Biomedical applications, perspectives and tag design concepts in the cell - silent Raman window.

Spectroscopic studies increasingly employ Raman tags exhibiting a signal in the cell - silent region of the Raman spectrum (1800-2800 cm-1), where bands arising from biological molecules are inherently absent. Raman tags bearing functional groups which contain a triple bond, such as alkyne and nitrile or a carbon-deuterium bond, have a distinct vibrational frequency in this region. Due to the lack of spectral background and cell-associated bands in the specific area, the implementation of those tags can help overcome the inherently poor signal-to-noise ratio and presence of overlapping Raman bands in measurements of biological samples. The cell - silent Raman tags allow for bioorthogonal imaging of biomolecules with improved chemical contrast and they have found application in analyte detection and monitoring, biomarker profiling and live cell imaging. This review focuses on the potential of the cell - silent Raman region, reporting on the tags employed for biomedical applications using variants of Raman spectroscopy.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy.

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

Saline dry fixation for improved cell composition analysis using Raman spectroscopy.

Raman spectroscopy enables the label-free assessment of cellular composition. While live cell analysis is the most accurate approach for cellular Raman spectroscopy, the analysis of fixed cells has proved to be very useful, particularly in collaborative projects where samples need to be serially examined by different laboratories or stored and reanalyzed at a later date. However, many chemicals that are widely used for cell fixation directly affect cellular biomolecules, yielding Raman spectra with missing or altered information. In this article, we compared the suitability of dry-fixation with saline versus chemical fixatives. We compared the Raman spectroscopy of saline dry-fixed cells with the more commonly used formaldehyde and methanol fixation and found that dry-fixed cell spectra preserved more cellular information than either chemical fixative. We also assessed the stability of dry-fixed cells over time and found that they were stable for at least 5 months. Finally, a comparison of dry-fixed and live cell spectra revealed effects due to the hydration state of the cells since they were recovered upon rehydrating dry-fixed samples. Thus, for fixed cell Raman spectroscopy, we recommend dry-fixation with unbuffered saline as a superior method to formaldehyde or methanol fixation.

Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra.

Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra.

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Assessing the quality of stored red blood cells using handheld Spatially Offset Raman spectroscopy with multisource correlation analysis.

In this work we employ Spatially Offset Raman Spectroscopy (SORS) to non-invasively identify storage-related changes in red blood cell concentrate (RCC) in-situ within standard plastic transfusion bags. To validate the measurements, we set up a parallel study comparing both bioanalytical data (obtained by blood-gas analysis, hematology analysis and spectrophotometric assays), and Raman spectrometry data from the same blood samples. We then employ Multisource Correlation Analysis (MuSCA) to correlate the different types of data in RCC. Our analysis confirmed a strong correlation of glucose, methemoglobin and oxyhemoglobin with their respective bioassay values in RCC units. Finally, by combining MuSCA with k-means clustering, we assessed changes in all Raman wavenumbers during cold storage in both RCC Raman data from the current study and parallel RCC supernatant Raman data previously acquired from the same units. Direct RCC quality monitoring during storage, would help to establish a basis for improved inventory management of blood products in blood banks and hospitals based on analytical data.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra.

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Multiphoton imaging and Raman spectroscopy of the bovine vertebral endplate.

The interface between the intervertebral disc and the vertebral body is important to the discs' biomechanics and physiology, and is widely implicated in its pathology. This study aimed to explore biochemically and structurally the bony endplate, cartilage endplate and intervertebral disc, below the nucleus and below the annulus in healthy bovine tails. Multiphoton imaging and spontaneous Raman spectroscopy were employed. Raman spectroscopy provided relative quantification of mineral and matrix components across the vertebral endplate and its adjacent areas with microscopic spatial resolution. Microscopy utilising second-harmonic generation (SHG) and two-photon fluorescence (TPF) allowed for the structural identification of distinct endplate regions. The cartilage endplate was revealed as structurally distinct from both the bone and disc, supporting its biomechanical function as a transition zone between the soft and hard tissue components. The collagen fibres were continuous across the tidemark which defines the interface between the mineralised and non-mineralised regions of the endplate. Raman spectroscopy revealed gradients in phosphate and carbonate content through the depth of the endplate and also differences beneath the nucleus and annulus consistent with a higher rate of remodelling under the annulus.

