New applications of image correlation spectroscopy to reveal mechanisms of cell membrane receptor regulation
Bibliographic record
Abstract
Achieving an understanding of the molecular dynamics and interactions controlling cellular functions has been a fundamental focus of molecular biophysics.In this thesis, we apply novel image correlation spectroscopy (ICS) techniques to analyze fluorescence microscopy images to quantify these molecular interactions in a variety of biological systems.ICS and its variants are a family of fluorescence fluctuation analysis methods that have been used to measure number density, aggregation state, speed and type of molecular transport, and colocalization of fluorescently tagged biomolecules of interest in cells and neurons.This thesis will present segmentation image cross-correlation spectroscopy (ICCS) applied to total internal reflection fluorescence (TIRF) microscopy images of fixed embryonic rat cortical neurons cultured on printed protein nanodots of the chemotropic guidance cue netrin-1.We investigate population distributions of F-actin, and the chemoattractive receptor, deleted in colorectal cancer (DCC).We reveal that v-SNARE VAMP2 is needed for DCC multimerization and colocalization to netrin-1.This is the first use of segmentation ICCS combined with patterned immobilized ligands to investigate local receptor distribution and signal transduction within specialized subcellular compartments.The author cultured the cells, designed and performed all the live cell imaging experiments, wrote the analysis codes for sliding window TICCS and performed the analysis.viii
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".