Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

In situ real-time pathway to study the polyethylene long-term degradation process by a marine fungus through confocal Raman quantitative imaging

Since plastic waste has become a worldwide pollution problem, studying the ability of marine microorganisms to degrade plastic waste is important. However, conventional methods are unable to in situ real-time study the ability of microorganisms to biodegrade plastics. In recent years, Raman spectroscopy has been widely used in the characterization of plastics as well as in the study of biological metabolism due to its low cost, rapidity, label-free, non-destructive, and water-independent features, which provides us with new ideas to address the above limitations. Here, we have established a method to study the degradation ability of microorganisms on plastics using confocal Raman imaging. Alternaria alternata FB1, a recently reported polyethylene (PE) degrading marine fungus, is used as a model to perform a long-term (up to 274 days) in situ real-time nondestructive inspection of its degradation process. We can prove the degradation of PE plastics from the following two aspects, visualization and analysis of the degradation process based on depth imaging and quantification of the degradation rate by crystallinity calculations. The findings also reveal unprecedented degradation details. The method is important for realizing high-throughput screening of microorganisms with potential to degrade plastics and studying the degradation process of plastics in the future.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Upregulated Palmitoleate and Oleate Production in Escherichia coli Promotes Gentamicin Resistance

Metabolic reprogramming mediates antibiotic efficacy. However, metabolic adaptation of microbes evolving from antibiotic sensitivity to resistance remains undefined. Therefore, untargeted metabolomics was conducted to unveil relevant metabolic reprogramming and potential intervention targets involved in gentamicin resistance. In total, 61 metabolites and 52 metabolic pathways were significantly altered in gentamicin-resistant E. coli. Notably, the metabolic reprogramming was characterized by decreases in most metabolites involved in carbohydrate and amino acid metabolism, and accumulation of building blocks for nucleotide synthesis in gentamicin-resistant E. coli. Meanwhile, fatty acid metabolism and glycerolipid metabolism were also significantly altered in gentamicin-resistant E. coli. Additionally, glycerol, glycerol-3-phosphate, palmitoleate, and oleate were separately defined as the potential biomarkers for identifying gentamicin resistance in E. coli. Moreover, palmitoleate and oleate could attenuate or even abolished killing effects of gentamicin on E. coli, and separately increased the minimum inhibitory concentration of gentamicin against E. coli by 2 and 4 times. Furthermore, palmitoleate and oleate separately decreased intracellular gentamicin contents, and abolished gentamicin-induced accumulation of reactive oxygen species, indicating involvement of gentamicin metabolism and redox homeostasis in palmitoleate/oleate-promoted gentamicin resistance in E. coli. This study identifies the metabolic reprogramming, potential biomarkers and intervention targets related to gentamicin resistance in bacteria.

VHL mutation drives human clear cell renal cell carcinoma progression through PI3K/AKT-dependent cholesteryl ester accumulation

BACKGROUND

Cholesteryl ester (CE) accumulation in intracellular lipid droplets (LDs) is an essential signature of clear cell renal cell carcinoma (ccRCC), but its molecular mechanism and pathological significance remain elusive.

METHODS

Enabled by the label-free Raman spectromicroscopy, which integrated stimulated Raman scattering microscopy with confocal Raman spectroscopy on the same platform, we quantitatively analyzed LD distribution and composition at the single cell level in intact ccRCC cell and tissue specimens in situ without any processing or exogenous labeling. Since we found that commonly used ccRCC cell lines actually did not show the CE-rich signature, primary cancer cells were isolated from human tissues to retain the lipid signature of ccRCC with CE level as high as the original tissue, which offers a preferable cell model for the study of cholesterol metabolism in ccRCC. Moreover, we established a patient-derived xenograft (PDX) mouse model that retained the CE-rich phenotype of human ccRCC.

FINDINGS

Surprisingly, our results revealed that CE accumulation was induced by tumor suppressor VHL mutation, the most common mutation of ccRCC. Moreover, VHL mutation was found to promote CE accumulation by upregulating HIFα and subsequent PI3K/AKT/mTOR/SREBPs pathway. Inspiringly, inhibition of cholesterol esterification remarkably suppressed ccRCC aggressiveness in vitro and in vivo with negligible toxicity, through the reduced membrane cholesterol-mediated downregulations of integrin and MAPK signaling pathways.

INTERPRETATION

Collectively, our study improves current understanding of the role of CE accumulation in ccRCC and opens up new opportunities for treatment.

FUNDING

This work was supported by National Natural Science Foundation of China (No. U23B2046 and No. 62027824), National Key R&D Program of China (No. 2023YFC2415500), Fundamental Research Funds for the Central Universities (No. YWF-22-L-547), PKU-Baidu Fund (No. 2020BD033), Peking University First Hospital Scientific and Technological Achievement Transformation Incubation Guidance Fund (No. 2022CX02), and Beijing Municipal Health Commission (No. 2020-2Z-40713).

