A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Neural Network-Based Filter Design for Compressive Raman Classification of Cells

Cell-based therapies are bound to revolutionize medicine, but significant technical hurdles must be overcome before wider adoption. In particular, nondestructive, label-free methods to characterize cells in real time are needed to optimize the production process and improve quality control. Raman spectroscopy, which provides a fingerprint of a cell's chemical composition, would be an ideal modality but is too slow for high-throughput applications. Compressive Raman techniques, which measure only linear combinations of Raman intensities, can be fast but require careful optimization to deliver high performance. Here, we develop a neural network model to identify optimal parameters for a compressive sensing scheme that reduces measurement time by 2 orders of magnitude. In a data set containing Raman spectra of three different cell types, it achieves up to 90% classification accuracy using only five linear combinations of Raman intensities. Our method thus unlocks the power of Raman spectroscopy for the characterization of cell products.

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.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Biologically Active Micropatterns of Biomolecules and Living Matter Using Microbubble Lithography

In situ patterning of biomolecules and living organisms while retaining their biological activity is extremely challenging, primarily because such patterning typically involves thermal stresses that could be substantially higher than the physiological thermal or stress tolerance level. Top-down patterning approaches are especially prone to these issues, while bottom-up approaches suffer from a lack of control in developing defined structures and the time required for patterning. A microbubble generated and manipulated by optical tweezers (microbubble lithography) is used to self-assemble and pattern living organisms in continuous microscopic structures in real-time, where the material thus patterned remains biologically active due to their ability to withstand elevated temperatures for short exposures. Successful patterns of microorganisms (Escherichia coli, Lactococcus. lactis and the Type A influenza virus) are demonstrated, as well as reporter proteins such as green fluorescent protein (GFP) on functionalized substrates with high signal-to-noise ratio and selectivity. Together, the data presented herein may open up fascinating possibilities in rapid in situ parallelized diagnostics of multiple pathogens and bioelectronics.

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

Raman spectroscopy analysis of human amniotic fluid cells from fetuses with myelomeningocele

Prenatal surgery for the treatment of spina bifida (myelomeningocele, MMC) significantly enhances the neurological prognosis of the patient. To ensure better protection of the spinal cord by large defects, the application of skin grafts produced with cells gained from the amniotic fluid is presently studied. In order to determine the most appropriate cells for this purpose, we tried to shed light on the extremely complex amniotic fluid cellular composition in healthy and MMC pregnancies. We exploited the potential of micro-Raman spectroscopy to analyse and characterize human amniotic fluid cells in total and putative (cKit/CD117-positive) stem cells of fetuses with MMC in comparison with amniotic fluid cells from healthy individuals, human fetal dermal fibroblasts and adult adipose derived stem cells. We found that (i) the differences between healthy and MMC amniocytes can be attributed to specific spectral regions involving collagen, lipids, sugars, tryptophan, aspartate, glutamate, and carotenoids, (ii) MMC amniotic fluid contains two particular cell populations which are absent or reduced in normal pregnancies, (iii) the cKit-negative healthy amniocyte subpopulation shares molecular features with human fetal fibroblasts. On the one hand we demonstrate a different amniotic fluid cellular composition in healthy and MMC pregnancies, on the other our work confirms micro-Raman spectroscopy to be a valuable tool for discriminating cell populations in unknown mixtures of cells.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Rapid and accurate identification of stem cell differentiation stages via SERS and convolutional neural networks

Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining surface-enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.

Single-cell mapping of lipid metabolites using an infrared probe in human-derived model systems

Understanding metabolic heterogeneity is the key to uncovering the underlying mechanisms of metabolic-related diseases. Current metabolic imaging studies suffer from limitations including low resolution and specificity, and the model systems utilized often lack human relevance. Here, we present a single-cell metabolic imaging platform to enable direct imaging of lipid metabolism with high specificity in various human-derived 2D and 3D culture systems. Through the incorporation of an azide-tagged infrared probe, selective detection of newly synthesized lipids in cells and tissue became possible, while simultaneous fluorescence imaging enabled cell-type identification in complex tissues. In proof-of-concept experiments, newly synthesized lipids were directly visualized in human-relevant model systems among different cell types, mutation status, differentiation stages, and over time. We identified upregulated lipid metabolism in progranulin-knockdown human induced pluripotent stem cells and in their differentiated microglia cells. Furthermore, we observed that neurons in brain organoids exhibited a significantly lower lipid metabolism compared to astrocytes.

