Quantitative volumetric Raman imaging of three dimensional cell cultures

Enhanced cellular viability and osteogenic activity in oxygen-self-generating and magnetically responsive alginate microgels as advanced cell carriers

Improving the accuracy of in vitro three-dimensional (3D) cellular cultures more closely replicates the in vivo microenvironment by mimicking the complex tissue structures, enhancing cell-cell interactions, and increasing differentiation potential along with functional capabilities. Natural materials aid in cell adhesion and proliferation within the 3D matrix, providing a more realistic growth environment. Oxygen availability is also critical for cell survival in 3D cultures, as a lack of oxygen can impede proliferation, reduce functionality, and ultimately result in cell death. To address the issue of oxygen supply in such systems, a novel magnetic alginate-based microcarrier that generates oxygen autonomously has been developed. This microcarrier contains calcium peroxide encapsulated within polylactic acid microspheres (CP), which act as an internal oxygen reservoir. The release of calcium ions results in weak interactions with alginate, thus improving structural integrity while also supporting bone marrow stromal cells (BMSCs) and creating a more in vivo-like microenvironment. Notably, when exposed to an external magnetic field, BMSCs on these CP/Fe3O4/SA microcarriers show improved viability, a marked decrease in hypoxia-inducible factor-1α (HIF-1α), and increased osteogenic gene expression. Therefore, the CP/Fe3O4/SA microcarriers represent a promising approach for enhancing in vitro 3D culture methods and offer significant potential for tissue repair and regeneration.

Noninvasive and in situ identification of the phenotypes and differentiation stages of individual living cells entrapped within hydrogels

Microscale screening platforms that allow cells to interact in three dimensions (3D) with their microenviroment have been developed as a tool for identifying the extrinsic cues that might stimulate stem cells to replicate without differentiating within artificial cultures. Though these platforms reduce the number of valuable stem cells that must be used for screening, analyzing the fate decisions of cells in these platforms can be challenging. New noninvasive approaches for identifying the lineage-specific differentiation stages of cells while they are entrapped in the hydrogels used for these 3D cultures are especially needed. Here we used Raman spectra acquired from individual, living cells entrapped within a hydrogel matrix and multivariate analysis to identify cell phenotype noninvasively and in situ. We collected a single Raman spectrum from each cell of interest while it was entrapped within a hydrogel matrix and used partial least-squares discriminant analysis (PLS-DA) of the spectra for cell phenotype identification. We first demonstrate that this approach enables identifying the lineages of individual, living cells from different laboratory lines entrapped within two different hydrogels that are used for 3D culture, collagen and gelatin methacrylate (gelMA). Then we use a hematopoietic progenitor cell line that differentiates into different types of macrophages to show that the lineage-specific differentiation stages of individual, living hematopoietic cells entrapped inside of gelMA scaffolds may be identified by PLS-DA of Raman spectra. This ability to noninvasively identify the lineage-specific differentiation stages of cells without removing them from a 3D culture could enable tracking the differentiation of the same cell over time.

Single-Molecule-Sensitive Three-Dimensional Atomic Heterostructures with Extreme Light-Matter Coupling

Three-dimensional heterostructures (3DHS) with controlled compositions and tuned properties are highly desired for fundamental studies and applications in optoelectronics, nanocatalysis, clean energy, and biomedicine. However, conventional nanostructure engineering is hindered by challenges such as poor structural control, time- and energy-intensive processes, the use of hazardous and expensive chemicals, and harsh conditions. Here, we report plasma-assisted epitaxy (PAE) engineering of a metal-organic 3DHS with extreme light-matter interaction for rapid single-molecule-level sensing. Plasmonic-active 3DHS composed of structure-tuned gold-silver core-shell nanoparticles (AuAgCSNPs) was precisely engineered using stable and scalable microplasma-enabled nanofabrication under ambient conditions. The engineered AuAgCSNP-based 3DHS possessed exceptional Raman enhancement under suitable laser excitation, leading to single-molecule detection of SARS-CoV-2 spike proteins in simulated human saliva via surface-enhanced Raman scattering (SERS). The developed plasma fabrication method allows the production of centimeter-scale SERS-active metal-organic 3DHS on disposable, flexible, lightweight, and cost-effective substrates, thereby opening a new avenue for next-generation biosensing, nanoelectronics, nanocatalysis, and biomedical applications.