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

Stimulated Raman Scattering Microscopy Reveals Aberrant Triglyceride Accumulation in Lymphatic Metastasis of Papillary Thyroid Carcinoma

Lipid metabolic alterations are known to play a crucial role in cancer metastasis. As a key hub in lipid metabolism, intracellular neutral lipid accumulation in lipid droplets (LDs) has become a signature of aggressive human cancers. Nevertheless, it remains unclear whether lipid accumulation displays distinctive features in metastatic lesions compared to the primary ones. Here, we integrated multicolor stimulated Raman scattering (SRS) imaging with confocal Raman spectroscopy on the same platform to quantitatively analyze the amount and composition of LDs in intact human thyroid tissues in situ without any processing or labeling. Inspiringly, we found aberrant accumulation of triglycerides (TGs) in lymphatic metastases but not in normal thyroid, primary papillary thyroid carcinoma (PTC), or normal lymph node. In addition, the unsaturation degree of unsaturated TGs was significantly higher in the lymphatic metastases from patients diagnosed with late-stage (T3/T4) PTC compared to those of patients diagnosed with early-stage (T1/T2) PTC. Furthermore, both public sequencing data analysis and our RNA-seq transcriptomic experiment showed significantly higher expression of alcohol dehydrogenase-1B (ADH1B), which is critical to lipid uptake and transport, in lymphatic metastases relative to the primary ones. In summary, these findings unravel the lipid accumulation as a novel marker and therapeutic target for PTC lymphatic metastasis that has a poor response to the regular radioactive iodine therapy.

Elucidating Iron Metabolism through Molecular Imaging

Iron is essential for many physiological processes, and the dysregulation of its metabolism is implicated in the pathogenesis of various diseases. Recent advances in iron metabolism research have revealed multiple complex pathways critical for maintaining iron homeostasis. Molecular imaging, an interdisciplinary imaging technique, has shown considerable promise in advancing research on iron metabolism. Here, we comprehensively review the multifaceted roles of iron at the cellular and systemic levels (along with the complex regulatory mechanisms of iron metabolism), elucidate appropriate imaging methods, and summarize their utility and fundamental principles in diagnosing and treating diseases related to iron metabolism. Utilizing molecular imaging technology to deeply understand the complexities of iron metabolism and its critical role in physiological and pathological processes offers new possibilities for early disease diagnosis, treatment monitoring, and the development of novel therapies. Despite technological limitations and the need to ensure the biological relevance and clinical applicability of imaging results, molecular imaging technology's potential to reveal the iron metabolic process is unparalleled, providing new insights into the link between iron metabolism abnormalities and various diseases.

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF -mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.

Establishment of a Method for the Introduction of Poorly Water-Soluble Drugs in Cells and Evaluation of Intracellular Concentration Distribution Using Resonance Raman Imaging

Label-free measurement is essential to understand the metabolism of drug molecules introduced into cells. Raman imaging is a powerful method to investigate intracellular drug molecules because it provides in situ label-free observation of introduced molecules. In this study, we propose that Raman imaging can be used not only to observe the intracellular distribution of drug molecules but also to quantitatively visualize the concentration distribution reflecting each organelle in a single living cell using the Raman band of extracellular water as an intensity standard. We dissolved poorly water-soluble all-trans-retinoic acid (ATRA) in water using a cytocompatible amphiphilic phospholipid polymer, poly[2-methacryloyloxyethyl phosphorylcholine-co-n-butyl methacrylate] (PMB) as a solubilizing reagent, introduced it into cells, and obtained the intracellular concentration distribution of ATRA. ATRA was concentrated in the cells and mainly localized to mitochondria and lipid droplets, interacting strongly with mitochondria and weakly with lipid droplets. Poorly water-soluble β-carotene was also introduced into cells using PMB but was not concentrated intracellularly, indicating that β-carotene does not interact specifically with intracellular molecules. We established a protocol for the solubilization and intracellular uptake of poorly water-soluble molecules using PMB and obtaining their concentration distribution using Raman microscopy.

Spectral fingerprinting of cellular lipid droplets using stimulated Raman scattering microscopy and chemometric analysis

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful method for direct visualisation and compositional analysis of cellular lipid droplets. Here we report the application of spectral phasor analysis as a convenient method for the segmentation of lipid droplets using the hyperspectral SRS spectrum in the high wavenumber and fingerprint region of the spectrum. Spectral phasor analysis was shown to discriminate six fatty acids based on vibrational spectroscopic features in solution. The methodology was then applied to studying fatty acid metabolism and storage in a mammalian cancer cell model and during drug-induced steatosis in a hepatocellular carcinoma cell model. The accumulation of fatty acids into cellular lipid droplets was shown to vary as a function of the degree of unsaturation, whilst in a model of drug-induced steatosis, the detection of increased saturated fatty acid esters was observed. Taking advantage of the fingerprint and high wavenumber regions of the SRS spectrum has yielded a greater insight into lipid droplet composition in a cellular context. This approach will find application in the label-free profiling of intracellular lipids in complex disease models.

