A High Performance, Cost-Effective, Open-Source Microscope for Scanning Two-Photon Microscopy that Is Modular and Readily Adaptable
Bibliographic record
Abstract
Two-photon laser scanning microscopy has revolutionized the ability to delineate cellular and physiological function in acutely isolated tissue and in vivo. However, there exist barriers for many laboratories to acquire two-photon microscopes. Additionally, if owned, typical systems are difficult to modify to rapidly evolving methodologies. A potential solution to these problems is to enable scientists to build their own high-performance and adaptable system by overcoming a resource insufficiency. Here we present a detailed hardware resource and protocol for building an upright, highly modular and adaptable two-photon laser scanning fluorescence microscope that can be used for in vitro or in vivo applications. The microscope is comprised of high-end componentry on a skeleton of off-the-shelf compatible opto-mechanical parts. The dedicated design enabled imaging depths close to 1 mm into mouse brain tissue and a signal-to-noise ratio that exceeded all commercial two-photon systems tested. In addition to a detailed parts list, instructions for assembly, testing and troubleshooting, our plan includes complete three dimensional computer models that greatly reduce the knowledge base required for the non-expert user. This open-source resource lowers barriers in order to equip more laboratories with high-performance two-photon imaging and to help progress our understanding of the cellular and physiological function of living systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".