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Advances in Optical Sensing for Biomedical and Biotechnological Applications

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 22413

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Dr. Maria Lepore

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Dipartimento di Medicina Sperimentale, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
Interests: fluorescence optical methods; vibrational spectroscopies; enzymatic optical biosensing; two-photon microscopy; optical properties of turbid media; biophotonics medical applications.
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Dr. Ines Delfino

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Dipartimento di Scienze Ecologiche e Biologiche, Università degli Studi della Tuscia, I-01100 Viterbo, Italy
Interests: optical spectroscopy and microscopy; Raman and SERS techniques; light scattering methods; optical biosensing; optical sensing approaches; diagnosis and disease follow-up and study of ionizing radiation on biosystems
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Special Issue Information

Dear Colleagues,

Optical methods are non-invasive tools that have allowed the development of a large number of sensors for quantitatively and qualitatively determining the components of analytes of interest in many fields of application including pharmaceutical research, medical diagnostics, environmental monitoring, agriculture, industry, food safety and security. In the last years, advances in experimental and data analysis techniques have enabled the development of new sensing schemes and devices characterized by superior working parameters (very low detection limit, high specificity and sensitivity) and innovative applicative approaches. The aim of this special issue is to offer an overview of the recent advances in the use of optical methods for biomedical and biotechnological applications. With this aim, original research papers, as well as review articles, will be published to show the diversity of the new developments in these areas and their wide dissemination in these fields. If you require clarifications, or wish to discuss your submission in advance, we encourage you to contact us. We look forward to and welcome your participation in this Special Issue.

Prof. Maria Lepore
Dr. Ines Delfino
Guest Editors

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Keywords

physical, chemical and biological sensors
Raman and SERS sensing
SEIRA sensing
Sensors based on colorimetry, evanescent wave and infrared spectroscopies
light-scattering sensing
fluorescence
label-free sensing
plasmonic based sensors
spectral analysis
multivariate data analysis
chemometrics

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16 pages, 2846 KiB

Open AccessArticle

Multivariate Analysis of Difference Raman Spectra of the Irradiated Nucleus and Cytoplasm Region of SH-SY5Y Human Neuroblastoma Cells

by Ines Delfino, Valerio Ricciardi, Lorenzo Manti, Maria Lasalvia and Maria Lepore

Sensors 2019, 19(18), 3971; https://doi.org/10.3390/s19183971 - 14 Sep 2019

Cited by 16 | Viewed by 3587

Abstract

Previous works showed that spatially resolved Raman spectra of cytoplasm and nucleus region of single cells exposed to X-rays evidence different features. The present work aims to introduce a new approach to profit from these differences to deeper investigate X-ray irradiation effects on single SH-SY5Y human neuroblastoma cells. For this aim, Raman micro-spectroscopy was performed in vitro on single cells after irradiation by graded X-ray doses (2, 4, 6, 8 Gy). Spectra from nucleus and cytoplasm regions were selectively acquired. The examination by interval Principal Component Analysis (i-PCA) of the difference spectra obtained by subtracting each cytoplasm-related spectrum from the corresponding one detected at the nucleus enabled us to reveal the subtle modifications of Raman features specific of different spatial cell regions. They were discussed in terms of effects induced by X-ray irradiation on DNA/RNA, lipids, and proteins. The proposed approach enabled us to evidence some features not outlined in previous investigations. Full article

(This article belongs to the Special Issue Advances in Optical Sensing for Biomedical and Biotechnological Applications)