Exploring the future of regenerative medicine: Unveiling the potential of optical microscopy for structural and functional imaging of stem cells

Regenerative medicine, which utilizes stem cells for tissue and organ repair, holds immense promise in healthcare. A comprehensive understanding of stem cell characteristics is crucial to unlock their potential. This study explores the pivotal role of optical microscopy in advancing regenerative medicine as a potent tool for stem cell research. Advanced optical microscopy techniques enable an in-depth examination of stem cell behavior, morphology, and functionality. The review encompasses current optical microscopy, elucidating its capabilities and constraints in stem cell imaging, while also shedding light on emerging technologies for improved stem cell visualization. Optical microscopy, complemented by techniques like fluorescence and multiphoton imaging, enhances our comprehension of stem cell dynamics. The introduction of label-free imaging facilitates noninvasive, real-time stem cell monitoring without external dyes or markers. By pushing the boundaries of optical microscopy, researchers reveal the intricate cellular mechanisms underpinning regenerative processes, thereby advancing more effective therapeutic strategies. The current study not only outlines the future of regenerative medicine but also underscores the pivotal role of optical microscopy in both structural and functional stem cell imaging.

CD47 and PD-L1 overexpression in proliferating human hepatocytes attenuated immune responses and ameliorated acute liver injury in mice

Hepatocyte transplantation has the potential to treat acute liver failure and correct liver-based metabolic disorders. Proliferating human hepatocytes (ProliHHs) provide a large-scale source as an alternative to primary human hepatocytes. However, host rejection led to inefficient graft survival and function, which hindered the clinical application of cell therapy. Herein, we employed the lentiviral system to overexpress immunomodulatory factors programmed death-ligand 1 (cluster of differentiation 274) (CD274) and cluster of differentiation 47 (CD47) in ProliHHs. CD47+274 overexpression inhibited macrophage and T cell responses in vitro. After transplantation into mice via the spleen without immunosuppression, CD47+274 ProliHHs accumulation in the liver significantly increased for 48 hours compared with ProliHHs. Consistent with the in vitro results, CD47+274 ProliHHs were less aggregated and infiltrated by macrophages and also recruited fewer T cells in the liver. Seven days after transplantation, the human albumin level of engineered ProliHHs doubled compared with control group. CD47+274 ProliHHs further ameliorated the liver injury induced using concanavalin A. Overall, our results suggested CD47+274 overexpression reduced innate and adaptive immune responses during hepatocyte transplantation, and the survival rate and graft function of transplanted hepatocyte-like cells were all significantly improved.

Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning

Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

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.

Molecular tracking of interactions between progenitor and endothelial cells via Raman and FTIR spectroscopy imaging: a proof of concept of a new analytical strategy for in vitro research

Circulating endothelial cell progenitors originating from the bone marrow are considered to be a powerful tool in the repair of endothelium damage. Due to their unique properties, endothelial progenitors are now broadly investigated to assess their clinical significance in diseases e.g., associated with brain endothelial dysfunction. However, their distinction in terms of the expression of specific markers remains ambiguous. Additionally, endothelial progenitor cells may change their repertoire of markers depending on the microenvironment of the tissue in which they are currently located. Here, we applied the label-free Raman and FTIR imaging to discriminate mice brain endothelium and endothelial progenitors. Cells cultured separately showed distinctly different spectral signatures extracted from the whole cellular interior as well as the detected intracellular compartments (nucleus, cytoplasm, perinuclear area, and lipid droplets). Then, we used these spectroscopic signals to examine the cells co-cultured for 24 h. Principal cluster analysis showed their grouping with the progenitor cells and segregation from brain endothelium at a level of the entire cell machinery (in FTIR images) which resulted from biochemical alternations in the cytoplasm and lipid droplets (in Raman images). The models included in partial least square regression indicated that lipid droplets are the key element for the classification of endothelial progenitor-brain endothelial cells interactions.

