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Fulltext Tunable Microfluidic Devices for Hydrodynamic Fractionation of Cells and Beads: A Review

Alvankarian J, Majlis BY

Sensors (Basel), 2015 Nov 24;15(11):29685-701.
PMID: 26610519 DOI: 10.3390/s151129685

The adjustable microfluidic devices that have been developed for hydrodynamic-based fractionation of beads and cells are important for fast performance tunability through interaction of mechanical properties of particles in fluid flow and mechanically flexible microstructures. In this review, the research works reported on fabrication and testing of the tunable elastomeric microfluidic devices for applications such as separation, filtration, isolation, and trapping of single or bulk of microbeads or cells are discussed. Such microfluidic systems for rapid performance alteration are classified in two groups of bulk deformation of microdevices using external mechanical forces, and local deformation of microstructures using flexible membrane by pneumatic pressure. The main advantage of membrane-based tunable systems has been addressed to be the high capability of integration with other microdevice components. The stretchable devices based on bulk deformation of microstructures have in common advantage of simplicity in design and fabrication process.

Matched MeSH terms: Single-Cell Analysis*
Fulltext Single Cell Electrical Characterization Techniques

Mansor MA, Ahmad MR

Int J Mol Sci, 2015;16(6):12686-712.
PMID: 26053399 DOI: 10.3390/ijms160612686

Electrical properties of living cells have been proven to play significant roles in understanding of various biological activities including disease progression both at the cellular and molecular levels. Since two decades ago, many researchers have developed tools to analyze the cell's electrical states especially in single cell analysis (SCA). In depth analysis and more fully described activities of cell differentiation and cancer can only be accomplished with single cell analysis. This growing interest was supported by the emergence of various microfluidic techniques to fulfill high precisions screening, reduced equipment cost and low analysis time for characterization of the single cell's electrical properties, as compared to classical bulky technique. This paper presents a historical review of single cell electrical properties analysis development from classical techniques to recent advances in microfluidic techniques. Technical details of the different microfluidic techniques are highlighted, and the advantages and limitations of various microfluidic devices are discussed.

Matched MeSH terms: Single-Cell Analysis/methods*
scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network

Huang Z, Wang J, Lu X, Mohd Zain A, Yu G

Brief Bioinform, 2023 Mar 19;24(2).
PMID: 36733262 DOI: 10.1093/bib/bbad040

Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.

Matched MeSH terms: Single-Cell Analysis/methods
Computational local stiffness analysis of biological cell: High aspect ratio single wall carbon nanotube tip

TermehYousefi A, Bagheri S, Shahnazar S, Rahman MH, Kadri NA

Mater Sci Eng C Mater Biol Appl, 2016 Feb;59:636-642.
PMID: 26652417 DOI: 10.1016/j.msec.2015.10.041

Carbon nanotubes (CNTs) are potentially ideal tips for atomic force microscopy (AFM) due to the robust mechanical properties, nanoscale diameter and also their ability to be functionalized by chemical and biological components at the tip ends. This contribution develops the idea of using CNTs as an AFM tip in computational analysis of the biological cells. The proposed software was ABAQUS 6.13 CAE/CEL provided by Dassault Systems, which is a powerful finite element (FE) tool to perform the numerical analysis and visualize the interactions between proposed tip and membrane of the cell. Finite element analysis employed for each section and displacement of the nodes located in the contact area was monitored by using an output database (ODB). Mooney-Rivlin hyperelastic model of the cell allows the simulation to obtain a new method for estimating the stiffness and spring constant of the cell. Stress and strain curve indicates the yield stress point which defines as a vertical stress and plan stress. Spring constant of the cell and the local stiffness was measured as well as the applied force of CNT-AFM tip on the contact area of the cell. This reliable integration of CNT-AFM tip process provides a new class of high performance nanoprobes for single biological cell analysis.

Matched MeSH terms: Single-Cell Analysis/instrumentation*; Single-Cell Analysis/methods
An impedance flow cytometry with integrated dual microneedle for electrical properties characterization of single cell

Mansor MA, Ahmad MR, Petrů M, Rahimian Koloor SS

Artif Cells Nanomed Biotechnol, 2023 Dec;51(1):371-383.
PMID: 37548425 DOI: 10.1080/21691401.2023.2239274

Electrical characteristics of living cells have been proven to reveal important details about their internal structure, charge distribution and composition changes in the cell membrane, as well as the extracellular context. An impedance flow cytometry is a common approach to determine the electrical properties of a cell, having the advantage of label-free and high throughput. However, the current techniques are complex and costly for the fabrication process. For that reason, we introduce an integrated dual microneedle-microchannel for single-cell detection and electrical properties extraction. The dual microneedles utilized a commercially available tungsten needle coated with parylene. When a single cell flows through the parallel-facing electrode configuration of the dual microneedle, the electrical impedance at multiple frequencies is measured. The impedance measurement demonstrated the differential of normal red blood cells (RBCs) with three different sizes of microbeads at low and high frequencies, 100 kHz and 2 MHz, respectively. An electrical equivalent circuit model (ECM) was used to determine the unique membrane capacitance of individual cells. The proposed technique demonstrated that the specific membrane capacitance of an RBC is 9.42 mF/m-2, with the regression coefficients, ρ at 0.9895. As a result, this device may potentially be used in developing countries for low-cost single-cell screening and detection.

