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Image- Two Photon Imaging

Two-photon excitation microscopy (TPEF or 2PEF) is a fluorescence imaging technique that requires simultaneous excitation by two photons with longer wavelength than the emitted light. The laser is focused onto a specific location in the tissue and scanned across the sample to sequentially produce the image. Due to the non-linearity of two-photon excitation, mainly fluorophores in the micrometer-sized focus of the laser beam are excited, which results in the spatial resolution of the image. This contrasts with confocal microscopy, where the spatial resolution is produced by the interaction of excitation focus and the confined detection with a pinhole.

Two-photon excitation microscopy typically uses near-infrared (NIR) excitation light which can also excite fluorescent dyes. Using infrared light minimizes scattering in the tissue because infrared light is scattered less in typical biological tissues. Due to the multiphoton absorption, the background signal is strongly suppressed. Both effects lead to an increased penetration depth for this technique.

Best practice for sharing this type of data:

Most suitable repositories:
Image data may be added to PRIDE, Proteome-pI, and Real-Time Quantitative PCR Primer Database.

Any tabular data obtained by quantifying brightness/density of these images should be saved as a .txt or .csv file. The first row(s) should contain information about the dataset, such as the data file name, author, today's date, when the data within the file were last modified, and companion file names. Please also state which symbol has been used to denote missing data (NA is preferred). Column headings should describe the content of each column and contain only numbers, letters, and underscores - no spaces or special characters. Lowercase letters are preferred. Row names should be consistent with those used in the article and in other related datasets.

Best practice for indicating re-use of existing data:
For public datasets please provide a DOI or other stable identified for the dataset itself *and* include a citation for the dataset in the reference list. Be sure to indicate exactly which data has been re-used, particularly when multiple versions of the dataset exist. In many cases, this is best achieved by sharing the code used to extract the part of the data that you analyzed. In some cases it may be best to share the exact dataset(s) you analyzed as well.

For access-controlled data authors should provide a link to instructions for obtaining access (e.g. here is the information page for ADNI (Alzheimer's Disease Neuroimaging Initiative):

When re-using a private dataset from a previous study please contact the data owners to discuss how the data can be made public.

data_type/image/two_photon_imaging.txt · Last modified: 2024/01/03 22:41 by souad