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy.

BACKGROUND: The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach. STUDY DESIGN AND METHODS: A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis. RESULTS: Raman bands of lactate (857 cm-1 ), glucose (787 cm-1 ), and hemolysis (1003 cm-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively. DISCUSSION: This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources.

Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.

Tissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review.

Raman spectroscopy is a group of analytical techniques, currently applied in several research fields, including clinical diagnostics. Tissue-mimicking optical phantoms have been established as an essential intermediate stage for medical applications with their employment from spectroscopic techniques to be constantly growing. This review outlines the types of tissue phantoms currently employed in different biomedical applications of Raman spectroscopy, focusing on their composition and optical properties. It is therefore an attempt to present an informed range of options for potential use to the researchers.

Correction: Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives.

Correction for 'Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives' by Shreyas Rangan et al., Analyst, 2020, DOI: 10.1039/c9an01811e.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives.

Therapies based on injecting living cells into patients offer a huge potential to cure many degenerative and deadly diseases, with hundreds of clinical trials ongoing. Due to their complex nature, a basic understanding of their biochemical and functional characteristics, how to manufacture them for safe and efficacious therapy, and how to effectively implement them in clinical settings are very challenging. Raman spectroscopy could provide an information-rich, non-invasive, non-destructive analytical method to complement the use of conventional sample-based, infrequent and destructive biochemical assays typically employed to analyze and validate the quality of therapeutic cells. This article provides an overview of the current state of emerging cell therapies, and then reviews the related Raman spectroscopic state of the art analysis of human cells. This includes spectroscopic data processing considerations, the scope offered by technical variants of Raman spectroscopy, and analytical difficulties encountered by spectroscopists working with therapeutic cells. Finally, we outline a number of salient challenges as cell therapy products are translated from the laboratory to the clinic, and propose how Raman spectroscopy-based solutions could address these challenges.

Defocused Spatially Offset Raman Spectroscopy in Media of Different Optical Properties for Biomedical Applications Using a Commercial Spatially Offset Raman Spectroscopy Device.

In this study, we show how defocused spatially offset Raman spectroscopy (SORS) can be employed to recover chemical information from media of biomedical significance within sealed plastic transfusion and culture bags using a commercial SORS instrument. We demonstrate a simple approach to recover subsurface spectral information through a transparent barrier by optimizing the spatial offset of the defocused beam. The efficiency of the measurements is assessed in terms of the SORS ratio and signal-to-noise ratio (S/N) through a simple manual approach and an ordinary least squares model. By comparing the results for three different biological samples (red blood cell concentrate, pooled red cell supernatant and a suspension of Jurkat cells), we show that there is an optimum value of the offset parameter which yields the maximum S/N depending on the barrier material and optical properties of the ensemble contents. The approach was developed in the context of biomedical applications but is generally applicable to any three-layer system consisting of turbid content between transparent thin plastic barriers (i.e., front and back bag surfaces), particularly where the analyte of interest is dilute or not a strong scatterer.

Fourier transform infrared spectroscopic imaging of colon tissues: evaluating the significance of amide I and C-H stretching bands in diagnostic applications with machine learning.

Fourier transform infrared (FTIR) spectroscopic imaging of colon biopsy tissues in transmission combined with machine learning for the classification of different stages of colon malignancy was carried out in this study. Two different approaches, an optical and a computational one, were applied for the elimination of the scattering background during the measurements and compared with the results of the machine learning model without correction for the scattering. Several different data processing pathways were implemented in order to obtain a high accuracy of the prediction model. This study demonstrates, for the first time, that C-H stretching and amide I bands are of little to no significance in the classification of the colon malignancy, based on the Gini importance values by random forest (RF). The best prediction outcome is found when supervised RF classification was carried out in the fingerprint region of the spectral data between 1500 and 1000 cm-1 (excluding the contribution of amide I and II bands). An overall prediction accuracy higher than 90% is achieved through the RF. The results also show that dysplastic and hyperplastic tissues are well distinguished. This leads to the insight that the important differences between hyperplastic and dysplastic colon tissues lie within the fingerprint region of FTIR spectra. In this study, computational correction performed better than optical correction, but the findings show that the disease states of colon biopsies can be distinguished effectively without elimination of Mie scattering effect. Graphical abstract.

