Spatiotemporal Organization Of Signaling: From Plasma Membrane To Chromatin
Bibliographic record
Abstract
Compartmentalization is essential for all complex forms of life. In eukaryotic cells, membrane‐bound organelles, as well as a multitude of protein‐ and nucleic acid‐rich subcellular structures, maintain boundaries and serve as enrichment zones to promote and regulate protein function. Consistent with the critical importance of these boundaries, alterations in the machinery that mediate protein transport between these compartments has been implicated in a number of diverse diseases. Signaling molecules are no exception, and must be targeted to specific locations for proper activation in time and place. Understanding the composition of each cellular “compartment” (be it a classical organelle or a large protein complex) remains a challenging task. For soluble protein complexes, approaches such as affinity purification other biochemical fractionation coupled to mass spectrometry provides important insight, including on the temporal regulation of signaling, but this is not the case for detergent‐insoluble components. Classically, both microscopy and organellar purifications have been employed for identifying the composition of these structures, but these approaches have limitations, notably in resolution for standard high‐throughput fluorescence microscopy and in the difficulty in purifying some of the structures (e.g. p‐bodies) for approaches based on biochemical isolations. Prompted by the recent implementation in vivo biotinylation approaches such as BioID, we have begun the systematic mapping of the composition of various subcellular structures, using as baits proteins (or protein fragments) which are well‐characterized markers for a specified location. We report here our low‐resolution map of a human cell (currently defined from BioID profiling of 100 marker proteins), including various membrane compartments, cytoskeletal structures and nuclear subdomains. We will also introduce a higher resolution map of RNA‐containing cellular structures, including the p‐bodies and the stress granules that regulate mRNA stability, and new data on signaling pathways that span insoluble compartments. We demonstrate in vivo biotinylation to be a scalable approach capable of revealing protein complexes as well as their association into larger structures. Support or Funding Information Work in the Gingras lab was primarily supported by the Canadian Institutes for Health Research (Foundation grant) and the Natural Sciences and Engineering Research Council of Canada (Discovery grant). Additional funding was provided by Genome Canada through Ontario Genomics.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".