Adaptive Raman spectral unmixing method based on Voigt peak compensation for quantitative analysis of cellular biochemical components

Raman spectroscopy, with its unique "molecular fingerprint" characteristics, is an essential tool for label-free, non-invasive biochemical analysis of cells. It provides precise information on cellular biochemical components, such as proteins, lipids, and nucleic acids by analyzing molecular vibrational modes. However, overlapping Raman spectral signals make spectral unmixing crucial for accurate quantification. Traditional unmixing methods face challenges: unsupervised algorithms yield poorly interpretable results, while supervised methods like BCA rely heavily on accurate reference spectra and are sensitive to environmental changes (e.g., pH, temperature, excitation wavelength), causing spectral distortion and reducing quantitative reliability. This study addresses these challenges by introducing a parameterized Voigt function into the linear spectral mixing model for element spectrum compensation, using iterative least-squares optimization for adaptive unmixing and quantitative analysis. Simulations show that the Voigt-compensated unmixing algorithm improves spectral fitting accuracy and robustness. Applied to Raman spectra from Hela cell apoptosis and iPSCs differentiation, the algorithm accurately tracks biochemical molecular changes, proving its applicability in cellular Raman spectral analysis and a precise, reliable, and versatile tool for quantitative biochemical analysis.

Phenotypic analysis of complex bioengineered 3D models

With advances in underlying technologies such as complex multicellular systems, synthetic materials, and bioengineering techniques, we can now generate in vitro miniaturized human tissues that recapitulate the organotypic features of normal or diseased tissues. Importantly, these 3D culture models have increasingly provided experimental access to diverse and complex tissues architectures and their morphogenic assembly in vitro. This review presents an analytical toolbox for biological researchers using 3D modeling technologies through which they can find a collation of currently available methods to phenotypically assess their 3D models in their normal state as well as their response to therapeutic or pathological agents.

Unveiling the Molecular Secrets: A Comprehensive Review of Raman Spectroscopy in Biological Research

Raman spectroscopy has been proven to be a fast, convenient, and nondestructive technique for advancing our understanding of biological systems. The Raman effect originates from the inelastic scattering of light which directly probe vibration/rotational states in biological molecules and materials. Despite numerous advantages over infrared spectroscopy and continuous technical as well as operational improvement in Raman spectroscopy, an advanced development of the device and more applications have become possible. In this review, we explore the principles, techniques, and myriad applications of Raman spectroscopy in the realm of biology. We begin by providing an overview of Raman spectroscopy, highlighting its significance in unraveling the complexities of biological research. The focus of this review is on Raman spectroscopy concepts and methods, clarifying the fundamentals of Raman scattering and spectral interpretation. The review also highlights the key experimental considerations for productive biological applications. We explore the broad range of Raman applications including molecular structure, biomolecular composition, disease detection, and medication discovery. The Raman imaging and mapping can also be used to visualize biological samples at the molecular level. Raman spectroscopy is still developing, giving fresh insights and remedies, from biosensing to its use in tissue engineering and regenerative medicine. This review sheds light on the past, present, and future of Raman spectroscopy; it also highlights promising directions of future research developments and serves as a thorough resource for all researchers.

Simple quantitation and spatial characterization of label free cellular images

Label-free imaging is routinely used during cell culture because of its minimal interference with intracellular biology and capability of observing cells over time. However, label-free image analysis is challenging due to the low contrast between foreground signals and background. So far various deep learning tools have been developed for label-free image analysis and their performance depends on the quality of training data. In this study, we developed a simple computational pipeline that requires no training data and is suited to run on images generated using high-content microscopy equipment. By combining classical image processing functions, Voronoi segmentation, Gaussian mixture modeling and automatic parameter optimization, our pipeline can be used for cell number quantification and spatial distribution characterization based on a single label-free image. We demonstrated the applicability of our pipeline in four morphologically distinct cell types with various cell densities. Our pipeline is implemented in R and does not require excessive computational power, providing novel opportunities for automated label-free image analysis for large-scale or repeated cell culture experiments.

Raman microscopy of cryofixed biological specimens for high-resolution and high-sensitivity chemical imaging

Raman microscopy is an emerging molecular imaging technology, yet its signal-to-noise ratio (SNR) in measurements of biological specimens is severely limited because of the small cross section of Raman scattering. Here, we present Raman imaging techniques of cryofixed specimens to overcome SNR limitations by enabling long exposure of specimens under highly stabilized low-temperature conditions. The observation of frozen specimens in a cryostat at a constant low temperature immediately after rapid freezing enabled the improvement of SNR and enhanced the spatial and spectral resolution. We also confirmed that the cryofixation can preserve physicochemical states of specimens by observing alkyne-labeled coenzyme Q in cytosol and hemeproteins in acute ischemic myocardium, which cannot be done by fixation using chemical reagents. Last, we applied the technique for multiplex Raman imaging of label-free endogenous molecules and alkyne-tagged molecules in cryofixed HeLa cells, demonstrating its capability of high-content imaging of complex biological phenomena while maintaining physiological conditions.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

Tunable Hybrid Hydrogels of Alginate and Cell-Derived dECM to Study the Impact of Matrix Alterations on Epithelial-to-Mesenchymal Transition

