Analyzing for Co-Localization of Proteins at a Cell Membrane
Bibliographic record
Abstract
Cell-to-cell communication is mediated by molecular interactions at the surface of the cell by soluble ligands released from distant cells or by cell surface molecules on adjacent cells. These interactions lead to activation of intracellular signaling pathways that subsequently can lead to activation of specific genes. This signal transduction process controls cellular activities as diverse as proliferation, differentiation and apoptosis, so we must understand the underlying molecular events in detail in order to understand broader questions related to development, uncontrolled growth in tumors, tissue regeneration and use of stem cells to name a few. Binding of a ligand in the extracellular space to a transmembrane receptor constitutes the first crucial step for activation of a signaling pathway within the cell. This binding can either lead to oligomerization of individual receptors, to reorganization of existing clusters of receptors or to changes in the protein conformations, which in turn results in recruitment of signaling molecules in the cytoplasm. While different membrane receptors activate different downstream signaling pathways, some receptors can activate more than one pathway and a particular pathway can be activated by different receptors. It appears that these processes are regulated either by agonists and antagonists in the extracellular medium, by receptor-receptor interactions in the membrane or by a number of signaling mediators in the cytoplasm of the cell. Our work has focused on understanding how the intermolecular interactions in the membrane can control the signal transduction process: Are there specialized structures on the surface that facilitate receptor-receptor interactions? Do the receptors exist as monomers or pre-existing complexes that enhance the probability of activation? Do different receptors associate in the same domains or are there distinct organizational principles for each receptor type. In order to address these questions, we seek to develop tools that allow us to examine intermolecular interactions and reactions directly on the cell surface, particularly on live cells in culture or in tissue. This review discusses some of the approaches that are currently available and highlights some of the key advantages and disadvantages they represent with particular focus on image cross correlation spectroscopy as a relatively new quantitative tool developed by us to address some of these issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".