Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy

Characterization of a Single-Capture Bright-Field and Off-Axis Digital Holographic Microscope for Biological Applications

We present a single-capture multimodal bright-field (BF) and quantitative phase imaging (QPI) approach that enables the analysis of large, connected, or extended samples, such as confluent cell layers or tissue sections. The proposed imaging concept integrates a fiber-optic Mach-Zehnder interferometer-based off-axis digital holographic microscopy (DHM) with an inverted commercial optical BF microscope. Utilizing 8-bit grayscale dynamic range multiplexing, we simultaneously capture both BF images and digital holograms, which are then demultiplexed numerically via Fourier filtering, phase aberration compensation, and weighted image subtraction procedures. Compared to previous BF-DHM systems, our system avoids synchronization challenges caused by multiple image recording devices, improves acquisition speed, and enhances versatility for fast imaging of large, connected, and rapidly moving samples. Initially, we perform a systematic characterization of the system's multimodal imaging performance by optimizing numerical as well as coherent and incoherent illumination parameters. Subsequently, the application capabilities are evaluated by multimodal imaging of living cells. The results highlight the potential of single-capture BF-DHM for fast biomedical imaging.

Correlative Raman Imaging: Development and Cancer Applications

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

Investigation of refractive index dynamics during in vitro embryo development using off-axis digital holographic microscopy

Embryo quality is a crucial factor affecting live birth outcomes. However, an accurate diagnostic for embryo quality remains elusive in the in vitro fertilization clinic. Determining physical parameters of the embryo may offer key information for this purpose. Here, we demonstrate that digital holographic microscopy (DHM) can rapidly and non-invasively assess the refractive index of mouse embryos. Murine embryos were cultured in either low- or high-lipid containing media and digital holograms recorded at various stages of development. The phase of the recorded hologram was numerically retrieved, from which the refractive index of the embryo was calculated. We showed that DHM can detect spatio-temporal changes in refractive index during embryo development that are reflective of its lipid content. As accumulation of intracellular lipid is known to compromise embryo health, DHM may prove beneficial in developing an accurate, non-invasive, multimodal diagnostic.

Reliable cell preparation protocol for Raman imaging to effectively differentiate normal leukocytes and leukemic blasts

Leukemias are a remarkably diverse group of malignancies originating from abnormal progenitor cells in the bone marrow. Leukemia subtypes are classified according to the cell type that has undergone neoplastic transformation using demanding and time-consuming methods. Alternative is Raman imaging that can be used both for living and fixed cells. However, considering the diversity of leukemic cell types and normal leukocytes, and the availability of different sample preparation protocols, the main objective of this work was to verify them for leukemia and normal blood cell samples for Raman imaging. The effect of glutaraldehyde (GA) fixation in a concentration gradient (0.1 %, 0.5 %, and 2.5 % GA) on the molecular structure of T-cell acute lymphoblastic leukemia (T-ALL) and peripheral blood mononuclear cells (PBMCs) was verified. Changes in the secondary structure of proteins within cells were indicated as the main effect of fixation, as shown by an increase in band intensity at 1041 cm^-1, characteristic for in-plane δ(CH) deformation in phenylalanine (Phe). Different sensitivity of mononuclear and leukemic cells to fixation was observed. While the 0.1 % concentration of GA was too low to preserve the cell structure for an extended period of time, a GA concentration of 0.5 % seemed optimal for both normal and malignant cells. Chemical changes in PBMCs samples stored for 11 days were also investigated, which manifested in numerous modifications in the secondary structure of proteins and the content of nucleic acids. The impact of cell preculturing for 72 h after unbanking was verified, and there was no significant effect on the molecular structure of cells fixed with 0.5 % GA. In summary, the developed protocol for the preparation of samples for Raman imaging allows for the effective differentiation of fixed normal leukocytes from malignant T lymphoblasts.