Epithelial-to-mesenchymal transition (EMT) is crucial for tumor progression, being linked to alterations in the extracellular matrix (ECM). Understanding the ECM's role in EMT can uncover new therapeutic targets, yet replicating these interactions in vitro remains challenging. It is shown that hybrid hydrogels of alginate (ALG) and cell-derived decellularized ECM (dECM), with independently tunable composition and stiffness, are useful 3D-models to explore the impact of the breast tumor matrix on EMT. Soft RGD-ALG hydrogels (200 Pa), used as neutral bulk material, supported mammary epithelial cells morphogenesis without spontaneous EMT, allowing to define the gene, protein, and biochemical profiles of cells at different TGFβ1-induced EMT states. To mimic the breast tumor composition, dECM from TGFβ1-activated fibroblasts (adECM) are generated, which shows upregulation of tumor-associated proteins compared to ndECM from normal fibroblasts. Using hybrid adECM-ALG hydrogels, it is shown that the presence of adECM induces partial EMT in normal epithelial cells, and amplifes TGF-β1 effects compared to ALG and ndECM-ALG. Increasing the hydrogel stiffness to tumor-like levels (2.5 kPa) have a synergistic effect, promoting a more evident EMT. These findings shed light on the complex interplay between matrix composition and stiffness in EMT, underscoring the utility of dECM-ALG hydrogels as a valuable in vitro platform for cancer research.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Label-free 3D molecular imaging of living tissues using Raman spectral projection tomography

The ability to image tissues in three dimensions (3D) with label-free molecular contrast at the mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with optical sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3D molecular contrast in transparent and semi-transparent samples. We also created a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data. Using these tools, we demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and living tissue-engineered constructs and explanted native cartilage tissues. We show that there exists a favorable balance wherein employing Raman spectroscopy, with its advantages in live cell imaging and label-free molecular contrast, outweighs the reduction in imaging resolution and blurring caused by diffuse photon propagation. Thus, RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

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.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

In situ monitoring of the shikimate pathway: a combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging

Sensing and visualization of metabolites and metabolic pathways in situ are significant requirements for tracking their spatiotemporal dynamics in a non-destructive manner. The shikimate pathway is an important cellular mechanism that leads to the de novo synthesis of many compounds containing aromatic rings of high importance such as phenylalanine, tyrosine, and tryptophan. In this work, we present a cost-effective and extraction-free method based on the principles of stable isotope-coupled Raman spectroscopy and hyperspectral Raman imaging to monitor and visualize the activity of the shikimate pathway. We also demonstrated the applicability of this approach for nascent aromatic amino acid localization and tracking turnover dynamics in both prokaryotic and eukaryotic model systems. This method can emerge as a promising tool for both qualitative and semi-quantitative in situ metabolomics, contributing to a better understanding of aromatic ring-containing metabolite dynamics across various organisms.

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Cardiac multiscale bioimaging: from nano- through micro- to mesoscales

Cardiac multiscale bioimaging is an emerging field that aims to provide a comprehensive understanding of the heart and its functions at various levels, from the molecular to the entire organ. It combines both physiologically and clinically relevant dimensions: from nano- and micrometer resolution imaging based on vibrational spectroscopy and high-resolution microscopy to assess molecular processes in cardiac cells and myocardial tissue, to mesoscale structural investigations to improve the understanding of cardiac (patho)physiology. Tailored super-resolution deep microscopy with advanced proteomic methods and hands-on experience are thus strategically combined to improve the quality of cardiovascular research and support future medical decision-making by gaining additional biomolecular information for translational and diagnostic applications.

Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy

The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.

Lactate- and immunomagnetic-purified hiPSC-derived cardiomyocytes generate comparable engineered cardiac tissue constructs

Three-dimensional engineered cardiac tissue (ECT) using purified human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has emerged as an appealing model system for the study of human cardiac biology and disease. A recent study reported widely used metabolic (lactate) purification of monolayer hiPSC-CM cultures results in an ischemic cardiomyopathy-like phenotype compared with magnetic antibody-based cell sorting (MACS) purification, complicating the interpretation of studies using lactate-purified hiPSC-CMs. Herein, our objective was to determine if use of lactate relative to MACS-purified hiPSC-CMs affects the properties of resulting hiPSC-ECTs. Therefore, hiPSC-CMs were differentiated and purified using either lactate-based media or MACS. Global proteomics revealed that lactate-purified hiPSC-CMs displayed a differential phenotype over MACS hiPSC-CMs. hiPSC-CMs were then integrated into 3D hiPSC-ECTs and cultured for 4 weeks. Structurally, there was no significant difference in sarcomere length between lactate and MACS hiPSC-ECTs. Assessment of isometric twitch force and Ca2+ transient measurements revealed similar functional performance between purification methods. High-resolution mass spectrometry-based quantitative proteomics showed no significant difference in protein pathway expression or myofilament proteoforms. Taken together, this study demonstrates that lactate- and MACS-purified hiPSC-CMs generate ECTs with comparable structural, functional, and proteomic features, and it suggests that lactate purification does not result in an irreversible change in a hiPSC-CM phenotype.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

