High-throughput Single-cell Analysis Research Articles

Highly efficient cell-microbead encapsulation using dielectrophoresis-assisted dual-nanowell array.

Recent advancements in micro/nanofabrication techniques have led to the development of portable devices for high-throughput single-cell analysis through the isolation of individual target cells, which are then paired with functionalized microbeads. Compared with commercially available benchtop instruments, portable microfluidic devices can be more widely and cost-effectively adopted in single-cell transcriptome and proteome analysis. The sample utilization and cell pairing rate (∼33%) of current stochastic-based cell-bead pairing approaches are fundamentally limited by Poisson statistics. Despite versatile technologies having been proposed to reduce randomness during the cell-bead pairing process in order to statistically beat the Poisson limit, improvement of the overall pairing rate of a single cell to a single bead is typically based on increased operational complexity and extra instability. In this article, we present a dielectrophoresis (DEP)-assisted dual-nanowell array (ddNA) device, which employs an innovative microstructure design and operating process that decouples the bead- and cell-loading processes. Our ddNA design contains thousands of subnanoliter microwell pairs specifically tailored to fit both beads and cells. Interdigitated electrodes (IDEs) are placed below the microwell structure to introduce a DEP force on cells, yielding high single-cell capture and pairing rates. Experimental results with human embryonic kidney cells confirmed the suitability and reproducibility of our design. We achieved a single-bead capture rate of >97% and a cell-bead pairing rate of >75%. We anticipate that our device will enhance the application of single-cell analysis in practical clinical use and academic research.

Full-electric microfluidic platform to capture, analyze and selectively release single cells.

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

High-throughput single-cell genomic analysis for archival tissue blocks

High-throughput single-cell genomic analysis for archival tissue blocks

Performance-enhanced clogging-free viscous sheath constriction impedance flow cytometry.

As a label-free and high-throughput single cell analysis platform, impedance flow cytometry (IFC) suffers from clogging caused by a narrow microchannel as mechanical constriction (MC). Current sheath constriction (SC) solutions lack systematic evaluation of the performance and proper guidelines for the sheath fluid. Herein, we hypothesize that the viscosity of the non-conductive liquid is the key to the performance of SC, and propose to employ non-conductive viscous sheath flow in SC to unlock the tradeoff between sensitivity and throughput, while ensuring measurement accuracy. By placing MC and SC in series in the same microfluidic chip, we established an evaluation platform to prove the hypothesis. Through modeling analysis and experiments, we confirmed the accuracy (error < 1.60% ± 4.71%) of SC w.r.t. MC, and demonstrated that viscous non-conductive PEG solution achieved an improved sensitivity (7.92×) and signal-to-noise ratio (1.42×) in impedance measurement, with the accuracy maintained and free of clogging. Viscous SC IFC also shows satisfactory ability to distinguish different types of cancer cells and different subtypes of human breast cancer cells. It is envisioned that viscous SC IFC paves the way for IFC to be really usable in practice with clogging-free, accurate, and sensitive performance.

Dental niche cells directly contribute to tooth reconstitution and morphogenesis.

Mammalian teeth develop from the inductive epithelial-mesenchymal interaction, an important mechanism shared by many organs. The cellular basis for such interaction remains elusive. Here, we generate a dual-fluorescence model to track and analyze dental cells from embryonic to postnatal stages, in which Pitx2+ epithelium and Msx1+ mesenchyme are sufficient for tooth reconstitution. Single-cell RNA sequencing and spatial mapping further revealed critical cellular dynamics during molar development, where tooth germs are organized by Msx1+Sdc1+ dental papilla and surrounding dental niche. Surprisingly, niche cells are more efficient in tooth reconstitution and can directly regenerate papilla cells through interaction with dental epithelium. Finally, from the dental niche, we identify a group of previously unappreciated migratory Msx1+ Sox9+ cells as the potential cell origin for dental papilla. Our results indicate that the dental niche cells directly contribute to tooth organogenesis and provide critical insights into the essential cell composition for tooth engineering.

526 The involvement of gremlin 1 in particulate matter-induced melanogenesis

Particulate matter (PM) is known as a major concern for human health because it is closely associated with mortality and morbidity from respiratory and cardio- and neurovascular diseases. Recently, it has been reported that PM can aggravate allergic skin diseases by affecting the skin barrier or inducing inflammation. Along with our previous reports that PM promotes melanin production via ER-mediated IRE1α signaling pathway, we also performed high-throughput single-cell RNA sequencing analysis using PM-delivered ex vivo human skin tissues to find out target molecules in different skin cell types, keratinocytes, melanocytes, fibroblasts, etc.

