Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

First, the potential role of Raman-based techniques in biomedicine is introduced. Second, an overview about the instrumentation for spontaneous and coherent Raman scattering microscopic imaging is given with a focus of recent developments. Third, imaging strategies are summarized including sequential registration with laser scanning microscopes, line imaging and global or wide-field imaging. Finally, examples of biomedical applications are presented in the context of single cells, laser tweezers, tissue sections, biopsies and whole animals.

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

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.

We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications. (1) HTS-RS-based differential white blood cell count. A classification model was trained using Raman spectra of 52 218 lymphocytes, 48 220 neutrophils, and 7 294 monocytes from four volunteers. The model was applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. (2) HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9, and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90, and 3:97, respectively. Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics.

Raman spectroscopy is a powerful tool for label-free, single cell characterization. In many reported studies, a Raman spectrum is acquired from a fraction of the cell volume and used as a representative signature of the whole cell to identify and discriminate between cell populations. It has remained an open question whether this is the most suitable approach since the spectra may not truly represent the cell as a whole and critical biochemical information could therefore be lost. To address this question, we developed a line-scan Raman microscope to acquire Raman images of single lymphocytes exposed to the chemotherapeutic drug doxorubicin for 24 to 96 hours. Principal component analysis was able to separate cells based on their drug-exposure times. Difference spectra on the mean data for the different time-points revealed that changes are related to a decrease in mean nucleic acid content and an increase in mean protein and lipid content. Vertex component analysis was used to extract the pure component spectra of lipids, nucleic acids, and proteins. Quantitative analysis of the data revealed that biochemical changes occurred at both local subcellular (i.e. molecular density) and global cellular (i.e. total observable molecular content) levels. However, significant differences between the trends in the local and global changes were observed. While local nucleic acid content decreased with increasing drug exposure time, the total cellular nucleic acid content remained relatively constant. For protein, local content remained relatively constant for all exposure times while the total protein content in the cell increased ∼3 fold. Lipid content in the entire cell increased ∼5 fold, compared to a smaller increase in lipid at the local level. These results show that valuable information about the biochemical changes throughout the entire cell can be missed if only Raman spectra of localized cell regions are used. These findings are expected to have a major impact on the future development of Raman spectroscopy for cytometry applications.

Cellular lipid droplets are the least studied and least understood cellular organelles in eukaryotic and prokaryotic cells. Despite a significant body of research studying the physiology of lipid droplets it has not yet been possible to fully determine the composition of individual cellular lipid droplets. In this paper we use Raman spectroscopy on single cellular lipid droplets and least-squares fitting of pure fatty acid spectra to determine the composition of individual lipid droplets in cells after treatment with different ratios of oleic and palmitic acid. We validate the results of the Raman spectroscopy-based single lipid droplet analysis with results obtained by gas chromatography analysis of millions of cells, and find that our approach can accurately predict the relative amount of a specific fatty acid in the lipid droplet. Based on these results we show that the fatty acid composition in individual lipid droplets is on average similar to that of all lipid droplets found in the sample. Furthermore, we expand this approach to the investigation of the lipid composition in single cellular peroxisomes. We determine the location of cellular peroxisomes based on two-photon excitation fluorescence (TPEF) imaging of peroxisomes labeled with the green fluorescent protein, and successive Raman spectroscopy of peroxisomes. We find that in some cases peroxisomes can produce a detectable CARS signal, and that the peroxisomal Raman spectra exhibit an oleic acid-like signature.

Raman spectroscopy provides label-free biochemical information from tissue samples without complicated sample preparation. The clinical capability of Raman spectroscopy has been demonstrated in a wide range of in vitro and in vivo applications. However, a challenge for in vivo applications is the simultaneous excitation of auto-fluorescence in the majority of tissues of interest, such as liver, bladder, brain, and others. Raman bands are then superimposed on a fluorescence background, which can be several orders of magnitude larger than the Raman signal. To eliminate the disturbing fluorescence background, several approaches are available. Among instrumentational methods shifted excitation Raman difference spectroscopy (SERDS) has been widely applied and studied. Similarly, computational techniques, for instance extended multiplicative scatter correction (EMSC), have also been employed to remove undesired background contributions. Here, we present a theoretical and experimental evaluation and comparison of fluorescence background removal approaches for Raman spectra based on SERDS and EMSC.