1
|
Fanous MJ, Pillar N, Ozcan A. Digital staining facilitates biomedical microscopy. FRONTIERS IN BIOINFORMATICS 2023; 3:1243663. [PMID: 37564725 PMCID: PMC10411189 DOI: 10.3389/fbinf.2023.1243663] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational "virtual" staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.
Collapse
Affiliation(s)
- Michael John Fanous
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
- Bioengineering Department, University of California, Los Angeles, CA, United States
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
- Bioengineering Department, University of California, Los Angeles, CA, United States
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, United States
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| |
Collapse
|
2
|
Rabel RAC, Marchioretto PV, Bangert EA, Wilson K, Milner DJ, Wheeler MB. Pre-Implantation Bovine Embryo Evaluation-From Optics to Omics and Beyond. Animals (Basel) 2023; 13:2102. [PMID: 37443900 DOI: 10.3390/ani13132102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Approximately 80% of the ~1.5 million bovine embryos transferred in 2021 were in vitro produced. However, only ~27% of the transferred IVP embryos will result in live births. The ~73% pregnancy failures are partly due to transferring poor-quality embryos, a result of erroneous stereomicroscopy-based morphological evaluation, the current method of choice for pre-transfer embryo evaluation. Numerous microscopic (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial-intelligence-based microscopy) and non-microscopic (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) methodologies have been tested to find an embryo evaluation technique that is superior to morphologic evaluation. Many of these research tools can accurately determine embryo quality/viability; however, most are invasive, expensive, laborious, technically sophisticated, and/or time-consuming, making them futile in the context of in-field embryo evaluation. However accurate they may be, using complex methods, such as RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy, at thousands of embryo production/collection facilities is impractical. Therefore, future research is warranted to innovate field-friendly, simple benchtop tests using findings already available, particularly from omics-based research methodologies. Time-lapse monitoring and artificial-intelligence-based automated image analysis also have the potential for accurate embryo evaluation; however, further research is warranted to innovate economically feasible options for in-field applications.
Collapse
Affiliation(s)
- R A Chanaka Rabel
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Paula V Marchioretto
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Bangert
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Kenneth Wilson
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Derek J Milner
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Matthew B Wheeler
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| |
Collapse
|
3
|
Wang W, Yan B, Wang H, Chen Y, Nie X, Yi C, Wang Z, Xu Z, Zeng J, Fan W. Wide-Field and Real-Time Super-Resolution Optical Imaging By Titanium Dioxide Nanoparticle-Assembled Solid Immersion Lens. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207596. [PMID: 36897007 DOI: 10.1002/smll.202207596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/12/2023] [Indexed: 06/08/2023]
Abstract
Super-resolution optical imaging techniques can break the optical diffraction limit, thus providing unique opportunities to visualize the microscopic world at the nanoscale. Although near-field optical microscopy techniques have been proven to achieve significantly improved imaging resolution, most near-field approaches still suffer from a narrow field of view (FOV) or difficulty in obtaining wide-field images in real time, which may limit their widespread and diverse applications. Here, the authors experimentally demonstrate an optical microscope magnification and image enhancement approach by using a submillimeter-sized solid immersion lens (SIL) assembled by densely-packed 15 nm TiO2 nanoparticles through a silicone oil two-step dehydration method. This TiO2 nanoparticle-assembled SIL can achieve both high transparency and high refractive index, as well as sufficient mechanical strength and easy-to-handle size, thus providing a fast, wide-field, real-time, non-destructive, and low-cost solution for improving the quality of optical microscopic observation of a variety of samples, including nanomaterials, cancer cells, and living cells or bacteria under conventional optical microscopes. This study provides an attractive alternative to simplify the fabrication and applications of high-performance SILs.
Collapse
Affiliation(s)
- Weicheng Wang
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Bing Yan
- School of Computer Science and Electronic Engineering, Bangor University, Bangor, LL57 1UT, UK
| | - Haiyan Wang
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Yue Chen
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Xiuyu Nie
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Changfeng Yi
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Zengbo Wang
- School of Computer Science and Electronic Engineering, Bangor University, Bangor, LL57 1UT, UK
| | - Zushun Xu
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| | - Jing Zeng
- Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200433, P. R. China
| | - Wen Fan
- Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan, 430062, P. R. China
| |
Collapse
|