0
Review Article

Single-Cell Analysis Using Hyperspectral Imaging Modalities

[+] Author and Article Information
Nishir Mehta, Shahensha Shaik, Manas Ranjan Gartia

Department of Mechanical Engineering,
Louisiana State University,
Baton Rouge, LA 70803

Ram Devireddy

Department of Mechanical Engineering,
Louisiana State University,
Baton Rouge, LA 70803
e-mail: devireddy@me.lsu.edu

1Corresponding author.

Manuscript received June 17, 2017; final manuscript received November 22, 2017; published online January 12, 2018. Editor: Victor H. Barocas.

J Biomech Eng 140(2), 020802 (Jan 12, 2018) (16 pages) Paper No: BIO-17-1262; doi: 10.1115/1.4038638 History: Received June 17, 2017; Revised November 22, 2017

Almost a decade ago, hyperspectral imaging (HSI) was employed by the NASA in satellite imaging applications such as remote sensing technology. This technology has since been extensively used in the exploration of minerals, agricultural purposes, water resources, and urban development needs. Due to recent advancements in optical re-construction and imaging, HSI can now be applied down to micro- and nanometer scales possibly allowing for exquisite control and analysis of single cell to complex biological systems. This short review provides a description of the working principle of the HSI technology and how HSI can be used to assist, substitute, and validate traditional imaging technologies. This is followed by a description of the use of HSI for biological analysis and medical diagnostics with emphasis on single-cell analysis using HSI.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Stender, A. S. , Marchuk, K. , Liu, C. , Sander, S. , Meyer, M. W. , Smith, E. A. , Neupane, B. , Wang, G. , Li, J. , and Cheng, J. , 2013, “ Single Cell Optical Imaging and Spectroscopy,” Chem. Rev., 113(4), pp. 2469–2527. [CrossRef] [PubMed]
Hickey, W. J. , Shetty, A. R. , Massey, R. J. , Toso, D. B. , and Austin, J. , 2017, “ Three‐Dimensional Bright‐Field Scanning Transmission Electron Microscopy Elucidate Novel Nanostructure in Microbial Biofilms,” J. Microsc., 265(1), pp. 3–10. [CrossRef] [PubMed]
Zheng, W. , Taylor, N. , Leiman, P. , and Egelman, E. , 2017, “ Cryo-EM of the Bacteriophage Tail Tube at Better Than 3.5 Å Resolution,” Biophys. J., 112(3), pp. 573a–574a. [CrossRef]
Galler, K. , Bräutigam, K. , Große, C. , Popp, J. , and Neugebauer, U. , 2014, “ Making a Big Thing of a Small Cell–Recent Advances in Single Cell Analysis,” Analyst, 139(6), pp. 1237–1273. [CrossRef] [PubMed]
Seidman, D. , 2007, “ Three-Dimensional Atom-Probe Tomography: Advances and Applications,” Annu. Rev. Mater. Res., 37, pp. 127–158. [CrossRef]
Kherlopian, A. , Song, T. , Duan, Q. , Neimark, M. , Po, M. , Gohagan, J. , and Laine, A. , 2008, “ A Review of Imaging Techniques for Systems Biology,” BMC Syst. Biol., 2(1), p. 74. [CrossRef] [PubMed]
Amemiya, S. , Bard, A. , Fan, F. , Mirkin, M. , and Unwin, P. , 2008, “ Scanning Electrochemical Microscopy,” Annu. Rev. Anal. Chem., 1, pp. 95–131. [CrossRef]
Zhu, Y. , Zhang, J. , Li, A. , Zhang, Y. , and Fan, C. , 2017, “ Synchrotron-Based X-Ray Microscopy for Sub-100 nm Resolution Cell Imaging,” Curr. Opin. Chem. Biol., 39, pp. 11–16. [CrossRef] [PubMed]
Downes, A. , Mouras, R. , Bagnaninchi, P. , and Elfick, A. , 2011, “ Raman Spectroscopy and CARS Microscopy of Stem Cells and Their Derivatives,” J. Raman Spectrosc., 42(10), pp. 1864–1870. [CrossRef] [PubMed]
Gerber, H.-P. , Malik, A. K. , Solar, G. P. , Sherman, D. , Liang, X. H. , Meng, G. , Hong, K. , Marsters, J. C. , and Ferrara, N. , 2002, “ VEGF Regulates Haematopoietic Stem Cell Survival by an Internal Autocrine Loop Mechanism,” Nature, 417(6892), pp. 954–958. [CrossRef] [PubMed]
Schultz, R. A. , Nielsen, T. , Zavaleta, J. R. , Ruch, R. , Wyatt, R. , and Garner, H. R. , 2001, “ Hyperspectral Imaging: A Novel Approach for Microscopic Analysis,” Cytometry, 43(4), pp. 239–247. [CrossRef] [PubMed]
Wang, X. , Cui, Y. , and Irudayaraj, J. , 2015, “ Single-Cell Quantification of Cytosine Modifications by Hyperspectral Dark-Field Imaging,” ACS Nano, 9(12), pp. 11924–11932. [CrossRef] [PubMed]
