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In the last couple of decades, the spatial resolution in optical microscopy has increased to unprecedented levels by exploiting the fluorescence properties of the probe. At about the same time, Raman imaging techniques have emerged as a way to image inherent chemical information in a sample without using fluorescent probes. However, in many applications, the achievable resolution is limited to about half the wavelength of excitation light. Here we report the use of structured illumination to increase the spatial resolution of label-free spontaneous Raman microscopy, generating highly detailed spatial contrast from the ensemble of molecular information in the sample. Using structured line illumination in slit-scanning Raman microscopy, we demonstrate a marked improvement in spatial resolution and show the applicability to a range of samples, including both biological and inorganic chemical component mapping. This technique is expected to contribute towards greater understanding of chemical component distributions in organic and inorganic materials. Raman imaging provides visualization of molecular content in a sample, but has been practically limited in resolution. Here, the authors implement structured line illumination in slit-scanning Raman microscopy, expanding its resolvable spatial frequencies without spoiling the advantage of the Raman approach.

The acquired immune system, mainly composed of T and B lymphocytes, plays a key role in protecting the host from infection. It is important and technically challenging to identify cell types and their activation status in living and intact immune cells, without staining or killing the cells. Using Raman spectroscopy, we succeeded in discriminating between living T cells and B cells, and visualized the activation status of living T cells without labeling. Although the Raman spectra of T cells and B cells were similar, they could be distinguished by discriminant analysis of the principal components. Raman spectra of activated T cells with anti-CD3 and anti-CD28 antibodies largely differed compared to that of naïve T cells, enabling the prediction of T cell activation status at a single cell level. Our analysis revealed that the spectra of individual T cells gradually change from the pattern of naïve T cells to that of activated T cells during the first 24 h of activation, indicating that changes in Raman spectra reflect slow changes rather than rapid changes in cell state during activation. Our results indicate that the Raman spectrum enables the detection of dynamic changes in individual cell state scattered in a heterogeneous population.

System level understanding of the cell requires detailed description of the cell state, which is often characterized by the expression levels of proteins. However, understanding the cell state requires comprehensive information of the cell, which is usually obtained from a large number of cells and their disruption. In this study, we used Raman spectroscopy, which can report changes in the cell state without introducing any label, as a non-invasive method with single cell capability. Significant differences in Raman spectra were observed at the levels of both the cytosol and nucleus in different cell-lines from mouse, indicating that Raman spectra reflect differences in the cell state. Difference in cell state was observed before and after the induction of differentiation in neuroblastoma and adipocytes, showing that Raman spectra can detect subtle changes in the cell state. Cell state transitions during embryonic stem cell (ESC) differentiation were visualized when Raman spectroscopy was coupled with principal component analysis (PCA), which showed gradual transition in the cell states during differentiation. Detailed analysis showed that the diversity between cells are large in undifferentiated ESC and in mesenchymal stem cells compared with terminally differentiated cells, implying that the cell state in stem cells stochastically fluctuates during the self-renewal process. The present study strongly indicates that Raman spectral morphology, in combination with PCA, can be used to establish cells' fingerprints, which can be useful for distinguishing and identifying different cellular states.

Osteoblastic mineralization occurs during the early stages of bone formation. During this mineralization, hydroxyapatite (HA), a major component of bone, is synthesized, generating hard tissue. Many of the mechanisms driving biomineralization remain unclear because the traditional biochemical assays used to investigate them are destructive techniques incompatible with viable cells. To determine the temporal changes in mineralization-related biomolecules at mineralization spots, we performed time-lapse Raman imaging of mouse osteoblasts at a subcellular resolution throughout the mineralization process. Raman imaging enabled us to analyze the dynamics of the related biomolecules at mineralization spots throughout the entire process of mineralization. Here, we stimulated KUSA-A1 cells to differentiate into osteoblasts and conducted time-lapse Raman imaging on them every 4 hours for 24 hours, beginning 5 days after the stimulation. The HA and cytochrome c Raman bands were used as markers for osteoblastic mineralization and apoptosis. From the Raman images successfully acquired throughout the mineralization process, we found that β-carotene acts as a biomarker that indicates the initiation of osteoblastic mineralization. A fluctuation of cytochrome c concentration, which indicates cell apoptosis, was also observed during mineralization. We expect time-lapse Raman imaging to help us to further elucidate osteoblastic mineralization mechanisms that have previously been unobservable.

Background Lipid/carbohydrate content and ratio are extremely important when engineering algal cells for liquid biofuel production. However, conventional methods for such determination and quantification are not only destructive and tedious, but also energy consuming and environment unfriendly. In this study, we first demonstrate that Raman spectroscopy is a clean, fast, and accurate method to simultaneously quantify the lipid/carbohydrate content and ratio in living microalgal cells. Results The quantification results of both lipids and carbohydrates obtained by Raman spectroscopy showed a linear correspondence with that obtained by conventional methods, indicating Raman can provide a similar accuracy to conventional methods, with a significantly shorter detection time. Furthermore, the subcellular resolution of Raman spectroscopy enabled not only the concentration mapping of lipid/carbohydrate content in single living cells, but also the evaluation of standard deviation between the biomass accumulation levels of individual algal cells. Conclusions In this study, we first demonstrate that Raman spectroscopy can be used for starch quantification in addition to lipid quantification in algal cells. Due to the easiness and non-destructive nature of Raman spectroscopy, it makes a perfect tool for the further study of starch-lipid shift mechanism.

Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications.

Our current understanding of molecular biology provides a clear picture of how the genome, transcriptome and proteome regulate each other, but how the chemical environment of the cell plays a role in cellular regulation remains much to be studied. Here we show an imaging method using hybrid fluorescence-Raman microscopy that measures the chemical micro-environment associated with protein expression patterns in a living cell. Simultaneous detection of fluorescence and Raman signals, realised by spectrally separating the two modes through the single photon anti-Stokes fluorescence emission of fluorescent proteins, enables the accurate correlation of the chemical fingerprint of a specimen to its physiological state. Subsequent experiments revealed the slight chemical differences that enabled the chemical profiling of mouse embryonic stem cells with and without Oct4 expression. Furthermore, using the fluorescent probe as localisation guide, we successfully analysed the detailed chemical content of cell nucleus and Golgi body. The technique can be further applied to a wide range of biomedical studies for the better understanding of chemical events during biological processes.

Using Raman spectral imaging, we visualized the cell state transition during differentiation and constructed hypothetical potential landscapes for attractors of cellular states on a state space composed of parameters related to the shape of the Raman spectra. As models of differentiation, we used the myogenic C2C12 cell line and mouse embryonic stem cells. Raman spectral imaging can validate the amounts and locations of multiple cellular components that describe the cell state such as proteins, nucleic acids and lipids; thus, it can report the state of a single cell. Herein, we visualized the cell state transition during differentiation using Raman spectral imaging of cell nuclei in combination with principal component analysis. During differentiation, cell populations with a seemingly homogeneous cell state before differentiation showed heterogeneity at the early stage of differentiation. At later differentiation stages, the cells returned to a homogeneous cell state that was different from the undifferentiated state. Thus, Raman spectral imaging enables us to illustrate the disappearance and reappearance of an attractor in a differentiation landscape, where cells stochastically fluctuate between states at the early stage of differentiation.

The most prevalent oral cancer globally is oral squamous cell carcinoma (OSCC). The invasion of adjacent bones and the metastasis to regional lymph nodes often lead to poor prognoses and shortened survival times in patients with OSCC. Encouraging immunotherapeutic responses have been seen with immune checkpoint inhibitors (ICIs); however, these positive responses to monotherapy have been limited to a small subset of patients. Therefore, it is urgent that further investigations into optimizing immunotherapies are conducted. Areas of research include identifying novel immune checkpoints and targets and tailoring treatment programs to meet the needs of individual patients. Furthermore, the advancement of combination therapies against OSCC is also critical. Thus, additional studies are needed to ensure clinical trials are successful. Mice models are advantageous in immunotherapy research with several advantages, such as relatively low costs and high tumor growth success rate. This review paper divided methods for establishing OSCC mouse models into four categories: syngeneic tumor models, chemical carcinogen induction, genetically engineered mouse, and humanized mouse. Each method has advantages and disadvantages that influence its application in OSCC research. This review comprehensively surveys the literature and summarizes the current mouse models used in immunotherapy, their advantages and disadvantages, and details relating to the cell lines for oral cancer growth. This review aims to present evidence and considerations for choosing a suitable model establishment method to investigate the early diagnosis, clinical treatment, and related pathogenesis of OSCC.

Hepatocellular carcinoma (HCC) is a lethal human malignancy and a leading cause of cancer‐related death worldwide. Patients with HCC are often diagnosed at an advanced stage, and the prognosis is usually poor. The multikinase inhibitor sorafenib is the first‐line treatment for patients with advanced HCC. However, cases of primary or acquired resistance to sorafenib have gradually increased, leading to a predicament in HCC therapy. Thus, it is critical to investigate the mechanism underlying sorafenib resistance. Transactivation response element RNA‐binding protein 2 (TARBP2) is a multifaceted miRNA biogenesis factor that regulates cancer stem cell (CSC) properties. The tumorigenicity and drug resistance of cancer cells are often enhanced due to the acquisition of CSC features. However, the role of TARBP2 in sorafenib resistance in HCC remains unknown. Our results demonstrate that TARBP2 is significantly downregulated in sorafenib‐resistant HCC cells. The TARBP2 protein was destabilized through autophagic–lysosomal proteolysis, thereby stabilizing the expression of the CSC marker protein Nanog, which facilitates sorafenib resistance in HCC cells. In summary, here we reveal a novel miRNA‐independent role of TARBP2 in regulating sorafenib resistance in HCC cells. Sorafenib is the first‐line treatment for patients with advanced hepatocellular carcinoma (HCC). The cases of sorafenib resistance have gradually increased, leading to a predicament HCC therapy. Our results demonstrate that transactivation response element RNA‐binding protein 2 (TARBP2) is downregulated in sorafenib‐resistant HCC cells. TARBP2 protein was destabilized through autophagic–lysosomal proteolysis, thereby stabilizing Nanog, which facilitates sorafenib resistance in HCC cells.