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.

Deposits in the lungs of Kwäday Dän Ts'ìnchi man: Characterization by a combination of analytical microscopical methods.

OBJECTIVES: The approximately 250 years old remains of the Kwäday Dän Ts'ìnchi man were found in a glacier in Canada. Studying the state of preservation of the corpse, we observed black deposits in his lung. Following this observation we wanted to determine: (1) location of the deposits in the lung tissue, (2) composition and origins of the deposits. METHODS: By light microscopy (LM) and transmission electron microscopy (TEM), we studied the deposits in the Kwäday Dän Ts'ìnchi man' s lung and compared it with distribution of anthracotic deposits in contemporary samples from the David Harwick Pathology Centre (DHPC). To determine chemical composition of the inclusions we used Raman spectroscopy. Scanning electron microscopy and elemental mapping was used for determine the chemical elements. RESULTS: The histopathological identification of anthracosis in the Kwäday Dän Ts'ìnchi man's lung allowed us to distinguish crushed parenchyma from conducting airway tissue and identification of particles using LM and TEM. Crystal particles were found using TEM. Ordered carbonaceous material (graphene and graphite), disordered carbonaceous material (soot) and what might be minerals (likely conglomerates) were found with Raman spectrometry. Gold and lead particles in the lung were discovered with scanning electron microscopy and elemental mapping. CONCLUSIONS: Presence of soot particles in anthracotic areas in the Kwäday Dän Ts'ìnchi man's lung probably were due to an inhalation of particles in open fires. Gold and lead particles are most likely of an environmental origin and may have been inhaled and could have impacted his health and his Champagne and Aishihik First Nations (CAFN) contemporaries.

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.

Using Raman spectroscopy to assess hemoglobin oxygenation in red blood cell concentrate: an objective proxy for morphological index to gauge the quality of stored blood?

Blood banking is an essential aspect of modern medical care. When red blood cells (RBCs) are stored, they become damaged by various chemical processes, such as accumulation of their own waste products and oxidative injury, among others. These processes lead to the development of the RBC storage lesion, a complex condition where the severity is reflected through the morphology of the stored cells. It was hypothesized that Raman spectroscopy could be used to monitor certain structural and compositional changes associated with such ageing effects and that a relationship between these features and traditional morphology (as measured using a morphology index) could be observed. The hypothesis was tested by measuring spectral features associated with hemoglobin oxygenation from dry-fixed smears and liquid RBCs for twenty-nine different donors (combined), and comparing the trends with morphological scoring from seven of these donors. After appropriately fitting the two data sets to either power or linear curves, the oxygenation state was shown to change in a manner that was donor-dependent and that closely tracked morphological changes. This study suggests Raman analysis has promise for providing a rapid and objective measure of the cell quality of stored RBCs through measurements of hemoglobin oxygenation that is comparable to traditional morphological assessment.

Raman spectroscopy as a novel tool for monitoring biochemical changes and inter-donor variability in stored red blood cell units.

Individual units of donated red blood cells (RBCs) do not ordinarily undergo analytical testing prior to transfusion. This study establishes the utility of Raman spectroscopy for analyzing the biochemistry of stored RBC supernatant and reveals interesting storage-related changes about the accumulation of lactate, a chemical species that may be harmful to certain patients. The data show measurable variations in supernatant composition and demonstrate that some units of donated RBCs accumulate lactate much more readily than others. The spectra also indicate a higher relative concentration of lactate in units collected from male donors than female donors (p = 0.004) and imply that there is a greater degree of variability at later stages of storage in units from older male donors (>45 years). The study proves that Raman analysis has promise for elucidating the relationship between the metabolism of stored RBCs and donor characteristics. It also suggests that there may be benefit in developing a Raman instrument for the rapid non-invasive assessment of blood-bag biochemistry by measuring through plastic over-layers.

Process Analytical Utility of Raman Microspectroscopy in the Directed Differentiation of Human Pancreatic Insulin-Positive Cells.

