Protein patterning: a comparison of direct spotting versus microcontact printing
Bibliographic record
Abstract
Protein microarrays are used various research areas including drug discovery, diagnosis, and analysis of protein-ligand interactions. Their efficacy depends on a well-defined pattern of immobilized proteins that also have retained their bioactivity. Protein microarrays are classically fabricated using the robotic spotting drop method (“pin printing”), which can lead to spots with uneven protein concentration within the spotted area, leading to difficult to quantify readings. Among the alternative techniques, microcontact printing (μCP) with a poly(dimethylsiloxane) (PDMS) stamp appears to deliver more defined protein patterns on surfaces, while maintaining bioactivity for a wide range of proteins. Here we have quantitatively compared the distribution of fluorescently labeled proteins deposited using direct pipetting, pin printing and μCP printing with flat stamps onto various functionalized glass surfaces of different contact angles through fluorescent microscopy. The uniformity of the deposited protein spots across deposition techniques was also qualitatively analyzed. It was found that with the use of either the direct pipetting or pin printing techniques that protein concentration on surfaces varied largely across surfaces with different contact angles, whereas adsorption did not vary significantly when using the μCP printing Furthermore, when μCP printing was performed with flat relief structures the spot inhomogeneity was lower than when classical methods were used, and even less so when a pyramid relief structure was used. This suggests that μCP printing with pyramid relief structures could produce protein patterns on various surfaces and with increased spot uniformity to enable more reliable protein microarrays.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".