SERS-Active Printable Hydrogel for 3D Cell Culture and Imaging

Hydrogel-based three-dimensional (3D) cell culture systems mimic the salient elements of extracellular matrices and promote native cell function. However, high-resolution 3D cell imaging that can provide biological information about multiple features of individual cells is yet to be realized. In this context, we incorporated plasmonic gold nanoparticles (AuNPs) into an alginate/gelatin hydrogel to produce surface-enhanced Raman spectroscopy (SERS)-active hydrogel inks for the 3D printing and culturing of Vero cells. Dense incorporation of AuNPs enables production of a printed 3D grid structure with 3D SERS performance, but with no measurable adverse effects on cell growth. Label-free SERS spectra were collected within a hydrogel, and a random forest binary classifier was developed to discriminate Vero cell signals from the hydrogel background with an accuracy of 87.5%. The results suggest that SERS signals from cellular components, such as proteins, lipids, and carbohydrates, account for this discrimination. We demonstrate visualization of cell shape, location, and density by combining predicted binary maps with peak feature intensity maps in 2D and 3D. SERS images with a resolution of ≈3 μm match well with the microscopy images and show clear increases in intensity with incubation time. We suggest that 3D SERS cell imaging is a promising means to examine the effect of external cell stimuli on cellular behavior for diagnostic purposes.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.

Strategies and Progress of Raman Technologies for Cellular Uptake Analysis of the Drug Delivery Systems

Nanoparticle (NP)-based drug delivery systems have the potential to significantly enhance the pharmacological and therapeutic properties of drugs. These systems enhance the bioavailability and biocompatibility of pharmaceutical agents via enabling targeted delivery to specific tissues or organs. However, the efficacy and safety of these systems are largely dependent on the cellular uptake and intracellular transport of NPs. Thus, it is crucial to monitor the intracellular behavior of NPs within a single cell. Yet, it is challenging due to the complexity and size of the cell. Recently, the development of the Raman instrumentation offers a versatile tool to allow noninvasive cellular measurements. The primary objective of this review is to highlight the most recent advancements in Raman techniques (spontaneous Raman scattering, bioorthogonal Raman scattering, coherence Raman scattering, and surface-enhanced Raman scattering) when it comes to assessing the internalization of NP-based drug delivery systems and their subsequent movement within cells.

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.

A novel chalcone derivative exerts anticancer effects by promoting apoptotic cell death of human pancreatic cancer cells

Aggressive pancreatic cancer is typically treated using chemotherapeutics to reduce the tumor pre-operatively and prevent metastasis post-operatively, as well as surgical approaches. In the present study, we synthesized a hydroxyl group-introduced chalcone derivative (1, IC50 = 32.1 μM) and investigated its potential as an anticancer drug candidate by evaluating its apoptosis-promoting effects on BXPC-3 cancer cells. The viability of BXPC-3 cells treated with 1 was measured using the water-soluble tetrazolium 1 reagent. BXPC-3 cells induced by 1 were stained with diverse probes or antibodies, such as ethidium homodimer-1, Hoechst, anti-Ki67, and MitoTracker. Protein expression was measured using an immunoblotting assay, and mRNA expression was determined using real-time polymerase chain reaction. Apoptotic molecular features, such as lipid accumulation and protein degradation, were monitored directly using stimulated Raman scattering microspectroscopy. Through incubation time- and concentration-dependent studies of 1, we found that it significantly reduced the proliferation and increased the number of apoptotic BXPC-3 cells. Compound 1 induced mitochondrial dysfunction, phosphorylation of p38, and caspase 3 cleavage. These results indicate that 1 is a potential therapeutic agent for pancreatic cancer, providing valuable insights into the development of new anticancer drug candidates.

Hybrid Polypyrrole and Polydopamine Nanosheets for Precise Raman/Photoacoustic Imaging and Photothermal Therapy

The development of near-infrared light responsive conductive polymers provides a useful theranostic platform for malignant tumors by maximizing spatial resolution with deep tissue penetration for diagnosis and photothermal therapy. Herein, the self-assembly of ultrathin 2D polypyrrole nanosheets utilizing dopamine as a capping agent and a monolayer of octadecylamine as a template is demonstrated. The 2D polypyrrole-polydopamine nanostructure has tunable size distribution which shows strong absorption in the first and second near-infrared windows, enabling photoacoustic imaging and photothermal therapy. The hybrid double-layer is demonstrated to increase Raman intensity for 3D Raman imaging (up to two orders of magnitude enhancement and spatial resolution up to 1 µm). The acidic environment drives reversible doping of polypyrrole, which can be detected by Raman spectroscopy. The combined properties of the nanosheets can substantially enhance performance in dual-mode Raman and photoacoustic guided photothermal therapy, as shown by the 69% light to heat conversion efficiency and higher cytotoxicity against cancer spheroids. These pH-responsive features highlight the potential of 2D conductive polymers for applications in accurate, highly efficient theranostics.