Matched MeSH terms: Single-Cell Analysis
Fulltext Tracking traction force changes of single cells on the liquid crystal surface

Soon CF, Tee KS, Youseffi M, Denyer MC

Biosensors (Basel), 2015 Mar;5(1):13-24.
PMID: 25808839 DOI: 10.3390/bios5010013

Cell migration is a key contributor to wound repair. This study presents findings indicating that the liquid crystal based cell traction force transducer (LCTFT) system can be used in conjunction with a bespoke cell traction force mapping (CTFM) software to monitor cell/surface traction forces from quiescent state in real time. In this study, time-lapse photo microscopy allowed cell induced deformations in liquid crystal coated substrates to be monitored and analyzed. The results indicated that the system could be used to monitor the generation of cell/surface forces in an initially quiescent cell, as it migrated over the culture substrate, via multiple points of contact between the cell and the surface. Future application of this system is the real-time assaying of the pharmacological effects of cytokines on the mechanics of cell migration.

Matched MeSH terms: Single-Cell Analysis/methods*
Fulltext A Review of Cell Adhesion Studies for Biomedical and Biological Applications

Khalili AA, Ahmad MR

Int J Mol Sci, 2015 Aug 05;16(8):18149-84.
PMID: 26251901 DOI: 10.3390/ijms160818149

Cell adhesion is essential in cell communication and regulation, and is of fundamental importance in the development and maintenance of tissues. The mechanical interactions between a cell and its extracellular matrix (ECM) can influence and control cell behavior and function. The essential function of cell adhesion has created tremendous interests in developing methods for measuring and studying cell adhesion properties. The study of cell adhesion could be categorized into cell adhesion attachment and detachment events. The study of cell adhesion has been widely explored via both events for many important purposes in cellular biology, biomedical, and engineering fields. Cell adhesion attachment and detachment events could be further grouped into the cell population and single cell approach. Various techniques to measure cell adhesion have been applied to many fields of study in order to gain understanding of cell signaling pathways, biomaterial studies for implantable sensors, artificial bone and tooth replacement, the development of tissue-on-a-chip and organ-on-a-chip in tissue engineering, the effects of biochemical treatments and environmental stimuli to the cell adhesion, the potential of drug treatments, cancer metastasis study, and the determination of the adhesion properties of normal and cancerous cells. This review discussed the overview of the available methods to study cell adhesion through attachment and detachment events.

Matched MeSH terms: Single-Cell Analysis/methods
Fulltext Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses

Liu S, Punthambaker S, Iyer EPR, Ferrante T, Goodwin D, Fürth D, et al.

Nucleic Acids Res, 2021 06 04;49(10):e58.
PMID: 33693773 DOI: 10.1093/nar/gkab120

We present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.

Matched MeSH terms: Single-Cell Analysis/methods*
Fulltext Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics

Chan MY, Efthymios M, Tan SH, Pickering JW, Troughton R, Pemberton C, et al.

Circulation, 2020 10 13;142(15):1408-1421.
PMID: 32885678 DOI: 10.1161/CIRCULATIONAHA.119.045158

BACKGROUND: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery.
METHODS: We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.
RESULTS: In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.
CONCLUSIONS: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

Matched MeSH terms: Single-Cell Analysis*
Fulltext On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Ng GYL, Tan SC, Ong CS

PLoS One, 2023;18(10):e0292961.
PMID: 37856458 DOI: 10.1371/journal.pone.0292961

Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.

Matched MeSH terms: Single-Cell Analysis/methods
Fulltext Numerical Analysis of Hydrodynamic Flow in Microfluidic Biochip for Single-Cell Trapping Application

Khalili AA, Ahmad MR

Int J Mol Sci, 2015;16(11):26770-85.
PMID: 26569218 DOI: 10.3390/ijms161125987

Single-cell analysis has become the interest of a wide range of biological and biomedical engineering research. It could provide precise information on individual cells, leading to important knowledge regarding human diseases. To perform single-cell analysis, it is crucial to isolate the individual cells before further manipulation is carried out. Recently, microfluidic biochips have been widely used for cell trapping and single cell analysis, such as mechanical and electrical detection. This work focuses on developing a finite element simulation model of single-cell trapping system for any types of cells or particles based on the hydrodynamic flow resistance (Rh) manipulations in the main channel and trap channel to achieve successful trapping. Analysis is carried out using finite element ABAQUS-FEA™ software. A guideline to design and optimize single-cell trapping model is proposed and the example of a thorough optimization analysis is carried out using a yeast cell model. The results show the finite element model is able to trap a single cell inside the fluidic environment. Fluid's velocity profile and streamline plots for successful and unsuccessful single yeast cell trapping are presented according to the hydrodynamic concept. The single-cell trapping model can be a significant important guideline in designing a new chip for biomedical applications.

