Deep learning-driven adaptive optics for single-molecule localization microscopy.

Journal: Nature methods
Published Date: Sep 28, 2023

Abstract

The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.

Authors

  • Peiyi Zhang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Donghan Ma
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Xi Cheng
    Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology (OCICB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of HealthRockville, MD, USA.
  • Andy P Tsai
    Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
  • Yu Tang
    School of Information Science and Engineering, Central South University, Changsha 410083, China. tangyu@csu.edu.cn.
  • Hao-Cheng Gao
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Li Fang
    Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Cheng Bi
    Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China.
  • Gary E Landreth
    Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA. glandret@iu.edu.
  • Alexander A Chubykin
    Department of Biological Sciences, Purdue University, West Lafayette, IN, USA. chubykin@purdue.edu.
  • Fang Huang

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