Misinterpretation in microplastic detection in biological tissues: When 2D imaging is not enough

The presence of microplastics in the food chain is a public concern worldwide, and its analysis is an analytical challenge. In our research, we apply Raman imaging to study the presence of 1 μm polystyrene microplastics in cryosections of Mytilus galloprovincialis due to its wide geographic distribution, widespread occurrence in the food web, and general high presence in the environment. Ingested microplastics are accumulated in the digestive tract, but a large number can also be rapidly eliminated. Some authors state that the translocation of microplastics to the epithelial cells is possible, increasing the risk of microplastics transmission along the food chain. However, as seen in our study, a surface imaging approach (2D) is probably not enough to confirm the internalization of particles and avoid misinterpretation. In fact, while some microplastic particles were detected in the epithelium by 2D Raman imaging, further 3D Raman imaging analysis demonstrated that those particles were dragged from the lumens to the epithelium during sample preparation due to the blade drag effect of the cryotome, and subsequently located on the surface of the analyzed cryosection, discarding the translocation to the epithelial cells. This effect can also happen when the samples are fortuitously contaminated during sample preparation. Several research articles that use similar analytical techniques have shown the presence of microplastics in different types of tissue. It is not our intention to put such results in doubt, but the present work points out the necessity of appropriate three-dimensional analytical methods including data interpretation and the need to go a step further than just surface imaging analysis.

Lactate and Immunomagnetic-purified iPSC-derived Cardiomyocytes Generate Comparable Engineered Cardiac Tissue Constructs

Three-dimensional engineered cardiac tissue (ECT) using purified human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has emerged as an appealing model system for the study of human cardiac biology and disease. A recent study reported widely-used metabolic (lactate) purification of monolayer hiPSC-CM cultures results in an ischemic cardiomyopathy-like phenotype compared to magnetic antibody-based cell sorting (MACS) purification, complicating the interpretation of studies using lactate-purified hiPSC-CMs. Herein, our objective was to determine if use of lactate relative to MACs-purified hiPSC-CMs impacts the properties of resulting hiPSC-ECTs. Therefore, hiPSC-CMs were differentiated and purified using either lactate-based media or MACS. After purification, hiPSC-CMs were combined with hiPSC-cardiac fibroblasts to create 3D hiPSC-ECT constructs maintained in culture for four weeks. There were no structural differences observed, and there was no significant difference in sarcomere length between lactate and MACS hiPSC-ECTs. Assessment of isometric twitch force, Ca 2+ transients, and β-adrenergic response revealed similar functional performance between purification methods. High-resolution mass spectrometry (MS)-based quantitative proteomics showed no significant difference in any protein pathway expression or myofilament proteoforms. Taken together, this study demonstrates lactate- and MACS-purified hiPSC-CMs generate ECTs with comparable molecular and functional properties, and suggests lactate purification does not result in an irreversible change in hiPSC-CM phenotype.

Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging

Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.

Study of Microbial Sulfur Metabolism in a Near Real-Time Pathway through Confocal Raman Quantitative 3D Imaging

As microbial sulfur metabolism significantly contributes to the formation and cycling of deep-sea sulfur, studying their sulfur metabolism is important for understanding the deep-sea sulfur cycle. However, conventional methods are limited in near real-time studies of bacterial metabolism. Recently, Raman spectroscopy has been widely used in studies on biological metabolism due to its low-cost, rapid, label-free, and nondestructive features, providing us with new approaches to solve the above limitation. Here, we used the confocal Raman quantitative 3D imaging method to nondestructively detect the growth and metabolism of Erythrobacter flavus 21-3 in the long term and near real time, which possessed a pathway mediating the formation of elemental sulfur in the deep sea, but the dynamic process was unknown. In this study, its dynamic sulfur metabolism was visualized and quantitatively assessed in near real time using 3D imaging and related calculations. Based on 3D imaging, the growth and metabolism of microbial colonies growing under both hyperoxic and hypoxic conditions were quantified by volume calculation and ratio analysis. Additionally, unprecedented details of growth and metabolism were uncovered by this method. Due to this successful application, this method is potentially significant for analyzing the in situ biological processes of microorganisms in the future. IMPORTANCE Microorganisms contribute significantly to the formation of deep-sea elemental sulfur, so studies on their growth and dynamic sulfur metabolism are important to understand the deep-sea sulfur cycle. However, near real-time in situ nondestructive metabolic studies of microorganisms remain a great challenge due to the limitations of existing methods. We thus used an imaging-related workflow by confocal Raman microscopy. More detailed descriptions of the sulfur metabolism of E. flavus 21-3 were disclosed, which perfectly complemented previous research results. Therefore, this method is potentially significant for analyzing the in-situ biological processes of microorganisms in the future. To our knowledge, this is the first label-free and nondestructive in situ technique that can provide temporally persistent 3D visualization and quantitative information about bacteria.