Episymbiotic Saccharibacteria induce intracellular lipid droplet production in their host bacteria

Saccharibacteria (formerly TM7) are a group of widespread and genetically diverse ultrasmall bacteria with highly reduced genomes that belong to Candidate Phyla Radiation, a large monophyletic lineage with poorly understood biology. Nanosynbacter lyticus type strain TM7x is the first Saccharibacteria member isolated from the human oral microbiome. With restrained metabolic capacities, TM7x lives on the surface of, and forms an obligate episymbiotic relationship with its bacterial host, Schaalia odontolytica strain XH001. The symbiosis allows TM7x to propagate but presents a burden to host bacteria by inducing stress response. Here, we employed super-resolution fluorescence imaging to investigate the physical association between TM7x and XH001. We showed that the binding with TM7x led to a substantial alteration in the membrane fluidity of XH001. We also revealed the formation of intracellular lipid droplets in XH001 when forming episymbiosis with TM7x, a feature that has not been reported in oral bacteria. The TM7x-induced lipid droplets accumulation in XH001 was confirmed by label-free Raman spectroscopy, which also unveiled additional phenotypical features when XH001 cells are physically associated with TM7x. Further exploration through culturing XH001 under various stress conditions showed that lipid droplets accumulation was a general response to stress. A survival assay demonstrated that the presence of lipid droplets plays a protective role in XH001, enhancing its survival under adverse conditions. In conclusion, our study sheds new light on the intricate interaction between Saccharibacteria and their host bacteria, highlighting the potential benefit conferred by TM7x to its host and further emphasizing the context-dependent nature of symbiotic relationships.

Chemical Approaches for Measuring and Manipulating Lipids at the Organelle Level

As the products of complex and often redundant metabolic pathways, lipids are challenging to measure and perturb using genetic tools. Yet by virtue of being the major constituents of cellular membranes, lipids are highly regulated in space and time. Chemists have stepped into this methodological void, developing an array of techniques for the precise quantification and manipulation of lipids at the subcellular, organelle level. Here, we survey the landscape of these methods. For measuring lipids, we summarize the use of metabolic labeling and click chemistry tagging, photoaffinity labeling, isotopic tagging for Raman microscopy, and chemoenzymatic labeling for tracking lipid production and interorganelle transport. For perturbing lipids, we describe synthetic photocaged lipids and membrane editing approaches using optogenetic enzymes for precise manipulation of lipid signaling. Collectively, these chemical and biochemical tools are revealing phenomena and mechanisms underlying lipid functions at the subcellular level.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Imagining the future of optical microscopy: everything, everywhere, all at once

The optical microscope has revolutionized biology since at least the 17th Century. Since then, it has progressed from a largely observational tool to a powerful bioanalytical platform. However, realizing its full potential to study live specimens is hindered by a daunting array of technical challenges. Here, we delve into the current state of live imaging to explore the barriers that must be overcome and the possibilities that lie ahead. We venture to envision a future where we can visualize and study everything, everywhere, all at once - from the intricate inner workings of a single cell to the dynamic interplay across entire organisms, and a world where scientists could access the necessary microscopy technologies anywhere.

Stimulated Raman photothermal microscopy toward ultrasensitive chemical imaging

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics, and couplings in complex systems. However, the sensitivity of SRS is fundamentally limited to the millimolar level due to shot noise and the small modulation depth. To overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrationally excited states. The subsequent relaxation heats up the surroundings and induces refractive index changes. By probing the refractive index changes with a laser beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved. The versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. SRP microscopy opens a way to perform vibrational spectroscopic imaging with ultrahigh sensitivity.

Impact of Airborne Pathogen-Derived Extracellular Vesicles on Macrophages Revealed by Raman Spectroscopy and Multiomics

Long-term exposure to the indoor environment may pose threats to human health due to the presence of pathogenic bacteria and their byproducts. Nanoscale extracellular vesicles (EVs) extensively secreted from pathogenic bacteria can traverse biological barriers and affect physio-pathological processes. However, the potential health impact of EVs from indoor dust and the underlying mechanisms remain largely unexplored. Here, Raman spectroscopy combined with multiomics (genomics and proteomics) was used to address these issues. Genomic analysis revealed that Pseudomonas was an efficient producer of EVs that harbored 68 types of virulence factor-encoding genes. Upon exposing macrophages to environmentally relevant doses of Pseudomonas aeruginosa PAO1-derived EVs, macrophage internalization was observed, and release of inflammatory factors was determined by RT-PCR. Subsequent Raman spectroscopy and unsupervised surprisal analysis of EV-affected macrophages distinguished metabolic alterations, particularly in proteins and lipids. Proteomic analysis further revealed differential expression of proteins in inflammatory and metabolism-related pathways, indicating that EV exposure induced macrophage metabolic reprogramming and inflammation. Collectively, our findings revealed that pathogen-derived EVs in the indoor environments can act as a new mediator for pathogens to exert adverse health effects. Our method of Raman integrated with multiomics offers a complementary approach for rapid and in-depth understanding of EVs' impact.

