Live Imaging of Dense-core Vesicles in Primary Cultured Hippocampal Neurons
Bibliographic record
Abstract
Observing and characterizing dynamic cellular processes can yield important information about cellular activity that cannot be gained from static images. Vital fluorescent probes, particularly green fluorescent protein (GFP) have revolutionized cell biology stemming from the ability to label specific intracellular compartments and cellular structures. For example, the live imaging of GFP (and its spectral variants) chimeras have allowed for a dynamic analysis of the cytoskeleton, organelle transport, and membrane dynamics in a multitude of organisms and cell types [1-3]. Although live imaging has become prevalent, this approach still poses many technical challenges, particularly in primary cultured neurons. One challenge is the expression of GFP-tagged proteins in post-mitotic neurons; the other is the ability to capture fluorescent images while minimizing phototoxicity, photobleaching, and maintaining general cell health. Here we provide a protocol that describes a lipid-based transfection method that yields a relatively low transfection rate (~0.5%), however is ideal for the imaging of fully polarized neurons. A low transfection rate is essential so that single axons and dendrites can be characterized as to their orientation to the cell body to confirm directionality of transport, i.e., anterograde v. retrograde. Our approach to imaging GFP expressing neurons relies on a standard wide-field fluorescent microscope outfitted with a CCD camera, image capture software, and a heated imaging chamber. We have imaged a wide variety of organelles or structures, for example, dense-core vesicles, mitochondria, growth cones, and actin without any special optics or excitation requirements other than a fluorescent light source. Additionally, spectrally-distinct, fluorescently labeled proteins, e.g., GFP and dsRed-tagged proteins, can be visualized near simultaneously to characterize co-transport or other coordinated cellular events. The imaging approach described here is flexible for a variety of imaging applications and can be adopted by a laboratory for relatively little cost provided a microscope is available.
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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".