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.

Microfluidic SERS devices: brightening the future of bioanalysis

A new avenue has opened up for applications of surface-enhanced Raman spectroscopy (SERS) in the biomedical field, mainly due to the striking advantages offered by SERS tags. SERS tags provide indirect identification of analytes with rich and highly specific spectral fingerprint information, high sensitivity, and outstanding multiplexing potential, making them very useful in in vitro and in vivo assays. The recent and innovative advances in nanomaterial science, novel Raman reporters, and emerging bioconjugation protocols have helped develop ultra-bright SERS tags as powerful tools for multiplex SERS-based detection and diagnosis applications. Nevertheless, to translate SERS platforms to real-world problems, some challenges, especially for clinical applications, must be addressed. This review presents the current understanding of the factors influencing the quality of SERS tags and the strategies commonly employed to improve not only spectral quality but the specificity and reproducibility of the interaction of the analyte with the target ligand. It further explores some of the most common approaches which have emerged for coupling SERS with microfluidic technologies, for biomedical applications. The importance of understanding microfluidic production and characterisation to yield excellent device quality while ensuring high throughput production are emphasised and explored, after which, the challenges and approaches developed to fulfil the potential that SERS-based microfluidics have to offer are described.

Bond-selective intensity diffraction tomography

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 s per volume, with a lateral and axial resolution of ~350 nm and ~1.1 µm, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 µm x 100 µm).

Super-Long SERS Active Single Silver Nanowires for Molecular Imaging in 2D and 3D Cell Culture Models

Establishing a systematic molecular information analysis strategy for cell culture models is of great significance for drug development and tissue engineering technologies. Here, we fabricated single silver nanowires with high surface-enhanced Raman scattering activity to extract SERS spectra in situ from two-dimensional (2D) and three-dimensional (3D) cell culture models. The silver nanowires were super long, flexible and thin enough to penetrate through multiple cells. A single silver nanowire was used in combination with a four-dimensional microcontroller as a cell endoscope for spectrally analyzing the components in cell culture models. Then, we adopted a machine learning algorithm to analyze the obtained spectra. Our results show that the abundance of proteins differs significantly between the 2D and 3D models, and that nucleic acid-rich and protein-rich regions can be distinguished with satisfactory accuracy.

Current strategies with implementation of three-dimensional cell culture: the challenge of quantification

From growing cells in spheroids to arranging them on complex engineered scaffolds, three-dimensional cell culture protocols are rapidly expanding and diversifying. While these systems may often improve the physiological relevance of cell culture models, they come with technical challenges, as many of the analytical methods used to characterize traditional two-dimensional (2D) cells must be modified or replaced to be effective. Here we review the advantages and limitations of quantification methods based either on biochemical measurements or microscopy imaging. We focus on the most basic of parameters that one may want to measure, the number of cells. Precise determination of this number is essential for many analytical techniques where measured quantities are only meaningful when normalized to the number of cells (e.g. cytochrome p450 enzyme activity). Thus, accurate measurement of cell number is often a prerequisite to allowing comparisons across different conditions (culturing conditions or drug and treatment screening) or between cells in different spatial states. We note that this issue is often neglected in the literature with little or no information given regarding how normalization was performed, we highlight the pitfalls and complications of quantification and call for more accurate reporting to improve reproducibility.

Tilt-angle stimulated Raman projection tomography

Stimulated Raman projection tomography is a label-free volumetric chemical imaging technology allowing three-dimensional (3D) reconstruction of chemical distribution in a biological sample from the angle-dependent stimulated Raman scattering projection images. However, the projection image acquisition process requires rotating the sample contained in a capillary glass held by a complicated sample rotation stage, limiting the volumetric imaging speed, and inhibiting the study of living samples. Here, we report a tilt-angle stimulated Raman projection tomography (TSPRT) system which acquires angle-dependent projection images by utilizing tilt-angle beams to image the sample from different azimuth angles sequentially. The TSRPT system, which is free of sample rotation, enables rapid scanning of different views by a tailor-designed four-galvo-mirror scanning system. We present the design of the optical system, the theory, and calibration procedure for chemical tomographic reconstruction. 3D vibrational images of polystyrene beads and C. elegans are demonstrated in the C-H vibrational region.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

Identification of the Differentiation Stages of Living Cells from the Six Most Immature Murine Hematopoietic Cell Populations by Multivariate Analysis of Single-Cell Raman Spectra

