Label-free biochemical characterization of stem cells using vibrational spectroscopy

Demixing and Analysis of Complex Biological Raman Hyperspectra Based on Peak Fitting, Amplitude Trend Clustering, and Spectrum Reconstruction

To better interpret the Raman spectra from mammalian cells, it is often desirable to reduce their complexity by decomposing them into the spectral contributions from individual macromolecules or types of macromolecules. Diverse methods exist for demixing complex spectra, each with different benefits and drawbacks. However, some methods require a library of component spectra that might not be available, while others are hampered by noise and peak congestion that includes many proximal overlapping peaks. Through rapid fitting of individual peaks in every spectrum of a Raman hyperspectral data set, we have obtained individual peak parameters from which we determined the trends for all the peak amplitudes. We then grouped similar trends with k-means clustering. Then we used the peak parameters of all the peaks in a given cluster to reconstruct a spectrum representative of that cluster. This method produced spectra that were less distorted by unrelated overlapping peaks or noise, were less congested than those in the hyperspectral set, and thereby improved peak identification and macromolecule recognition. We have demonstrated the application of the method with Raman spectra from a perchlorate-polystyrene model system and extended it to complex spectra from methanol-fixed mammalian cells. We were able to recover independent spectra of perchlorate and polystyrene in the model system and spectra pertaining to individual macromolecular types (proteins, nucleic acids, lipids) from the mammalian cell data. We discuss how imperfections in spectral preprocessing and peak fitting can adversely affect the results. In summary, we have provided a proof-of-concept for a novel mixture resolution method with different attributes than extant ones.

Harnessing Raman spectroscopy for cell therapy bioprocessing

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Spheroids as a 3D in vitro model to study bone and bone mineralization

Traumas, fractures, and diseases can severely influence bone tissue. Insight into bone mineralization is essential for the development of therapies and new strategies to enhance bone regeneration. 3D cell culture systems, in particular cellular spheroids, have gained a lot of interest as they can recapitulate crucial aspects of the in vivo tissue microenvironment, such as the extensive cell-cell and cell-extracellular matrix (ECM) interactions found in tissue. The potential of combining spheroids and various classes of biomaterials opens also new opportunities for research within bone tissue engineering. Characterizing cellular organization, ECM structure, and ECM mineralization is a fundamental step for understanding the biological processes involved in bone tissue formation in a spheroid-based model system. Still, many experimental techniques used in this field of research are optimized for use with monolayer cell cultures. There is thus a need to develop new and improving existing experimental techniques, for applications in 3D cell culture systems. In this review, bone composition and spheroids properties are described. This is followed by an insight into the techniques that are currently used in bone spheroids research and how these can be used to study bone mineralization. We discuss the application of staining techniques used with optical and confocal fluorescence microscopy, molecular biology techniques, second harmonic imaging microscopy, Raman spectroscopy and microscopy, as well as electron microscopy-based techniques, to evaluate osteogenic differentiation, collagen production and mineral deposition. Challenges in the applications of these methods in bone regeneration and bone tissue engineering are described. STATEMENT OF SIGNIFICANCE: 3D cell cultures have gained a lot of interest in the last decades as a possible technique that can be used to recreate in vitro in vivo biological process. The importance of 3D environment during bone mineralization led scientists to use this cell culture to study this biological process, to obtain a better understanding of the events involved. New and improved techniques are also required for a proper analysis of this cell model and the process under investigation. This review summarizes the state of the art of the techniques used to study bone mineralization and how 3D cell cultures, in particular spheroids, are tested and analysed to obtain better resolved results related to this complex biological process.

Recent Advances in Monitoring Stem Cell Status and Differentiation Using Nano-Biosensing Technologies

In regenerative medicine, cell therapies using various stem cells have received attention as an alternative to overcome the limitations of existing therapeutic methods. Clinical applications of stem cells require the identification of characteristics at the single-cell level and continuous monitoring during expansion and differentiation. In this review, we recapitulate the application of various stem cells used in regenerative medicine and the latest technological advances in monitoring the differentiation process of stem cells. Single-cell RNA sequencing capable of profiling the expression of many genes at the single-cell level provides a new opportunity to analyze stem cell heterogeneity and to specify molecular markers related to the branching of differentiation lineages. However, this method is destructive and distorted. In addition, the differentiation process of a particular cell cannot be continuously tracked. Therefore, several spectroscopic methods have been developed to overcome these limitations. In particular, the application of Raman spectroscopy to measure the intrinsic vibration spectrum of molecules has been proposed as a powerful method that enables continuous monitoring of biochemical changes in the process of the differentiation of stem cells. This review provides a comprehensive overview of current analytical methods employed for stem cell engineering and future perspectives of nano-biosensing technologies as a platform for the in situ monitoring of stem cell status and differentiation.