Automated detection of patterned single-cells within hydrogel using deep learning

Single-cell analysis has been widely used in various biomedical engineering applications, ranging from cancer diagnostics, and immune response monitoring to drug screening. Single-cell isolation is fundamental for observing single-cell activities and an automatic finding method of accurate and reliable cell detection with few possible human errors is also essential. This paper reports trapping single cells into photo patternable hydrogel microwell arrays and isolating them. Additionally, we present an object detection-based DL algorithm that detects single cells in microwell arrays and predicts the presence of cells in resource-limited environments at the highest possible mAP (mean average precision) of 0.989 with an average inference time of 0.06 s. This algorithm leads to the enhancement of the high-throughput single-cell analysis, establishing high detection precision and reduced experimentation time.

High-throughput single cell metabolomics and cellular heterogeneity exploration by inertial microfluidics coupled with pulsed electric field-induced electrospray ionization-high resolution mass spectrometry

Single cell metabolomics can obtain the metabolic profiles of individual cells and reveal cellular heterogeneity. However, high-throughput single-cell mass spectrometry (MS) analysis under physiological conditions remains a great challenge due to the presence of complex matrix and extremely small cell volumes. Herein, a serpentine channel microfluidic device which was designed to achieve continuous cell separation and inertial focusing, was coupled with a pulsed electric field-induced electrospray ionization-high resolution MS (PEF-ESI-HRMS) to achieve high-throughput single cell analysis. The pulsed square wave electric field was applied to realize on-line cell disruption and induce electrospray ionization. Single cells were analyzed under near-physiological conditions at a throughput of up to 80 cells min−1. More than 900 features were detected and approximately 120 metabolites were tentatively identified from a single cell. Further, by continually analyzing more than 3000 MCF7 and HepG2 cells, discrimination of different cancer cells based on their individual metabolic profiles was achieved by using the principal component analysis. The PEF-ESI-HRMS method was also applied for the analysis of single yeast cells, and more than 40 metabolites were annotated. This method is versatile and has good robustness, which is promising for high-throughput single cell metabolomics analysis.

Impedance-Enabled Camera-Free Intrinsic Mechanical Cytometry.

Mechanical properties of single cells are important label-free biomarkers normally measured by expensive and complex imaging systems. To unlock this limit and allow mechanical properties comparable across different measurement platforms, camera-free intrinsic mechanical cytometry (CFIMC) is proposed for on-the-fly measurement of two major intrinsic mechanical parameters, that is, Young's modulus E and fluidity β, of single cells. CFIMC adopts a framework that couples the impedance electrodes with the constriction channel spatially, so that the impedance signals contain the dynamic deformability information of the cell squeezing through the constriction channel. Deformation of the cell is thus extracted from the impedance signals and used to derive the intrinsic mechanical parameters. With reasonably high throughput (>500 cells min-1 ), CFIMC can successfully reveal the mechanical difference in cancer and normal cells (i.e., human breast cell lines MCF-10A, MCF-7, and MDA-MB-231), living and fixed cells, and pharmacological perturbations of the cytoskeleton. It is further found that 1µM level concentration of Cytochalasin B may be the threshold for the treated cells to induce a significant cytoskeleton effect reflected by the mechanical parameters. It is envisioned that CFIMC provides an alternative avenue for high-throughput and real-time single-cell intrinsic mechanical analysis.

Single-cell genomics identifies distinct B1 cell developmental pathways and reveals aging-related changes in the B-cell receptor repertoire

BackgroundB1 cells are self-renewing innate-like B lymphocytes that provide the first line of defense against pathogens. B1 cells primarily reside in the peritoneal cavity and are known to originate from various fetal tissues, yet their developmental pathways and the mechanisms underlying maintenance of B1 cells throughout adulthood remain unclear.ResultsWe performed high-throughput single-cell analysis of the transcriptomes and B-cell receptor repertoires of peritoneal B cells of neonates, young adults, and elderly mice. Gene expression analysis of 31,718 peritoneal B cells showed that the neonate peritoneal cavity contained many B1 progenitors, and neonate B cell specific clustering revealed two trajectories of peritoneal B1 cell development, including pre-BCR dependent and pre-BCR independent pathways. We also detected profound age-related changes in B1 cell transcriptomes: clear difference in senescence genetic program was evident in differentially aged B1 cells, and we found an example that a B1 subset only present in the oldest mice was marked by expression of the fatty-acid receptor CD36. We also performed antibody gene sequencing of 15,967 peritoneal B cells from the three age groups and discovered that B1 cell aging was associated with clonal expansion and two B1 cell clones expanded in the aged mice had the same CDR-H3 sequence (AGDYDGYWYFDV) as a pathogenically linked cell type from a recent study of an atherosclerosis mouse model.ConclusionsBeyond offering an unprecedent data resource to explore the cell-to-cell variation in B cells, our study has revealed that B1 precursor subsets are present in the neonate peritoneal cavity and dissected the developmental pathway of the precursor cells. Besides, this study has found the expression of CD36 on the B1 cells in the aged mice. And the single-cell B-cell receptor sequencing reveals B1 cell aging is associated with clonal expansion.