Recent advancements in single-cell metabolic analysis for pharmacological research

Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology. Pharmacological research is being advanced by single-cell metabolic analysis, which offers a technique to identify variations in RNA, proteins, metabolites, and drug molecules in cells. In this review, the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized. High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods, such as droplet microfluidics, microchamber, open microfluidic probe, and digital microfluidics. They are used in tandem with variety of detection techniques, including optical imaging, Raman spectroscopy, electrochemical detection, RNA sequencing, and mass spectrometry, to evaluate single-cell metabolic changes in response to drug administration. The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis. These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway, therapeutic targets discovery, and in vitro disease model evaluation.

Raman Spectroscopy Profiling of Splenic T-Cells in Sepsis and Endotoxemia in Mice

Sepsis is a life-threatening condition that results from an overwhelming and disproportionate host response to an infection. Currently, the quality and extent of the immune response are evaluated based on clinical symptoms and the concentration of inflammatory biomarkers released or expressed by the immune cells. However, the host response toward sepsis is heterogeneous, and the roles of the individual immune cell types have not been fully conceptualized. During sepsis, the spleen plays a vital role in pathogen clearance, such as bacteria by an antibody response, macrophage bactericidal capacity, and bacterial endotoxin detoxification. This study uses Raman spectroscopy to understand the splenic T-lymphocyte compartment profile changes during bona fide bacterial sepsis versus hyperinflammatory endotoxemia. The Raman spectral analysis showed marked changes in splenocytes of mice subjected to septic peritonitis principally in the DNA region, with minor changes in the amino acids and lipoprotein areas, indicating significant transcriptomic activity during sepsis. Furthermore, splenocytes from mice exposed to endotoxic shock by injection of a high dose of lipopolysaccharide showed significant changes in the protein and lipid profiles, albeit with interindividual variations in inflammation severity. In summary, this study provided experimental evidence for the applicability and informative value of Raman spectroscopy for profiling the immune response in a complex, systemic infection scenario. Importantly, changes within the acute phase of inflammation onset (24 h) were reliably detected, lending support to the concept of early treatment and severity control by extracorporeal Raman profiling of immunocyte signatures.

Raman spectroscopy provides valuable process insights for cell-derived and cellular products

Two of the big challenges in modern bioprocesses are process economics and in-depth process understanding. Getting access to online process data helps to understand process dynamics and monitor critical process parameters (CPPs). This is an important part of the quality-by- design concept that was introduced to the pharmaceutical industry in the last decade. Raman spectroscopy has proven to be a versatile tool to allow noninvasive measurements and access to a broad spectrum of analytes. This information can then be used for enhanced process control strategies. This review article will focus on the latest applications of Raman spectroscopy in established protein production bioprocesses as well as show its potential for virus, cell therapy, and mRNA processes.

Highly selective and quantitative in situ monitoring of cell surface proteins by SERS immunoassay system

Due to their pivotal roles in many biological functions, cell surface proteins (CSPs) are often used for cancer prognosis, as evidenced by a number of studies that have reported significant changes in the expression levels of specific surface proteins depending on the stage of tumorigenesis and selection/variety of reprogrammed cells during cell fate conversion. Current CSP detection strategies suffer from poor selectivity and lack the ability for in situ analysis but maintain the spatial information between cells. Here, we have fabricated nanoprobes for surface-enhanced Raman scattering (SERS) immunoassays by conjugating a specific antibody onto silica-coated gold nanoparticles incorporating an individual Raman reporter (Au-tag@SiO₂-Ab NPs) for highly sensitive and selective in situ detection in different types of cells. When multiple HEK293 cell lines stably expressing different levels of the CSP, ACE2, were investigated by the SERS immunoassay, we demonstrated that the level of ACE2 expression in each cell line could be statistically distinguished from that in the other cell lines, indicating the quantitative feature of this biosensing system. When detecting living cells without cell lysis or fixation, as well as fixed cells, the levels of the epithelial CSPs, EpCAM (epithelial cell adhesion molecule) and E-cadherin, were successfully determined using our Au-tag@SiO₂-Ab NPs and SERS immunoassay system in a highly selective and quantitative manner without significant cytotoxicity. Hence, our work provides technical insight into the development of a biosensing platform for a variety of biomedical applications, such as cancer metastasis prognosis and the in situ monitoring of stem cell reprogramming and differentiation.

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.