Cell-based drug delivery systems and their in vivo fate

Cell-based drug delivery systems (DDSs) have received attention recently because of their unique biological properties and self-powered functions, such as excellent biocompatibility, low immunogenicity, long circulation time, tissue-homingcharacteristics, and ability to cross biological barriers. A variety of cells, including erythrocytes, stem cells, and lymphocytes, have been explored as functional vectors for the loading and delivery of various therapeutic payloads (e.g., small-molecule and nucleic acid drugs) for subsequent disease treatment. These cell-based DDSs have their own unique in vivo fates, which are attributed to various factors, including their biological properties and functions, the loaded drugs and loading process, physiological and pathological circumstances, and the body's response to these carrier cells, which result in differences in drug delivery efficiency and therapeutic effect. In this review, we summarize the main cell-based DDSs and their biological properties and functions, applications in drug delivery and disease treatment, and in vivo fate and influencing factors. We envision that the unique biological properties, combined with continuing research, will enable development of cell-based DDSs as friendly drug vectors for the safe, effective, and even personalized treatment of diseases.

Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation

Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.

Characterization of the biofilm matrix composition of psychrotrophic, meat spoilage pseudomonads

Psychrotrophic Pseudomonas species are the key spoilage bacteria of aerobically stored chilled meat. These organisms readily form biofilms on meat under refrigerated conditions leading to consumer rejection and associated economic losses. Limited information is available on the matrix composition of the biofilms formed by these bacteria. We quantified and characterized the main components of the matrix of mono-species biofilms of selected Pseudomonas fragi and Pseudomonas lundensis strains using chemical analysis and Raman spectroscopy. The biofilms were grown at 10 °C and 25 °C on nitro-cellulose membranes placed on surface sterilized beef cuts. Extra-cellular polymeric substances of the matrix were extracted in soluble and bound forms and were chemically assessed for total carbohydrates, proteins and extra-cellular DNA. Both Pseudomonas species showed a significant increase in total carbohydrates and total proteins when grown at 10 °C as compared to 25 °C. Extra-cellular DNA did not show a strong correlation with growth temperature. Raman spectra were obtained from planktonic bacteria and membrane grown biofilms at 10 °C and 25 °C. Higher levels of guanine were detected in planktonic cells as compared to biofilm cells. This study suggests that psychrotrophic Pseudomonas species may respond to cold stress by increasing extra-cellular polymer secretions.

Infrared spectroscopy of live cells from a flowing solution using electrically-biased plasmonic metasurfaces

Spectral cytopathology (SCP) is a promising label-free technique for diagnosing diseases and monitoring therapeutic outcomes using FTIR spectroscopy. In most cases, cells must be immobilized on a substrate prior to spectroscopic interrogation. This creates significant limitations for high throughput phenotypic whole-cell analysis, especially for the non-adherent cells. Here we demonstrate how metasurface-enhanced infrared reflection spectroscopy (MEIRS) can be applied to a continuous flow of live cell solution by applying AC voltage to metallic metasurfaces. By integrating metasurfaces with microfluidic delivery channels and attracting the cells to the metasurface via dielectrophoretic (DEP) force, we collect the infrared spectra of cells in real time within a minute, and correlate the spectra with simultaneously acquired images of the attracted cells. The resulting DEP-MEIRS technique paves the way for rapid SCP of complex cell-containing body fluids with low cell concentrations, and for the development of a wide range of label-free liquid biopsies.

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

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Influence of chemical fixation process on primary mesenchymal stem cells evidenced by Raman spectroscopy

In investigation of (patho)physiological processes, cells represent frequently used analyte as an exceptional source of information. However, spectroscopic analysis of live cells is still very seldom in clinics, as well as in research studies. Among others, the reasons are long acquisition time during which autolysis process is activated, necessity of specified technical equipment, and inability to perform analysis in a moment of sample preparation. Hence, an optimal method of preserving cells in the existing state is of extreme importance, having in mind that selection of fixative is cell lineage dependent. In this study, two commonly used chemical fixatives, formaldehyde and methanol, are used for preserving primary mesenchymal stem cells extracted from periodontal ligament, which are valuable cell source for reconstructive dentistry. By means of Raman spectroscopy, cell samples were probed and the impact of these fixatives on their Raman response was analyzed and compared. Different chemical mechanisms are the core processes of formaldehyde and methanol fixation and certain Raman bands are shifted and/or of changed intensity when Raman spectra of cells fixed in that manner are compared. In order to get clearer picture, comprehensive statistical analysis was performed.