Mapping Immune-Tumor Bidirectional Dialogue Using Ultrasensitive Nanosensors for Accurate Diagnosis of Lung Cancer

Lung cancer is one of the most common cancers with high mortality worldwide despite the development of molecularly targeted therapies and immunotherapies. A significant challenge in managing lung cancer is the accurate diagnosis of cancerous lesions owing to the lack of sensitive and specific biomarkers. The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT) and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells.

Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells

Particles in biopharmaceutical formulations remain a hot topic in drug product development. With new product classes emerging it is crucial to discriminate particulate active pharmaceutical ingredients from particulate impurities. Technical improvements, new analytical developments and emerging tools (e.g., machine learning tools) increase the amount of information generated for particles. For a proper interpretation and judgment of the generated data a thorough understanding of the measurement principle, suitable application fields and potential limitations and pitfalls is required. Our review provides a comprehensive overview of novel particle analysis techniques emerging in the last decade for particulate impurities in therapeutic protein formulations (protein-related, excipient-related and primary packaging material-related), as well as particulate biopharmaceutical formulations (virus particles, virus-like particles, lipid nanoparticles and cell-based medicinal products). In addition, we review the literature on applications, describe specific analytical approaches and illustrate advantages and drawbacks of currently available techniques for particulate biopharmaceutical formulations.

In vivo optical molecular imaging of inflammation and immunity

Inflammation is the phenotypic form of various diseases. Recent development in molecular imaging provides new insights into the diagnostic and therapeutic evaluation of different inflammatory diseases as well as diseases involving inflammation such as cancer. While conventional imaging techniques used in the clinical setting provide only indirect measures of inflammation such as increased perfusion and altered endothelial permeability, optical imaging is able to report molecular information on diseased tissue and cells. Optical imaging is a quick, noninvasive, nonionizing, and easy-to-use diagnostic technology which has been successfully applied for preclinical research. Further development of optical imaging technology such as optoacoustic imaging overcomes the limitations of mere fluorescence imaging, thereby enabling pilot clinical applications in humans. By means of endogenous and exogenous contrast agents, sites of inflammation can be accurately visualized in vivo. This allows for early disease detection and specific disease characterization, enabling more rapid and targeted therapeutic interventions. In this review, we summarize currently available optical imaging techniques used to detect inflammation, including optical coherence tomography (OCT), bioluminescence, fluorescence, optoacoustics, and Raman spectroscopy. We discuss advantages and disadvantages of the different in vivo imaging applications with a special focus on targeting inflammation including immune cell tracking.

Molecular monitoring of glioblastoma's immunogenicity using a combination of Raman spectroscopy and chemometrics

Raman spectroscopy (RS) has been used as a powerful diagnostic and non-invasive tool in cancer diagnosis as well as in discrimination of cancer and immune cells. In this study RS in combination with chemometrics was applied to cellular Raman spectral data to distinguish the phenotype of T-cells and monocytes after incubation with media conditioned by glioblastoma stem-cells (GSCs) showing different molecular background. For this purpose, genetic modulations of epithelial-to-mesenchymal transition (EMT) process and expression of immunomodulator CD73 were introduced. Principal component analysis of the Raman spectral data showed that T-cells and monocytes incubated with tumour-conditioned media (TCMs) of GSCs with inhibited EMT activator ZEB1 or CD73 formed distinct clusters compared to controls highlighting their differences. Further discriminatory analysis performed using linear discriminant analysis (LDA) and support vector machine classification (SVM), yielded sensitivities and specificities of over 70 and 67% respectively upon validation against an independent test set. Supporting those results, flow cytometric analysis was performed to test the influence of TCMs on cytokine profile of T-cells and monocytes. We found that ZEB1 and CD73 influence T-cell and monocyte phenotype and promote monocyte differentiation into a population of mixed pro- and anti-tumorigenic macrophages (MΦs) and dendritic cells (DCs) respectively. In conclusion, Raman spectroscopy in combination with chemometrics enabled tracking T-cells and monocytes.