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Figure 1

Figure 1
Spectra obtained by averaging all the spectra taken from cytoplasm (red line, a spectrum) and from nucleus (black line, b spectrum) in the fingerprint region (400–1750 cm−1) from control cells. Main peaks are labeled; for tentative assignment see <a href="#sensors-19-03971-t001" class="html-table">Table 1</a>. Full article ">Figure 2
Spectra obtained by averaging all the spectra taken from cytoplasm (red line, a spectrum) and from nucleus (black line, b spectrum) in the high Raman shift region (HSR) (2650–3200 cm−1) from control cells. Main peaks are labeled; for tentative assignment see <a href="#sensors-19-03971-t001" class="html-table">Table 1</a>. Full article ">Figure 3
Results of Principal Component Analysis (PCA) on the whole dataset of nucleus- and cytoplasm-related spectra detected for control and irradiated samples. 2D score plot representations (for the two PCs) of the data separately evaluated for cytoplasm-related and nucleus-related spectra. Full article ">Figure 4
Results of interval Principal Component Analysis (i-PCA) on the whole dataset (control and exposed cells) including nucleus- and cytoplasm-related spectra for the intervals of the fingerprint region in which a separation between spectra from nucleus and cytoplasm region is obtained by using the scores of the first two interval principal components. PC2 vs. PC1 score plot representation of the data for cytoplasm-related (black squares) and nucleus-related (red squares) spectra for the following spectral ranges (intervals): (a) 601–816 cm−1, (c) 116–1214 cm−1, (e) 1303–1438 cm−1, (g) 1672–1737 cm−1. The corresponding loadings of the second component in the same intervals are reported. PC2 loadings in the following intervals: (b) 601–816 cm−1, (d) 116–1214 cm−1, (f) 1303–1438 cm−1, (h) 1674–1737 cm−1. According to Hostelling’s T-square test, for all the shown PC score plots, the cluster of nucleus-related scores and cytoplasm-related scores are statistically separated (p values lower than 0.0001 were obtained). Full article ">Figure 5
Results of i-PCA on the whole dataset including nucleus- and cytoplasm-related spectra (control and exposed cells) for the intervals of the fingerprint region in which a separation between spectra from the nucleus and cytoplasm region is obtained by using the scores of the first two interval principal components. PC2 vs. PC1 score plot representation of the data for cytoplasm-related (black squares) and nucleus-related (red squares) spectra for the following spectral ranges (intervals): (a) 2824–2905 cm−1, (c) 2909–3030 cm−1. The corresponding loadings of the second component in the same intervals are reported. PC2 loadings in the following intervals: (b) 2824–2905 cm−1, (d) 2909–3030 cm−1. According to Hotelling’s T-square test, for all the shown PC score plots, the cluster of nucleus-related scores and cytoplasm-related scores are statistically separated (p values lower than 0.0001 were obtained). Full article ">Figure 6
Average difference Raman spectra (for each coupled pair of spectra the different spectrum is obtained by subtracting the cytoplasm-related spectrum to the corresponding nucleus-related one) are reported for the different irradiation doses. Main features are labelled. For the sake of clarity, difference spectra for 2-Gy-, 4-Gy-, 6-Gy-, and 8-Gy-X-ray irradiated cells were shifted along the y axis; for each difference spectra the y = 0 level is represented by the dashed line with the same color of the corresponding spectrum. Full article ">Figure 7
Results of i-PCA analysis of difference spectra for the selected spectral regions: box plot representation of the i-PC scores separately evaluated for each “irradiation treatment”, i.e., 0, 2, 4, 6, and 8 Gy of irradiation dose. Full article ">