Conti, M. , Scanferlato, R. , Louka, M. , Sansone, A. , Marzetti, C. , and Ferreri, C. , 2016, “ Building Up Spectral Libraries for Mapping Erythrocytes by Hyperspectral Dark Field Microscopy,” Biomed. Spectrosc. Imaging, 5(2), pp. 175–184. [CrossRef]
More, S. S. , and Vince, R. , 2015, “ Hyperspectral Imaging Signatures Detect Amyloidopathy in Alzheimer's Mouse Retina Well Before Onset of Cognitive Decline,” ACS Chem. Neurosci., 6(2), pp. 306–315. [CrossRef] [PubMed]
Lu, G. , and Fei, B. , 2014, “ Medical Hyperspectral Imaging: A Review,” J. Biomed. Opt., 19(1), p. 010901. [CrossRef]
Vo-Dinh, T. , 2004, “ A Hyperspectral Imaging System for In Vivo Optical Diagnostics,” IEEE Eng. Med. Biol. Mag., 23(5), pp. 40–49. [CrossRef] [PubMed]
Li, Q. , He, X. , Wang, Y. , Liu, H. , Xu, D. , and Guo, F. , 2013, “ Review of Spectral Imaging Technology in Biomedical Engineering: Achievements and Challenges,” J. Biomed. Opt., 18(10), p. 100901. [CrossRef] [PubMed]
Oh, E. S. , Heo, C. , Kim, J. S. , Suh, M. , Lee, Y. H. , and Kim, J.-M. , 2013, “ Hyperspectral Fluorescence Imaging for Cellular Iron Mapping in the In Vitro Model of Parkinson's Disease,” J. Biomed. Opt., 19(5), p. 051207. [CrossRef]
Verebes, G. S. , Melchiorre, M. , Garcia‐Leis, A. , Ferreri, C. , Marzetti, C. , and Torreggiani, A. , 2013, “ Hyperspectral Enhanced Dark Field Microscopy for Imaging Blood Cells,” J. Biophotonics, 6(11–12), pp. 960–967. [CrossRef] [PubMed]
Vermaas, W. F. , Timlin, J. A. , Jones, H. D. , Sinclair, M. B. , Nieman, L. T. , Hamad, S. W. , Melgaard, D. K. , and Haaland, D. M. , 2008, “ In Vivo Hyperspectral Confocal Fluorescence Imaging to Determine Pigment Localization and Distribution in Cyanobacterial Cells,” Proc. Natl. Acad. Sci., 105(10), pp. 4050–4055. [CrossRef]
Boldrini, B. , Kessler, W. , Rebner, K. , and Kessler, R. W. , 2012, “ Hyperspectral Imaging: A Review of Best Practice, Performance and Pitfalls for In-Line and On-Line Applications,” J. Near Infrared Spectrosc., 20(5), pp. 483–508. [CrossRef]
Schnarr, K. , Mooney, R. , Weng, Y. , Zhao, D. , Garcia, E. , Armstrong, B. , Annala, A. J. , Kim, S. U. , Aboody, K. S. , and Berlin, J. M. , 2013, “ Gold Nanoparticle‐Loaded Neural Stem Cells for Photothermal Ablation of Cancer,” Adv. Healthcare Mater., 2(7), pp. 976–982. [CrossRef]
Gupta, N. , 2011, “ Development of Staring Hyperspectral Imagers,” IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, Oct. 11–13, pp. 1–8.
Weitzel, L. , Krabbe, A. , Kroker, H. , Thatte, N. , Tacconi-Garman, L. , Cameron, M. , and Genzel, R. , 1996, “ 3D: The Next Generation Near-Infrared Imaging Spectrometer,” Astron. Astrophys. Suppl. Ser., 119(3), pp. 531–546. [CrossRef]
Owen, D. M. , Manning, H. B. , de Beule, P. , Talbot, C. , Requejo-Isidro, J. , Dunsby, C. , McGinty, J. , Benninger, R. K. , Elson, D. S. , and Munro, I. , 2007, “ Development of a Hyperspectral Fluorescence Lifetime Imaging Microscope and Its Application to Tissue Imaging,” Proc. SPIE, 6441, p. 64411K.
Yudovsky, D. , Nouvong, A. , Schomacker, K. , and Pilon, L. , 2011, “ Assessing Diabetic Foot Ulcer Development Risk With Hyperspectral Tissue Oximetry,” J. Biomed. Opt., 16(2), p. 026009. [CrossRef] [PubMed]
Greenman, R. L. , Panasyuk, S. , Wang, X. , Lyons, T. E. , Dinh, T. , Longoria, L. , Giurini, J. M. , Freeman, J. , Khaodhiar, L. , and Veves, A. , 2005, “ Early Changes in the Skin Microcirculation and Muscle Metabolism of the Diabetic Foot,” Lancet, 366(9498), pp. 1711–1717. [CrossRef] [PubMed]
Shah, S. , Bachrach, N. , Spear, S. , Letbetter, D. , Stone, R. , Dhir, R. , Prichard, J. , Brown, H. , and LaFramboise, W. , 2003, “ Cutaneous Wound Analysis Using Hyperspectral Imaging,” Biotechniques, 34(2), pp. 408–413. https://www.biotechniques.com/multimedia/archive/00010/03342pf01_10632a.pdf [PubMed]
Afromowitz, M. A. , Callis, J. B. , Heimbach, D. M. , DeSoto, L. A. , and Norton, M. K. , 1988, “ Multispectral Imaging of Burn Wounds: A New Clinical Instrument for Evaluating Burn Depth,” IEEE Trans. Biomed. Eng., 35(10), pp. 842–850. [CrossRef] [PubMed]