High-throughput analysis of single human cells reveals the complex nature of DNA replication timing control

DNA replication initiates from replication origins firing throughout S phase. Debate remains about whether origins are a fixed set of loci, or a loose agglomeration of potential sites used stochastically in individual cells, and about how consistent their firing time is. We develop an approach to profile DNA replication from whole-genome sequencing of thousands of single cells, which includes in silico flow cytometry, a method for discriminating replicating and non-replicating cells. Using two microfluidic platforms, we analyze up to 2437 replicating cells from a single sample. The resolution and scale of the data allow focused analysis of replication initiation sites, demonstrating that most occur in confined genomic regions. While initiation order is remarkably similar across cells, we unexpectedly identify several subtypes of initiation regions in late-replicating regions. Taken together, high throughput, high resolution sequencing of individual cells reveals previously underappreciated variability in replication initiation and progression.

Negative Magnetophoresis Focusing Microchips Online-Coupled with ICP-MS for High-Throughput Single-Cell Analysis.

High-throughput single-cell analysis is critical to elucidate the cell heterogeneity. Recently, droplet microchips using oil/gas phases to generate single-cell encapsulated droplets have been combined with inductively coupled plasma-mass spectrometry (ICP-MS) for determination of trace elements in single cells with a throughput of dozens of cells per min. To improve the sample throughput and avoid the oil phase introduced into ICP-MS, herein, a negative magnetophoresis focusing microchip was established and online-coupled to ICP-MS for single-cell analysis. MCF-7 cells in the paramagnetic salt solution were introduced into the designed focusing microchannel, in which they were focused into a single stream under both the magnetic repulsion force and inertial lift force, and then were introduced into ICP-MS for online single-cell analysis. The important parameters including the chip design, the concentration of the paramagnetic salt solution, flow rate, cell density, and dwell time were optimized. Under the optimal conditions, a high sample throughput of 1390 cells min-1 was obtained. The established online analytical system was applied to study the uptake behaviors of MCF-7 cells for Zn2+ and ZnO nanoparticles (NPs) at a single-cell level. The single-cell analysis results indicate that MCF-7 cells displayed more remarkable heterogeneity when they were treated with ZnO NPs, and the uptake content of ZnO NPs by MCF-7 cells was less than that of Zn2+. Compared with other droplet microdevice-ICP-MS analysis systems, the developed system has the advantages of simple design and fabrication, no organic phase, a high throughput, and a low sample consumption (only 5 μL).

CellNet: A Lightweight Model towards Accurate LOC-Based High-Speed Cell Detection

Label-free cell separation and sorting in a microfluidic system, an essential technique for modern cancer diagnosis, resulted in high-throughput single-cell analysis becoming a reality. However, designing an efficient cell detection model is challenging. Traditional cell detection methods are subject to occlusion boundaries and weak textures, resulting in poor performance. Modern detection models based on convolutional neural networks (CNNs) have achieved promising results at the cost of a large number of both parameters and floating point operations (FLOPs). In this work, we present a lightweight, yet powerful cell detection model named CellNet, which includes two efficient modules, CellConv blocks and the h-swish nonlinearity function. CellConv is proposed as an effective feature extractor as a substitute to computationally expensive convolutional layers, whereas the h-swish function is introduced to increase the nonlinearity of the compact model. To boost the prediction and localization ability of the detection model, we re-designed the model’s multi-task loss function. In comparison with other efficient object detection methods, our approach achieved state-of-the-art 98.70% mean average precision (mAP) on our custom sea urchin embryos dataset with only 0.08 M parameters and 0.10 B FLOPs, reducing the size of the model by 39.5× and the computational cost by 4.6×. We deployed CellNet on different platforms to verify its efficiency. The inference speed on a graphics processing unit (GPU) was 500.0 fps compared with 87.7 fps on a CPU. Additionally, CellNet is 769.5-times smaller and 420 fps faster than YOLOv3. Extensive experimental results demonstrate that CellNet can achieve an excellent efficiency/accuracy trade-off on resource-constrained platforms.

Characterization of adipose depot-specific stromal cell populations by single-cell mass cytometry

SummaryThe increased prevalence of obesity and metabolic diseases has heightened interest in adipose tissue biology and its potential as a therapeutic target. To better understand cellular heterogeneity and complexity of white adipose tissue (WAT), we employed cytometry by time-of-flight (CyTOF) to characterize immune and stromal cells in visceral and subcutaneous WAT depots under normal and high-fat diet feeding, by quantifying the expression levels of 32 surface marker proteins. We observed comparable proportions of immune cells in two WAT depots under steady state, but depot-distinct subtypes of adipose precursor cells (APC), suggesting differences in their adipogenic and fibrogenic potential. Furthermore, in addition to pro-inflammatory immune cell shifts, significant pro-fibrotic changes were observed in APCs under high-fat diet, suggesting that APCs are early responders to dietary challenges. We propose CyTOF as a complementary and alternative tool to current high-throughput single-cell transcriptomic analyses to better understand the function and plasticity of adipose tissue.