Revealing the Preventable Effects of Fu-Zheng-Qu-Xie Decoction against Recurrence and Metastasis of Postoperative Early-Stage Lung Adenocarcinoma Based on Network Pharmacology Coupled with Metabolomics Analysis

Fu-Zheng-Qu-Xie (FZQX) decoction is a traditional Chinese herbal prescription for the treatment of lung cancer and exerts proapoptotic and immunomodulatory effects. It has been clinically suggested to be effective in improving the survival of postoperative early-stage lung adenocarcinoma (LUAD), but the mechanism remains unclear. In this study, we used network pharmacology coupled with metabolomics approaches to explore the pharmacological action and effective mechanism of FZQX against the recurrence and metastasis of postoperative early-stage LUAD. Network pharmacology analysis showed that FZQX could prevent the recurrence and metastasis of postoperative early-stage LUAD by regulating a series of targets involving vascular endothelial growth factor receptor 2, estrogen receptor 1, sarcoma gene, epidermal growth factor receptor, and protein kinase B and by influencing the Ras, PI3K-Akt, and mitogen-activated protein kinase signaling pathways. In liquid chromatography-mass spectrometry analysis, 11 differentially expressed metabolites, including PA(12:0/18:4(6Z,9Z,12Z,15Z)), PC(16:0/0:0)[U], LysoPC(18:1(11Z)), and LysoPC(18:0), were discovered in the FZQX-treated group compared to those in the model group before treatment or normal group. They were enriched in cancer metabolism-related signaling pathways such as central carbon metabolism in cancer, choline metabolism, and glycerol phospholipid metabolism. Collectively, our results suggest that the multicomponent and multitarget interaction network of FZQX inhibits the recurrence and metastasis of postoperative early-stage LUAD by activating the receptor signal transduction pathway to inhibit proliferation, induce cell apoptosis, inhibit aerobic glycolysis, and reprogram tumor lipid metabolism.

Multistage microfluidic cell sorting method and chip based on size and stiffness

High performance sorting of circulating tumor cells (CTCs) from peripheral blood is key to liquid biopsies. Size-based deterministic lateral displacement (DLD) technique is widely used in cell sorting. But conventional microcolumns have poor fluid regulation ability, which limits the sorting performance of DLD. When the size difference between CTCs and leukocytes is small (e.g., less than 3 μm), not only DLD, many size-based separation techniques fail due to low specificity. CTCs have been confirmed to be softer than leukocytes, which could serve as a basis for sorting. In this study, we presented a multistage microfluidic CTCs sorting method, first sorting CTCs using a size-based two-array DLD chip, then purifying CTCs mixed by leukocytes using a stiffness-based cone channel chip, and finally identifying cell types using Raman techniques. The entire CTCs sorting and analysis process was label free, highly pure, high-throughput and efficient. The two-array DLD chip employed a droplet-shaped microcolumn (DMC) developed by optimization design rather than empirical design. Attributed to the excellent fluid regulation capability of DMC, the CTCs sorter system developed by parallelizing four DMC two-array DLD chips was able to process a sample of 2.5 mL per minute with a recovery efficiency of 96.30 ± 2.10% and a purity of 98.25 ± 2.48%. To isolate CTCs mixed dimensionally by leukocytes, a cone channel sorting method and chip were developed based on solid and hydrodynamic coupled analysis. The cone channel chip allowed CTCs to pass through the channel and entrap leukocytes, improving the purity of CTCs mixed by leukocytes by 1.8-fold.

Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution

Spatial transcriptomics of histological sections have revolutionized research in life sciences and enabled unprecedented insights into genetic processes involved in tissue reorganization. However, in contrast to genomic analysis, the actual biomolecular composition of the sample has fallen behind, leaving a gap of potentially highly valuable information. Raman microspectroscopy provides untargeted spatiomolecular information at high resolution, capable of filling this gap. In this study we demonstrate spatially resolved Raman "spectromics" to reveal homogeneity, heterogeneity and dynamics of cell matrix on molecular levels by repurposing state-of-the-art bioinformatic analysis tools commonly used for transcriptomic analyses. By exploring sections of murine myocardial infarction and cardiac hypertrophy, we identify myocardial subclusters when spatially approaching the pathology, and define the surrounding metabolic and cellular (immune-) landscape. Our innovative, label-free, non-invasive "spectromics" approach could therefore open perspectives for a profound characterization of histological samples, while additionally allowing the combination with consecutive downstream analyses of the very same specimen.