Renkoski, T. E. , Hatch, K. D. , and Utzinger, U. , 2012, “ Wide-Field Spectral Imaging of Human Ovary Autofluorescence and Oncologic Diagnosis Via Previously Collected Probe Data,” J. Biomed. Opt., 17(3), p. 036003. [CrossRef] [PubMed]
Akbari, H. , Halig, L. V. , Schuster, D. M. , Osunkoya, A. , Master, V. , Nieh, P. T. , Chen, G. Z. , and Fei, B. , 2012, “ Hyperspectral Imaging and Quantitative Analysis for Prostate Cancer Detection,” J. Biomed. Opt., 17(7), p. 0760051. [CrossRef]
Akbari, H. , Uto, K. , Kosugi, Y. , Kojima, K. , and Tanaka, N. , 2011, “ Cancer Detection Using Infrared Hyperspectral Imaging,” Cancer Sci., 102(4), pp. 852–857. [CrossRef] [PubMed]
Panasyuk, S. V. , Yang, S. , Faller, D. V. , Ngo, D. , Lew, R. A. , Freeman, J. E. , and Rogers, A. E. , 2007, “ Medical Hyperspectral Imaging to Facilitate Residual Tumor Identification During Surgery,” Cancer Biol. Ther., 6(3), pp. 439–446. [CrossRef] [PubMed]
Kong, S. G. , Martin, M. , and Vo-Dinh, T. , 2006, “ Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection,” Etri J., 28(6), pp. 770–776. [CrossRef]
Benavides, J. M. , Chang, S. , Park, S. Y. , Richards-Kortum, R. , Mackinnon, N. , MacAulay, C. , Milbourne, A. , Malpica, A. , and Follen, M. , 2003, “ Multispectral Digital Colposcopy for In Vivo Detection of Cervical Cancer,” Opt. Express, 11(10), pp. 1223–1236. [CrossRef] [PubMed]
Randeberg, L. L. , Baarstad, I. , Løke, T. , Kaspersen, P. , and Svaasand, L. O. , 2006, “ Hyperspectral Imaging of Bruised Skin,” Proc. SPIE, 6078, p. 607800.
Randeberg, L. L. , and Hernandez-Palacios, J. , 2012, “ Hyperspectral Imaging of Bruises in the SWIR Spectral Region,” Proc. SPIE, 8207, p. 82070N.
Dicker, D. T. , Lerner, J. , Van Belle, P. , Guerry, T. , DuPont , Herlyn, M. , Elder, D. E. , and El-Deiry, W. S. , 2006, “ Differentiation of Normal Skin and Melanoma Using High Resolution Hyperspectral Imaging,” Cancer Biol. Ther., 5(8), pp. 1033–1038. [CrossRef] [PubMed]
Li, Q. , Wang, Y. , Liu, H. , and Chen, Z. , 2012, “ Nerve Fibers Identification Based on Molecular Hyperspectral Imaging Technology,” IEEE International Conference on Computer Science and Automation Engineering (CSAE) Zhangjiajie, China, May 25–27, pp. 15–17.
Usenik, P. , Bürmen, M. , Fidler, A. , Pernuš, F. , and Likar, B. , 2012, “ Evaluation of Cross-Polarized Near Infrared Hyperspectral Imaging for Early Detection of Dental Caries,” Proc. SPIE, 8208, p. 82080G.
Martin, R. , Thies, B. , and Gerstner, A. O. , 2012, “ Hyperspectral Hybrid Method Classification for Detecting Altered Mucosa of the Human Larynx,” Int. J. Health Geographics, 11(1), p. 21. [CrossRef]
Larsen, E. L. , Randeberg, L. L. , Olstad, E. , Haugen, O. A. , Aksnes, A. , and Svaasand, L. O. , 2011, “ Hyperspectral Imaging of Atherosclerotic Plaques In Vitro,” J. Biomed. Opt., 16(2), p. 026011.
Akbari, H. , Kosugi, Y. , Kojima, K. , and Tanaka, N. , 2010, “ Detection and Analysis of the Intestinal Ischemia Using Visible and Invisible Hyperspectral Imaging,” IEEE Trans. Biomed. Eng., 57(8), pp. 2011–2017. [CrossRef] [PubMed]
Johnson, W. R. , Wilson, D. W. , Fink, W. , Humayun, M. , and Bearman, G. , 2007, “ Snapshot Hyperspectral Imaging in Ophthalmology,” J. Biomed. Opt., 12(1), p. 014036. [CrossRef] [PubMed]
Sorg, B. S. , Moeller, B. J. , Donovan, O. , Cao, Y. , and Dewhirst, M. W. , 2005, “ Hyperspectral Imaging of Hemoglobin Saturation in Tumor Microvasculature and Tumor Hypoxia Development,” J. Biomed. Opt., 10(4), p. 044004. [CrossRef]
Ferris, D. G. , Lawhead, R. A. , Dickman, E. D. , Holtzapple, N. , Miller, J. A. , Grogan, S. , Bambot, S. , Agrawal, A. , and Faupel, M. L. , 2001, “ Multimodal Hyperspectral Imaging for the Noninvasive Diagnosis of Cervical Neoplasia,” J. Lower Genital Tract Dis., 5(2), pp. 65–72.
Roth, G. A. , Tahiliani, S. , Neu‐Baker, N. M. , and Brenner, S. A. , 2015, “ Hyperspectral Microscopy as an Analytical Tool for Nanomaterials,” Wiley Interdiscip. Rev.: Nanomed. Nanobiotechnol., 7(4), pp. 565–579. [CrossRef] [PubMed]
Seng, P. , Drancourt, M. , Gouriet, F. , La Scola, B. , Fournier, P.-E. , Rolain, J. M. , and Raoult, D. , 2009, “ Ongoing Revolution in Bacteriology: Routine Identification of Bacteria by Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry,” Clin. Infect. Dis., 49(4), pp. 543–551. [CrossRef] [PubMed]
