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.

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.

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.

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.

Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High-Quality Procedure.

Theoretical probability distributions are often fitted to the individual peaks of Raman spectra to decompose them and facilitate further analyses. Fitting has the additional advantage of eliminating noise. We have exploited this noise-eliminating attribute of fitting procedures in an automated algorithm to smooth Raman spectra. An initial smoothing was performed by fitting Voigt distributions to every channel in a spectrum. The Voigt distribution characters used were strongly Gaussian, the distribution widths equal to the spectral resolution, and their initial amplitudes equal to the spectral intensities at the channels where they were located. The smoothed spectrum was then subtracted from the original noisy spectrum to obtain a residual. For channels where the residual exceeded a limit-of-detection threshold, the distribution width was decreased. The fitting was then repeated until a secondary, lower limit distribution width was reached. The residual was then smoothed repeatedly in the same manner until the minimum distribution width was reached. After each repetition, the smoothed residual was added to the smoothed spectrum. The process was continued until a combined limit of detection and chi-squared stopping criterion was reached. Although slower in comparison to spline- and Savitzky-Golay-based methods, the smoothing quality was significantly better allowing the majority of smoothed spectra, in contrast to these methods, to pass a stringent smoothing quality test.

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples.

Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.

Types of cell death and apoptotic stages in Chinese Hamster Ovary cells distinguished by Raman spectroscopy.

Cell death is the ultimate cause of productivity loss in bioreactors that are used to produce therapeutic proteins. We investigated the ability of Raman spectroscopy to detect the onset and types of cell death for Chinese Hamster Ovary (CHO) cells-the most widely used cell type for therapeutic protein production. Raman spectroscopy was used to compare apoptotic, necrotic, autophagic, and control CHO cells. Several specific nucleic acid-, protein-, and lipid-associated marker bands within the 650-850 cm-1 spectral region were identified that distinguished among cells undergoing different modes of cell death; supporting evidence was provided by principal component analysis (PCA) of the full spectral data. In addition to comparing the different modes of cell death, normal cells were compared to cells sorted at several stages of apoptosis, in order to explore the potential for early detection of apoptosis. Different stages of apoptosis could be distinguished via Raman spectroscopy, with multiple changes observed in nucleic acid peaks at early stages whereas an increase in lipid signals was a feature of late apoptosis/secondary necrosis.

Impaired glucose tolerance alters functional ability of peripheral blood-derived mononuclear cells in Asian Indian men.

AIM: To compare the adhesion, migration and endothelial differentiation potential of peripheral blood-derived mononuclear cells (PBMCs) obtained from drug-naive normal glucose tolerance (NGT) and impaired glucose tolerance (IGT) Asian Indian men. METHODS: Based on the 75-g oral glucose tolerance test, 30 NGT and 31 IGT subjects were recruited into the study. PBMCs were isolated from fasting blood using histopaque density gradient centrifugation. Isolated PBMCs were analysed for their ability to adhere to extracellular matrices, incorporation into tubular structures formed by matured endothelial cells and differentiation into endothelial cells upon 7-day culture in endothelial-specific growth medium. RESULTS: PBMCs obtained from IGT subjects exhibit poor adherence to fibronectin and reduced incorporation into tubular structures. Migration towards stromal cell-derived factor-1α (SDF-1α) in a trans-well filter assembly was also reduced for these cells. Semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) analysis revealed decreased expression of CXCR4 and ß2 integrin and increased expression of arginase II in IGT subjects. No differences were observed with regard to endothelial differentiation; however, cultured PBMCs of IGT subjects had decreased intracellular nitric oxide (NO) production. CONCLUSION: In pre-diabetic, Asian Indian men, PBMCs exhibit defective migration and homing potential.