13 pages, 1938 KiB

Open AccessArticle

A Smartphone-Based Whole-Cell Array Sensor for Detection of Antibiotics in Milk

by Mei-Yi Lu, Wei-Chen Kao, Shimshon Belkin and Ji-Yen Cheng

Sensors 2019, 19(18), 3882; https://doi.org/10.3390/s19183882 - 9 Sep 2019

Cited by 33 | Viewed by 5037

Abstract

We present an integral smartphone-based whole-cell biosensor, LumiCellSense (LCS), which incorporates a 16-well biochip with an oxygen permeable coating, harboring bioluminescent Escherichia coli bioreporter cells, a macro lens, a lens barrel, a metal heater tray, and a temperature controller, enclosed in a light-impermeable case. The luminescence emitted by the bioreporter cells in response to the presence of the target chemicals is imaged by the phone’s camera, and a dedicated phone-embedded application, LCS_Logger, is employed to calculate photon emission intensity and plot it in real time on the device’s screen. An alert is automatically given when light intensity increases above the baseline, indicating the presence of the target. We demonstrate the efficacy of this system by the detection of residues of an antibiotic, ciprofloxacin (CIP), in whole milk, with a detection threshold of 7.2 ng/mL. This value is below the allowed maximum as defined by European Union regulations. Full article

(This article belongs to the Special Issue Advances in Optical Sensing for Biomedical and Biotechnological Applications)

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Full article ">Figure 1
The LumiCellSense (LCS) system. (a) Photo of the LCS system. All of the system’s components except the smartphone are enclosed in a chamber for protection from ambient light. (b) A schematic diagram of the bioreactor and smartphone. The macro lens is aligned with the smartphone’s camera. The alginate-immobilized bacteria and the sample are loaded into the wells of the bacterial chip (BacChip), which is sandwiched between a polydimethylsiloxane (PDMS) layer and an adhesive film and then inserted into the heater tray. Full article ">Figure 2
Measurement of absolute photon detection sensitivity. Luminescence intensity was acquired by measuring light from a LED illuminating through a single well of the BacChip, at an exposure time of 150 s. The inset magnifies the data at the low photon intensities range. A rational exponent fitting curve (red dots) was plotted to calculate the detection limit. The dashed horizontal line indicates luminescence intensity that is higher than the background by three standard deviations (SDs) of the background signal intensity. Full article ">Figure 3
Stability and homogeneity of the LCS system. (a) Signal stability of a typical dark well with immobilized bacteria (1.32 × 107 cells per well) without stimulation. (b) Temporal SD of relative luminescence intensity (RLI) of different wells in the BacChip. The red dashed line indicates the average SD of 0.0016 RLI. Inset: Close-up photo of a BacChip and the numbering of the wells. Full article ">Figure 4
Color separation for image analysis. (a–d): Smartphone-obtained images showing the (a) image of luminescence from phosphor tape for identification of the well positions. (b) Image of luminescence from the immobilized bacteria (1.32 × 107 cells per well) stimulated with ciprofloxacin (CIP) for 180 min. The number at each well indicates the CIP concentration in ng/mL. Image (b) was isolated into red and green channels, displayed in (c) and (d), respectively. (e) Time-lapse RLI of the white light (W) and of red (R), blue (B), or green (G) channel are from the immobilized bacteria stimulated with 32 ng/mL of CIP. Arrows indicate the on-time calculated for each channel. (f) Screenshot of the smartphone showing the time-lapse RLI of each well. The blue lines mark the on-time calculated for each well. The white number indicates the concentration of CIP. Full article ">Figure 5
Detection of CIP in whole milk. (a) Luminescence of immobilized bacteria (1.32 × 107 cells per well) stimulated with 0, 8, 16, or 32 ng/mL of CIP in whole milk was continuously recorded for up to 30 h. (b) Magnified details in the early 180 min of time-lapse luminescence. (c) Luminescence at 180 min in response to a broader range of CIP concentrations. The inset shows a linear fit to the data points and extrapolation to determine the detection threshold. Full article ">