Vater, J. , Kablitz, B. , Wilde, C. , Franke, P. , Mehta, N. , and Cameotra, S. S. , 2002, “ Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry of Lipopeptide Biosurfactants in Whole Cells and Culture Filtrates of Bacillus Subtilis C-1 Isolated From Petroleum Sludge,” Appl. Environ. Microbiol., 68(12), pp. 6210–6219. [CrossRef] [PubMed]
Welker, M. , and Moore, E. R. , 2011, “ Applications of Whole-Cell Matrix-Assisted Laser-Desorption/Ionization Time-of-Flight Mass Spectrometry in Systematic Microbiology,” Syst. Appl. Microbiol., 34(1), pp. 2–11. [CrossRef] [PubMed]
Murakoshi, M. , Iida, K. , Kumano, S. , and Wada, H. , 2009, “ Immune Atomic Force Microscopy of Prestin-Transfected CHO Cells Using Quantum Dots,” Pflügers Archiv—Eur. J. Physiol., 457(4), p. 885. [CrossRef]
Gartia, M. R. , Hsiao, A. , Sivaguru, M. , Chen, Y. , and Liu, G. L. , “ Enhanced 3D Fluorescence Live Cell Imaging on Nanoplasmonic Substrate,” Nanotechnology, 22(36), p. 365203. [CrossRef] [PubMed]
Chen, J. , and Irudayaraj, J. , 2010, “ Fluorescence Lifetime Cross Correlation Spectroscopy Resolves EGFR and Antagonist Interaction in Live Cells,” Anal. Chem., 82(15), pp. 6415–6421. [CrossRef] [PubMed]
Schober, Y. , Guenther, S. , Spengler, B. , and Römpp, A. , 2012, “ Single Cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging,” Anal. Chem., 84(15), pp. 6293–6297. [CrossRef] [PubMed]
Kim, A. , and Wilson, B. C. , 2010, “ Measurement of Ex Vivo and In Vivo Tissue Optical Properties: Methods and Theories,” Optical-Thermal Response of Laser-Irradiated Tissue, Springer, Dordrecht, The Netherlands, pp. 267–319. [CrossRef]
Sandell, J. L. , and Zhu, T. C. , 2011, “ A Review of In‐Vivo Optical Properties of Human Tissues and Its Impact on PDT,” J. Biophotonics, 4(11–12), pp. 773–787. [CrossRef] [PubMed]
Bashkatov, A. N. , Genina, E. A. , and Tuchin, V. V. , 2011, “ Optical Properties of Skin, Subcutaneous, and Muscle Tissues: A Review,” J. Innovative Opt. Health Sci., 4(1), pp. 9–38. [CrossRef]
Jacques, S. L. , 2013, “ Optical Properties of Biological Tissues: A Review,” Phys. Med. Biol., 58(11), p. R37. [CrossRef] [PubMed]
Luu, Y. K. , Capilla, E. , Rosen, C. J. , Gilsanz, V. , Pessin, J. E. , Judex, S. , and Rubin, C. T. , 2009, “ Mechanical Stimulation of Mesenchymal Stem Cell Proliferation and Differentiation Promotes Osteogenesis While Preventing Dietary‐Induced Obesity,” J. Bone Miner. Res., 24(1), pp. 50–61. [CrossRef] [PubMed]
Palonpon, A. F. , Ando, J. , Yamakoshi, H. , Dodo, K. , Sodeoka, M. , Kawata, S. , and Fujita, K. , 2013, “ Raman and SERS Microscopy for Molecular Imaging of Live Cells,” Nat. Protoc., 8(4), pp. 677–692. [CrossRef] [PubMed]
Smus, J. P. , Moura, C. C. , McMorrow, E. , Tare, R. S. , Oreffo, R. O. , and Mahajan, S. , 2015, “ Tracking Adipogenic Differentiation of Skeletal Stem Cells by Label-Free Chemically Selective Imaging,” Chem. Sci., 6(12), pp. 7089–7096. [CrossRef]
Peterson, S. M. , and Freeman, J. L. , 2009, “ RNA Isolation From Embryonic Zebrafish and cDNA Synthesis for Gene Expression Analysis,” J. Visualized Exp., 30, p. 1470. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3152201/
Iandolino, A. , Goes da Silva, F. , Lim, H. , Choi, H. , Williams, L. , and Cook, D. , 2004, “ High-Quality RNA, cDNA, and Derived Est Libraries From Grapevine (Vitis Vinifera L.),” Plant Mol. Biol. Reporter, 22(3), pp. 269–278. [CrossRef]
Haaland, D. M. , and Thomas, E. V. , 1988, “ Partial Least-Squares Methods for Spectral Analyses. 1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information,” Anal. Chem., 60(11), pp. 1193–1202. [CrossRef]
Ruckebusch, C. , and Blanchet, L. , 2013, “ Multivariate Curve Resolution: A Review of Advanced and Tailored Applications and Challenges,” Anal. Chim. Acta, 765, pp. 28–36. [CrossRef] [PubMed]
Wang, W. , Foley, K. , Shan, X. , Wang, S. , Eaton, S. , Nagaraj, V. J. , Wiktor, P. , Patel, U. , and Tao, N. , 2011, “ Single Cells and Intracellular Processes Studied by a Plasmonic-Based Electrochemical Impedance Microscopy,” Nat. Chem., 3(3), pp. 249–255. [CrossRef] [PubMed]
