Laser Tweezers Raman Microspectroscopy of Single Cells and Biological Particles

Liposomes characterization for market approval as pharmaceutical products: Analytical methods, guidelines and standardized protocols

Liposomes are nano-sized lipid-based vesicles widely studied for their drug delivery capabilities. Compared to standard carries they exhibit better properties such as improved site-targeting and drug release, protection of drugs from degradation and clearance, and lower toxic side effects. At present, scientific literature is rich of studies regarding liposomes-based systems, while 14 types of liposomal products have been authorized to the market by EMA and FDA and many others have been approved by national agencies. Although the interest in nanodevices and nanomedicine has steadily increased in the last two decades the development of documentation regulating and standardizing all the phases of their development and quality control still suffers from major inadequacy due to the intrinsic complexity of nano-systems characterization. Many generic documents (Type 1) discussing guidelines for the study of nano-systems (lipidic and not) have been proposed while there is a lack of robust and standardized methods (Type 2 documents). As a result, a widespread of different techniques, approaches and methodologies are being used, generating results of variable quality and hard to compare with each other. Additionally, such documents are often subject to updates and rewriting further complicating the topic. Within this context the aim of this work is focused on bridging the gap in liposome characterization: the most recent standardized methodologies suitable for liposomes characterization are here reported (with the corresponding Type 2 documents) and revised in a short and pragmatical way focused on providing the reader with a practical background of the state of the art. In particular, this paper will put the accent on the methodologies developed to evaluate the main critical quality attributes (CQAs) necessary for liposomes market approval.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

Laser tweezer Raman spectroscopy combined with deep neural networks for identification of liver cancer cells

Rapid identification of cancer cells is crucial for clinical treatment guidance. Laser tweezer Raman spectroscopy (LTRS) that provides biochemical characteristics of cells can be used to identify cell phenotypes through classification models in a non-invasive and label-free manner. However, traditional classification methods require extensive reference databases and clinical experience, which is challenging when sampling at inaccessible locations. Here, we describe a classification method combing LTRS with deep neural network (DNN) for differential and discriminative analysis of multiple liver cancer (LC) cells. By using LTRS, we obtained high-quality single-cell Raman spectra of normal hepatocytes (HL-7702) and liver cancer cell lines (SMMC-7721, Hep3B, HepG2, SK-Hep1 and Huh7). The tentative assignment of Raman peaks indicated that arginine content was elevated and phenylalanine, glutathione and glutamate content was decreased in liver cancer cells. Subsequently, we randomly selected 300 spectra from each cell line for DNN model analysis, achieving a mean accuracy of 99.2%, a mean sensitivity of 99.2% and a mean specificity of 99.8% for the identification and classification of multiple LC cells and hepatocyte cells. These results demonstrate the combination of LTRS and DNN is a promising method for rapid and accurate cancer cell identification at single cell level.

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective

Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.

Quantitative Approach for Protein Analysis in Small Cell Ensembles by an Integrated Microfluidic Chip with MALDI Mass Spectrometry

Increasing evidence has demonstrated that cells are individually heterogeneous. Advancing the technologies for single-cell analysis will improve our ability to characterize cells, study cell biology, design and screen drugs, and aid cancer diagnosis and treatment. Most current single-cell protein analysis approaches are based on fluorescent antibody-binding technology. However, this technology is limited by high background and cross-talk of multiple tags introduced by fluorescent labels. Stable isotope labels used in mass cytometry can overcome the spectral overlap of fluorophores. Nevertheless, the specificity of each antibody and heavy-metal-tagged antibody combination must be carefully validated to ensure detection of the intended target. Thus, novel single-cell protein analysis methods without using labels are urgently needed. Moreover, the labeling approach targets already known motifs, hampering the discovery of new biomarkers relevant to single-cell population variation. Here, we report a combined microfluidic and matrix-assisted laser desorption and ionization (MALDI) mass spectrometric approach for the analysis of protein biomarkers suitable for small cell ensembles. All necessary steps for cell analysis including cell lysis, protein capture, and digestion as well as MALDI matrix deposition are integrated on a microfluidic chip prior to the downstream MALDI-time-of-flight (TOF) detection. For proof of principle, this combined method is used to assess the amount of Bcl-2, an apoptosis regulator, in metastatic breast cancer cells (MCF-7) by using an isotope-labeled peptide as an internal standard. The proposed approach will eventually provide a new means for proteome studies in small cell ensembles with the potential for single-cell analysis and improve our ability in disease diagnosis, drug discovery, and personalized therapy.

Analytical characterization of liposomes and other lipid nanoparticles for drug delivery

Lipid nanoparticles, especially liposomes and lipid/nucleic acid complexed nanoparticles have shown great success in the pharmaceutical industry. Their success is attributed to stable drug loading, extended pharmacokinetics, reduced off-target side effects, and enhanced delivery efficiency to disease targets with formidable blood-brain or plasma membrane barriers. Therefore, they offer promising formulation options for drugs limited by low therapeutic indexes in traditional dosage forms and current "undruggable" targets. Recent development of siRNA, antisense oligonucleotide, or the CRISPR complex-loaded lipid nanoparticles and liposomal vaccines also shed light on their potential in enabling versatile formulation platforms for new pharmaceutical modalities. Analytical characterization of these nanoparticles is critical to drug design, formulation development, understanding in vivo performance, as well as quality control. The multi-lipid excipients, unique core-bilayer structure, and nanoscale size all underscore their complicated critical quality attributes, including lipid species, drug encapsulation efficiency, nanoparticle characteristics, product stability, and drug release. To address these challenges and facilitate future applications of lipid nanoparticles in drug development, we summarize available analytical approaches for physicochemical characterizations of lipid nanoparticle-based pharmaceutical modalities. Furthermore, we compare advantages and challenges of different techniques, and highlight the promise of new strategies for automated high-throughput screening and future development.

