> For the complete documentation index, see [llms.txt](https://bakingtray.mouse.vision/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bakingtray.mouse.vision/users/introduction.md). # Introduction Serial section 2-photon is a microscopy technique for unattended imaging whole organs at high resolution. See the [gallery](https://bakingtray.mouse.vision/gallery) for example data. ## How does serial section 2-photon work? A sample is embedded in agar, glued to a slide, mounted in a water bath, and placed under the microscope. A vibratome associated with the microscope exposes the top of the sample block. The microscope performs a tile scan over the exposed sample surface then slices a thin section off the block. The microscope alternates between tile scanning and imaging until the whole sample has been acquired. Physical sections are taken approximately every 50 microns and optical sectioning is possible to allow for finer resolution in z. The slices sink down to the bottom of the water bath and are usually discarded once acquisition is complete. ## What sort of samples are suitable? Any sample that can be embedded in agar, cut on a vibratome, and contains a fluorescent signal can be imaged. Fixed tissue exhibits rich autofluorescence so label-free structural imaging is possible. Imaging of a wide range of fluorophores is, of course, also possible. The sample should ideally be brightly labeled to allow for short integration times and therefore faster imaging. Typical use cases that work well include counting labeled somata, mapping dye-labeled electrode tracks in brains, tracing bulk fibre projections in brains. Higher resolution scenarios, such as brain-wide tracing of individual axons is possible but very challenging. ## How does serial section imaging compare to conventional approaches? Datasets created by automated microscopy techniques can potentially be very large and so will usually require automated analysis techniques in order to extract meaning. It becomes particularly important to select imaging resolutions that are appropriate for the question at hand otherwise dataset size can quickly enter the TB range and acquisitions stretch into multiple days. Parameters such as voxel size and integration time should be carefully chosen to be adequate for the downstream analysis pipeline and not over-sampled. Often a quality that is perfectly adequate seems noisy or low resolution to those more familiar with confocal microscopy or even automated slide scanners. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://bakingtray.mouse.vision/users/introduction.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.