Wood, R. W. , 1902, “ On a Remarkable Case of Uneven Distribution of Light in a Diffraction Grating Spectrum,” Proc. Phys. Soc. London, 18(1), p. 269. [CrossRef]
Otto, A. , 1968, “ Excitation of Nonradiative Surface Plasma Waves in Silver by the Method of Frustrated Total Reflection,” Z. Phys., 216(4), pp. 398–410. [CrossRef]
Kretschmann, E. , and Raether, H. , 1968, “ Notizen: Radiative Decay of Non Radiative Surface Plasmons Excited by Light,” Z. Naturforsch. A, 23(12), pp. 2135–2136. [CrossRef]
Chu, X. , and Chu, S.-I. , 2001, “ Time-Dependent Density-Functional Theory for Molecular Processes in Strong Fields: Study of Multiphoton Processes and Dynamical Response of Individual Valence Electrons of N 2 in Intense Laser Fields,” Phys. Rev. A, 64(6), p. 063404. [CrossRef]
Wang, W. , Wang, S. , Liu, Q. , Wu, J. , and Tao, N. , 2012, “ Mapping Single-Cell–Substrate Interactions by Surface Plasmon Resonance Microscopy,” Langmuir, 28(37), pp. 13373–13379. [CrossRef] [PubMed]
Berguiga, L. , Streppa, L. , Boyer-Provera, E. , Martinez-Torres, C. , Schaeffer, L. , Elezgaray, J. , Arneodo, A. , and Argoul, F. , 2016, “ Time-Lapse Scanning Surface Plasmon Microscopy of Living Adherent Cells With a Radially Polarized Beam,” Appl. Opt., 55(6), pp. 1216–1227. [CrossRef] [PubMed]
Homola, J. , 2008, “ Surface Plasmon Resonance Sensors for Detection of Chemical and Biological Species,” Chem. Rev., 108(2), pp. 462–493. [CrossRef] [PubMed]
Chattopadhyay, P. K. , Gierahn, T. M. , Roederer, M. , and Love, J. C. , 2014, “ Single-Cell Technologies for Monitoring Immune Systems,” Nat. Immunol., 15(2), pp. 128–135. [CrossRef] [PubMed]
Laplatine, L. , Leroy, L. , Calemczuk, R. , Baganizi, D. , Marche, P. N. , Roupioz, Y. , and Livache, T. , 2014, “ Spatial Resolution in Prism-Based Surface Plasmon Resonance Microscopy,” Opt. Express, 22(19), pp. 22771–22785. [CrossRef] [PubMed]
Siddiqi, A. M. , Li, H. , Faruque, F. , Williams, W. , Lai, K. , Hughson, M. , Bigler, S. , Beach, J. , and Johnson, W. , 2008, “ Use of Hyperspectral Imaging to Distinguish Normal, Precancerous, and Cancerous Cells,” Cancer Cytopathol., 114(1), pp. 13–21. [CrossRef]
Aaron, J. , Travis, K. , Harrison, N. , and Sokolov, K. , 2009, “ Dynamic Imaging of Molecular Assemblies in Live Cells Based on Nanoparticle Plasmon Resonance Coupling,” Nano Lett., 9(10), pp. 3612–3618. [CrossRef] [PubMed]
Weinkauf, H. , and Brehm‐Stecher, B. F. , 2009, “ Enhanced Dark Field Microscopy for Rapid Artifact‐Free Detection of Nanoparticle Binding to Candida Albicans Cells and Hyphae,” Biotechnol. J., 4(6), pp. 871–879. [CrossRef] [PubMed]
Patskovsky, S. , Bergeron, E. , Rioux, D. , and Meunier, M. , 2015, “ Wide‐Field Hyperspectral 3D Imaging of Functionalized Gold Nanoparticles Targeting Cancer Cells by Reflected Light Microscopy,” J. Biophotonics, 8(5), pp. 401–407. [CrossRef] [PubMed]
Ma, L. L. , Feldman, M. D. , Tam, J. M. , Paranjape, A. S. , Cheruku, K. K. , Larson, T. A. , Tam, J. O. , Ingram, D. R. , Paramita, V. , and Villard, J. W. , 2009, “ Small Multifunctional Nanoclusters (Nanoroses) for Targeted Cellular Imaging and Therapy,” ACS Nano, 3(9), pp. 2686–2696. [CrossRef] [PubMed]
Goh, D. , Gong, T. , Dinish, U. , Maiti, K. K. , Fu, C. Y. , Yong, K.-T. , and Olivo, M. , 2012, “ Pluronic Triblock Copolymer Encapsulated Gold Nanorods as Biocompatible Localized Plasmon Resonance-Enhanced Scattering Probes for Dark-Field Imaging of Cancer Cells,” Plasmonics, 7(4), pp. 595–601. [CrossRef]
Gong, T. , Olivo, M. , Dinish, U. , Goh, D. , Kong, K. V. , and Yong, K.-T. , 2013, “ Engineering Bioconjugated Gold Nanospheres and Gold Nanorods as Label-Free Plasmon Scattering Probes for Ultrasensitive Multiplex Dark-Field Imaging of Cancer Cells,” J. Biomed. Nanotechnol., 9(6), pp. 985–991. [CrossRef] [PubMed]
Mahlein, A.-K. , Steiner, U. , Hillnhütter, C. , Dehne, H.-W. , and Oerke, E.-C. , 2012, “ Hyperspectral Imaging for Small-Scale Analysis of Symptoms Caused by Different Sugar Beet Diseases,” Plant Methods, 8(1), p. 3. [CrossRef] [PubMed]