Decoding Optical Data with Machine Learning

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

Raman Microscopy: Progress in Research on Cancer Cell Sensing

In the last decade, Raman Spectroscopy (RS) was demonstrated to be a label-free, non-invasive and non-destructive optical spectroscopy allowing the improvement in diagnostic accuracy in cancer and analytical assessment for cell sensing. This review discusses how Raman spectra can lead to a deeper molecular understanding of the biochemical changes in cancer cells in comparison to non-cancer cells, analyzing two key examples, leukemia and breast cancer. The reported Raman results provide information on cancer progression and allow the identification, classification, and follow-up after chemotherapy treatments of the cancer cells from the liquid biopsy. The key obstacles for RS applications in cancer cell diagnosis, including quality, objectivity, number of cells and velocity of the analysis, are considered. The use of multivariant analysis, such as principal component analysis (PCA) and linear discriminate analysis (LDA), for an automatic and objective assessment without any specialized knowledge of spectroscopy is presented. Raman imaging for cancer cell mapping is shown and its advantages for routine clinical pathology practice and live cell imaging, compared to single-point spectral analysis, are debated. Additionally, the combination of RS with microfluidic devices and high-throughput screening for improving the velocity and the number of cells analyzed are also discussed. Finally, the combination of the Raman microscopy (RM) with other imaging modalities, for complete visualization and characterization of the cells, is described.

Detection and Differentiation of Bacterial and Fungal Infection of Neutrophils from Peripheral Blood Using Raman Spectroscopy

Neutrophils are important cells of the innate immune system and the major leukocyte subpopulation in blood. They are responsible for recognizing and neutralizing invading pathogens, such as bacteria or fungi. For this, neutrophils are well equipped with pathogen recognizing receptors, cytokines, effector molecules, and granules filled with reactive oxygen species (ROS)-producing enzymes. Depending on the pathogen type, different reactions are triggered, which result in specific activation states of the neutrophils. Here, we aim to establish a label-free method to indirectly detect infections and to identify the cause of infection by spectroscopic characterization of the neutrophils. For this, isolated neutrophils from human peripheral blood were stimulated in an in vitro infection model with heat-inactivated Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacterial pathogens as well as with heat-inactivated and viable fungi (Candida albicans). Label-free and nondestructive Raman spectroscopy was used to characterize neutrophils on a single cell level. Phagocytized fungi could be visualized in a few high-resolution false color images of individual neutrophils using label-free Raman spectroscopic imaging. Using a high-throughput screening Raman spectroscope (HTS-RS), Raman spectra of more than 2000 individual neutrophils from three different donors were collected in each treatment group, yielding a data set of almost 20 000 neutrophil spectra. Random forest classification models were trained to differentiate infected and noninfected cells with high accuracy (90%). Among the neutrophils challenged with pathogens, even the cause of infection, bacterial or fungal, was predicted correctly with 92% accuracy. Therefore, Raman spectroscopy enables reliable neutrophil phenotyping and infection diagnosis in a label-free manner. In contrast to the microbiological diagnostic standard, where the pathogen is isolated in time-consuming cultivation, this Raman-based method could potentially be blood-culture independent, thus saving precious time in bloodstream infection diagnostics.

Polarization-Sensitive Digital Holographic Imaging for Characterization of Microscopic Samples: Recent Advances and Perspectives

Polarization-sensitive digital holographic imaging (PS-DHI) is a recent imaging technique based on interference among several polarized optical beams. PS-DHI allows simultaneous quantitative three-dimensional reconstruction and quantitative evaluation of polarization properties of a given sample with micrometer scale resolution. Since this technique is very fast and does not require labels/markers, it finds application in several fields, from biology to microelectronics and micro-photonics. In this paper, a comprehensive review of the state-of-the-art of PS-DHI techniques, the theoretical principles, and important applications are reported.

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.