Stimulated Raman Photothermal Microscopy towards Ultrasensitive Chemical Imaging

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics and coupling in a complex system. However, the bond-detection sensitivity of SRS microscopy is fundamentally limited to milli-molar level due to the shot noise and the small modulation depth in either pump or Stokes beam4. Here, to overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrational excited states. The thereafter relaxation heats up the surrounding and induces a change in refractive index. By probing the refractive index change with a continuous wave beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved on dimethyl sulfide with conserved average power. Versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. With much improved signal to noise ratio compared to SRS, SRP microscopy opens a new way to perform vibrational spectroscopic imaging with ultrahigh sensitivity and minimal water absorption.

Episymbiotic bacterium induces intracellular lipid droplet production in its host bacteria

Saccharibacteria (formerly TM7) Nanosynbacter lyticus type strain TM7x exhibits a remarkably compact genome and an extraordinarily small cell size. This obligate epibiotic parasite forms a symbiotic relationship with its bacterial host, Schaalia odontolytica, strain XH001 (formerly Actinomyces odontolyticus strain XH001). Due to its limited genome size, TM7x possesses restrained metabolic capacities, predominantly living on the surface of its bacterial host to sustain this symbiotic lifestyle. To comprehend this intriguing, yet understudied interspecies interaction, a thorough understanding of the physical interaction between TM7x and XH001 is imperative. In this study, we employed super-resolution fluorescence imaging to investigate the physical association between TM7x and XH001. We found that the binding with TM7x led to a substantial alteration in the membrane fluidity of the host bacterium XH001. Unexpectedly, we revealed the formation of intracellular lipid droplets in XH001 when forming episymbiosis with TM7x, a feature not commonly observed in oral bacteria cells. The TM7x-induced LD accumulation in XH001 was further confirmed by label-free non-invasive Raman spectroscopy, which also unveiled additional phenotypical features when XH001 cells are physically associated with TM7x. Further exploration through culturing host bacterium XH001 alone under various stress conditions showed that LD accumulation was a general response to stress. Intriguingly, a survival assay demonstrated that the presence of LDs likely plays a protective role in XH001, enhancing its overall survival under adverse conditions. In conclusion, our study sheds new light on the intricate interaction between Saccharibacteria and its host bacterium, highlighting the potential benefit conferred by TM7x to its host, and further emphasizing the context-dependent nature of symbiotic relationships.

Genomic Mysteries of Giant Bacteria: Insights and Implications

Bacteria and Archaea are traditionally regarded as organisms with a simple morphology constrained to a size of 2-3 µm. Nevertheless, the history of microbial research is rich in the description of giant bacteria exceeding tens and even hundreds of micrometers in length or diameter already from its early days, for example, Beggiatoa spp., to the present, for example, Candidatus Thiomargarita magnifica. While some of these giants are still being studied, some were lost to science, with merely drawings and photomicrographs as evidence for their existence. The physiology and biogeochemical role of giant bacteria have been studied, with a large focus on those involved in the sulfur cycle. With the onset of the genomic era, no special emphasis has been given to this group, in an attempt to gain a novel, evolutionary, and molecular understanding of the phenomenon of bacterial gigantism. The few existing genomic studies reveal a mysterious world of hyperpolyploid bacteria with hundreds to hundreds of thousands of chromosomes that are, in some cases, identical and in others, extremely different. These studies on giant bacteria reveal novel organelles, cellular compartmentalization, and novel mechanisms to combat the accumulation of deleterious mutations in polyploid bacteria. In this perspective paper, we provide a brief overview of what is known about the genomics of giant bacteria and build on that to highlight a few burning questions that await to be addressed.

Intracellular spatiotemporal metabolism in connection to target engagement

The metabolism in eukaryotic cells is a highly ordered system involving various cellular compartments, which fluctuates based on physiological rhythms. Organelles, as the smallest independent sub-cell unit, are important contributors to cell metabolism and drug metabolism, collectively designated intracellular metabolism. However, disruption of intracellular spatiotemporal metabolism can lead to disease development and progression, as well as drug treatment interference. In this review, we systematically discuss spatiotemporal metabolism in cells and cell subpopulations. In particular, we focused on metabolism compartmentalization and physiological rhythms, including the variation and regulation of metabolic enzymes, metabolic pathways, and metabolites. Additionally, the intricate relationship among intracellular spatiotemporal metabolism, metabolism-related diseases, and drug therapy/toxicity has been discussed. Finally, approaches and strategies for intracellular spatiotemporal metabolism analysis and potential target identification are introduced, along with examples of potential new drug design based on this.

Coherent Raman scattering microscopy of lipid droplets in cells and tissues

Lipid droplets (LDs) play a key role as the hub for lipid metabolism to maintain cellular metabolic homeostasis. Understanding the functions and changes of LDs in different pathological conditions is crucial for identifying new markers for diagnosis and discovering new targets for treatment. In recent years, coherent Raman scattering (CRS) microscopy has been popularized for the imaging and quantification of LDs in live cells. Compared to spontaneous Raman scattering microscopy, CRS microscopy offers a much higher imaging speed while maintaining similar chemical information. Due to the high lipid density, LDs usually have strong CRS signals and therefore are the most widely studied organelle in the CRS field. In this review, we discuss recent achievements using CRS to study the quantity, distribution, composition, and dynamics of LDs in various systems.

Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity

Metabolic reprogramming in a subpopulation of cancer cells is a hallmark of tumor chemoresistance. However, single-cell metabolic profiling is difficult because of the lack of a method that can simultaneously detect multiple metabolites at the single-cell level. In this study, through hyperspectral stimulated Raman scattering (hSRS) imaging in the carbon-hydrogen (C-H) window and sparsity-driven hyperspectral image decomposition, we demonstrate a high-content hSRS (h2SRS) imaging approach that enables the simultaneous mapping of five major biomolecules, including proteins, carbohydrates, fatty acids, cholesterol, and nucleic acids at the single-cell level. h2SRS imaging of brain and pancreatic cancer cells under chemotherapy revealed acute and adapted chemotherapy-induced metabolic reprogramming and the unique metabolic features of chemoresistance. Our approach is expected to facilitate the discovery of therapeutic targets to combat chemoresistance. This study illustrates a high-content, label-free chemical imaging approach that measures metabolic profiles at the single-cell level and warrants further research on cellular metabolism.

Label-Free Raman Spectromicroscopy Unravels the Relationship between MGMT Methylation and Intracellular Lipid Accumulation in Glioblastoma

Temozolomide (TMZ) is considered a first line chemotherapy drug for glioblastoma (GBM). Unfortunately, the GBM without methylation of O6-methylguanine-DNA methyltransferase (MGMT), accounting for about 70% of all GBM, shows an inherent resistance to TMZ treatment. Aberrant accumulation of neutral lipids, primarily triglycerides (TGs) and cholesteryl esters (CEs), in lipid droplets (LDs) has been recognized as metabolic vulnerability for GBM therapy. However, it is not known whether MGMT methylation affects lipid accumulation in GBM. Herein, we employed label-free Raman spectromicroscopy, which integrated stimulated Raman scattering (SRS) microscopy and confocal Raman spectroscopy, to quantitatively analyze both the amount and composition of intracellular LDs in intact GBM tissues obtained from patients who had undergone resection surgery. Our results showed significant reductions in both the LD amount and the CE percentage in MGMT unmethylated GBMs (MGMT methylation < 15%) compared to MGMT methylated ones (MGMT methylation ≥ 15%). Due to a big variation of lipid accumulation in the MGMT methylated GBMs, these patients were further divided into hypermethylated group (MGMT methylation ≥ 50%) and intermediate-methylated group (MGMT methylation 15∼50%), according to the significantly different median survival rates of these two groups. Remarkable differences in LD amount, CE percentage, and also lipid saturation degree were found between the hypermethylated group and the other two groups, but not between the unmethylated and intermediate-methylated groups. To elucidate the possible underlying mechanism, we analyzed the differential expression of lipid metabolism-related genes in GBM with different levels of MGMT methylation using The Cancer Genome Atlas Program (TCGA) dataset. It was shown that the genes related to lipid oxidation and lipid efflux were upregulated, and the genes related to lipid synthesis were downregulated in unmethylated group. These findings unravel the relationship between MGMT methylation and lipid accumulation in GBM, which may offer new opportunities for the diagnosis and treatment of TMZ-resistant GBM.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

Raman Imaging Reveals Insights into Membrane Phase Biophysics in Cells

Cellular membranes are essential components of all living organisms. They are composed of a complex mixture of lipids with diverse chemical structures and crucial biological functions. The dynamic and heterogeneous nature of cellular membranes presents a challenge for studying their biophysical properties and organization in vivo. Raman imaging, particularly coherent Raman scattering techniques such as stimulated Raman scattering (SRS) microscopy, have emerged as powerful tools for studying cellular membranes with high spatial and temporal resolution and minimal perturbation. In this Review, we discuss the scientific importance and technical challenges of characterizing membrane composition in cellular contexts and how the advances of Raman imaging can provide unique insights into membrane phase behavior and organization. We also highlight recent applications of Raman imaging in studying cellular membranes and implications in diseases. In particular, the discovery of phase separation and a solid-phase intracellular membrane on endoplasmic reticulum is reviewed in detail, shedding light on the biology of lipotoxicity.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Analytical Techniques for Single-Cell Biochemical Assays of Lipids

Lipids are essential cellular components forming membranes, serving as energy reserves, and acting as chemical messengers. Dysfunction in lipid metabolism and signaling is associated with a wide range of diseases including cancer and autoimmunity. Heterogeneity in cell behavior including lipid signaling is increasingly recognized as a driver of disease and drug resistance. This diversity in cellular responses as well as the roles of lipids in health and disease drive the need to quantify lipids within single cells. Single-cell lipid assays are challenging due to the small size of cells (∼1 pL) and the large numbers of lipid species present at concentrations spanning orders of magnitude. A growing number of methodologies enable assay of large numbers of lipid analytes, perform high-resolution spatial measurements, or permit highly sensitive lipid assays in single cells. Covered in this review are mass spectrometry, Raman imaging, and fluorescence-based assays including microscopy and microseparations.