Sotiriou, G. A. , Starsich, F. , Dasargyri, A. , Wurnig, M. C. , Krumeich, F. , Boss, A. , Leroux, J. C. , and Pratsinis, S. E. , 2014, “ Photothermal Killing of Cancer Cells by the Controlled Plasmonic Coupling of Silica‐Coated Au/Fe2O3 Nanoaggregates,” Adv. Funct. Mater., 24(19), pp. 2818–2827. [CrossRef]
Mortimer, M. , Gogos, A. , Bartolomé, N. , Kahru, A. , Bucheli, T. D. , and Slaveykova, V. I. , 2014, “ Potential of Hyperspectral Imaging Microscopy for Semi-Quantitative Analysis of Nanoparticle Uptake by Protozoa,” Environ. Sci. Technol., 48(15), pp. 8760–8767. [CrossRef] [PubMed]
Vetten, M. A. , Tlotleng, N. , Rascher, D. T. , Skepu, A. , Keter, F. K. , Boodhia, K. , Koekemoer, L.-A. , Andraos, C. , Tshikhudo, R. , and Gulumian, M. , 2013, “ Label-Free In Vitro Toxicity and Uptake Assessment of Citrate Stabilised Gold Nanoparticles in Three Cell Lines,” Part. Fibre Toxicol., 10(1), p. 50. [CrossRef] [PubMed]
Lee, J. Y. , Clarke, M. L. , Tokumasu, F. , Lesoine, J. F. , Allen, D. W. , Chang, R. , Litorja, M. , and Hwang, J. , 2012, “ Absorption-Based Hyperspectral Imaging and Analysis of Single Erythrocytes,” IEEE J. Select. Top. Quantum Electron., 18(3), pp. 1130–1139. [CrossRef]
More, S. S. , Beach, J. M. , and Vince, R. , 2016, “ Early Detection of Amyloidopathy in Alzheimer's Mice by Hyperspectral Endoscopy,” Invest. Ophthalmol. Visual Sci., 57(7), pp. 3231–3238. [CrossRef]
Fu, D. , Yang, W. , and Xie, X. S. , 2017, “ Label-Free Imaging of Neurotransmitter Acetylcholine at Neuromuscular Junctions With Stimulated Raman Scattering,” J. Am. Chem. Soc., 139(2), pp. 583–586. [CrossRef] [PubMed]
Chaudhari, K. , and Pradeep, T. , 2014, “ Spatiotemporal Mapping of Three Dimensional Rotational Dynamics of Single Ultrasmall Gold Nanorods,” Sci. Rep., 4, p. 5948.
Mortimer, M. , Kahru, A. , and Slaveykova, V. I. , 2014, “ Uptake, Localization and Clearance of Quantum Dots in Ciliated Protozoa Tetrahymena Thermophila,” Environ. Pollut., 190, pp. 58–64. [CrossRef] [PubMed]
Misra, S. K. , Ostadhossein, F. , Daza, E. , Johnson, E. V. , and Pan, D. , 2016, “ Hyperspectral Imaging Offers Visual and Quantitative Evidence of Drug Release From Zwitterionic‐Phospholipid‐Nanocarbon When Concurrently Tracked in 3D Intracellular Space,” Adv. Funct. Mater., 26(44), pp. 8031–8041. [CrossRef]
Gosnell, M. E. , Anwer, A. G. , Mahbub, S. B. , Perinchery, S. M. , Inglis, D. W. , Adhikary, P. P. , Jazayeri, J. A. , Cahill, M. A. , Saad, S. , and Pollock, C. A. , 2016, “ Quantitative Non-Invasive Cell Characterisation and Discrimination Based on Multispectral Autofluorescence Features,” Sci. Reports, 6, p. 23453.
Khaodhiar, L. , Dinh, T. , Schomacker, K. T. , Panasyuk, S. V. , Freeman, J. E. , Lew, R. , Vo, T. , Panasyuk, A. A. , Lima, C. , Giurini, J. M. , Lyons, T. E. , and Veves, A. , 2007, “ The Use of Medical Hyperspectral Technology to Evaluate Microcirculatory Changes in Diabetic Foot Ulcers and to Predict Clinical Outcomes,” Diabetes Care, 30(4), pp. 903–910. https://www.ncbi.nlm.nih.gov/pubmed/17303790
Liu, L. , and Ngadi, M. O. , 2013, “ Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging,” Food Bioprocess Technol., 6(9), pp. 2503–2513. [CrossRef]
Thirumala, S. , Gimble, J. M. , and Devireddy, R. V. , 2010, “ Cryopreservation of Stromal Vascular Fraction of Adipose Tissue in a Serum‐Free Freezing Medium,” J. Tissue Eng. Regener. Med., 4(3), pp. 224–232. [CrossRef]
Shaik, S. , Hayes, D. , Gimble, J. , and Devireddy, R. , 2017, “ Inducing Heat Shock Proteins Enhances the Stemness of Frozen-Thawed Adipose Tissue Derived Stem Cells,” Stem Cells Dev., 26(8), pp. 608–616. [CrossRef] [PubMed]
Manolakis, D. , and Shaw, G. , 2002, “ Detection Algorithms for Hyperspectral Imaging Applications,” IEEE Signal Process. Mag., 19(1), pp. 29–43. [CrossRef]
Segers, V. F. , and Lee, R. T. , 2008, “ Stem-Cell Therapy for Cardiac Disease,” Nature, 451(7181), pp. 937–942. [CrossRef] [PubMed]
Langer, R. , 2007, “ Editorial: Tissue Engineering: Perspectives, Challenges, and Future Directions,” Tissue Eng., 13(1), pp. 1–2. [CrossRef] [PubMed]
Downes, A. , Mouras, R. , and Elfick, A. , 2010, “ Optical Spectroscopy for Noninvasive Monitoring of Stem Cell Differentiation,” BioMed Res. Int., 2010, p. 101864.