Modern Microscopic Approaches to Astrocytes

Microscopy started as the histological analysis based on intrinsic optical properties of tissues such as the refractive index and light absorption, and is expanding to include the visualization of organelles by chemical staining, localization of molecules by immunostaining, physiological measurements such as Ca²⁺ imaging, functional manipulation by optogenetics, and comprehensive analysis of chemical composition by Raman spectra. The microscope is one of the most important tools in neuroscience, which aims to reveal the complex intercellular communications underlying brain function and pathology. Many aspects of astrocytes, including the structures of their fine processes and physiological activities in concert with neurons and blood vessels, were revealed in the course of innovations in modern microscopy. The evolution of modern microscopy is a consequence of breakthroughs in spatiotemporal resolutions and expansions in molecular and physiological targets due to the progress in optics and information technology, as well as the inventions of probes using organic chemistry and molecular biology. This review overviews the modern microscopic approach to astrocytes.

Single-cell infrared vibrational analysis by optical trapping mid-infrared photothermal microscopy

Single-cell analysis by means of vibrational spectroscopy combined with optical trapping is a reliable platform for unveiling cell-to-cell heterogeneities in vast populations. Although infrared (IR) vibrational spectroscopy provides rich molecular fingerprint information on biological samples in a label-free manner, its application with optical trapping has never been achieved due to weak gradient forces generated by the diffraction-limited focused IR beam and strong background of water absorption. Herein, we present single-cell IR vibrational analysis that incorporates mid-infrared photothermal (MIP) microscopy with optical trapping. Optically trapped single polymer particles and red blood cells (RBCs) in blood could be chemically identified owing to their IR vibrational fingerprints. This single-cell IR vibrational analysis further allowed us to probe the chemical heterogeneities of RBCs originating from the variation in the intracellular characteristics. Our demonstration paves the way for the IR vibrational analysis of single cells and chemical characterization in various fields.

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.

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.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Direct imaging of intracellular RNA, DNA, and liquid-liquid phase separated membraneless organelles with Raman microspectroscopy

Methodologies for direct intracellular imaging of RNA and DNA are necessary for the advancement of bioimaging. Here we show direct label-free imaging of RNA and DNA in single cells by isolating their accurate Raman spectra. Raman images of DNA from interphase cells show intact nucleus, while those from mitotic cells reveal condensed chromosome. The condensed chromosome images are accurate enough to assign the stage of mitotic cell division (e.g., metaphase). Raman spectral features indicate B-DNA double helical conformational form in all the cell lines investigated here. The Raman images of RNAs, on the other hand, reveal liquid-liquid phase separated (LLPS) membraneless organelles in interphase cells, which disappears during mitosis. Further, the Raman spectrum of proteins from the intracellular LLPS organelles indicates slight enrichment of amyloid-like secondary structural features. Vibrational imaging of intracellular DNA and RNA simultaneously would open myriad of opportunities for examining functional biochemical aspects of cells and organelles.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis

The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

Comparative Transcriptome Analysis to Investigate the Immunotoxicity Mechanism Triggered by Dimethomorph on Human Jurkat T Cell Lines

Dimethomorph (DMM) is a broad-spectrum fungicide used globally in agricultural production, but little is known regarding the immunotoxicity of DMM in humans. In this study, the immunotoxicity of DMM on human Jurkat T cells was evaluated in vitro. The results indicated that the half-effective concentration (EC₅₀) of DMM for Jurkat cells was 126.01 mg/L (0.32 mM). To further elucidate the underlying mechanism, transcriptomics based on RNA sequencing for exposure doses of EC₂₅ (M21) and EC₁₀ (L4) was performed. The results indicated that compared to untreated samples (Ctr), 121 genes (81 upregulated, 40 downregulated) and 30 genes (17 upregulated, 13 downregulated) were significantly differentially regulated in the L4 and M21 samples, respectively. A gene ontology analysis indicated that the significantly differentially expressed genes (DEGs) were mostly enriched in the negative regulation of cell activities, and a KEGG pathway analysis indicated that the DEGs were mainly enriched in the immune regulation and signal transduction pathways. A quantitative real-time PCR for the selected genes showed that compared to the high-dose exposure (M21), the effect of the low-dose DMM exposure (L4) on gene expression was more significant. The results indicated that DMM has potential immunotoxicity for humans, and this toxicity cannot be ignored even at low concentrations.