Probing primary mesenchymal stem cells differentiation status by micro-Raman spectroscopy

We have employed micro-Raman spectroscopy to get insight into intrinsic biomolecular profile of individual mesenchymal stem cell isolated from periodontal ligament. Furthermore, these cells were stimulated towards adipogenic, chondrogenic, and osteogenic lineages and their status of differentiation was assessed using micro-Raman spectroscopy. In both cases, glass coverslips were used as substrates, due to their wide availability and cost effectiveness. In all sample groups, the same type of behavior was observed, manifested as changes in Raman spectra: the increase of relative intensity of protein/lipid bands and decrease of nucleic acid bands. Comprehensive statistical analysis in the form of principal component analysis was performed, which revealed noticeable grouping of cells with the similar features. Despite the inhomogeneity of primary stem cells and their differentiated lineages, we demonstrated that micro-Raman spectroscopy is sufficient for distinguishing cells' status, which can be valuable for medical and clinical application.

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.

Probe of Alcohol Structures in the Gas and Liquid States Using C⁻H Stretching Raman Spectroscopy

Vibrational spectroscopy is a powerful tool for probing molecular structures and dynamics since it offers a unique fingerprint that allows molecular identification. One of important aspects of applying vibrational spectroscopy is to develop the probes that can characterize the related properties of molecules such as the conformation and intermolecular interaction. Many examples of vibrational probes have appeared in the literature, including the azide group (⁻N₃), amide group (⁻CONH₂), nitrile groups (⁻CN), hydroxyl group (⁻OH), ⁻CH group and so on. Among these probes, the ⁻CH group is an excellent one since it is ubiquitous in organic and biological molecules and the C⁻H stretching vibrational spectrum is extraordinarily sensitive to the local molecular environment. However, one challenge encountered in the application of C⁻H probes arises from the difficulty in the accurate assignment due to spectral congestion in the C⁻H stretching region. In this paper, recent advances in the complete assignment of C⁻H stretching spectra of aliphatic alcohols and the utility of C⁻H vibration as a probe of the conformation and weak intermolecular interaction are outlined. These results fully demonstrated the potential of the ⁻CH chemical group as a molecular probe.

Infrared Spectroscopy and Imaging in Stem Cells and Aging Research

The effect of aging process on stem cell function is crucial because of their critical role in tissue regeneration and repair. The impact of aging on stem cells needs to be understood clearly for the success of clinical application and obtaining desired therapeutic outcome throughout the novel stem cell based therapies. The existing methods used to monitor and characterize the stem cells have some unwanted effects on the properties of stem cells and these methods also do not provide real-time information about cellular conditions. These challenges enforce the usage of nondestructive, rapid, sensitive, high-quality, label-free, cheep, and innovative chemical monitoring methods. In this context, vibrational spectroscopy provides promising alternative to get new information into the field of stem cell biology for chemical analysis, quantification, and imaging of stem cells. Infrared spectroscopy and imaging coupled with chemometric methods can be used as novel and complimentary methods to obtain new insight into stem cell studies for future therapeutic and regenerative medicine.

Recent advances in the use of microfluidic technologies for single cell analysis

The inherent heterogeneity in cell populations has become of great interest and importance as analytical techniques have improved over the past decades. With the advent of personalized medicine, understanding the impact of this heterogeneity has become an important challenge for the research community. Many different microfluidic approaches with varying levels of throughput and resolution exist to study single cell activity. In this review, we take a broad view of the recent microfluidic developments in single cell analysis based on microwell, microchamber, and droplet platforms. We cover physical, chemical, and molecular biology approaches for cellular and molecular analysis including newly emerging genome-wide analysis.

Empirical Factors Affecting the Quality of Non-Negative Matrix Factorization of Mammalian Cell Raman Spectra

Mammalian cells contain various macromolecules that can be investigated non-invasively with Raman spectroscopy. The particular mixture of major macromolecules present in a cell being probed are reflected in the measured Raman spectra. Determining macromolecular identities and estimating their concentrations from these mixture Raman spectra can distinguish cell types and otherwise enable biological research. However, the application of canonical multivariate methods, such as principal component analysis (PCA), to perform spectral unmixing yields mathematical solutions that can be difficult to interpret. Non-negative matrix factorization (NNMF) improves the interpretability of unmixed macromolecular components, but can be difficult to apply because ambiguities produced by overlapping Raman bands permit multiple solutions. Furthermore, theoretically sound methods can be difficult to implement in practice. Here we examined the effects of a number of empirical approaches on the quality of NNMF results. These approaches were evaluated on simulated mammalian cell Raman hyperspectra and the results were used to develop an enhanced procedure for implementing NNMF. We demonstrated the utility of this procedure using a Raman hyperspectral data set measured from human islet cells to recover the spectra of insulin and glucagon. This was compared to the relatively inferior PCA of these data.

