BrainPhys neuronal medium optimized for imaging and optogenetics in vitro
Bibliographic record
Abstract
Abstract The capabilities of imaging technologies, fluorescent sensors, and optogenetics tools for cell biology have improved exponentially in the last ten years. At the same time, advances in cellular reprogramming and organoid engineering have quickly expanded the use of human neuronal models in vitro . Altogether this creates an increasing need for tissue culture conditions better adapted to live-cell imaging. Here, we identified multiple caveats of traditional media when used for live imaging and functional assays on neuronal cultures (e.g., phototoxicity, suboptimal fluorescence signals, and unphysiological neuronal activity). To overcome these issues, we developed a new neuromedium, “BrainPhys™ Imaging”, in which we adjusted fluorescent and phototoxic compounds. The new medium is based on the formulation of the original BrainPhys medium, which we designed to better support the neuronal activity of human neurons in vitro 1 . We tested the new imaging-optimized formulation on human neurons cultured in monolayers or organoids, and rat primary neurons. BrainPhys Imaging enhanced fluorescence signals and reduced phototoxicity throughout the entire light spectrum. Importantly, consistent with standard BrainPhys, we showed that the new imaging medium optimally supports the electrical and synaptic activity of midbrain and human cortical neurons in culture. We also benchmarked the capacity of the new medium for functional calcium imaging and optogenetic control of human neurons. Altogether, our study shows that the new BrainPhys Imaging improves the quality of a wide range of fluorescence imaging applications with live neurons in vitro while supporting cell viability and neuronal functions.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".