Recent Advances in Raman Spectral Imaging in Cell Diagnosis and Gene Expression Prediction

Normal and tumor regions within cancer tissue can be distinguished using various methods, such as histological analysis, tumor marker testing, X-ray imaging, or magnetic resonance imaging. Recently, new discrimination methods utilizing the Raman spectra of tissues have been developed and put into practical use. Because Raman spectral microscopy is a non-destructive and non-labeling method, it is potentially compatible for use in the operating room. In this review, we focus on the basics of Raman spectroscopy and Raman imaging in live cells and cell type discrimination, as these form the bases for current Raman scattering-based cancer diagnosis. We also review recent attempts to estimate the gene expression profile from the Raman spectrum of living cells using simple machine learning. Considering recent advances in machine learning techniques, we speculate that cancer type discrimination using Raman spectroscopy will be possible in the near future.

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.

Cell Behavioral Dynamics as a Cue in Optimizing Culture Stabilization in the Bioprocessing of Pluripotent Stem Cells

Pluripotent stem cells (PSCs) are important for future regenerative medicine therapies. However, in the production of PSCs and derivatives, the control of culture-induced fluctuations in the outcome of cell quality remains challenging. A detailed mechanistic understanding of how PSC behaviors are altered in response to biomechanical microenvironments within a culture is necessary for rational bioprocessing optimization. In this review, we discuss recent insights into the role of cell behavioral and mechanical homeostasis in modulating the states and functions of PSCs during culture processes. We delineate promising ways to manipulate the culture variability through regulating cell behaviors using currently developed tools. Furthermore, we anticipate their potential implementation for designing a culture strategy based on the concept of Waddington's epigenetic landscape that may provide a feasible solution for tuning the culture quality and stability in the bioprocessing space.

Raman Spectroscopy for Chemical Biology Research

In chemical biology research, various fluorescent probes have been developed and used to visualize target proteins or molecules in living cells and tissues, yet there are limitations to this technology, such as the limited number of colors that can be detected simultaneously. Recently, Raman spectroscopy has been applied in chemical biology to overcome such limitations. Raman spectroscopy detects the molecular vibrations reflecting the structures and chemical conditions of molecules in a sample and was originally used to directly visualize the chemical responses of endogenous molecules. However, our initial research to develop "Raman tags" opens a new avenue for the application of Raman spectroscopy in chemical biology. In this Perspective, we first introduce the label-free Raman imaging of biomolecules, illustrating the biological applications of Raman spectroscopy. Next, we highlight the application of Raman imaging of small molecules using Raman tags for chemical biology research. Finally, we discuss the development and potential of Raman probes, which represent the next-generation probes in chemical biology.

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.

Molecular Crosstalk between T Cells and Tumor Uncovers GBM-Specific T Cell Signatures in Blood: Noninvasive GBM Diagnosis Using Immunosensors

Glioblastoma (GBM) is the most common and aggressive stage IV brain cancer with a poor prognosis and survival rate. The blood-brain barrier (BBB) in GBM prevents the entry and exit of biomarkers, limiting its treatment options. Hence, GBM diagnosis is pivotal for timely clinical management. Currently, there exists no clinically validated biomarker for GBM diagnosis. T cells exhibit the potential to escape a leaky BBB in GBM patients. These T cells infiltrating the GBM interact with the heterogeneous population of tumor cells, display a symbiotic interaction resulting in intertwined molecular crosstalk, and display a GBM-associated signature while entering the peripheral circulation. Therefore, we hypothesize that studying these distinct molecular changes is critical to enable T cells to be a diagnostic marker for accurate detection of GBM from patient blood. We demonstrated this by utilizing the phenotypic and immunological landscape changes in T cells associated with glioblastoma tumors. GBM exhibits a high level of heterogeneity with diverse subtypes of cells within the tumor, enabling immune infiltration and different degrees of interactions with the tumor. To accurately detect these subtle molecular differences in T cells, we designed an immunosensor with a high detection sensitivity and repeatability. Hence in this study, we investigated the characteristic behavior of T cells to establish two preclinically validated biomarkers: GBM-associated T cells (GBMAT) and GBM stem cell-associated T cells (GSCAT). A comprehensive investigation was conducted by mimicking the tumor microenvironment in vitro by coculturing T cells with cancer cells and cancer stem cells to study the distinct variation in GBMAT and GSCAT. Preclinical investigation of T cells from GBM patient blood shows similar characteristics to our established biomarkers (GBMAT, GSCAT). Further evaluating the relative attributes of T cells in patient blood and tissue biopsy confirms the infiltrating ability of T cells across the BBB. A pilot validation using a SERS-based machine learning algorithm was accomplished by training the model with GBMAT and GSCAT as diagnostic markers. Using GBMAT as a biomarker, we achieved a sensitivity and specificity of 93.3% and 97.4%, respectively, whereas applying GSCAT yielded a sensitivity and specificity of 100% and 98.7%, respectively. We also validated this diagnostic methodology by using conventional biological assays to study the change in expression levels of T cell surface markers (CD4 and CD8) and cytokine levels in T cells (IL6, IL10, TNFα, INFγ) from GBM patients. This study introduces T cells as GBM-specific immune biomarkers to diagnose GBM using patient liquid biopsy. This preclinical validation study presents a better translatability into clinical reality that will enable rapid and noninvasive glioblastoma detection from patient blood.

Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

Glycogen metabolism reprogramming promotes inflammation in coal dust-exposed lung

Long-term coal dust exposure triggers complex inflammatory processes in the coal workers' pneumoconiosis (CWP) lungs. The progress of the inflammation is reported to be affected by disordered cell metabolism. However, the changes in the metabolic reprogramming associated with the pulmonary inflammation induced by the coal dust particles are unknown. Herein, we show that coal dust exposure causes glycogen accumulation and the reprogramming of glucose metabolism in the CWP lung. The glycogen accumulation caused by coal dust is mainly due to macrophages, which reprogram glycogen metabolism and trigger an inflammatory response. In addition, 2-deoxy-D-glucose (2-DG) reduced glycogen content in macrophages, which was accompanied by mitigated inflammation and restrained NF-κB activation. Accordingly, we have pinpointed a novel and crucial metabolic pathway that is an essential regulator of the inflammatory phenotype of coal dust-exposed macrophages. These results shed light on new ways to regulate CWP inflammation.

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.

Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma

Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.

Data-Driven Identification of Biomarkers for In Situ Monitoring of Drug Treatment in Bladder Cancer Organoids

Three-dimensional (3D) organoid culture recapitulating patient-specific histopathological and molecular diversity offers great promise for precision medicine in cancer. In this study, we established label-free imaging procedures, including Raman microspectroscopy (RMS) and fluorescence lifetime imaging microscopy (FLIM), for in situ cellular analysis and metabolic monitoring of drug treatment efficacy. Primary tumor and urine specimens were utilized to generate bladder cancer organoids, which were further treated with various concentrations of pharmaceutical agents relevant for the treatment of bladder cancer (i.e., cisplatin, venetoclax). Direct cellular response upon drug treatment was monitored by RMS. Raman spectra of treated and untreated bladder cancer organoids were compared using multivariate data analysis to monitor the impact of drugs on subcellular structures such as nuclei and mitochondria based on shifts and intensity changes of specific molecular vibrations. The effects of different drugs on cell metabolism were assessed by the local autofluorophore environment of NADH and FAD, determined by multiexponential fitting of lifetime decays. Data-driven neural network and data validation analyses (k-means clustering) were performed to retrieve additional and non-biased biomarkers for the classification of drug-specific responsiveness. Together, FLIM and RMS allowed for non-invasive and molecular-sensitive monitoring of tumor-drug interactions, providing the potential to determine and optimize patient-specific treatment efficacy.

Inflammation and fibrosis in the coal dust-exposed lung described by confocal Raman spectroscopy

Background

Coal workers' pneumoconiosis (CWP) is an occupational disease that severely damages the life and health of miners. However, little is known about the molecular and cellular mechanisms changes associated with lung inflammation and fibrosis induced by coal dust. As a non-destructive technique for measuring biological tissue, confocal Raman spectroscopy provides accurate molecular fingerprints of label-free tissues and cells. Here, the progression of lung inflammation and fibrosis in a murine model of CWP was evaluated using confocal Raman spectroscopy.