Efforts to expand hematopoietic stem and progenitor cells (HSPCs) in vitro are motivated by their use in the treatment of leukemias and other blood and immune system diseases. The combinations of extrinsic cues within the hematopoietic stem cell (HSC) niche that lead to HSC fate decisions remain unknown. New noninvasive and location-specific techniques are needed to enable identification of the differentiation stages of individual hematopoietic cells on biomaterial microarray screening platforms that minimize the usage of rare HSCs. Here, we show that a combination of Raman microspectroscopy and partial least-squares discriminant analysis (PLS-DA) enables the location-specific identification of individual living cells from the six most immature hematopoietic cell populations, HSC, multipotent progenitor (MPP)-1, MPP-2, MPP-3, common myeloid progenitor, and common lymphoid progenitor. Better than 90% accuracy was achieved. We show that the accuracy of this differentiation stage identification was based on spectral features associated with cell biochemistries. This work establishes that PLS-DA can capture the subtle spectral variations between as many as six closely related cell populations in the presence of potentially significant within-population spectral variation. This noninvasive approach can be used to screen HSC fate decisions elicited by extrinsic cues within biomaterial microarray screening platforms.

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

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

Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications

Raman spectroscopy combined with augmented reality and mixed reality to reconstruct molecular information of tissue surface.

Looking Inside Non-Destructively: Label-Free, Raman-Based Visualization of Intracellular Coxiella burnetii

The life cycle of intracellular pathogens is often complex and can include different morphoforms. Treatment of intracellular infections and unperturbed studying of the pathogen inside the host cell are frequently challenging. Here, we present a Raman-based, label-free, non-invasive, and non-destructive method to localize, visualize, and even quantify intracellular bacteria in 3D within intact host cells in a Coxiella burnetii infection model. C. burnetii is a zoonotic obligate intracellular pathogen that causes infections in ruminant livestock and humans with an acute disease known as Q fever. Using statistical data analysis, no isolation is necessary to gain detailed information on the intracellular pathogen's metabolic state. High-quality false color image stacks with diffraction-limited spatial resolution enable a 3D spatially resolved single host cell analysis that shows excellent agreement with results from transmission electron microscopy. Quantitative analysis at different time points post infection allows to follow the infection cycle with the transition from the large cell variant (LCV) to the small cell variant (SCV) at around day 6 and a gradual change in the lipid composition during vacuole maturation. Spectral characteristics of intracellular LCV and SCV reveal a higher lipid content of the metabolically active LCV.

Autostereoscopic-Raman Spectrometry-Based Three-Dimensional Metrology System for Measurements, Tracking and Identification in a Volume

Three-dimensional compound measurement within a volume of interest is of great importance in industrial manufacturing and the biomedical field. However, there is no current method that can simultaneously perform spatial localization and 3D measurement in a non-scanning manner as well as the identification of material in a volume. In this paper, an Autostereoscopic-Raman Spectrometry-based (ARS) three-dimensional measurement system is proposed. The target object in a large depth range is initially positioned by the autostereoscopic 3D measurement method, and then the accurate position information is cross-checked and obtained by combining the spectral signal. Meanwhile, the spectral signal at the precise excitation position guided by the autostereoscopic signal also carries the material composition information. In order to verify the proposed ARS method, an associated measurement system was developed, and experimental studies of detecting various fibers of different depths in multi-layer glass structure were conducted. The spatial locations and dimensional information of multiple different targets can be measured in a volume, and their material can also be identified at the same time. The average error between the calculated position processed by the ARS system and the actual spatial position is within sub-micron levels, and the success rate of spectrum acquisition reaches 98%.

Academic User View: Organ-on-a-Chip Technology

Organ-on-a-Chip (OoC) systems bring together cell biology, engineering, and material science for creating systems that recapitulate the in vivo microenvironment of tissues and organs. The versatility of OoC systems enables in vitro models for studying physiological processes, drug development, and testing in both academia and industry. This paper evaluates current platforms from the academic end-user perspective, elaborating on usability, complexity, and robustness. We surveyed 187 peers in 35 countries and grouped the responses according to preliminary knowledge and the source of the OoC systems that are used. The survey clearly shows that current commercial OoC platforms provide a substantial level of robustness and usability-which is also indicated by an increasing adaptation of the pharmaceutical industry-but a lack of complexity can challenge their use as a predictive platform. Self-made systems, on the other hand, are less robust and standardized but provide the opportunity to develop customized and more complex models, which are often needed for human disease modeling. This perspective serves as a guide for researchers in the OoC field and encourages the development of next-generation OoCs.