10 pages, 5018 KiB

Open AccessFeature PaperArticle

Raman Analysis of Tear Fluid Alteration Following Contact Lense Use

by Angela Capaccio, Antonio Sasso and Giulia Rusciano

Sensors 2019, 19(15), 3392; https://doi.org/10.3390/s19153392 - 2 Aug 2019

Cited by 16 | Viewed by 3648

Abstract

Tear fluid is a heterogeneous solution containing mainly proteins, lipids, mucins and electrolytes, which regulates the physiology of the human eye. The complex composition of tears can be altered in the presence of eye inflammations. The use of contact lenses is one of the most frequent causes of inflammatory responses of the eye, with the related discomfort often causing the wearer to give up using them. In this paper, we exploit the potentiality of Raman Spectroscopy to analyse the biochemical changes in tear fluid in a contact lens wearer. In particular, we analysed the tear fluid collected from a volunteer as a function of the wearing time for two types of monthly contact lenses (Hydrogel and Si-Hydrogel). Our experimental results show an alteration of the relative concentrations of proteins and lipids in both of the analysed cases. More importantly, our results highlight the diagnostic sensitivity of Raman analysis to select the proper contact lens type for each wearer and optimise the lens wearing conditions. Full article

(This article belongs to the Special Issue Advances in Optical Sensing for Biomedical and Biotechnological Applications)

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13 pages, 1878 KiB

Open AccessArticle

Biochemical Changes in Human Cells Exposed to Low Concentrations of Gold Nanoparticles Detected by Raman Microspectroscopy

by Maria Lasalvia, Giuseppe Perna and Vito Capozzi

Sensors 2019, 19(10), 2418; https://doi.org/10.3390/s19102418 - 27 May 2019

Cited by 7 | Viewed by 3352

Abstract

The toxicological implications of nanoparticles deserve accurate scientific investigation for the protection of human health. Although toxic effects involve specific organs, the events that cause them have their origin from biochemical modifications of some cellular constituents. Therefore, a first analysis to evaluate the effects due to the action of nanoparticles is achieved by investigation of in vitro cells, which allows the identification of the cellular modifications caused by nanoparticles (NPs) even at much lower doses than the lethal ones. This work evaluated the Raman microspectroscopy capability to monitor biochemical changes occurring in human cells as a consequence of exposure to a suspension of gold nanoparticles with a non-cytotoxic concentration. Human keratinocyte cells were used as a model cell line, because they are mainly involved in environmental exposure. A trypan blue assay revealed that the investigated concentration, 650 ng/mL, is non-cytotoxic (about 5% of cells died after 48 h exposure). Specific Raman spectral markers to represent the cell response to nanoparticle exposure were found (at 1450 and 2865 cm⁻¹) in the cytoplasm spectra, with the aid of ratiometric and principal component analysis. Full article

(This article belongs to the Special Issue Advances in Optical Sensing for Biomedical and Biotechnological Applications)

