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Microfluidic Techniques for High-Throughput Cell Analysis
Published in Hyun Jung Kim, Biomimetic Microengineering, 2020
Dongwei Chen, Juanli Yun, Yuxin Qiao, Jian Wang, Ran Hu, Beiyu Hu, Wenbin Du
Single-cell genomics is a powerful tool to enlarge our understanding of genetics, and it elucidates the metabolic, genotypic, and evolutionary diversity and heterogeneity at the single-cell resolution. Single-cell analysis can be used to identify heterogeneous responses or to compare cell states between complex samples with unknown population structure (Gawad et al. 2016). This approach is highly useful in analyzing rare cell types such as circulating tumor cells and uncultured microbes. The application of single-cell genomic analysis has grown remarkably in recent years, indicating that single-cell genomics is robust in biology fields, especially in basic and clinical research, which are closely related to human disease diagnostics.
Nanorobotic Manipulation for a Single Biological Cell
Published in Yunhui Liu, Dong Sun, Biologically Inspired, 2017
Toshio Fukuda, Masahiro Nakajima, Mohd Ridzuan Ahmad
Recently, the evaluation of bio-samples has received much attention for nano-bio applications in nanobiotechnology (Leary Liu, and Apuzzo 2006; Staples et al. 2006). Single-cell analysis has received a lot of attention for its potential to reveal unknown biological aspects of individual cells. Nanomanipulation techniques are a promising way to develop nano-bio applications on the single-cell level for drug delivery, nanotherapy, nanosurgery and so on.
Emerging applications of microfluidic techniques for in vitro toxicity studies of atmospheric particulate matter
Published in Aerosol Science and Technology, 2021
Fobang Liu, Nga Lee Ng, Hang Lu
Flow cytometry is often the choice of technology for single‐cell analysis, as it is high‐throughput and can distinguish subpopulations of cells. However, this technology is neither capable of providing spatial information (e.g., unable to resolve subcellular components such as the mitochondria and the nuclei) nor monitoring the temporal change within the same cell. In comparison, a number of microfluidic techniques have been developed that allow for the analysis of cell heterogeneity and the tracking of single-cell temporal behavior (Chingozha et al. 2014; Chung et al. 2011; Di Carlo, Aghdam, and Lee 2006; Hosokawa et al. 2011; Kniss-James et al. 2017; Li, Motschman et al. 2020). Although the designs of these microfluidic platforms are different, they share some common purposes, i.e., efficiently capture single cells, retain them in a specific location, and control the environment surrounding them. These newly developed tools are suitable for investigating single‐cell response to PM, not only because they can provide high-resolution and high-content information, but because microfluidics is also a convenient method for controlling the exact cellular environment and experimental conditions over time.
Modelling acute myeloid leukaemia in a continuum of differentiation states
Published in Letters in Biomathematics, 2018
H. Cho, K. Ayers, L. de Pills, Y.-H. Kuo, J. Park, A. Radunskaya, R. C. Rockne
The recent advance of single-cell RNA-sequencing (scRNA-Seq) technologies has enabled a new, high-dimensional definition of cell states. In contrast to conventional gene expression analyses based on measuring the average levels in a tissue or given cell population, single-cell analysis captures the cellular heterogeneity and provides resolution at the level of individual cells within the tissue or cell population. This level of resolution coupled with genome-wide gene expression provides an unprecedented opportunity to quantitatively probe cellular behaviour, cellular variation and dynamics in a wide range of biological contexts.
Design of artificial cells: artificial biochemical systems, their thermodynamics and kinetics properties
Published in Egyptian Journal of Basic and Applied Sciences, 2022
Adamu Yunusa Ugya, Lin Pohan, Qifeng Wang, Kamel Meguellati
Secondary metabolites are compounds that play a crucial role in the antioxidant response of an organism in counteracting oxidative effects. The types of secondary metabolites include alkaloids, terpenoids, steroids, glycosides, natural phenols, phenoazines, biphenyls, dibenzofurans, beta-lactams, to name just a few examples [89]. The process used in the systematic study of secondary metabolites is referred to as metabolomics. The objective of metabolomics techniques is to discover and characterize secondary metabolites in their metabolite state in natural and engineered bio-systems. The production of cryptic metabolites is fixed in the newly sequenced genomes in the modular biosynthesis apparatus found in nature, resulting in the production of novel chemistry and new strategies towards ambitious re-engineering. Consequently, these are the approaches used in the field of synthetic biology for the production of a secondary metabolite [90]. By all means, within a living cell, the products of biochemical reactions (metabolites) determine the relationships among different pathways [91]. The molecular mechanisms of development, reaction, and resistance to therapeutics in disease research are broadly carried out with the help of personal omic profiling as it is easy to control, low cost, and infer in both human and animal subjects [92]. The single cell analysis resulted in a detailed understanding of cell function. It helps to identify the differences between cells among the cellular populace. In individual bacterial cells, metabolites are detected by Raman microspectroscopy (RMS), secondary ion mass spectrometry (SIMS), and Fourier transform infrared spectroscopy (FTIRS) [93]. Using capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS), 300 distinct signals are detected in individual animal neurons [94]. The potential applications and advantages for cell testing are in the metabolic analysis of cell cultures [95]. In particular, the production of antibodies is carried out by cell culture optimization methods, cell transfections, apoptosis determination, novel metabolic pathway identification, lung cancer cell phenotype comparison, and drug testing are the main examples of metabolomics [96]. To point out, some of the major challenges faced in cell metabolomics are the unknown facts about the physical and chemical properties of metabolites, extraction techniques, metabolic pathways, and inaccessible reference libraries of standard compounds [97].