Raman spectroscopy and coherent anti-Stokes Raman scattering imaging: prospective tools for monitoring skeletal cells and skeletal regeneration

The use of skeletal stem cells (SSCs) for cell-based therapies is currently one of the most promising areas for skeletal disease treatment and skeletal tissue repair. The ability for controlled modification of SSCs could provide significant therapeutic potential in regenerative medicine, with the prospect to permanently repopulate a host with stem cells and their progeny. Currently, SSC differentiation into the stromal lineages of bone, fat and cartilage is assessed using different approaches that typically require cell fixation or lysis, which are invasive or even destructive. Raman spectroscopy and coherent anti-Stokes Raman scattering (CARS) microscopy present an exciting alternative for studying biological systems in their natural state, without any perturbation. Here we review the applications of Raman spectroscopy and CARS imaging in stem-cell research, and discuss the potential of these two techniques for evaluating SSCs, skeletal tissues and skeletal regeneration as an exemplar.

Active antioxidants in ex-vivo examination of burn wound healing by means of IR and Raman spectroscopies-Preliminary comparative research

Being a complex traumatic event, burn injury also affects other organ systems apart from the skin. Wounds undergo various pathological changes which are accompanied by alterations in the molecular environment. Information about molecules may be obtained with the use of Raman spectroscopy and Fourier-transform infrared spectroscopy, and when combined, both methods are a powerful tool for providing material characterization. Alterations in the molecular environment may lead to identifying objective markers of acute wound healing. In general, incubation of samples in solutions of l-ascorbic acid and 5% and 7% orthosilicic acid organizes the collagen structure, whereas the increased intensity of the Raman bands in the region of 1500-800cm^-1 reveals regeneration of the burn tissue. Since oxidative damage is one of the mechanisms responsible for local and distant pathophysiological events after burn, antioxidant therapy can prove to be beneficial in minimizing burn wounds, which was examined on the basis of human skin samples and chicken skin samples, the latter being subject to modification when heated to a temperature sufficient for the simulation of a burn incident.

FTIR Spectroscopic and Molecular Analysis during Differentiation of Pluripotent Stem Cells to Pancreatic Cells

Some of the greatest challenges in stem cells (SCs) biology and regenerative medicine are differentiation control of SCs and ensuring the purity of differentiated cells. In this work, we differentiated mouse pluripotent stem cells (mPSCs) toward pancreatic cells characterizing this differentiation process by molecular and spectroscopic technics. Both mPSCs and Differentiated Pancreatic Cells (DPCs) were subjected to a genetic, phenotypic, and biochemical analysis by real-time quantitative PCR (RT-qPCR), immunocytochemistry, and Fourier Transform Infrared (FTIR) spectroscopy. Cultured mPCSs expressed pluripotent genes and proteins (Nanog and SOX2). DPCs expressed endodermal genes (SOX17 and Pdx1) at day 11, an inductor gene of embryonic pancreas development (Pdx1) at day 17 and pancreas genes and proteins (Insulin and Glucagon) at day 21 of differentiation. Likewise, FTIR spectra of mPSCs and DPCs at different maturation stages (11, 17, and 21 days) were obtained and showed absorption bands related with different types of biomolecules. These FTIR spectra exhibited significant spectral changes agreeing with the differentiation process, particularly in proteins and nucleic acids bands. In conclusion, the obtained DPCs passed through the chronological stages of embryonic pancreas development and FTIR spectra provide a new biophysical parameter based on molecular markers indicating the differentiation process of mPSCs to specialized cells.

Synchrotron FTIR microspectroscopy reveals early adipogenic differentiation of human mesenchymal stem cells at single-cell level

Human mesenchymal stem cells (hMSCs) have been used as an ideal in vitro model to study human adipogenesis. However, little knowledge of the early stage differentiation greatly hinders our understanding on the mechanism of the adipogenesis processes. In this study, synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy was applied to track the global structural and compositional changes of lipids, proteins and nucleic acids inside individual hMSCs along the time course. The multivariate analysis of the SR-FTIR spectra distinguished the dynamic and significant changes of the lipids and nucleic acid at early differentiation stage. Importantly, changes of lipid structure during early days (Day 1-3) of differentiation might serve as a potential biomarker in identifying the state in early differentiation at single cell level. These results proved that SR-FTIR is a powerful tool to study the stem cell fate determination and early lipogenesis events.