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Figure 1

Figure 1
Absorbance spectra of 40 nm gold nanoparticles (NPs) stabilized in 0.1 mM phosphate buffered saline (PBS), measured as received (black line) and by suspending them in the cell medium, at the beginning of the incubation time (red line), after 24 h (blue line), and 48 h (green line) of incubation. The NPs-PBS suspension/medium volume ratio is 1:2. The absorption spectrum of cell medium is also shown (pink line). The spectra have been shifted on the vertical axis for clarity. Full article ">Figure 2
Trypan blue assay for HuKe cells exposed for different times to the gold NP suspension. Full article ">Figure 3
Normalized and averaged Raman spectrum of about 30 unexposed HuKe cells, measured in the low wavenumber (a) and high wavenumber (b) spectral range, by focusing the exciting laser on nucleus (black lines) and outside nucleus (red lines). The labels refer to the attribution of the most important spectral features reported in [<a href="#B30-sensors-19-02418" class="html-bibr">30</a>]. Full article ">Figure 4
Average Raman spectra of control (black lines) and gold NP-exposed (red lines) HuKe cells, with exposure time of 24 h (a) and 48 h (b). Each spectrum is the average of spectra collected from about 30 different cells, by focusing the exciting laser beam on the cell nucleus. On the right hand side of each Raman spectrum is shown the corresponding comparison between mean values of the I784/I1003 (dots), I1090/I1003 (squares), I1340/I1260 (triangles up), and I1580/I1450 (triangles down) intensity ratios of Raman peaks of control and exposed cells. The values are means ± standard error. The values of intensity ratios which are significantly different are labelled (*). Full article ">Figure 5
Average Raman spectra of control (black lines) and gold NP-exposed (red lines) HuKe cells, with exposure time of 24 h (a) and 48 h (b). Each spectrum is the average of spectra collected from about 30 different cells, by focusing the exciting laser beam outside of the cell nucleus. An enlarged detail of the spectra for the 1425–1475 cm−1 spectral range is shown in the inset. On the right hand side of each Raman spectrum is shown the corresponding comparison between mean values of the I1300/I1260 (dots), I1450/I1660 (squares), and I2865/I2940 (triangles up) intensity ratios of Raman peaks of control and exposed cells. The values are means ± standard error. The values of intensity ratios which are significantly different are labelled (*). Full article ">Figure 6
Score plots of the principal component analysis (PCA) of Raman spectra measured in the 700–1800 cm−1 (a) and 2700–3150 cm−1 (c) wavenumber range, by focusing the exciting laser beam outside the nucleus. The data refer to the spectra of the 48 h unexposed (black dots) and gold NP exposed (red dots). Loading plots of PC1 (black line) and difference Raman spectra (red line) between 48 h unexposed and exposed cells, measured in the 700–1800 cm−1 (b) and 2700–3150 cm−1 (d) wavenumber range. A multiplicative factor has been applied to the difference spectrum to better visualize the comparison with the corresponding loading 1 spectra. Full article ">Figure 6 Cont.
Score plots of the principal component analysis (PCA) of Raman spectra measured in the 700–1800 cm−1 (a) and 2700–3150 cm−1 (c) wavenumber range, by focusing the exciting laser beam outside the nucleus. The data refer to the spectra of the 48 h unexposed (black dots) and gold NP exposed (red dots). Loading plots of PC1 (black line) and difference Raman spectra (red line) between 48 h unexposed and exposed cells, measured in the 700–1800 cm−1 (b) and 2700–3150 cm−1 (d) wavenumber range. A multiplicative factor has been applied to the difference spectrum to better visualize the comparison with the corresponding loading 1 spectra. Full article ">

19 pages, 2983 KiB

Open AccessArticle

A Handheld Real-Time Photoacoustic Imaging System for Animal Neurological Disease Models: From Simulation to Realization

by Yu-Hang Liu, Yu Xu, Lun-De Liao, Kim Chuan Chan and Nitish V. Thakor

Sensors 2018, 18(11), 4081; https://doi.org/10.3390/s18114081 - 21 Nov 2018

Cited by 13 | Viewed by 5888

Abstract

This article provides a guide to design and build a handheld, real-time photoacoustic (PA) imaging system from simulation to realization for animal neurological disease models. A pulsed laser and array-based ultrasound (US) platform were utilized to develop the system for evaluating vascular functions in rats with focal ischemia or subcutaneous tumors. To optimize the laser light delivery, finite element (FE)-based simulation models were developed to provide information regarding light propagation and PA wave generation in soft tissues. Besides, simulations were also conducted to evaluate the ideal imaging resolution of the US system. As a result, a PA C-scan image of a designed phantom in 1% Lipofundin was reconstructed with depth information. Performance of the handheld PA system was tested in an animal ischemia model, which revealed that cerebral blood volume (CBV) changes at the cortical surface could be monitored immediately after ischemia induction. Another experiment on subcutaneous tumors showed the anomalous distribution of the total hemoglobin concentration (HbT) and oxygen saturation (SO₂), while 3D and maximum intensity projection (MIP) PA images of the subcutaneous tumors are also presented in this article. Overall, this system shows promise for monitoring disease progression in vascular functional impairments. Full article

(This article belongs to the Special Issue Advances in Optical Sensing for Biomedical and Biotechnological Applications)

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