Determination of Depth in Transmission Raman Spectroscopy in Turbid Media Using a Beam Enhancing Element.

This study demonstrates experimentally a method to enable prediction of depth of a chemical species buried in a turbid medium by using transmission Raman spectroscopy alone. The method allows the prediction of the depth of a single, chemically distinct layer within a turbid matrix by performing two measurements, with and without a beam enhancing element, or "photon diode." The samples employed consisted of two different polymers, of total thickness 3.6 mm, whose optical properties are loosely relevant to pharmaceutical applications. A polymer layer of low-density polyethylene (LDPE) was placed at different positions within multiple layers of the polytetrafluoroethylene (PTFE) matrix and Raman spectra were recorded in each case. Both univariate and multivariate analyses were utilized to determine whether the depth of the LDPE layer could be predicted using the obtained data. The best-achieved RMSE of prediction was 4.2% of the total sample size (i.e., +/- 0.15 mm) with the multivariate approach.

Subwavelength Terahertz Imaging of Graphene Photoconductivity.

Using a spatially structured, optical pump pulse with a terahertz (THz) probe pulse, we are able to determine spatial variations of the ultrafast THz photoconductivity with subwavelength resolution (75 µm ≈ λ/5 at 0.8 THz) in a planar graphene sample. We compare our results to Raman spectroscopy and correlate the existence of the spatial inhomogeneities between the two measurements. We find a strong correlation with inhomogeneity in electron density. This demonstrates the importance of eliminating inhomogeneities in doping density during CVD growth and fabrication for photoconductive devices.

Characterisation of signal enhancements achieved when utilizing a photon diode in deep Raman spectroscopy of tissue.

We characterise the performance of a beam enhancing element ('photon diode') for use in deep Raman spectroscopy (DRS) of biological tissues. The optical component enhances the number of laser photons coupled into a tissue sample by returning escaping photons back into it at the illumination zone. The method is compatible with transmission Raman spectroscopy, a deep Raman spectroscopy concept, and its implementation leads to considerable enhancement of detected Raman photon rates. In the past, the enhancement concept was demonstrated with a variety of samples (pharmaceutical tablets, tissue, etc) but it was not systematically characterized with biological tissues. In this study, we investigate the enhancing properties of the photon diode in the transmission Raman geometry as a function of: a) the depth and b) the optical properties of tissue samples. Liquid tissue phantoms were employed to facilitate systematic variation of optical properties. These were chosen to mimic optical properties of human tissues, including breast and prostate. The obtained results evidence that a photon diode can enhance Raman signals of tissues by a maximum of × 2.4, although it can also decrease the signals created towards the back of samples that exhibit high scattering or absorption properties.

Studying the distribution of deep Raman spectroscopy signals using liquid tissue phantoms with varying optical properties.

In this study we employed large volume liquid tissue phantoms, consisting of a scattering agent (Intralipid), an absorption agent (Indian ink) and a synthesized calcification powder (calcium hydroxyapatite (HAP)) similar to that found in cancerous tissues (e.g. breast and prostate), to simulate human tissues. We studied experimentally the magnitude and origin of Raman signals in a transmission Raman geometry as a function of optical properties of the medium and the location of calcifications within the phantom. The goal was to inform the development of future noninvasive cancer screening applications in vivo. The results provide insight into light propagation and Raman scattering distribution in deep Raman measurements, exploring also the effect of the variation of relative absorbance of laser and Raman photons within the phantoms. Most notably when modeling breast and prostate tissues it follows that maximum signals is obtained from the front and back faces of the tissue with the central region contributing less to the measured spectrum.