Label-free cellular structure imaging with 82 nm lateral resolution using an electron-beam excitation-assisted optical microscope

We present label-free and high spatial-resolution imaging for specific cellular structures using an electron-beam excitation-assisted optical microscope (EXA microscope). Images of the actin filament and mitochondria of stained HeLa cells, obtained by fluorescence and EXA microscopy, were compared to identify cellular structures. Based on these results, we demonstrated the feasibility of identifying label-free cellular structures at a spatial resolution of 82 nm. Using numerical analysis, we calculated the imaging depth region and determined the spot size of a cathodoluminescent (CL) light source to be 83 nm at the membrane surface.

Label-free and non-invasive discrimination of HaCaT and melanoma cells in a co-culture model by hyperspectral confocal reflectance microscopy

A novel hyperspectral confocal microscopy method to separate different cell populations in a co-culture model is presented here. The described methodological and instrumental approach allows discrimination of different cell types using a non-invasive, label free method with good accuracy with a single cell resolution. In particular, melanoma cells are discriminated from HaCaT cells by hyperspectral confocal imaging, principal component analysis and optical frequencies signing, as confirmed by fluorescence labelling cross check. The identification seems to be quite robust to be insensitive to the cellular shape within the studied samples, enabling to separate cells according to their cytotype down to a single cell sensitivity. Set of hyperspectral images of melanoma-keratinocytes co-culture model (left), score plot of principal component analysis and spectral analysis of principal components coefficients (center), label-free spectral identification of cell populations (right).

Label-free nonlinear optical microscopy detects early markers for osteogenic differentiation of human stem cells

Tissue engineering by stem cell differentiation is a novel treatment option for bone regeneration. Most approaches for the detection of osteogenic differentiation are invasive or destructive and not compatible with live cell analysis. Here, non-destructive and label-free approaches of Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) and second harmonic generation (SHG) microscopy were used to detect and image osteogenic differentiation of human neural crest-derived inferior turbinate stem cells (ITSCs). Combined CARS and SHG microscopy was able to detect markers of osteogenesis within 14 days after osteogenic induction. This process increased during continued differentiation. Furthermore, Raman spectroscopy showed significant increases of the PO4(3-) symmetric stretch vibrations at 959 cm(-1) assigned to calcium hydroxyapatite between days 14 and 21. Additionally, CARS microscopy was able to image calcium hydroxyapatite deposits within 14 days following osteogenic induction, which was confirmed by Alizarin Red-Staining and RT- PCR. Taken together, the multimodal label-free analysis methods Raman spectroscopy, CARS and SHG microscopy can monitor osteogenic differentiation of adult human stem cells into osteoblasts with high sensitivity and spatial resolution in three dimensions. Our findings suggest a great potential of these optical detection methods for clinical applications including in vivo observation of bone tissue-implant-interfaces or disease diagnosis.

Detection of hepatic maturation by Raman spectroscopy in mesenchymal stromal cells undergoing hepatic differentiation

Introduction

Mesenchymal stromal cells (MSCs) are well known for their application potential in tissue engineering. We previously reported that MSCs are able to differentiate into hepatocytes in vitro. However, conventional methods for estimating the maturation of hepatic differentiation require relatively large amounts of cell samples. Raman spectroscopy (RS), a photonic tool for acquisition of cell spectra by inelastic scattering, has been recently used as a label-free single-cell detector for biological applications including phenotypic changes and differentiation of cells and diagnosis. In this study, RS is used to real-time monitor the maturation of hepatic differentiation in live MSCs.

Methods

The MSCs were cultured on the type I collagen pre-coating substrate and differentiated into hepatocytes in vitro using a two-step protocol. The Raman spectra at different time points are acquired in the range 400–3000 cm^–1and analyzed by quantification methods and principle component analysis during hepatic differentiation from the MSCs.

Results

The intensity of the broad band in the range 2800–3000 cm^–1 reflects the amount of glycogen within lipochrome in differentiated hepatocytes. A high correlation coefficient between the glycogen amount and hepatic maturation was exhibited. Moreover, principle component analysis of the Raman spectra from 400 to 3000 cm^–1 indicated that MSC-derived hepatocytes were close to the primary hepatocytes and were distinct from the undifferentiated MSCs.

Conclusions

In summary, RS can serve as a rapid, non-invasive, real-time and label-free biosensor and reflects changes in live cell components during hepatic differentiation. The use of RS may thus facilitate the detection of hepatic differentiation and maturation in stem cells. Such an approach may substantially improve the feasibility as well as shorten the time required compared to the conventional molecular biology methods.

