High-throughput microcontact printing of proteins in microwell cell culture plates
Bibliographic record
Abstract
Microcontact printing (MCP) is used to pattern a surface with a specific compound, allowing the spatially restricted response of cells to be assayed as they encounter a molecule of interest. MCP is a relatively low-cost and accessible technique that uses commercially available reagents and common cell culture equipment. However, it can be technically challenging, slow, and incompatible with microwell cell culture plates that are widely used for screening and other applications. Here, we describe a novel protocol using medical biopsy punches to transfer patterns into standard 96-well plates via polydimethylsiloxane (PDMS) cutouts. We demonstrate that this method can be used to deposit patterns of poly-D-lysine (PDL) into the microwells of glass-bottom plates. As a proof-of-concept, we show that cultured rodent glial cells preferentially grow and extend processes on the pattern. This method will allow larger scale MCP experiments in which different patterns, proteins, or other factors can be assayed in parallel.•Biopsy punches enable both cutting out small circular stamps and plunging them into tissue culture microwells to transfer proteins.•Compared to standard MCP, this method offers a more rapid workflow to pattern proteins onto substrates, and allows use of microwell plates that permits larger-scale experiments.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".