Live-imaging of PKC Translocation in Sf9 Cells and in Aplysia Sensory Neurons
Bibliographic record
Abstract
Protein kinase Cs (PKCs) are serine threonine kinases that play a central role in regulating a wide variety of cellular processes such as cell growth and learning and memory. There are four known families of PKC isoforms in vertebrates: classical PKCs (α, βI, βII and γ), novel type I PKCs (ε and η), novel type II PKCs (δ and θ), and atypical PKCs (ζ and ι). The classical PKCs are activated by Ca(2+) and diacylclycerol (DAG), while the novel PKCs are activated by DAG, but are Ca(2+)-independent. The atypical PKCs are activated by neither Ca(2+) nor DAG. In Aplysia californica, our model system to study memory formation, there are three nervous system specific PKC isoforms one from each major class, namely the conventional PKC Apl I, the novel type I PKC Apl II and the atypical PKC Apl III. PKCs are lipid-activated kinases and thus activation of classical and novel PKCs in response to extracellular signals has been frequently correlated with PKC translocation from the cytoplasm to the plasma membrane. Therefore, visualizing PKC translocation in real time in live cells has become an invaluable tool for elucidating the signal transduction pathways that lead to PKC activation. For instance, this technique has allowed for us to establish that different isoforms of PKC translocate under different conditions to mediate distinct types of synaptic plasticity and that serotonin (5HT) activation of PKC Apl II requires production of both DAG and phosphatidic acid (PA) for translocation (1-2). Importantly, the ability to visualize the same neuron repeatedly has allowed us, for example, to measure desensitization of the PKC response in exquisite detail (3). In this video, we demonstrate each step of preparing Sf9 cell cultures, cultures of Aplysia sensory neurons have been described in another video article (4), expressing fluorescently tagged PKCs in Sf9 cells and in Aplysia sensory neurons and live-imaging of PKC translocation in response to different activators using laser-scanning microscopy.
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 | 0.000 |
| 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 | 0.000 | 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".