Autonomous adaptive data acquisition for scanning hyperspectral imaging
Abstract
Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.
Additional Information
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 25 February 2020; Accepted 18 September 2020; Published 18 November 2020. We thank Dr. Hans Bechtel and ALS Beamline 1.4.3 staff for their instrumentation support, Drs. Peter Zwart and Derek R. Holman for discussion, and the reviewers for their constructive comments. This research used resources of the Berkeley Synchrotron Infrared Structural Biology (BSISB) Imaging program, the Molecular Foundry, and the Advanced Light Source, DOE Office of Science User Facilities, under contract no. DE-AC02-05CH11231. E.A.H. was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301 and the Howard Hughes Medical Institute under Grant No. 047-101, with which P.W.S. was an investigator. Y.-S.F., L.C., and H.-Y.N.H. were supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. Data availability: Infrared spectral data are available through the CaltechDATA repository (https://doi.org/10.22002/D1.1609). The 11 high-resolution spectral maps used for calibration simulations are not included in the repository, since they are undergoing spectral analysis and interpretation in a different manuscript. Any remaining data is available from the corresponding author upon reasonable request. Code availability: This proprietary adaptive sampling code and GUI are specific to the Infrared Beamline 1.4.3 at the Advanced Light Source (https://als.lbl.gov/). They are available to IR beamline users through the DOE-supported Berkeley Synchrotron Infrared Structural Biology (BSISB) Imaging Program (https://bsisb.lbl.gov/wordpress/). Further requests concerning this code can be directed to H.-Y.N.H. Author Contributions: These authors contributed equally: Elizabeth A. Holman, Yuan-Sheng Fang. H.-Y.N.H. conceived the idea. H.-Y.N.H., and Y.-S.F. designed adaptive data acquisition. Y.-S.F. developed and wrote the algorithm, designed, and performed simulations. Y.-S.F., H.-Y.N.H., and E.A.H. implemented algorithm at ALS Beamline 1.4.3. Y.-S.F. and E.A.H. designed and tested IR processing module. Y.-S.F. wrote IR processing module. E.A.H. designed and performed proof-of-principle experiments. L.C. and E.A.H. performed IR data processing. E.A.H. performed IR spectral analysis, gathered materials from all authors, and wrote the manuscript. M.D., L.C., and H.-Y.N.H. supervised Y.-S.F. P.W.S. supervised E.A.H. The authors declare no competing interests.Attached Files
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Supplemental Material - 42003_2020_1385_MOESM1_ESM.pdf
Supplemental Material - 42003_2020_1385_MOESM2_ESM.pdf
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Additional details
- Eprint ID
- 106729
- Resolver ID
- CaltechAUTHORS:20201118-141320800
- DE-AC02-05CH11231
- Department of Energy (DOE)
- DGE-1745301
- NSF Graduate Research Fellowship
- 047-101
- Howard Hughes Medical Institute (HHMI)
- Created
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2020-11-18Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering