Researchers have developed a combination of commercially available hardware and open-source software, named PySight, to improve rapid 2D and 3D imaging of neuronal activity in the living brain and other tissues. PySight serves as an add-on for laser scanning microscopes. Such an advancement in microscopy could help scientists better understand brain dynamics and discover new treatments for health problems such as stroke, epilepsy, and dementia.
A laser-based imaging technique known as multiphoton microscopy has often been used to study the rapid activity patterns of neurons, blood vessels, and other cells at high resolution over time because it can image deep into tissue. This microscopy method uses laser pulses to excite fluorescent probes, eliciting the emission of photons, some of which are detected and used to form 2D and 3D images. Trying to capture the full breadth of neuronal activity with multiphoton microscopy forces scientists to image faster. As a result, fewer and fewer photons become available to form images, much like taking a photo with shorter and shorter exposure times. The challenge then becomes how to get meaningful images under these dim conditions.
"To tackle this challenge, microscopists have used a detector-readout method called photon counting," said research team leader Pablo Blinder from Tel Aviv University in Israel. "However, because its implementation required extensive electronics knowledge and custom components, photon counting has never been widely adopted. In addition, commercially available photon counting solutions were ill-suited to perform very fast imaging such as required for 3D imaging. PySight's easy installation procedure and its integration with state-of-the-art hardware eliminate such concerns."
In addition to advancing neural imaging research, PySight's improved sensitivity could facilitate rapid intraoperative identification of malignant cells in human patients using multiphoton microscopy. PySight's novel approach for reconstructing 3D scenes could also improve performance of light detection and ranging, or LIDAR. This could help lower the costs of self-driving cars that use LIDAR to map their surroundings.