Matched MeSH terms: Single-Cell Analysis
Single-cell analysis on stromal fibroblasts in the microenvironment of solid tumours

Musa M

Adv Med Sci, 2020 Mar;65(1):163-169.
PMID: 31972467 DOI: 10.1016/j.advms.2019.12.001

Besides malignant cells, the tumour microenvironment consists of various stromal cells such as cancer-associated fibroblasts (CAFs) and myofibroblasts. Accumulation of heterogeneous populations of stromal cells in solid tumours is associated with lower survival rates and cancer recurrence in patients. Certain limitations presented by conventional experimental designs and techniques in cancer research have led to poor understanding of the fundamental basis of cancer niche. Recent developments in single-cell techniques allow more in-depth studies of the tumour microenvironment. Analyses at the single-cell level enables the detection of rare cell types, characterization of intra-tumour cellular heterogeneity and analysis of the lineage output of malignant cells. This subsequently, provides valuable insights on better diagnostic methods and treatment avenues for cancer. This review explores the recent advancements and applications of single-cell technologies in cancer research pertaining to the study of stromal fibroblasts in the microenvironment of solid tumours.

Matched MeSH terms: Single-Cell Analysis/methods*
Fulltext In vitro differentiation between oxytocin- and vasopressin-secreting magnocellular neurons requires more than one experimental criterion

da Silva MP, Merino RM, Mecawi AS, Moraes DJ, Varanda WA

Mol Cell Endocrinol, 2015 Jan 15;400:102-11.
PMID: 25451978 DOI: 10.1016/j.mce.2014.11.004

The phenotypic differentiation between oxytocin (OT)- and vasopressin (VP)-secreting magnocellular neurosecretory cells (MNCs) from the supraoptic nucleus is relevant to understanding how several physiological and pharmacological challenges affect their electrical activity. Although the firing patterns of OT and VP neurons, both in vivo and in vitro, may appear different from each other, much is assumed about their characteristics. These assumptions make it practically impossible to obtain a confident phenotypic differentiation based exclusively on the firing patterns. The presence of a sustained outward rectifying potassium current (SOR) and/or an inward rectifying hyperpolarization-activated current (IR), which are presumably present in OT neurons and absent in VP neurons, has been used to distinguish between the two types of MNCs in the past. In this study, we aimed to analyze the accuracy of the phenotypic discrimination of MNCs based on the presence of rectifying currents using comparisons with the molecular phenotype of the cells, as determined by single-cell RT-qPCR and immunohistochemistry. Our results demonstrated that the phenotypes classified according to the electrophysiological protocol in brain slices do not match their molecular counterparts because vasopressinergic and intermediate neurons also exhibit both outward and inward rectifying currents. In addition, we also show that MNCs can change the relative proportion of each cell phenotype when the system is challenged by chronic hypertonicity (70% water restriction for 7 days). We conclude that for in vitro preparations, the combination of mRNA detection and immunohistochemistry seems to be preferable when trying to characterize a single MNC phenotype.

Matched MeSH terms: Single-Cell Analysis
Fulltext Single-Cell Gene Profiling Reveals Social Status-Dependent Modulation of Nuclear Hormone Receptors in GnRH Neurons in a Male Cichlid Fish

Ogawa S, Parhar IS

Int J Mol Sci, 2020 Apr 15;21(8).
PMID: 32326396 DOI: 10.3390/ijms21082724

Gonadotropin-releasing hormone (GnRH) is essential for the initiation and maintenance of reproductive functions in vertebrates. To date, three distinct paralogue lineages, GnRH1, GnRH2, and GnRH3, have been identified with different functions and regulatory mechanisms. Among them, hypothalamic GnRH1 neurons are classically known as the hypophysiotropic form that is regulated by estrogen feedback. However, the mechanism of action underlying the estrogen-dependent regulation of GnRH1 has been debated, mainly due to the coexpression of low levels of estrogen receptor (ER) genes. In addition, the role of sex steroids in the modulation of GnRH2 and GnRH3 neurons has not been fully elucidated. Using single-cell real-time PCR, we revealed the expression of genes for estrogen, androgen, glucocorticoid, thyroid, and xenobiotic receptors in GnRH1, GnRH2, and GnRH3 neurons in the male Nile tilapia Oreochromis niloticus. We further quantified expression levels of estrogen receptor genes (ERα, ERβ, and ERγ) in three GnRH neuron types in male tilapia of two different social statuses (dominant and subordinate) at the single cell level. In dominant males, GnRH1 mRNA levels were positively proportional to ERγ mRNA levels, while in subordinate males, GnRH2 mRNA levels were positively proportional to ERβ mRNA levels. These results indicate that variations in the expression of nuclear receptors (and possibly steroid sensitivities) among individual GnRH cells may facilitate different physiological processes, such as the promotion of reproductive activities through GnRH1 neurons, and the inhibition of feeding and sexual behaviors through GnRH2 neurons.

Matched MeSH terms: Single-Cell Analysis

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