Microfluidic Device for Single-Cell Analysis

Wheeler, Aaron R.; Throndset, William; Whelan, Rebecca J.; Leach, Andrew M.; Zare, Richard N.; Liao, Yish Hann; Farrell, Kevin; Manger, Ian D.; Daridon, Antoine

doi:10.1021/ac0340758

Cited by 548 publications

(409 citation statements)

References 32 publications

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“…However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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Section: A N a Ly S I Smentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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“…This makes it very suitable for medical studies and for dealing with cellular matter. Microfluidics is currently being used to study single cell 1 and single molecule biophysics 2 . A great amount of research work is currently being done towards producing cheap and accurate lab-on-a-chip devices that may be used as diagnostic tools or for other analytical tasks 3 .…”

Section: ____________ This Thesis Follows the Style Of Lab On A Chipmentioning

confidence: 99%

Monodispersed polygonal water droplets in microchannel

Mehrotra

Nan

Kameoka

2008

Applied Physics Letters

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In this thesis, we present a method to fabricate droplets using shear focusing in an oil medium alternatively from two channels facing each other. The droplets produced are non-circular in shape, and their shape dynamically alters as they travel in the microfluidic channel. The size of the droplets can be controlled by the ratio of oil and water flow rates.Microscopic images have been presented that show the non-spherical shape of the droplets at the point of fabrication. Images taken at two points further along the iv microfluidic channel show how the shapes of these droplets change as they travel in the channel. There were three regimes of droplet shapes, circular, triangular and rectangular shapes that were determined by the packing ratio of water droplet in oil phase in microfluidic channels. All droplets formed in this experiment were monodispersed.

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“…Recent research has accomplished this task quite elegantly on a multilayer PDMS microchip (19). Cells are focused between two streams of buffer and guided into a T-junction.…”

Section: Cell Trapping and Reagent Deliverymentioning

confidence: 99%

Chemical Analysis of Single Mammalian Cells with Microfluidics

Price¹,

Culbertson²

2007

Anal. Chem.

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of Single Mammalian Cells with Microfluidics C ells are the fundamental building blocks of life. All basic physiological functions of multicellular organisms reside ultimately in the cell. The misregulation of cellular physiology results in disease at the organism level. Thus, comprehending cell physiology is key to understanding and curing diseases. Many physiological processes can be studied using populations of cells. Others occur either on short timescales (e.g., kinase signaling cascades) or nonsynchronously (e.g., response to an external chemical gradient), so that taking a population average will not lead to an understanding of how the cellular chemistry occurs. These types of processes require single-cell analysis, and thus discretion must be exercised when deciding under which circumstances bulk versus singlecell analyses are more appropriate (1). In addition, many diseases like cancer start with a single cell; therefore, if one would like to find the rare mutations in populations of cells that herald the inception of a disease, then cells must be examined individually.Probing behavior at the single-cell level, however, is a very challenging task, primarily because of the small sample volume, the low abundance of material, and the fragile nature of the cell itself. Analyzing the contents of a single cell requires sensitive detection techniques and handling procedures that do not stress or damage it. Additionally, no proper blank exists that can be used, so truly quantitative studies are difficult. Intense interest in single-cell physiology, however, is driving the analytical and biomedical engineering fields to improve the technology for examining cells. One of the most popular and promising areas is lab-on-a-chip devices to manipulate and analyze single cells.Since Jorgenson's groundbreaking work in 1989, CE has been used to examine the contents of single cells (2-4). However, many procedures for cell injection and lysis are time-consuming, and accuracy and reproducibility rely heavily on the skill of the researcher. Furthermore, the limited number of architectures provided by microbore tubing and its relatively large volume restrict the types of processes that can be investigated. Conversely, microfluidics, or lab-on-a-chip technology, offers a versatile format in which biological cells can be analyzed.These miniaturized devices provide several analytical and operational advantages over conventional macroscale systems (5, 6). Microfluidic architectures provide precise spatial control over reagents and samples, are capable of fast analysis times, can be automated, and can precisely manipulate picoliter volumes of material without dilution. In addition, microfluidic systems are amenable to many different detection schemes, can be manufactured from many different materials at relatively low cost, allow flexibility of design, and provide the capability of integrating a series of multiple tasks (sample preparation, mixing, separation, etc.) in both serial and parallel schemes. Portable versions of these sys...

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Microfluidic Device for Single-Cell Analysis

Cited by 548 publications

References 32 publications

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Monodispersed polygonal water droplets in microchannel

Chemical Analysis of Single Mammalian Cells with Microfluidics

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