Discrimination and classification of liver cancer cells and proliferation states by Raman spectroscopic imaging

Discrimination of nodular lesions in cirrhotic liver is a challenge in the histopathologic diagnostics. For this reason, there is an urgent need for new detection methods to improve the accuracy of the diagnosis of liver cancer. Raman imaging allows to determine the spatial distribution of a variety of molecules in cells or tissue label-free and to correlate this molecular information with the morphological structures at the same sample location. This study reports investigations of two liver cancer cell lines, - HepG2 and SK-Hep1, - as well as HepG2 cells in different cellular growth phases using Raman micro-spectroscopic imaging. Spectral data of all cells were recorded as a color-coded image and subsequentially analyzed by hierarchical cluster and principal component analysis. A support vector machine-based classification algorithm reliably predicts previously unknown cancer cells and cell cycle phases. By including selectively the Raman spectra of the cytoplasmic lipids in the classifier, the accuracy has been improved. The main spectral differences that were found in the comparative analysis can be attributed to a higher expression of unsaturated fatty acids in the hepatocellular carcinoma cells and during the proliferation phase. This corresponds to the already examined de novo lipogenesis in cells of liver cancer.

Vibrational spectroscopic methods for cytology and cellular research

The use of vibrational spectroscopy, FTIR and Raman, for cytology and cellular research has the potential to revolutionise the approach to cellular analysis. Vibrational spectroscopy is non-destructive, simple to operate and provides direct information. Importantly it does not require expensive exogenous labels that may affect the chemistry of the cell under analysis. In addition, the advent of spectroscopic microscopes provides the ability to image cells and acquire spectra with a subcellular resolution. This introductory review focuses on recent developments within this fast paced field and highlights potential for the future use of FTIR and Raman spectroscopy. We particularly focus on the development of live cell research and the new technologies and methodologies that have enabled this.

Label-free and non-invasive monitoring of porcine trophoblast derived cells: differentiation in serum and serum-free media

Traditional approaches to characterize stem cell differentiation are time-consuming, lengthy and invasive. Here, Raman microspectroscopy (RM) and atomic force microscopy (AFM) - both considered as non-invasive techniques - are applied to detect the biochemical and biophysical properties of trophoblast derived stem-like cells incubated up to 10 days under conditions designed to induce differentiation. Significant biochemical and biophysical differences between control cells and differentiated cells were observed. Quantitative real time PCR was also applied to analyze gene expression. The relationship between cell differentiation and associated cellular biochemical and biomechanical changes were discussed. Monitoring trophoblast cells differentiation.

Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra

The development of new methods for the early diagnosis of cartilage disease could offer significant improvement in patient care. Raman spectroscopy is an emerging biomedical technology with unique potential to recognize disease tissues, though difficulty in obtaining the samples needed to train a diagnostic and excessive signal noise could slow its development into a clinical tool. In the current report we detail the use of principal component analysis--linear discriminant analysis (PCA-LDA) on spectra from pairs of materials modeling cartilage disease to create multiple spectral scoring metrics, which could limit the reliance on primary training data for identifying disease in low signal-to-noise-ratio (SNR) Raman spectra. Our proof-of-concept experiments show that combinations of these model-metrics has the potential to improve the classification of low-SNR Raman spectra from human normal and osteoarthritic (OA) cartilage over a single metric trained with spectra from the same healthy and OA tissues. Scatter plot showing the PCA-LDA derived human-disease-metric scores versus rat-model-metric scores for 7656 low signal-to-noise spectra from healthy (blue) and osteoarthritic (red) cartilage. Light vertical and horizontal lines represent the optimized single metric classification boundary. Dark diagonal line represents the classification of boundary resulting from the optimized combination of the two metrics.

Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation

Raman spectroscopy has emerged as a promising tool for real-time clinical diagnosis of malignant skin tumors offering a number of potential advantages: it is non-intrusive, it requires no sample preparation, and it features high chemical specificity with minimal water interference. However, in vivo tissue evaluation and accurate histopathological classification remain a challenging task for the successful transition from laboratory prototypes to clinical devices. In the literature, there are numerous reports on the applications of Raman spectroscopy to biomedical research and cancer diagnostics. Nevertheless, cases where real-time, portable instrumentations have been employed for the in vivo evaluation of skin lesions are scarce, despite their advantages in use as medical devices in the clinical setting. This paper reviews the advances in real-time Raman spectroscopy for the in vivo characterization of common skin lesions. The translational momentum of Raman spectroscopy towards the clinical practice is revealed by (i) assembling the technical specifications of portable systems and (ii) analyzing the spectral characteristics of in vivo measurements.