Continued advances toward cell-based therapies for human disease generate a growing need for unbiased and label-free monitoring of cellular characteristics. We used Raman microspectroscopy to characterize four important stages in the 26-day directed differentiation of human embryonic stem cells (hESCs) to insulin-positive cells. The extent to which the cells retained spectroscopic features of pluripotent cells or developed spectroscopic features suggestive of pancreatic endocrine cells, as well as assessing the homogeneity of the cell populations at these developmental stages, were of particular interest. Such information could have implications for the utility of Raman microspectroscopy process analysis for the generation of insulin-positive cells from hESCs. Because hESC seeding density influences the subsequent pancreatic development, three different seeding density cultures were analyzed. Transcription factor and other marker analyses assessed the progress of the cells through the relevant developmental stages. Increases in the Raman protein-to-nucleic acid band ratios were observed at the final endocrine stage analyzed, but this increase was less than expected. Also, high glycogen band intensities, somewhat unexpected in pancreatic endocrine cells, suggested the presence of a substantial number of glycogen containing cells. We discuss the potential process analytical technology application of these findings and their importance for cell manufacturing.

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra.

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Fully automated decomposition of Raman spectra into individual Pearson's type VII distributions applied to biological and biomedical samples.

Rapid technological advances have made the acquisition of large numbers of spectra not only feasible, but also routine. As a result, a significant research effort is focused on semi-automated and fully automated spectral processing techniques. However, the need to provide initial estimates of the number of peaks, their band shapes, and the initial parameters of these bands presents an obstacle to the full automation of peak fitting and its incorporation into fully automated spectral-preprocessing workflows. Moreover, the sensitivity of peak-fit routines to initial parameter settings and the resultant variations in solution quality further impede user-free operation. We have developed a technique to perform fully automated peak fitting on fully automated preconditioned spectra-specifically, baseline-corrected and smoothed spectra that are free of cosmic-ray-induced spikes. Briefly, the tallest peak in a spectrum is located and a Gaussian peak-fit is performed. The fitted peak is then subtracted from the spectrum, and the procedure is repeated until the entire spectrum has been processed. In second and third passes, all the peaks in the spectrum are fitted concurrently, but are fitted to a Pearson Type VII model using the parameters for the model established in the prior pass. The technique is applied to a synthetic spectrum with several peaks, some of which have substantial overlap, to test the ability of the method to recover the correct number of peaks, their true shape, and their appropriate parameters. Finally the method is tested on measured Raman spectra collected from human embryonic stem cells and samples of red blood cells.

Silicon-gold-silica lamellar structures for sample substrates that provide an internal standard for Raman microspectroscopy.

Crystalline silicon, widely used in the electronic industry, is also a very popular material for calibrating Raman spectrometry instruments. Silicon chips cut or cleaved from commercially available silicon wafers are low-cost monolithic monocrystalline materials that give a strong Raman line at 521 cm(-1) with almost no background. Such chips have at least one optically flat surface and can be used in place of glass microscope slides as sample substrates that provide an internal calibration standard during Raman measurements. The Raman signal intensity from the silicon can be selectively attenuated by depositing a gold layer on top of the silicon surface with variable thickness such that the far-field silicon Raman signal is comparable with the Raman signal of an investigated material adjacent to this structure. This gold layer provides the additional advantage of increased sensitivity of the spectral signal from the sample due to the reflectivity of the gold surface, which allows forward and backscattered analyte Raman excitation and signal collection. An additional thin encapsulating overlayer of SiO2 provides a protective and biocompatible surface to facilitate Raman microspectroscopic investigation of live cells.

A two-dimensionally coincident second difference cosmic ray spike removal method for the fully automated processing of Raman spectra.

Charge-coupled device detectors are vulnerable to cosmic rays that can contaminate Raman spectra with positive going spikes. Because spikes can adversely affect spectral processing and data analyses, they must be removed. Although both hardware-based and software-based spike removal methods exist, they typically require parameter and threshold specification dependent on well-considered user input. Here, we present a fully automated spike removal algorithm that proceeds without requiring user input. It is minimally dependent on sample attributes, and those that are required (e.g., standard deviation of spectral noise) can be determined with other fully automated procedures. At the core of the method is the identification and location of spikes with coincident second derivatives along both the spectral and spatiotemporal dimensions of two-dimensional datasets. The method can be applied to spectra that are relatively inhomogeneous because it provides fairly effective and selective targeting of spikes resulting in minimal distortion of spectra. Relatively effective spike removal obtained with full automation could provide substantial benefits to users where large numbers of spectra must be processed.