Methods

A mouse model of CWP was constructed and biochemical analysis in lungs exposed to coal dust after 1 month (CWP-1M) and 3 months (CWP-3M) vs control tissues (NS) were used by confocal Raman spectroscopy. H&E, immunohistochemical and collagen staining were used to evaluate the histopathology alterations in the lung tissues.

Results

The CWP murine model was successfully constructed, and the mouse lung tissues showed progression of inflammation and fibrosis, accompanied by changes in NF-κB, p53, Bax, and Ki67. Meanwhile, significant differences in Raman bands were observed among the different groups, particularly changes at 1,248, 1,448, 1,572, and 746 cm^-1. These changes were consistent with collagen, Ki67, and Bax levels in the CWP and NS groups.

Conclusion

Confocal Raman spectroscopy represented a novel approach to the identification of the biochemical changes in CWP lungs and provides potential biomarkers of inflammation and fibrosis.

A Single-Cell Raman Spectroscopy Analysis of Bone Marrow Mesenchymal Stem/Stromal Cells to Identify Inter-Individual Diversity

The heterogeneity of stem cells represents the main challenge in regenerative medicine development. This issue is particularly pronounced when it comes to the use of primary mesenchymal stem/stromal cells (MSCs) due to a lack of identification markers. Considering the need for additional approaches in MSCs characterization, we applied Raman spectroscopy to investigate inter-individual differences between bone marrow MSCs (BM-MSCs). Based on standard biological tests, BM-MSCs of analyzed donors fulfill all conditions for their characterization, while no donor-related specifics were observed in terms of BM-MSCs morphology, phenotype, multilineage differentiation potential, colony-forming capacity, expression of pluripotency-associated markers or proliferative capacity. However, examination of BM-MSCs at a single-cell level by Raman spectroscopy revealed that despite similar biochemical background, fine differences in the Raman spectra of BM-MSCs of each donor can be detected. After extensive principal component analysis (PCA) of Raman spectra, our study revealed the possibility of this method to diversify BM-MSCs populations, whereby the grouping of cell populations was most prominent when cell populations were analyzed in pairs. These results indicate that Raman spectroscopy, as a label-free assay, could have a huge potential in understanding stem cell heterogeneity and sorting cell populations with a similar biochemical background that can be significant for the development of personalized therapy approaches.

LONGL-Net: temporal correlation structure guided deep learning model to predict longitudinal age-related macular degeneration severity

Age-related macular degeneration (AMD) is the principal cause of blindness in developed countries, and its prevalence will increase to 288 million people in 2040. Therefore, automated grading and prediction methods can be highly beneficial for recognizing susceptible subjects to late-AMD and enabling clinicians to start preventive actions for them. Clinically, AMD severity is quantified by Color Fundus Photographs (CFP) of the retina, and many machine-learning-based methods are proposed for grading AMD severity. However, few models were developed to predict the longitudinal progression status, i.e. predicting future late-AMD risk based on the current CFP, which is more clinically interesting. In this paper, we propose a new deep-learning-based classification model (LONGL-Net) that can simultaneously grade the current CFP and predict the longitudinal outcome, i.e. whether the subject will be in late-AMD in the future time-point. We design a new temporal-correlation-structure-guided Generative Adversarial Network model that learns the interrelations of temporal changes in CFPs in consecutive time-points and provides interpretability for the classifier's decisions by forecasting AMD symptoms in the future CFPs. We used about 30,000 CFP images from 4,628 participants in the Age-Related Eye Disease Study. Our classifier showed average 0.905 (95% CI: 0.886-0.922) AUC and 0.762 (95% CI: 0.733-0.792) accuracy on the 3-class classification problem of simultaneously grading current time-point's AMD condition and predicting late AMD progression of subjects in the future time-point. We further validated our model on the UK Biobank dataset, where our model showed average 0.905 accuracy and 0.797 sensitivity in grading 300 CFP images.

