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Li C, Rai MR, Cai Y, Ghashghaei HT, Greenbaum A. Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning. INTELLIGENT COMPUTING (WASHINGTON, D.C.) 2024; 3:0095. [PMID: 39099879 PMCID: PMC11298055 DOI: 10.34133/icomputing.0095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/22/2024] [Indexed: 08/06/2024]
Abstract
Light-sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times, enabling high-resolution 3-dimensional imaging of large tissue-cleared samples. Inherent to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, which only illuminates a thin section of the sample. Therefore, substantial efforts are dedicated to identifying slender, nondiffracting beam profiles that yield uniform and high-contrast images. An ongoing debate concerns the identification of optimal illumination beams for different samples: Gaussian, Bessel, Airy patterns, and/or others. However, comparisons among different beam profiles are challenging as their optimization objectives are often different. Given that our large imaging datasets (approximately 0.5 TB of images per sample) are already analyzed using deep learning models, we envisioned a different approach to the problem by designing an illumination beam tailored to boost the performance of the deep learning model. We hypothesized that integrating the physical LSFM illumination model (after passing it through a variable phase mask) into the training of a cell detection network would achieve this goal. Here, we report that joint optimization continuously updates the phase mask and results in improved image quality for better cell detection. The efficacy of our method is demonstrated through both simulations and experiments that reveal substantial enhancements in imaging quality compared to the traditional Gaussian light sheet. We discuss how designing microscopy systems through a computational approach provides novel insights for advancing optical design that relies on deep learning models for the analysis of imaging datasets.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Yuheng Cai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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2
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Li C, Rai MR, Cai Y, Ghashghaei HT, Greenbaum A. Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569329. [PMID: 38077074 PMCID: PMC10705487 DOI: 10.1101/2023.11.29.569329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Light sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times for imaging of tissue-cleared specimen. This allows for high-resolution 3D imaging of large tissue volumes. Inherently to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, with the notion that the illumination beam only illuminates a thin section that is being imaged. Therefore, substantial efforts are dedicated to identifying slender, non-diffracting beam profiles that can yield uniform and high-contrast images. An ongoing debate concerns the employment of the most optimal illumination beam; Gaussian, Bessel, Airy patterns and/or others. Comparisons among different beam profiles is challenging as their optimization objective is often different. Given that our large imaging datasets (~0.5TB images per sample) is already analyzed using deep learning models, we envisioned a different approach to this problem by hypothesizing that we can tailor the illumination beam to boost the deep learning models performance. We achieve this by integrating the physical LSFM illumination model after passing through a variable phase mask into the training of a cell detection network. Here we report that the joint optimization continuously updates the phase mask, improving the image quality for better cell detection. Our method's efficacy is demonstrated through both simulations and experiments, revealing substantial enhancements in imaging quality compared to traditional Gaussian light sheet. We offer valuable insights for designing microscopy systems through a computational approach that exhibits significant potential for advancing optics design that relies on deep learning models for analysis of imaging datasets.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Yuheng Cai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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3
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Jeffet J, Mondal S, Federbush A, Tenenboim N, Neaman M, Deek J, Ebenstein Y, Bar-Sinai Y. Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures. ACS Sens 2023; 8:3781-3792. [PMID: 37791886 PMCID: PMC10616852 DOI: 10.1021/acssensors.3c01234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023]
Abstract
MicroRNAs (miRs) are small noncoding RNAs that regulate gene expression and are emerging as powerful indicators of diseases. MiRs are secreted in blood plasma and thus may report on systemic aberrations at an early stage via liquid biopsy analysis. We present a method for multiplexed single-molecule detection and quantification of a selected panel of miRs. The proposed assay does not depend on sequencing, requires less than 1 mL of blood, and provides fast results by direct analysis of native, unamplified miRs. This is enabled by a novel combination of compact spectral imaging and a machine learning-based detection scheme that allows simultaneous multiplexed classification of multiple miR targets per sample. The proposed end-to-end pipeline is extremely time efficient and cost-effective. We benchmark our method with synthetic mixtures of three target miRs, showcasing the ability to quantify and distinguish subtle ratio changes between miR targets.
