Non-specific Adsorption of Crude Cell Lysate on Surface Plasmon Resonance Sensors
Bibliographic record
Abstract
Non-specific adsorption of the molecular components of biofluids is ubiquitous in the area of biosensing technologies, severely limiting the use of biosensors in real-world applications. The surface chemistries developed to prevent non-specific adsorption of crude serum are not necessarily suited for sensing in other biosamples. In particular, the diagnostic potential of differential expression of proteins in tissues makes cell lysate attractive for disease diagnostics using solid biopsies. However, crude cell lysate poses a significant challenge for surface chemistries because of a large concentration of highly adherent lipids. Contrary to the non-specific adsorption in crude serum being suppressed by hydrophilic surfaces, the surface plasmon resonance (SPR) analysis of serine-, aspartic-acid-, histidine-, leucine-, and phenylalanine-based peptide monolayers revealed that hydrophobic and positively charged peptides decreased non-specific adsorption when using lysate from HEK 293FT cells. A polyethylene glycol (PEG) monolayer resulted in 2-fold greater fouling than the best peptide [3-MPA-(His)2(Leu)2(Phe)2-OH] under the same conditions. Matrix-assisted laser desorption ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF MS) analysis of the adsorbate from cell lysate confirmed that lipids are the main source of non-specific adsorption. Importantly, the mass spectrometry (MS) study revealed that both the number of lipids identified and their intensity decreased with decreasing non-specific adsorption. A peptide monolayer thus provides an efficient mean to suppress non-specific adsorption from this human cell lysate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".