Increased connectivity of hiPSC-derived neural networks in multiphase granular hydrogel scaffolds

To reflect human development, it is critical to create a substrate that can support long-term cell survival, differentiation, and maturation. Hydrogels are promising materials for 3D cultures. However, a bulk structure consisting of dense polymer networks often leads to suboptimal microenvironments that impedes nutrient exchange and cell-to-cell interaction. Herein, granular hydrogel-based scaffolds were used to support 3D human induced pluripotent stem cell (hiPSC)-derived neural networks. A custom designed 3D printed toolset was developed to extrude hyaluronic acid hydrogel through a porous nylon fabric to generate hydrogel granules. Cells and hydrogel granules were combined using a weaker secondary gelation step, forming self-supporting cell laden scaffolds. At three and seven days, granular scaffolds supported higher cell viability compared to bulk hydrogels, whereas granular scaffolds supported more neurite bearing cells and longer neurite extensions (65.52 ± 11.59 μm) after seven days compared to bulk hydrogels (22.90 ± 4.70 μm). Long-term (three-month) cultures of clinically relevant hiPSC-derived neural cells in granular hydrogels supported well established neuronal and astrocytic colonies and a high level of neurite extension both inside and beyond the scaffold. This approach is significant as it provides a simple, rapid and efficient way to achieve a tissue-relevant granular structure within hydrogel cultures.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Structural and Biochemical Changes in Pericardium upon Genipin Cross-Linking Investigated Using Nondestructive and Label-Free Imaging Techniques

Tissue cross-linking represents an important and often used technique to enhance the mechanical properties of biomaterials. For the first time, we investigated biochemical and structural properties of genipin (GE) cross-linked equine pericardium (EP) using optical imaging techniques in tandem with quantitative atomic force microscopy (AFM). EP was cross-linked with GE at 37 °C, and its biochemical and biomechanical properties were observed at various time points up to 24 h. GE cross-linked EP was monitored by the normalized ratio between its second-harmonic generation (SHG) and two-photon autofluorescence emissions and remained unchanged for untreated EP; however, a decreasing ratio due to depleted SHG and elevated autofluorescence and a fluorescence band at 625 nm were found for GE cross-linked EP. The mean autofluorescence lifetime of GE cross-linked EP also decreased. The biochemical signature of GE cross-linker and shift in collagen bands were detected and quantified using shifted excitation Raman difference spectroscopy as an innovative approach for tackling artifacts with high fluorescence backgrounds. AFM images indicated a higher and increasing Young's modulus correlated with cross-linking, as well as collagen structural changes in GE cross-linked EP, qualitatively explaining the observed decrease in the second-harmonic signal. In conclusion, we obtained detailed information about the biochemical, structural, and biomechanical effects of GE cross-linked EP using a unique combination of optical and force microscopy techniques in a nondestructive and label-free manner.

A polymer-based chemical tongue for the non-invasive monitoring of osteogenic stem-cell differentiation by pattern recognition of serum-supplemented spent media

The development of non-invasive techniques to characterize cultured cells is invaluable not only to ensure the reproducibility of cell research, but also for quality assurance of industrial cell products for...

Classification and Identification of Archaea Using Single-Cell Raman Ejection and Artificial Intelligence: Implications for Investigating Uncultivated Microorganisms

Archaea can produce special cellular components such as polyhydroxyalkanoates, carotenoids, rhodopsin, and ether lipids, which have valuable applications in medicine and green energy production. Most of the archaeal species are uncultivated, posing challenges to investigating their biomarker components and biochemical properties. In this study, we applied Raman spectroscopy to examine the biological characteristics of nine archaeal isolates, including halophilic archaea (Haloferax larsenii, Haloarcula argentinensis, Haloferax mediterranei, Halomicrobium mukohataei, Halomicrobium salinus, Halorussus sp., Natrinema gari), thermophilic archaea (Sulfolobus acidocaldarius), and marine group I (MGI) archaea (Nitrosopumilus maritimus). Linear discriminant analysis of the Raman spectra allowed visualization of significant separations among the nine archaeal isolates. Machine-learning classification models based on support vector machine achieved accuracies of 88-100% when classifying the nine archaeal species. The predicted results were validated by DNA sequencing analysis of cells isolated from the mixture by Raman-activated cell sorting. Raman spectra of uncultured archaea (MGII) were also obtained based on Raman spectroscopy and fluorescence in situ hybridization. The results combining multiple Raman-based techniques indicated that MGII may have the ability to produce lipids distinct from other archaeal species. Our study provides a valuable approach for investigating and classifying archaea, especially uncultured species, at the single-cell level.