Figures

Grahic Jump Location
Fig. 5

Label-free imaging of single cell using Raman spectroscopy. (a) Raman scattering images of unstained living HeLa cells at 753 cm−1 (showing cytochrome c), 1686 cm−1 (showing protein), and 2852 cm−1 (showing lipid). The fourth image is an overlay image merging intracellular distribution of cytochrome c, protein, and lipid colored as green, blue, and red channels, respectively. The excitation laser wavelength was 532 nm [60]. The authors demonstrated an easy to construct Raman microscope with high resolution as well as dynamic surface imaging. (b) Comparison of Coherent anti-Stokes Raman scattering imaging and Oil Red O stained images for adipogenic differentiation of skeletal stem cells (SSCs). SSCs were cultured in adipogenic media for 1, 3, 7, and 14 days. Scale bars correspond to 20 μm. Coherent anti-Stokes Raman scattering provided a chemically selective and label-free approach as well as higher sensitivity toward detection of small lipid droplets (blue) [61]. This study demonstrated an enhanced resolution and early analysis of adipogenesis in SSCs as well as delineating the lipid droplet changes within the stem cell. The caption text refers to online color version of the figure.

Grahic Jump Location
Fig. 4

Conventional cell imaging approaches. Images of CHO cells using (a) DIC [51]. DIC is an optical microscopy methodology based on beam-shearing interference system, i.e., optical rays illuminated at oblique angles create interference patterns to generate 3D images. Unlike HSI, DIC does not capture the surface (or spectral) variations between samples. (b) SEM [52]. Compared to HSI, SEM is an expensive electron gun-assisted imaging technique and suffers from sample distortion and biological vaporization due to the high voltage current applied. (c) AFM. Contact or noncontact probes in AFM do generate precise results for relatively hard surface topography but the probing pressure has the potential to alter results obtained on biological membranes due to their soft and pliable nature. Additionally, interior (subsurface) abnormalities and dynamic physiological process are hard if not impossible to quantify using AFM methods. (d) Laser scanning confocal fluorescence microscopy (LSCFM) [52]. Unlike HSI, living specimens for LSCFM require special care. Additionally, LSCFM can generate false labels when the specimens are sensitive to fluorophores and photobleaching and also result in weak signals due to low concentration of fluorescent markers limiting the scanning probe data. (e) FCCS to perform single-cell analysis to monitor the dynamic motion of biomolecules [53]. Unlike HSI, FCCS cannot capture both location (spatial) and surface (spectral) information, and data can only be obtained in a limited region potentially missing vital information at other locations within the sample. (f) Image of 3,3′-Dihexyloxacarbocyanine iodide [DIOC6(3)] inside a single cell using MALDI-MS [54]. Although. MALDI is an excellent tool to understand the chemical composition of cellular components, the sample preparation method is rather involved and cumbersome. MALDI also does not enable repeated measurements from the same sample due to laser vaporization of the sample during the measurement process and also does not allow in vivo analysis. The interested reader is referred to the cited references for further details on the imaging modalities.

Grahic Jump Location
Fig. 3

Schematic showing different approaches used for HSI [17]: (a) Whiskbroom, (b) push broom, (c) staring, (d) snapshot. Briefly, the dispersive element for whiskbroom, push broom, and snapshot is either a prism, a grating, or a prism gating prism while for spectral scan, it is a tunable filter or an interferometer. The wavelength range is wide for whiskbroom, push broom, and snapshot while it is medium for staring. The wavelength selection is partial for both whiskbroom and push broom and complete for staring and unavailable in snapshot. The spectral resolution is high for both whiskbroom and push broom while it is low for snapshot and medium for staring. Whiskbroom and staring are hyperspectral while snapshot is multispectral. The throughput is high for whiskbroom, push broom, and snapshot and low for staring. The data cube collection is relatively long for both whiskbroom and push broom while it is short for staring and fast for snapshot. However, the complexity is high for whiskbroom and push broom while it is simple for staring and medium for snapshot. The associated costs are low for both whiskbroom and push broom, medium for snapshot, and high for staring.

Grahic Jump Location
Fig. 2

The various components in a CytoViva HSI system [14]. Briefly, the components are (i) a light source; (ii) a high resolution light collimator or adapter; (ii) mirror(s) to generate plane-polarized light or light dispersive elements to disperse incident white light into its constitutive spectra; (iii) a microscopy stage for the specimen; (iv) an optical module for bright- and dark-field analysis; (v) visible and near infra-red spectrometer to collect and convert electromagnetic energy into electrical signals for image formation; (vi) an image capture modality (see Fig. 3) and (vii) computer for data collection and software analysis (not shown).

Grahic Jump Location
Fig. 1

(a) Schematic showing the features of monochrome, red-green-blue (RGB), spectroscopy, multispectral, and HSI [17]. As shown in the figure, both spectroscopy and HSI can store wavelength information over the entire spectrum. However, spectroscopy cannot provide precise spatial (location within the sample) information. RGB imaging does not allow for spectral information at all (information across multiple wavelengths) and is insensitive to components that are at different wavelengths than red (630 nm), green (545 nm) and blue (435 nm) but does enable spatial information. Spectroscopy allows for spectral information to be gleaned but doesn't allow for spatial information. HSI (300–2600 nm) can collect spatial, spectral, multicomponent while being sensitive to a variety of different wavelengths or components. (b) Detailed comparison showing the differences between HSI and RGB imaging [15]. The figure depicts light reflectance curve of a single pixel from an arbitrary sample imaged using hyperspectral spectroscopy and RGB imaging. The hyperspectral image contains information in a continuous visible near-infrared spectrum compared to the intensity curve from RGB imaging that provides data at only three prominent wavelengths. The additional spectral information contained with the continuous hyperspectral image can be utilized to more accurately analyze and understand micro- and nanoscale features that are not feasible using the discrete RGB imaging dataset. The caption text refers to online color version of the figure.