Cytological and spectroscopic characteristics of c-KIT N822K mutation in core binding factor acute myeloid leukemia cells

The frequency of N822K mutation is high in the A-loop region of c-KIT which is highly associated with poor prognosis of core binding factor acute myeloid leukemia. The current work used common assays including cell cycle, apoptosis, clone formation and western blot to perform cytological detection for HL60 (wild type), NB4 (carrying t[15;17] chromosome translocation) and Kasumi-1 (with c-KIT N822K mutation); and meanwhile, the laser tweezers Raman spectroscopy (LTRS) was also used to perform label-free detection of single living cells. The results demonstrated that Kasumi-1 cell line bearing c-KIT N822K mutation has a stable cell cycle, while there was a significant difference between early and late apoptosis within 48 hours. The LTRS detection initially reflected the spectral differences induced by genetic abnormalities and highlighted progressive patterns of DNA and amino acids band contents which were appropriately consistent with that of cell clone ratio and the c-KIT phosphorylation level. It is concluded that methodology of LTRS-based single living cell characterization could be potential and effective to reveal gene mutation-induced cell differentiation.

Red blood cells under varying extracellular tonicity conditions: an optical tweezers combined with micro-Raman study

Extracellular tonicity has a significant influence on human red blood cell deformation capability. Advancements in the area of laser physics and optical trapping have opened up a plethora of applications for understanding cell structure and dynamics. Here, Raman Tweezers technique was employed to investigate the impact of extracellular tonicity by exposing human red blood cells to both hypertonic and hypotonic intravenous fluids. Heme aggregation was observed in hypertonic saline solution, accompanied with damage in membrane protein. Loss of intracellular hemoglobin in hypotonic solution was evident from the decrease in porphyrin breathing mode present at 752 cm^-1. Oxygen binding to the central iron in the red blood cell heme was also affected under both hyper/hypo tonicity conditions. Morphological deviation of discocytes to echinocytes/spherocytes were also evident from quantitative phase imaging. Principal component analysis have showed clear differentiation of samples in order to classify the control erythrocytes and the tonicity stressed erythrocytes. Present study has also demonstrated the application of Raman Tweezers spectroscopy as a potential tool for probing red blood cell under different stress conditions.

Single-cell impedance analysis of osteogenic differentiation by droplet-based microfluidics

Single-cell analysis is critical to understanding its heterogeneity and biological processes, such as stem cell differentiation, and elucidating the underlying mechanisms of cellular metabolism. New tools to promote intercellular variability studies help elucidate cellular regulation mechanisms. Here an impedance measurement and analysis system was built to monitor the osteogenic differentiation of single bone marrow mesenchymal stem cells (BM-MSCs) in droplets. The biochip including a microelectrode array was designed based on droplet microfluidics and fabricated. A novel theoretical electrical model was proposed to simulate the electrical properties of cells in the droplets. Impedance measurements showed that single cells are substantially heterogeneous during osteoblast differentiation at different stages (days 0, 7, 14 and 21) and different cell passages (passages 6, 7 and 11). This result was consistent with the appearance of two biomarkers (alkaline phosphatase and calcium nodules), which are the gold standard biomarkers of osteoblastogenesis and differentiation. The device enabled highly efficient single-cell trapping, accurate positioning, and sensitive, label-free and noninvasive impedance measurements of individual cells with multiple channels. This system provides a strategy for exploring the processes of osteoblastogenesis and differentiation at the single-cell level and has substantial potential for applications in the biomedical field.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

Rapid discrimination of colon cancer cells with single base mutation in KRAS gene segment using laser tweezers Raman spectroscopy

Laser tweezers Raman spectroscopy (LTRS) as a label-free and noninvasive technology has been proven to be an ideal tool for analysis of single living cells, which provides important fingerprint information without interference from surrounding environments. For the first time, LTRS system was successfully used to examine the colon cancer cells with single base mutation in KRAS gene segment, including DKS-8 (KRAS wild-type [WT]) and DLD-1 (KRAS mutant-type [MT]), HKE-3 (KRAS WT) and HCT-116 (KRAS MT). Spectra changes of some vital biomolecules due to the gene mutation state were sensitively recorded by our home-made LTRS system. As a result of the comparison between DKS-8 and DLD-1 cells, an index of 97.5% of correct classification was obtained by combining LTRS with principle component analysis coupled with linear discriminant analysis (PCA-LDA) statistical analysis, while an index of 97.0% of correct classification was achieved between HKE-3 and HCT-116 cells. Moreover, between WT cells (DKS-8 and HKE-3) vs MT cells (DLD-1 and HCT-116), the index of correct classification was 81.2%, which was quite encouraging. Our preliminary results showed that the LTRS system coupled with PCA-LDA analysis will have a great potential for further applications in the rapid and label-free detection of circulating tumor cells in liquid biopsy.