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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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].

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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.




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