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Affiliation(s)
- Jonathan Jeffet
- School
of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact
Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Center
for Nanoscience and Nanotechnology, Tel
Aviv University, Tel Aviv 6997801, Israel
| | - Sayan Mondal
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Center
for Nanoscience and Nanotechnology, Tel
Aviv University, Tel Aviv 6997801, Israel
| | - Amit Federbush
- School
of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact
Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Nadav Tenenboim
- School
of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact
Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Center
for Nanoscience and Nanotechnology, Tel
Aviv University, Tel Aviv 6997801, Israel
| | - Miriam Neaman
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Department
of Hematology, Tel Aviv Sourasky Medical
Center, Tel Aviv 6423906, Israel
| | - Jasline Deek
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yuval Ebenstein
- School
of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Department
of Biomedical Engineering, Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Center
for Nanoscience and Nanotechnology, Tel
Aviv University, Tel Aviv 6997801, Israel
- Center
for AI & Data Science (TAD), Tel Aviv
University, Tel Aviv 6997801, Israel
| | - Yohai Bar-Sinai
- School
of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact
Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel
- Center
for AI & Data Science (TAD), Tel Aviv
University, Tel Aviv 6997801, Israel
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Singh K, Dudley A, Forbes A. Versatile all-digital transport-of-intensity based wavefront sensor and adaptive optics using a DMD. OPTICS EXPRESS 2023; 31:8987-8997. [PMID: 36860001 DOI: 10.1364/oe.481767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Measuring and correcting wavefront aberrations is an important process in a wide variety of disciplines, from ophthalmology, laser cutting, and astronomy to free-space communication and microscopy, and always relies on measuring intensities to infer phase. One approach is to use the transport-of-intensity as a means for phase retrieval, exploiting the connection between observed energy flow in optical fields and their wavefronts. Here we present a simple scheme, using a digital micro-mirror device (DMD), to perform angular spectrum propagation and extract the wavefront of optical fields at various wavelengths, dynamically, with high resolution and tuneable sensitivity. We verify the capability of our approach by extracting common Zernike aberrations, turbulent phase screens, and lens phases under static and dynamic conditions at multiple wavelengths and polarizations. We use this setup for adaptive optics, correcting distortion using a second DMD to apply conjugate phase modulation. We observed effective wavefront recovery under a variety of conditions which allowed for convenient real-time adaptive correction in a compact arrangement. Our approach provides an all-digital system that is versatile, cheap, fast, accurate, broadband and polarization invariant.
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Liu X, Jiang Y, Cui Y, Yuan J, Fang X. Deep learning in single-molecule imaging and analysis: recent advances and prospects. Chem Sci 2022; 13:11964-11980. [PMID: 36349113 PMCID: PMC9600384 DOI: 10.1039/d2sc02443h] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/19/2022] [Indexed: 09/19/2023] Open
Abstract
Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.
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Affiliation(s)
- Xiaolong Liu
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
- University of Chinese Academy of Sciences Beijing 100049 P. R. China
| | - Yifei Jiang
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences Hangzhou 310022 Zhejiang China
| | - Yutong Cui
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
- University of Chinese Academy of Sciences Beijing 100049 P. R. China
| | - Jinghe Yuan
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
| | - Xiaohong Fang
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
- University of Chinese Academy of Sciences Beijing 100049 P. R. China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences Hangzhou 310022 Zhejiang China
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Kozawa Y, Nakamura T, Uesugi Y, Sato S. Wavefront engineered light needle microscopy for axially resolved rapid volumetric imaging. BIOMEDICAL OPTICS EXPRESS 2022; 13:1702-1717. [PMID: 35415006 PMCID: PMC8973193 DOI: 10.1364/boe.449329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/09/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
Increasing the acquisition speed of three-dimensional volumetric images is important-particularly in biological imaging-to unveil the structural dynamics and functionalities of specimens in detail. In conventional laser scanning fluorescence microscopy, volumetric images are constructed from optical sectioning images sequentially acquired by changing the observation plane, limiting the acquisition speed. Here, we present a novel method to realize volumetric imaging from two-dimensional raster scanning of a light needle spot without sectioning, even in the traditional framework of laser scanning microscopy. Information from multiple axial planes is simultaneously captured using wavefront engineering for fluorescence signals, allowing us to readily survey the entire depth range while maintaining spatial resolution. This technique is applied to real-time and video-rate three-dimensional tracking of micrometer-sized particles, as well as the prompt visualization of thick fixed biological specimens, offering substantially faster volumetric imaging.
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Affiliation(s)
- Yuichi Kozawa
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Tomoya Nakamura
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Yuuki Uesugi
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Shunichi Sato
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
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Abstract
After first describing the issue contents (Biophysical Reviews-Volume 12 Issue 6), this Editorial goes on to provide a short round-up of the activities of the journal in 2020. Directly following this Editorial are two obituaries marking the recent deaths of Prof. Fumio Oosawa (Japan) and Dr. Herbert Tabor (USA)-two major figures in Biophysical/Biochemical science from the last 100 years.
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Affiliation(s)
- Damien Hall
- Department of Life Sciences and Applied Chemistry, Nagoya Institute of Technology, Gokiso Showa, Nagoya, 466-8555 Japan
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