TEL AVIV, Israel, Sept. 18, 2018 — A new add-on for laser-scanning microscopes, built from commercially available hardware and open-source software, could improve the quality of 2D and 3D imaging of neuronal activity in the living brain, even under photon-deprived imaging conditions. PySight, developed by researchers at Tel Aviv University, enables rapid imaging of the brain and other large tissue areas while using multiphoton microscopy, without impairing image resolution.
PySight embeds time-stamped photon counting into most existing multiphoton imaging systems. It uniquely time stamps each photon detection event with 100 picosecond accuracy, resulting in a modest data throughput, while exceeding the spatiotemporal resolution of existing volumetric imaging setups. It produces a data stream that scales to the number of detected photons, rather than to the volume or area being imaged. If no photons are detected, nothing gets written to disk. “This allows researchers to conduct rapid imaging of large volumes over long sessions, without compromising spatial or temporal resolution,” said research team leader Pablo Binder.
To reconstruct a 3D image, the PySight software reads a list of photon arrival times along with timing signals from the scanning elements, determines the origin of each photon within the sample, and generates the corresponding 3D movies. This approach helps to simplify 3D image reconstruction because the different scanning elements do not need to be synchronized.
The photon arrival times are generated by a multiple-event time digitizer, or multiscaler, which records the arrival times to within 100 picoseconds. An off-the-shelf resonant axial scanning lens — another key PySight component — enables the focal plane to be changed hundreds of thousands of times per second. The team used this lens to rapidly scan a laser beam across different depths within the brain and reconstruct continuous 3D images.
“The multiscaler we used hasn’t been applied to neuroimaging because the output isn’t easy to interpret, and using a resonant axial scanning lens for bioimaging has required custom-made scanning synchronization hardware or proprietary code to obtain the 3D data,” said Blinder. “PySight turns the output from both components into a 3D movie effortlessly.”
To test whether PySight was truly plug and play, the researchers plugged their device into a different imaging lab’s multiphoton microscope, downloaded the PySight software, and started recording. PySight’s data streaming architecture allowed the team to image a fruit fly’s brain olfactory response over 234 μm × 600 μm × 330 μm at 73 volumes per second, while retaining over 200× lower data rates than those of a conventional data acquisition system with comparable voxel sizes.
Capturing the breadth of neuronal activity with multiphoton microscopy requires fast imaging, resulting in fewer photons being available to form images. PySight overcomes dim conditions without photo counting, opening the way for easier implementation of multiphoton microscopy in almost any lab. It requires no electronics expertise or custom synchronization boards, and its open-source software is extensible to any imaging method based on single-pixel detectors.
In addition to advancing neural imaging research, PySight’s novel approach to reconstructing 3D scenes could potentially be used to improve lidar performance.
The researchers would like to add support for other microscopy imaging methods, such as fluorescence lifetime imaging, to PySight’s capabilities. Because the PySight software is open source and provides direct access to photon arrival times, it enables other scientists to add new features to meet their specific needs.
The research was published in Optica (doi:10.1364/OPTICA.5.001104).
1.2 μm×1.2 μm×2.2 μm