Label-free optical hemogram of granulocytes enhanced by artificial neural networks

An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to identify immune cell subsets. To achieve high accuracy, these techniques require a post-processing step using linear methods of multivariate processing, such as principal component analysis. Here we demonstrate for the first time a comparison between artificial neural networks and principal component analysis (PCA) to classify the key granulocyte cell lineages of neutrophils and eosinophils using both digital holographic microscopy and Raman spectroscopy. Artificial neural networks can offer advantages in terms of classification accuracy and speed over a PCA approach. We conclude that digital holographic microscopy with convolutional neural networks based analysis provides a route to a robust, stand-alone and high-throughput hemogram with a classification accuracy of 91.3 % at a throughput rate of greater than 100 cells per second.

Flow cytometry and micro-Raman spectroscopy: Identification of hemocyte populations in the mussel Mytilus galloprovincialis (Bivalvia: Mytilidae) from Faro Lake and Tyrrhenian Sea (Sicily, Italy)

Immunological and structural characteristics of hemocyte populations in the mussel Mytilus galloprovincialis (Bivalvia: Mytilidae), going from two different Sicilian habitats (Faro Lake and Tyrrhenian sea), was investigated by means of two different techniques (flow cytometric and micro-Raman spectroscopy analyses). For this purpose, three hundred and sixty mussels Mytilus galloprovincialis were analyzed during November 2017. They were divided into two equal groups (triplicate sample) on the basis of the site of collection (n = 60 caught in Faro Lake - group A, and n = 60 caught in Tyrrhenian Sea - group B). Some several differences between the species of Faro Lake and Tyrrhenian Sea are observed and ascribed to the disruption of immune parameters induced by the variations of some qualitative water parameters (temperature, salinity, dissolved oxygen, pH, ammonium 10, free chlorine, total chlorine, total phosphate, orthofhosphate) recorded in the two habitats. This study is relevant for monitoring the conditions of the sea and Faro Lake, which is strongly influenced by the currents of the Tyrrhenian Sea. Faro lake is well known for the cultivation of mussels and this is part of a coastal habitat of particular interest, consisted of a peculiar biocenotic complex. Further, for the first time, significant different arrangement in the mussels cell structural organization was evidenced by simply following their highly reproducible Raman biomolecular signatures.

Combined Raman and polarization sensitive holographic imaging for a multimodal label-free assessment of human sperm function

Raman microspectroscopy (RM) and polarization sensitive digital holographic imaging (PSDHI) are valuable analytical tools in biological and medical research, allowing the detection of both biochemical and morphological variations of the sample without labels or long sample preparation. Here, using this multi-modal approach we analyze in vitro human sperm capacitation and the acrosome reaction induced by heparin. The multimodal microscopy provides morphofunctional information that can assess the sperms ability to respond to capacitation stimuli (sperm function). More precisely, the birefringence analysis in sperm cells can be used as an indicator of its structural normality. Indeed, digital holography applied for polarization imaging allows for revelation of the polarization state of the sample, showing a total birefringence of the sperm head in non-reacted spermatozoa, and a birefringence localized in the post-acrosomal region in reacted spermatozoa. Additionally, RM allows the detection and spectroscopic characterization of protein/lipid delocalization in the plasma and acrosomal membranes that can be used as valuable Raman biomarkers of sperm function. Interestingly, these spectral variations can be correlated with different time phases of the cell capacitation response. Although further experimentation is required, the proposed multimodal approach could represent a potential label-free diagnostic tool for use in reproductive medicine and the diagnosis of infertility.

Raman spectroscopy reveals LPS-induced changes of biomolecular composition in monocytic THP-1 cells in a label-free manner

