Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning.

Published on May 24, 2021in Nature Communications14.919
· DOI :10.1038/S41467-021-23202-Z
Haonan Lin9
Estimated H-index: 9
(BU: Boston University),
Hyeon Jeong Lee11
Estimated H-index: 11
(ZJU: Zhejiang University)
+ 8 AuthorsJi-Xin Cheng84
Estimated H-index: 84
(BU: Boston University)
Sources
Abstract
Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400–1800 cm−1) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm−1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice. The authors employ a polygon-based ultrafast delay scanner and a deep learning framework for acquiring stimulated Raman scattering spectrum with high spectral and temporal resolution. They demonstrate high-speed imaging and tracking of multiple biomolecules in the fingerprint region.
References60
Newest
#1Jianglai Wu (University of California, Berkeley)H-Index: 9
#2Yajie Liang (HHMI: Howard Hughes Medical Institute)H-Index: 22
Last. Na JiH-Index: 31
view all 10 authors...
Understanding information processing in the brain requires monitoring neuronal activity at high spatiotemporal resolution. Using an ultrafast two-photon fluorescence microscope empowered by all-optical laser scanning, we imaged neuronal activity in vivo at up to 3,000 frames per second and submicrometer spatial resolution. This imaging method enabled monitoring of both supra- and subthreshold electrical activity down to 345 μm below the brain surface in head-fixed awake mice. High-speed two-phot...
Source
#1Todd C Hollon (UM: University of Michigan)H-Index: 11
#2Balaji Pandian (UM: University of Michigan)H-Index: 6
Last. Daniel A. Orringer (UM: University of Michigan)H-Index: 24
view all 37 authors...
Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5–7, a label-...
Source
#1Bryce Manifold (UW: University of Washington)H-Index: 5
#2Elena C. Thomas (UW: University of Washington)H-Index: 2
Last. Dan Fu (UW: University of Washington)H-Index: 25
view all 5 authors...
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Source
#1Tiebin Wang (BU: Boston University)H-Index: 4
#2Mary J. Dunlop (BU: Boston University)H-Index: 19
Individual cells within a population can display diverse phenotypes due to differences in their local environment, genetic variation, and stochastic expression of genes. Understanding this cell-to-cell variation is important for metabolic engineering applications because variability can impact production. For instance, recent studies have shown that production can be highly heterogeneous among engineered cells, and strategies that manage this diversity improve yields of biosynthetic products. Th...
Source
#1Lili Zhang (Fudan University)H-Index: 6
#2Yongzheng Wu (Fudan University)H-Index: 1
Last. Minbiao JiH-Index: 24
view all 14 authors...
: Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothesized that deep-learning based stimulated Raman scattering (SRS) microscopy could provide automated and accurate diagnosis of laryngeal squamous cell carcinoma on fresh, unprocessed surgical specimens without fixation, sectioning or staining. Method...
Source
#1Fernando Soldevila (James I University)H-Index: 8
#2Jonathan Dong ('ENS Paris': École Normale Supérieure)H-Index: 10
Last. Hilton B. de Aguiar ('ENS Paris': École Normale Supérieure)H-Index: 19
view all 5 authors...
Raman microscopy is a powerful method combining non-invasiveness with no special sample preparation. Because of this remarkable simplicity, it has been widely exploited in many fields, ranging from life and materials sciences to engineering. Notoriously, due to the required imaging speeds for bio-imaging, it has remained a challenge how to use this technique for dynamic and large-scale imaging. Recently, a supervised compressive Raman framework has been put forward, allowing for fast imaging, th...
Source
#1Benjamin Figueroa (UW: University of Washington)H-Index: 5
#2Walter Fu (Cornell University)H-Index: 9
Last. Dan Fu (UW: University of Washington)H-Index: 25
view all 7 authors...
Hyperspectral stimulated Raman scattering (hsSRS) microscopy has recently emerged as a powerful non-destructive technique for the label-free chemical imaging of biological samples. In most hsSRS imaging experiments, the SRS spectral range is limited by the total bandwidth of the excitation laser to ~300 cm−1 and a spectral resolution of ~25 cm−1. Here we present a novel approach for broadband hsSRS microscopy based on parabolic fiber amplification to provide linearly chirped broadened Stokes pul...
Source
#1Martin Weigert (MPG: Max Planck Society)H-Index: 71
#2Uwe Schmidt (MPG: Max Planck Society)H-Index: 16
Last. Eugene W. Myers (TUD: Dresden University of Technology)H-Index: 65
view all 21 authors...
Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microsco...
Source
#1Haonan Lin (BU: Boston University)H-Index: 9
#2Chien-Sheng Liao (BU: Boston University)H-Index: 14
Last. Ji-Xin Cheng (BU: Boston University)H-Index: 84
view all 5 authors...
Increasing image acquisition speed of a spectroscopic imaging technique opens the door for real-time detection of molecules in living cells. Spectroscopic stimulated Raman scattering imaging is a nondestructive, label-free technique for detecting the chemical fingerprints of biological molecules. However, its relatively slow image acquisition speed has limited its use. This led Ji-Xin Cheng and colleagues from Boston University and Purdue University in the United States to develop a method that ...
Source
Oct 1, 2017 in ICCV (International Conference on Computer Vision)
#1Zhaofan Qiu (USTC: University of Science and Technology of China)H-Index: 17
#2Ting Yao (Microsoft)H-Index: 41
Last. Tao Mei (Microsoft)H-Index: 75
view all 3 authors...
Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is ...
Source
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