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based "automated hyperspectral Raman analysis framework" to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen's abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

Direct monitoring of single-cell response to biomaterials by Raman spectroscopy

There is continued focus on the development of new biomaterials and associated biological testing methods needed to reduce the time taken for their entry to clinical use. The application of Raman spectroscopy to the study of individual cells that have been in contact with biomaterials offers enhanced in vitro information in a potentially non-destructive testing regime. The work presented here reports the Raman spectral analysis of discreet U-2 OS bone cells after exposure to hydroxyapatite (HA) coated titanium (Ti) substrates in both the as-deposited and thermally annealed states. These data show that cells that were in contact with the bioactive HA surface for 7 days had spectral markers similar to those cultured on the Ti substrate control for the same period. However, the spectral features for those cells that were in contact with the annealed HA surface had indicators of significant differentiation at day 21 while cells on the as-deposited surface did not show these Raman changes until day 28. The cells adhered to pristine Ti control surface showed no spectral changes at any of the timepoints studied. The validity of these spectroscopic results has been confirmed using data from standard in vitro cell viability, adhesion, and proliferation assays over the same 28-day culture period. In this case, cell maturation was evidenced by the formation of natural bone apatite, which precipitated intracellularly for cells exposed to both types of HA-coated Ti at 21 and 28 days, respectively. The properties of the intracellular apatite were markedly different from that of the synthetic HA used to coat the Ti substrate with an average particle size of 230 nm, a crystalline-like shape and Ca/P ratio of 1.63 ± 0.5 as determined by SEM-EDX analysis. By comparison, the synthetic HA particles used as a control had an average size of 372 nm and were more-rounded in shape with a Ca/P ratio of 0.8 by XPS analysis and 1.28 by SEM-EDX analysis. This study shows that Raman spectroscopy can be employed to monitor single U-2 OS cell response to biomaterials that promote cell maturation towards de novo bone thereby offering a label-free in vitro testing method that allows for non-destructive analyses.

Multimodal Label-Free Monitoring of Adipogenic Stem Cell Differentiation Using Endogenous Optical Biomarkers

Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping-based machine learning analysis is reported to distinguish differentiating human adipose-derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single-cell resolution. The label-free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton-based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix-assisted laser desorption/ionization-mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution.

Aortic aneurysm evaluation by scanning acoustic microscopy and Raman spectroscopy

Aortic aneurysm is observed as a result of the extensive alteration in the elasticity of the aortic wall due to the breakdown of elastin and collagen. In this study, we studied the feasibility of scanning acoustic microscopy (SAM) and Raman spectroscopy (RS) in characterizing the dilated segments of the aorta from male and female patients with aortic aneurysm. SAM determined the acoustic property variation in the aorta by calculating the acoustic impedance values of aorta samples of 18 patients. RS determined the disease states by analyzing the chemical variation especially in the peaks related to elastin and collagen using the k-means classification method. Consequently, we assume that combining these two techniques in clinics will help to investigate the dilated segment of the aorta with micrometer resolution, which will reduce the possibility of new aneurysm formation due to a segment not excised during the surgery.

Microfluidic-like fabrication of metal ion-cured bioadhesives by mussels

[Figure: see text].

Ratiometric Raman nanotags enable intraoperative detection of metastatic sentinel lymph node

Sentinel lymph node (SLN) imaging and biopsy has been advocated as an important technique to evaluate the metastatic status of regional lymph nodes and determine subsequent surgical procedure for many cancers, yet there is no reliable means to provide accurate and rapid diagnosis of metastatic SLN during surgery. Here we develop a new approach, named "Ratiometric Raman dual-nanotag strategy", that using folic acid functionalized targeted and nontargeted gap-enhanced Raman tags (FA-GERTs and Nt-GERTs) to detect metastatic SLN based on Raman imaging combined with classical least square data processing methods. By using this strategy, with built-in self-calibration for signal correction, rather than absolute intensity-dependent signal readout, we realize the visualization and prompt intraoperative diagnosis of metastatic SLN with a high accuracy of 87.5 % when the cut-off value of ratio (FA-GERTs/Nt-GERTs) set at 1.255. This approach may outperform the existing histopathological assessment in diagnosing SLN metastasis and is promising for guiding surgical procedure in the future.

Label-free breast cancer detection using fiber probe-based Raman spectrochemical biomarker-dominated profiles extracted from a mixture analysis algorithm

We report the development of a label-free, simple, and high efficiency breast cancer detection platform with multimodal biomarker analytic algorithms on a portable 785 nm Raman setup with an endoscopic Raman-lensed fiber optic probe. We propose a multimodal biomarker extraction algorithm (PCMA) implemented by combining a multivariate statistics principal component analysis (PCA) algorithm and a multivariate curve resolution-alternating least squares (MCR-ALS) computational model for extraction of the biomarker information hidden in Raman spectrochemical data. We show that the six Raman spectrochemical peaks at 1009, 1270, 1305/1443, 1658, and 1750 cm-1 assigned to phenylalanine, amide III in proteins, CH2 deformation in lipids, amide I in proteins, and carbonyl, respectively, can be used as a biomarker for breast cancer diagnosis using the biomarker-dominated PCMA spectrochemical spectra of breast tissues. From 20 human breast tissues, the PCMA-linear discriminant analysis (PCMA-LDA) identification method achieved high classification performance with a sensitivity and specificity >99% along with an improvement of approximately 4.5% compared to the performance without the PCMA mixture analysis algorithm. Our label-free breast cancer detection method has the potential for clinical application to diagnose breast cancer in real-time during surgery.