Grahic Jump Location
Fig. 6

Impedance images of a human cervical cell using SPRi and electrical impedance microscopy (EIM)—see text and Wang et al. [66] for further details. (a) Bright field (b) SPR (c) EIM at 0 min (top row), 30 min (middle row), and 75 min (bottom row) after apoptosis treatment. EIM is a label-free, noninvasive imaging methodology with high spatial and temporal resolutions and provides localized impedance information not previously available [66]. (d) Schematic illustration showing the evanescent field of SPRi mainly localized near the bottom portion of a cell. (e) Simulated EIM (top) and SPR (bottom) images.

Grahic Jump Location
Fig. 7

Hyperspectral dark-field imaging using plasmonic nanoprobes to quantify 5-carboxylcytosine (5caC) modification on DNA in single cells [12]. The study by Wang et al. [12] revealed the distribution of 5caC at different cell-cycle stages and demonstrated that 5caC is an inherited epigenetic marker. As stated by the authors, the hyperspectral dark-field imaging efficiently removes scattering noises from nonspecifically aggregated nanoprobes. The image shows the filter function applied to: (a) Raw image (b) converted to spectrally mapped images from 520–620 nm, (c) image of cell at a wavelength above 635 nm, and (d) number of gold nanoparticle (shown as green dots) inside the cell. The caption text refers to online color version of the figure.

Grahic Jump Location
Fig. 8

Study of 3D rotational dynamics of gold nanorods inside live HEK293 cells using CytoViva HSI system by Chaudhari and Pradeep [90]. (a) Scattering spectra of a single gold nanorod attached on the cell membrane. The inset shows the corresponding hyperspectral image. (b) Scattering spectra of the gold nanorod in (a) after being absorbed by the cell. The inset shows the corresponding hyperspectral image. (c) Actual image of cell being monitored to study rotational dynamics. The gold nanorod is marked with a square. Inset shows an enlarged view of the gold nanorod. Light scattered in the Z direction was collected through analyzer, whose orientation is shown by yellow double arrow. (d) Time variation of scattering intensity of the gold nanorod. Time scale corresponds to the axis of the graph shown below. Pink vertical bars show the region where microscope focus was adjusted on the particle after it went out of the focal plane. (e) Time variation of width of gold nanorod spot in two-dimensional Gaussian width of the gold nanorod. See Chaudhari and Pradeep [90] for further details. (f) Representation of gold nano particle path inside the HEK293 cell. Green arrow shows the time point from where temporal data of the GNR is shown. Color of the trace corresponds to the time scale of graphs (D, E). Please note that the background image is just to give a rough idea of the position of GNR inside the cell.

Grahic Jump Location
Fig. 9

Hyperspectral fluorescence imaging of SHSY5Y cells containing iron from Oh et al. [18]. (a) Dark-field images of the SHSY5Ycells incubated with iron (specifically ferric ammonium nitrate) for 1 h. The areas mapped with HSI are marked by colored boxes. (b) Spectral profiles collected from each region shown in (a). Peaks are mainly observed between 450 to 650 nm. All bulk iron areas have a peak absorbance near 600 nm, whereas the peak signal in the cells is near 500 nm and the signal on the glass plate is almost zero. (c) Magnified or zoomed-in images (17×) of pixels containing HSI data from glass, cytoplasm, nucleus, and bulk iron.

Grahic Jump Location
Fig. 10

Application of HSI modality for mapping nanoparticle drug delivery from Misra et al. [92]. (a) Three different conditions of carbon nanoparticle (CNP) encapsulated with drugs: drug encapsulated CNP denoted as drug-CNP, doxorubicin encapsulated CNP denoted as prodrug-CNP in the figure and SN2-lipase labile bexarotene prodrug passivated CNP denoted as lipid-CNP in the figure. Lipid-CNP represents a control nanoparticle without inclusion of any drug. (b) Molecular representation of prodrug-CNP. (c) Mapped spectral image containing information about distinct materials exhibiting differently colored pixels for drug delivery in MCF-7 breast cancer cells. (d) 3D representation of HSI on MCF-7 cells treated with drug-CNP for 4 h at 37 °C, 99% humidity and 5% CO2 environment. Localization of drug-CNP (red, white arrow) in 3D intracellular space showing cellular building blocks as described by Misra et al. [92].

Grahic Jump Location
Fig. 11

Label-free dark-field HSI of human RBCs from Conti et al. [13]: (a) Hyperspectral image of erythrocyte sample. (b) Spectral library composed of different endmembers with random color code. (c) Zoomed-in image of one erythrocyte. (d) Color mapping matching spectra of the spectral library. (e) Mapping five main components of RBCs namely phospholipid, cholesterol, hemoglobin, spectrin, and protoporphyrin. Briefly, 5 μl of whole blood was loaded in the center of glass slide and sandwiched with coverslip. After 120 min, to allow for image stability, the optical acquisition was started. Each image consisted of approximately 30 regularly shaped RBC as shown in (a) with no other cells. One RBC was chosen as shown in (c), (d), and (e) for further image analysis. For the RBCs, eight spectra (b) were individuated with optimal coverage of the optical image (d). Applying the SAM function, the spectral distribution of the 8 endmember spectra in the samples was then determined (data not shown) as described in Conti et al. [13]. This study demonstrated a fast, easy, and repeatable protocol to study large number of cells and to the possibility of mapping single molecules, proteins as well as structure of cell membranes with applications in personalized medicine and membrane-targeted therapies [13]. The caption text refers to online color version of the figure.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In