@article {69044, title = {Blue-shifted genetically encoded Ca2+ indicator with enhanced two-photon absorption}, journal = {Neurophotonics}, year = {2024}, month = {2024 Apr 11}, abstract = {

Significance:\ Genetically encoded calcium ion (Ca2+) indicators (GECIs) are powerful tools for monitoring intracellular\ Ca2+\ concentration changes in living cells and model organisms. In particular, GECIs have found particular utility for monitoring the transient increase of\ Ca2+concentration that is associated with the neuronal action potential. However, the palette of highly optimized GECIs for imaging of neuronal activity remains relatively limited. Expanding the selection of available GECIs to include new colors and distinct photophysical properties could create new opportunities for\ in vitro\ and\ in vivo\ fluorescence imaging of neuronal activity. In particular, blue-shifted variants of GECIs are expected to have enhanced two-photon brightness, which would facilitate multiphoton microscopy.

Aim:\ We describe the development and applications of T-GECO1-a high-performance blue-shifted GECI based on the\ Clavularia sp.-derived mTFP1.

Approach:\ We use protein engineering and extensive directed evolution to develop T-GECO1. We characterize the purified protein and assess its performance\ in vitro\ using one-photon excitation in cultured rat hippocampal neurons,\ in vivo\ using one-photon excitation fiber photometry in mice, and\ ex vivo\ using two-photon\ Ca2+\ imaging in hippocampal slices.

Results:\ The\ Ca2+-bound state of T-GECO1 has an excitation peak maximum of 468 nm, an emission peak maximum of 500 nm, an extinction coefficient of\ 49,300M-1cm-1, a quantum yield of 0.83, and two-photon brightness approximately double that of EGFP. The\ Ca2+-dependent fluorescence increase is 15-fold, and the apparent\ Kd\ for\ Ca2+\ is 82 nM. With two-photon excitation conditions at 850 nm, T-GECO1 consistently enabled the detection of action potentials with higher signal-to-noise (SNR) than a late generation GCaMP variant.

Conclusions:\ T-GECO1 is a high-performance blue-shifted GECI that, under two-photon excitation conditions, provides advantages relative to late generation GCaMP variants.

Keywords:\ blue-shifted fluorescence; genetically encoded calcium ion indicator; neuronal activity imaging; protein engineering; two-photon excitation.

}, doi = {10.1117/1.NPh.11.2.024207}, url = {https://doi.org/10.1117/1.NPh.11.2.024207}, author = {Abhi Aggarwal and Smrithi Sunil and Imane Bendifallah and Michael Moon and Mikhail Drobizhev and Landon Zarowny and Jihong Zheng and Sheng-Yi Wu and Alexander W. Lohman and Alison G. Tebo and Valentina Emiliani and Kaspar Podgorski and Yi Shen and Robert E. Campbell} } @conference {68678, title = {FourierNets enable the design of highly non-local optical encoders for computational imaging}, booktitle = {Advances in Neural Information Processing Systems}, year = {2022}, month = {10/2022}, abstract = {

Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170{\texttimes}\ to 7372{\texttimes}\ larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.

}, keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering, Machine Learning (cs.LG)}, doi = {https://doi.org/10.48550/arXiv.2104.10611}, url = {https://openreview.net/forum?id=Kx1VCs1treH}, author = {Diptodip Deb and Zhenfei Jiao and Ruth R Sims and Alex Bo-Yuan Chen and Michael Broxton and Misha Ahrens and Kaspar Podgorski and Srinivas C Turaga}, editor = {Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho} }