Spatially Selective Dissection of Signal Transduction in Neurons Grown on Netrin-1 Printed Nanoarrays <i>via</i> Segmented Fluorescence Fluctuation Analysis
Bibliographic record
Abstract
Axonal growth cones extend during neural development in response to precise distributions of extracellular cues. Deleted in colorectal cancer (DCC), a receptor for the chemotropic guidance cue netrin-1, directs F-actin reorganization, and is essential for mammalian neural development. To elucidate how the extracellular distribution of netrin-1 influences the distribution of DCC and F-actin within axonal growth cones, we patterned nanoarrays of substrate bound netrin-1 using lift-off nanocontact printing. The distribution of DCC and F-actin in embryonic rat cortical neuron growth cones was then imaged using total internal reflection fluorescence (TIRF) microscopy. Fluorescence fluctuation analysis via image cross-correlation spectroscopy (ICCS) was applied to extract the molecular density and aggregation state of DCC and F-actin, identifying the fraction of DCC and F-actin colocalizing with the patterned netrin-1 substrate. ICCS measurement of spatially segmented images based on the substrate nanodot patterns revealed distinct molecular distributions of F-actin and DCC in regions directly overlying the nanodots compared to over the reference surface surrounding the nanodots. Quantifiable variations between the populations of DCC and F-actin on and off the nanodots reveal specific responses to the printed protein substrate. We report that nanodots of substrate-bound netrin-1 locally recruit and aggregate DCC and direct F-actin organization. These effects were blocked by tetanus toxin, consistent with netrin-1 locally recruiting DCC to the plasma membrane via a VAMP2-dependent mechanism. Our findings demonstrate the utility of segmented ICCS image analysis, combined with precisely patterned immobilized ligands, to reveal local receptor distribution and signaling within specialized subcellular compartments.
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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 | 0.000 | 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 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".