Spectral focusing-based stimulated Raman scattering microscopy using compact glass blocks for adjustable dispersion

Spectral focusing is a well-established technique for increasing spectral resolution in coherent Raman scattering microscopy. However, current methods for tuning optical chirp in setups using spectral focusing, such as glass rods, gratings, and prisms, are very cumbersome, time-consuming to use, and difficult to align, all of which limit more widespread use of the spectral focusing technique. Here, we report a stimulated Raman scattering (SRS) configuration which can rapidly tune optical chirp by utilizing compact adjustable-dispersion TIH53 glass blocks. By varying the height of the blocks, the number of bounces in the blocks and therefore path length of the pulses through the glass can be quickly modulated, allowing for a convenient method of adjusting chirp with almost no necessary realignment. To demonstrate the flexibility of this configuration, we characterize our system's signal-to-noise ratio and spectral resolution at different chirp values and perform imaging in both the carbon-hydrogen stretching region (MCF-7 cells) and fingerprint region (prostate cores). Our findings show that adjustable-dispersion glass blocks allow the user to effortlessly modify their optical system to suit their imaging requirements. These blocks can be used to significantly simplify and miniaturize experimental configurations utilizing spectral focusing.

Mechanism of Cytotoxic Action of Gold Nanorods Photothermal Therapy for A549 Cell

Photothermal therapy has developed into an important field of tumor treatment research, and numerous studies have focused on the preparation of photothermal therapeutic agents, tumor targeting, diagnosis, and treatment integration. However, there are few studies on the mechanism of photothermal therapy acting on cancer cells. Here we investigated the metabolomics of lung cancer cell A549 during gold nanorod (GNR) photothermal treatment by high-resolution LC/MS, and several differential metabolites and corresponding metabolic pathways during photothermal therapy were found. The main differential metabolites contained 18-hydroxyoleate, beta-alanopine and cis-9,10-epoxystearic acid, and phosphorylcholine. Pathway analysis also showed metabolic changes involving cutin, suberine, and wax biosynthesis, pyruvate and glutamic acid synthesis, and choline metabolism. Analysis also showed that the photothermal process of GNRs may induce cytotoxicity by affecting pyruvate and glutamate synthesis, normal choline metabolism, and ultimately apoptosis.

Stimulated Raman Scattering Imaging Sheds New Light on Lipid Droplet Biology

A lipid droplet (LD) is a dynamic organelle closely associated with cellular functions and energy homeostasis. Dysregulated LD biology underlies an increasing number of human diseases, including metabolic disease, cancer, and neurodegenerative disorder. Commonly used lipid staining and analytical tools have difficulty providing the information regarding LD distribution and composition at the same time. To address this problem, stimulated Raman scattering (SRS) microscopy uses the intrinsic chemical contrast of biomolecules to achieve both direct visualization of LD dynamics and quantitative analysis of LD composition with high molecular selectivity at the subcellular level. Recent developments of Raman tags have further enhanced sensitivity and specificity of SRS imaging without perturbing molecular activity. With these advantages, SRS microscopy has offered great promise for deciphering LD metabolism in single live cells. This article overviews and discusses the latest applications of SRS microscopy as an emerging platform to dissect LD biology in health and disease.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Raman spectroscopy as a promising diagnostic method for rheumatoid arthritis

Background: Diagnosis of rheumatoid arthritis (RA) basically relies on clinical symptoms and autoantibodies, especially anti-citrullinated protein antibodies (ACPAs) and rheumatoid factor (RF). However, the lack of autoantibodies is still a dilemma clinically in seronegative RA, especially in the early stage of the disease. This study aimed to provide a unique disease fingerprint with high diagnostic value to discriminate RA based on Raman spectroscopy. Methods: Raman spectroscopy provides a repertoire of biomolecules in serum from RA. Multivariate dimension-reducing methods and machine-learning algorithms were exploited to reveal the intrinsic differences and the potential discrimination power. The underlying differential biomolecules were retrieved by the assignment of Raman peaks. Moreover, the correlations between the spectral differences and RA patient's clinical and immunological manifestations were also analyzed. Results: RA patients exhibited unique Raman spectra characterized by biomolecular alterations during the disease progression. The discrimination power yielded 97.3% sensitivity and 94.8% specificity for RA diagnosis. In the recognition of ACPA-negative RA, the sensitivity and specificity also reached 95.6% and 92.8%, respectively. In particular, the differential Raman spectrum peaks of RA patients mainly represented lipids, amino acids, glycogen, and fatty acids. Further analysis showed that the different serum Raman spectra correlated with the clinical features of RA, including disease duration, RF, anticyclic citrullinated peptide antibodies (anti-CCPs), IgA, IgM, IgG, tender joint count, and swollen joint count (|rs| = 0.15-0.52, p < 0.05). Conclusions: Raman spectroscopy was revealed to be a promising diagnostic method for RA, especially for ACPA-negative patients.