The human innate immune system is able to recognize pathogen-associated molecular patterns like lipopolysaccharides (LPS) leading to the activation of signal cascades and the release of different cytokines. Activation of the immune cells can be assessed in different ways which are either indirect (ELISA of cytokine release), require staining protocols (flow cytometry) or lysis of the cells (mRNA analysis). Here, Raman spectroscopy as a non-destructive spectroscopic method is presented to enable direct and label-free monitoring of changes in cellular metabolism, biomolecular composition as well as morphology. Exemplarily, the potential of Raman spectroscopy is presented for the characterization of LPS-stimulation of monocytic THP-1 cells over a time course of 16 h. The cell culture stimulation model is characterized using gene transcription and expression of the two cytokines TNFα and IL-1β. After 1 h, 3 h, 8 h and 16 h specific Raman spectroscopic fingerprints are generated which encode cell activation pattern after TLR4 stimulation. Most prevalent changes in the spectra occur after 8 h, but slight differences are already detectable after 1 h. Spatially highly resolved Raman scans are used to generate false-color Raman images which provide spatial information of the biochemical state of the cells and changes over time. One of the most significant observed differences is an increase in Raman signal from DNA/RNA content in LPS-stimulated cells when compared to unstimulated cells. The systematic assignment of Raman spectroscopic profiles of LPS-activated cells to cellular activation assessed by cytokine gene transcription and expression opens new ways for label-free and direct immunological studies of specific pathogen recognizing receptors and their downstream signaling pathways.

Detection of the T cell activation state using nonlinear optical microscopy

The ability to monitor the activation state of T-cells during immunotherapy is of great importance. Although specific activation markers do exist, their abundance and complicated regulation cannot definitely define the activation state of the cells. Previous studies have shown that Third Harmonic Generation (THG) imaging could distinguish between activated versus resting microglia and healthy versus cancerous cells, mainly based on their lipid-body profiles. In the present study, mitogen or antigen-stimulated T-cells were subjected to THG imaging microscopy. Qualitative and quantitative analysis showed statistically significant increase of THG mean area and intensity in activated versus resting T-cells. The connection of THG imaging to chemical information was achieved using Raman spectroscopy, which showed significant differences between the activation processes and controls, correlating of THG signal area with cholesterol and lipid compounds, but not with triglycerides. The obtained results suggested a potential employment of nonlinear microscopy in evaluating of T-cell activation, which is expected to be largely appreciated in the clinical practice.

Characterization of Spatial Light Modulator Based on the Phase in Fourier Domain of the Hologram and Its Applications in Coherent Imaging

Although digital holography is used widely at present, the information contained in the digital hologram is still underutilized. For example, the phase values of the Fourier spectra of the hologram are seldom used directly. In this paper, we take full advantage of them for characterizing the phase modulation of a spatial light modulator (SLM). Incident plane light beam is divided into two beams, one of which passes the SLM and interferes with the other one. If an image with a single grey scale loads on the SLM, theoretical analysis proves that the phase of the Fourier spectra of the obtained hologram contains the added phase and a constant part relative to the optical distance. By subtracting the phase for the image with the grey scale of 0 from that for the image with other grey scales, the phase modulation can be characterized. Simulative and experimental results validate that the method is effective. The SLM after characterization is successfully used for coherent imaging, which reconfirms that this method is exact in practice. When compared to the traditional method, the new method is much faster and more convenient.

Towards automated cancer screening: Label-free classification of fixed cell samples using wavelength modulated Raman spectroscopy

The ability to provide quantitative, objective and automated pathological analysis would provide enormous benefits for national cancer screening programmes, in terms of both resource reduction and improved patient wellbeing. The move towards molecular pathology through spectroscopic methods shows great promise, but has been restricted by spectral quality, acquisition times and lack of direct clinical application. In this paper, we present the application of wavelength modulated Raman spectroscopy for the automated label- and fluorescence-free classification of fixed squamous epithelial cells in suspension, such as those produced during a cervical smear test. Direct comparison with standard Raman spectroscopy shows marked improvement of sensitivity and specificity when considering both human papillomavirus (sensitivity +12.0%, specificity +5.3%) and transformation status (sensitivity +10.3%, specificity +11.1%). Studies on the impact of intracellular sampling location and storage effects suggest that wavelength modulated Raman spectroscopy is sufficiently robust to be used in fixed cell classification, but requires further investigations of potential sources of molecular variation in order to improve current clinical tools.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.