Variable penetrance of Nab3 granule accumulation quantified by a new tool for high-throughput single-cell granule analysis.

Reorganization of cellular proteins into subcellular compartments, such as the concentration of RNA-binding proteins into cytoplasmic stress granules and P-bodies, is a well-recognized, widely studied physiological process currently under intense investigation. One example of this is the induction of the yeast Nab3 transcription termination factor to rearrange from its pan-nucleoplasmic distribution to a granule at the nuclear periphery in response to nutrient limitation. Recent work in many cell types has shown that protein condensation in the nucleus is functionally important for transcription initiation, RNA processing, and termination. However, little is known about how subnuclear compartments form. Here, we have quantitatively analyzed this dynamic process in living yeast using a high-throughput computational tool and fluorescence microscopy. This analysis revealed that Nab3 granule accumulation varies in penetrance across yeast strains. A concentrated single granule is formed from at least a quarter of the nuclear Nab3 drawn from the rest of the nucleus. Levels of granule accumulation were inversely correlated with a growth defect in the absence of glucose. Importantly, the basis for some of the variation in penetrance was attributable to a defect in mitochondrial function. This publicly available computational tool provides a rigorous, reproducible, and unbiased examination of Nab3 granule accumulation that should be widely applicable to a variety of fluorescent images. Thousands of live cells can be readily examined enabling rigorous statistical verification of significance. With it, we describe a new feature of inducible subnuclear compartment formation for RNA-binding transcription factors and an important determinant of granule biogenesis.

Single-cell isoform analysis in human immune cells

High-throughput single-cell analysis today is facilitated by protocols like the 10X Genomics platform or Drop-Seq which generate cDNA pools in which the origin of a transcript is encoded at its 5′ or 3′ end. Here, we used R2C2 to sequence and demultiplex 12 million full-length cDNA molecules generated by the 10X Genomics platform from ~3000 peripheral blood mononuclear cells. We use these reads, independent from Illumina data, to identify B cell, T cell, and monocyte clusters and generate isoform-level transcriptomes for cells and cell types. Finally, we extract paired adaptive immune receptor sequences unique to each T and B cell.

Acoustofluidic interferometric device for rapid single-cell physical phenotyping.

High-throughput single-cell analysis based on physical properties (such as morphology or mechanics) is emerging as a powerful tool to inform clinical research, with a great potential for translation towards diagnosis. Here we present a novel microfluidic approach adopting acoustic waves to manipulate and mechanically stimulate single cells, and interferometry to track changes in the morphology and measure size, deformability, and refractive index of non-adherent cells. The method is based on the integration within the acoustofluidic channel of a low-finesse Fabry-Perot resonator, providing very high sensitivity and a speed potentially suitable to obtain the high-throughput necessary to handle the variability stemming from the biological diversity of single cells. The proposed approach is applied to a set of different samples: reference polystyrene beads, algae and yeast. The results demonstrate the capability of the acoustofluidic interferometric device to detect and quantify optomechanical properties of single cells with a throughput suitable to address label-free single-cell clinical analysis.

Deciphering impedance cytometry signals with neural networks.

Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes.

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.

Controlled generation of droplets using an electric field in a flow-focusing paper-based device.

Droplet-based microfluidics is a modular platform in high-throughput single-cell and small sample analyses. However, this droplet microfluidic system was widely fabricated using soft lithography or glass capillaries, which is expensive and technically demanding for various applications, limiting use in resource-poor settings. Besides, the variation in droplet size is also restricted due to the limitations on the operating forces that the paper-based platform is able to withstand. Herein, we develop a fully integrated paper-based droplet microfluidic platform for conducting droplet generation and cell encapsulation in independent aqueous droplets dispersed in a carrier oil by incorporating electric fields. Through imposing an electric field, the droplet size would decrease with increasing the electric field and smaller droplets can be produced at high applied voltage. The droplet diameter can be adjusted by the ratio of inner and outer flow velocities as well as the applied electric field. We also demonstrated the proof of concept encapsulation application of our paper device by encapsulating yeast cells under an electric field. Using a simple wax printing method, carbon electrodes can be integrated on the paper. The integrated paper-based microfluidic platform can be fabricated easily and conducted outside of centralized laboratories. This microfluidic system shows great potential in drug and cell investigations by encapsulating cells in resource-limited environments.