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Zhang J, Newman J, Wang Z, Qian Y, Feliciano-Ramos P, Guo W, Honda T, Chen ZS, Linghu C, Etienne-Cummings R, Fossum E, Boyden E, Wilson M. Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. Nat Commun 2024; 15:4480. [PMID: 38802338 DOI: 10.1038/s41467-024-48765-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
High-speed wide-field fluorescence microscopy has the potential to capture biological processes with exceptional spatiotemporal resolution. However, conventional cameras suffer from low signal-to-noise ratio at high frame rates, limiting their ability to detect faint fluorescent events. Here, we introduce an image sensor where each pixel has individually programmable sampling speed and phase, so that pixels can be arranged to simultaneously sample at high speed with a high signal-to-noise ratio. In high-speed voltage imaging experiments, our image sensor significantly increases the output signal-to-noise ratio compared to a low-noise scientific CMOS camera (~2-3 folds). This signal-to-noise ratio gain enables the detection of weak neuronal action potentials and subthreshold activities missed by the standard scientific CMOS cameras. Our camera with flexible pixel exposure configurations offers versatile sampling strategies to improve signal quality in various experimental conditions.
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Affiliation(s)
- Jie Zhang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
| | - Jonathan Newman
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Zeguan Wang
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Yong Qian
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Pedro Feliciano-Ramos
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Wei Guo
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Takato Honda
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Zhe Sage Chen
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Changyang Linghu
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Eric Fossum
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
| | - Edward Boyden
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Matthew Wilson
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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Liu C, Lu J, Wu Y, Ye X, Ahrens AM, Platisa J, Pieribone VA, Chen JL, Tian L. DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594448. [PMID: 38798473 PMCID: PMC11118583 DOI: 10.1101/2024.05.16.594448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Significance Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground truth, but usually suffer from the tradeoff between spatial and temporal performance. Aim We present DeepVID v2, a novel self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging. Approach DeepVID v2 is built on our original DeepVID framework,1,2 which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. The network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. Additionally, DeepVID v2 introduces a new edge extraction branch to capture fine structural details in order to learn high spatial resolution information. Results We demonstrate that DeepVID v2 is able to overcome the tradeoff between spatial and temporal performance, and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 is able to generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios (SNRs) and in extreme low-photon conditions. Conclusions Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.
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Affiliation(s)
- Chang Liu
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Jiayu Lu
- Boston University, Department of Electrical and Computer Engineering, Boston, MA 02215, USA
| | - Yicun Wu
- Boston University, Department of Computer Science, Boston, MA 02215, USA
| | - Xin Ye
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
- Neurophotonics Center, Boston University, Boston, MA 02215, USA
| | | | - Jelena Platisa
- Yale University, Department of Cellular and Molecular Physiology, New Haven, CT 06520, USA
- The John B. Pierce Laboratory, New Haven, CT 06519, USA
| | - Vincent A Pieribone
- Yale University, Department of Cellular and Molecular Physiology, New Haven, CT 06520, USA
- The John B. Pierce Laboratory, New Haven, CT 06519, USA
- Yale University, Department of Neuroscience, New Haven, CT 06520, USA
| | - Jerry L Chen
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
- Neurophotonics Center, Boston University, Boston, MA 02215, USA
- Boston University, Department of Biology, Boston, MA 02215, USA
| | - Lei Tian
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
- Boston University, Department of Electrical and Computer Engineering, Boston, MA 02215, USA
- Neurophotonics Center, Boston University, Boston, MA 02215, USA
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Wang Z, Zhang J, Symvoulidis P, Guo W, Zhang L, Wilson MA, Boyden ES. Imaging the voltage of neurons distributed across entire brains of larval zebrafish. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571964. [PMID: 38168290 PMCID: PMC10760087 DOI: 10.1101/2023.12.15.571964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Neurons interact in networks distributed throughout the brain. Although much effort has focused on whole-brain calcium imaging, recent advances in genetically encoded voltage indicators (GEVIs) raise the possibility of imaging voltage of neurons distributed across brains. To achieve this, a microscope must image at high volumetric rate and signal-to-noise ratio. We present a remote scanning light-sheet microscope capable of imaging GEVI-expressing neurons distributed throughout entire brains of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage of ∼1/3 of the neurons of the brain, distributed throughout. We observed that neurons firing at different times during a sequence were located at different brain locations, for sequences elicited by a visual stimulus, which mapped onto locations throughout the optic tectum, as well as during stimulus-independent bursts, which mapped onto locations in the cerebellum and medulla. Whole-brain voltage imaging may open up frontiers in the fundamental operation of neural systems.
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