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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".