chamber of a SPRi instrument and proteins were transferred to the SPRi chip. The SPR chip was then imaged using MALDI imaging MS to identify the biomolecules that were transferred to the SPRi chip.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".