Non-linear optical flow cytometry using a scanned, Bessel beam light-sheet

Modern flow cytometry instruments have become vital tools for high-throughput analysis of single cells. However, as issues with the cellular labeling techniques often used in flow cytometry have become more of a concern, the development of label-free modalities for cellular analysis is increasingly desired. Non-linear optical phenomena (NLO) are of growing interest for label-free analysis because of the ability to measure the intrinsic optical response of biomolecules found in cells. We demonstrate that a light-sheet consisting of a scanned Bessel beam is an optimal excitation geometry for efficiently generating NLO signals in a microfluidic environment. The balance of photon density and cross-sectional area provided by the light-sheet allowed significantly larger two-photon fluorescence intensities to be measured in a model polystyrene microparticle system compared to measurements made using other excitation focal geometries, including a relaxed Gaussian excitation beam often used in conventional flow cytometers.

Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors

The capability of saliva protein analysis, based on membrane protein purification and surface-enhanced Raman spectroscopy (SERS), for detecting benign and malignant breast tumors is presented in this paper. A total of 97 SERS spectra from purified saliva proteins were acquired from samples obtained from three groups: 33 healthy subjects; 33 patients with benign breast tumors; and 31 patients with malignant breast tumors. Subtle but discernible changes in the mean SERS spectra of the three groups were observed. Tentative assignments of the saliva protein SERS spectra demonstrated that benign and malignant breast tumors led to several specific biomolecular changes of the saliva proteins. Multiclass partial least squares–discriminant analysis was utilized to analyze and classify the saliva protein SERS spectra from healthy subjects, benign breast tumor patients, and malignant breast tumor patients, yielding diagnostic sensitivities of 75.75%, 72.73%, and 74.19%, as well as specificities of 93.75%, 81.25%, and 86.36%, respectively. The results from this exploratory work demonstrate that saliva protein SERS analysis combined with partial least squares–discriminant analysis diagnostic algorithms has great potential for the noninvasive and label-free detection of breast cancer.

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.

The extracellular matrix: Structure, composition, age-related differences, tools for analysis and applications for tissue engineering

The extracellular matrix is a structural support network made up of diverse proteins, sugars and other components. It influences a wide number of cellular processes including migration, wound healing and differentiation, all of which is of particular interest to researchers in the field of tissue engineering. Understanding the composition and structure of the extracellular matrix will aid in exploring the ways the extracellular matrix can be utilised in tissue engineering applications especially as a scaffold. This review summarises the current knowledge of the composition, structure and functions of the extracellular matrix and introduces the effect of ageing on extracellular matrix remodelling and its contribution to cellular functions. Additionally, the current analytical technologies to study the extracellular matrix and extracellular matrix–related cellular processes are also reviewed.

Label-free identification of the glioma stem-like cell fraction using Fourier-transform infrared spectroscopy

PURPOSE

Vibrational spectroscopy enables the label-free characterization of cells and tissue by probing the biochemical composition. Here, we evaluated these techniques to identify glioblastoma stem cells.

MATERIALS AND METHODS

The biochemical fingerprints of glioblastoma cells were established in human cell lines with high and low content of CD133 (cluster of differentiation 133)-positive cells using attenuated total reflection Fourier-transform infrared (ATR FT-IR) on vital cells and FT-IR mapping, which delivers spatially resolved spectroscopic datasets. After data preprocessing, unsupervised cluster analysis was applied. CD133 was addressed with flow cytometry and immunohistochemistry and used as a stemness marker.

RESULTS

In all preparations, the algorithm was able to correctly classify the spectra, differentiating CD133-rich and -poor populations. The main spectral differences were found in the region of 1000 cm(- 1) to 1150 cm(- 1) that can be assigned to vibrations of chemical bonds of DNA, RNA, carbohydrates and phospholipids. Interestingly, this spectral region is a key feature to discern glioblastoma from normal brain parenchyma, as FT-IR spectroscopic mapping of experimental brain tumors demonstrated.

CONCLUSIONS

We were able to show biochemical differences between glioblastoma cell populations with high and low content of cancer stem cells that are presumably related to changes in the RNA/DNA content.

Label-free identification and characterization of murine hair follicle stem cells located in thin tissue sections with Raman micro-spectroscopy

Stem cells offer tremendous opportunities for regenerative medicine.