Raman Spectroscopy as a Tool to Study the Pathophysiology of Brain Diseases

The Raman phenomenon is based on the spontaneous inelastic scattering of light, which depends on the molecular characteristics of the dispersant. Therefore, Raman spectroscopy and imaging allow us to obtain direct information, in a label-free manner, from the chemical composition of the sample. Since it is well established that the development of many brain diseases is associated with biochemical alterations of the affected tissue, Raman spectroscopy and imaging have emerged as promising tools for the diagnosis of ailments. A combination of Raman spectroscopy and/or imaging with tagged molecules could also help in drug delivery and tracing for treatment of brain diseases. In this review, we first describe the basics of the Raman phenomenon and spectroscopy. Then, we delve into the Raman spectroscopy and imaging modes and the Raman-compatible tags. Finally, we center on the application of Raman in the study, diagnosis, and treatment of brain diseases, by focusing on traumatic brain injury and ischemia, neurodegenerative disorders, and brain cancer.

The role of altered lipid composition and distribution in liver fibrosis revealed by multimodal nonlinear optical microscopy

Intracellular lipid accumulation is commonly seen in fibrotic livers, but its exact role in liver fibrosis remains elusive. Here, we established a multimodal nonlinear optical microscopy to quantitatively map distribution of biomolecules in fibrotic livers. Our data revealed that unsaturated triglycerides were predominantly accumulated in central vein area during liver fibrosis but not in portal vein area. Moreover, the lipid homeostasis was remarkably dysregulated in the late-stage compared to the early-stage fibrosis, including increased unsaturated triglycerides with decreased lipid unsaturation degree and decreased membrane fluidity. Such alterations were likely due to up-regulated lipogenesis, desaturation, and peroxidation, which consequently led to endoplasmic reticulum stress and cell death. Inspiringly, injured hepatocyte could be rescued by remodeling lipid homeostasis via either supply of unsaturated fatty acids or enhancement of membrane fluidity. Collectively, our study improves current understanding of the role of lipid homeostasis in fibrosis and open opportunities for treatment.

Pterostilbene-Mediated Inhibition of Cell Proliferation and Cell Death Induction in Amelanotic and Melanotic Melanoma

Melanoma is one of the fastest-growing cancers worldwide. Treatment of advanced melanoma is very difficult; therefore, there is growing interest in the identification of new therapeutic agents. Pterostilbene is a natural stilbene that has been found to have several pharmacological activities. The aim of this study was to evaluate the influence of pterostilbene on the proliferation and apoptosis of human melanoma cells. Proliferation of pterostilbene-treated amelanotic (C32) and melanotic (A2058) melanoma cells was determined by BRDU assay. Flow cytometric analyses were used to determine cell cycle progression, and further molecular investigations were performed using real-time RT-qPCR. The expression of the p21 protein and the DNA fragmentation assay were determined by the ELISA method. The results revealed that pterostilbene reduced the proliferation of both amelanotic and melanotic melanoma cells. Pterostilbene induced apoptosis in amelanotic C32 melanoma cells, and this effect was mediated by an increase in the expression of the BAX, CASP9, and CASP9 genes; induction of caspase 3 activity; and DNA degradation. Pterostilbene did not affect the activation of apoptosis in the A2058 cell line. It may be concluded that pterostilbene has anticancer potential against human melanoma cells; however, more studies are still needed to fully elucidate the effects of pterostilbene on amelanotic and melanotic melanoma cells.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

Raman spectroscopy differ leukemic cells from their healthy counterparts and screen biomarkers in acute leukemia

Precision medicine is important in the treatment of acute leukemia (AL). The target therapies of AL provide an opportunity to reduce the mortality of AL. How AL cells differ from their healthy counterparts is the basis for the development of therapies and the outcome of AL patients. Therefore, a label-free and noninvasive single-cell Raman platform was used to characterize cell molecular profiles and found potential biomarkers from three healthy people and twelve AL patients with more than 90% accuracy. We analyzed myeloblasts, abnormal promyelocytes, monoblasts and B-ALL cells respectively, compared with their healthy counterparts, which could be distinguished by their intrinsic phenotypic Raman spectra using orthogonal partial least squares discriminate analysis (OPLS-DA). Most importantly, we selected statistically significant markers of the four leukemia models. Further analysis of leukemic granulocytes, we found that a combination of the 1003, 1341 and 1579 cm^-1 Raman peaks could discriminate myeloblasts and abnormal promyelocytes from normal granulocytes. The assignments of 1579 cm^-1 gave us a clue to find potential important variables myeloperoxidase related with AL diagnosis. Our study demonstrates the capability of the Raman platform to characterize leukemia cells with non-invasively probing metabolites. The biomarker we identified could be extensible to other blood cells and potentially have a high impact on leukemia therapy.