A review of computational reconstruction of interaction in superresolution microscopy: from colocalization to closing the multichannel gap
Bibliographic record
Abstract
AbstractCellular function is defined by pathways that, in turn, are determined by distance-mediated interactions between and within subcel-lular organelles, protein complexes, and macromolecular structures. Multichannel Super Resolution Microscopy (SRM) is uniquelyplaced to quantify distance-mediated interactions at the nanometer scale with its ability to label individual biological targets withindependent markers that fluoresce in different spectra. We review novel computational methods that quantify interaction from mul-tichannel SRM data in both point-cloud and voxel form to meet the increasing adoption of multichannel SRM in life sciences. SRMspecific factors that can compromise interaction analysis are discussed in detail. Different classes of interactions are decomposedbased on distinct representative cell biology use cases, the underappreciated non-linear physics of their scale, and the developmentof specialized methods for those use case. An abstract mathematical model is introduced to facilitate the comparison and evaluationof interaction reconstruction methods and to quantify the computational bottlenecks. We discuss the different strategies for valida-tion of interaction analysis results with sparse or incomplete ground truth data. Finally, evolving trends and future directions arepresented, highlighting the ‘multichannel gap’, where interaction analysis is trailing behind the rapid increase in novel multichannelSRM acquisitions.This preprint was submitted to Cell Press Patterns 10/10/2024, it is derived in part from work published in the first author's thesis (see pdf).
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 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 | 0.001 |
| 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 | 0.001 |
| 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".