Quantitative, label-free characterization of stem cell differentiation at the single-cell level by broadband coherent anti-Stokes Raman scattering microscopy

We use broadband coherent anti-Stokes Raman scattering (BCARS) microscopy to characterize lineage commitment of individual human mesenchymal stem cells cultured in adipogenic, osteogenic, and basal culture media. We treat hyperspectral images obtained by BCARS in two independent ways, obtaining robust metrics for differentiation. In one approach, pixel counts corresponding to functional markers, lipids, and minerals, are used to classify individual cells as belonging to one of the three lineage groups: adipocytes, osteoblasts, and undifferentiated stem cells. In the second approach, we use multivariate analysis of Raman spectra averaged exclusively over cytosol regions of individual cells to classify the cells into the same three groups, with consistent results. The exceptionally high speed of spectral imaging with BCARS allows us to chemically map a large number of cells with high spatial resolution, revealing not only the phenotype of individual cells, but also population heterogeneity in the degree of phenotype commitment.

Label-free detection of insulin and glucagon within human islets of Langerhans using Raman spectroscopy

Intrahepatic transplantation of donor islets of Langerhans is a promising therapy for patients with type 1 diabetes. It is of critical importance to accurately monitor islet quality before transplantation, which is currently done by standard histological methods that are performed off-line and require extensive sample preparation. As an alternative, we propose Raman spectroscopy which is a non-destructive and label-free technique that allows continuous real-time monitoring of the tissue to study biological changes as they occur. By performing Raman spectroscopic measurements on purified insulin and glucagon, we showed that the 520 cm(-1) band assigned to disulfide bridges in insulin, and the 1552 cm(-1) band assigned to tryptophan in glucagon are mutually exclusive and could therefore be used as indirect markers for the label-free distinction between both hormones. High-resolution hyperspectral Raman imaging for these bands showed the distribution of disulfide bridges and tryptophan at sub-micrometer scale, which correlated with the location of insulin and glucagon as revealed by conventional immunohistochemistry. As a measure for this correlation, quantitative analysis was performed comparing the Raman images with the fluorescence images, resulting in Dice coefficients (ranging between 0 and 1) of 0.36 for insulin and 0.19 for glucagon. Although the use of separate microscope systems with different spatial resolution and the use of indirect Raman markers cause some image mismatch, our findings indicate that Raman bands for disulfide bridges and tryptophan can be used as distinctive markers for the label-free detection of insulin and glucagon in human islets of Langerhans.

Methods and applications of Raman microspectroscopy to single-cell analysis

Raman spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biological cells in the absence of fluorescent stains. In this review, we summarize some of the most recent developments in the noninvasive biochemical characterization of single cells by spontaneous Raman scattering. Different instrumentation strategies utilizing confocal detection optics, multispot, and line illumination have been developed to improve the speed and sensitivity of the analysis of single cells by Raman spectroscopy. To analyze and visualize the large data sets obtained during such experiments, sophisticated multivariate statistical analysis tools are necessary to reduce the data and extract components of interest. We highlight the most recent applications of single cell analysis by Raman spectroscopy and their biomedical implications that have enabled the noninvasive characterization of specific metabolic states of eukaryotic cells, the identification and characterization of stem cells, and the rapid identification of bacterial cells. We conclude the article with a brief look into the future of this rapidly evolving research area.

Apoptosis of colorectal cancer UTC116 cells induced by Cantharidinate

Effects of Cantharidinate on apoptosis of human colorectal cancer UTC-116 cells were investigated by means of 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay, H and E staining, flow cytometry, and Raman Spectra analysis. The results showed Cantharidinate to exert inhibitory action on proliferation of human colorectal cancer UTC-116 cells, inducing apoptosis, arresting cells in G1 phase, with decline of S and G2 phases. In addition, the results of Raman spectrum showed significant changes in the UTC-116 cells chemical structure with stretching after the application of Cantharidinate. Taken together, these results suggest that the treatment of human colorectal cancer with Cantharidinate may be associated with multiple molecular mechanisms for apoptosis. Furthermore, similar to fluorouracil, Cantharidinate should be considered as novel assistant drug for controlling the growth of human colorectal cancer UTC-116 cells.

Stem cell metabolic and spectroscopic profiling

Stem cells offer great potential for regenerative medicine because they regenerate damaged tissue by cell replacement and/or by stimulating endogenous repair mechanisms. Although stem cells are defined by their functional properties, such as the potential to proliferate, to self-renew, and to differentiate into specific cell types, their identification based on the expression of specific markers remains vague. Here, profiles of stem cell metabolism might highlight stem cell function more than the expression of single genes/markers. Thus, systematic approaches including spectroscopy might yield insight into stem cell function, identity, and stemness. We review the findings gained by means of metabolic and spectroscopic profiling methodologies, for example, nuclear magnetic resonance spectroscopy (NMRS), mass spectrometry (MS), and Raman spectroscopy (RS), with a focus on neural stem cells and neurogenesis.