Label-free determination of the cell cycle phase in human embryonic stem cells by Raman microspectroscopy.

The cell cycle is a series of integrated and coordinated physiological events that results in cell growth and replication. Besides observing the event of cell division it is not feasible to determine the cell cycle phase without fatal and/or perturbing invasive procedures such as cell staining, fixing, and/or dissociation. Raman microspectroscopy (RMS) is a chemical imaging technique that exploits molecular vibrations as a contrast mechanism; it can be applied to single living cells noninvasively to allow unperturbed analysis over time. We used RMS to determine the cell cycle phase based on integrating the composite 783 cm(-1) nucleic acid band intensities across individual cell nuclei. After correcting for RNA contributions using the RNA 811 cm(-1) band, the measured intensities essentially reflected DNA content. When quantifying Raman images from single cells in a population of methanol-fixed human embryonic stem cells, the histogram of corrected 783 cm(-1) band intensities exhibited a profile analogous to that obtained using flow-cytometry with nuclear stains. The two population peaks in the histogram occur at Raman intensities corresponding to a 1-fold and 2-fold diploid DNA complement per cell, consistent with a distribution of cells with a population peak due to cells at the end of G1 phase (1-fold) and a peak due to cells entering M phase (2-fold). When treated with EdU to label the replicating DNA and block cell division, cells with higher EdU-related fluorescence generally had higher integrated Raman intensities. This provides proof-of-principle of an analytical method for label-free RMS determination in situ of cell cycle phase in adherent monolayers or even single adherent cells.

Label-free imaging of mammalian cell nucleoli by Raman microspectroscopy.

The nucleolus is a prominent subnuclear structure whose major function is the transcription and assembly of ribosome subunits. The size of the nucleolus varies with the cell cycle, proliferation rate and stress. Changes in nucleolar size, number, chemical composition, and shape can be used to characterize malignant cells. We used spontaneous Raman microscopy as a label-free technique to examine nucleolar spatial and chemical features. Raman images of the 1003 cm(-1) phenylalanine band revealed large, well-defined subnuclear protein structures in MFC-7 breast cancer cells. The 783 cm(-1) images showed that nucleic acids were similarly distributed, but varied more in intensity, forming observable high-intensity regions. High subnuclear RNA concentrations were observed within some of these regions as shown by 809 cm(-1) Raman band images. Principal component analyses of sub-images and library spectra validated the subnuclear presence of RNA. They also revealed that an actin-like protein covaried with DNA within the nucleolus, a combination that accounted for 64% or more of the spectral variance. Embryonic stem cells are another rapidly proliferating cell type, but their nucleoli were not as large or well defined. Estimating the size of the larger MCF-7 nucleolus was used to show the utility of Raman microscopy for morphometric analyses. It was concluded that imaging based on Raman microscopy provides a promising new method for the study of nucleolar function and organization, in the evaluation of drug and experimental effects on the nucleolus, and in clinical diagnostics and prognostics.

A fast, automated, polynomial-based cosmic ray spike-removal method for the high-throughput processing of Raman spectra.

Raman spectra often contain undesirable, randomly positioned, intense, narrow-bandwidth, positive, unidirectional spectral features generated when cosmic rays strike charge-coupled device cameras. These must be removed prior to analysis, but doing so manually is not feasible for large data sets. We developed a quick, simple, effective, semi-automated procedure to remove cosmic ray spikes from spectral data sets that contain large numbers of relatively homogenous spectra. Although some inhomogeneous spectral data sets can be accommodated--it requires replacing excessively modified spectra with the originals and removing their spikes with a median filter instead--caution is advised when processing such data sets. In addition, the technique is suitable for interpolating missing spectra or replacing aberrant spectra with good spectral estimates. The method is applied to baseline-flattened spectra and relies on fitting a third-order (or higher) polynomial through all the spectra at every wavenumber. Pixel intensities in excess of a threshold of 3× the noise standard deviation above the fit are reduced to the threshold level. Because only two parameters (with readily specified default values) might require further adjustment, the method is easily implemented for semi-automated processing of large spectral sets.