Solving the “World-to-Chip” Interface Problem with a Microfluidic Matrix

Liu, Jian; Hansen, Carl L.; Quake, Stephen R.

doi:10.1021/ac0346407

Cited by 259 publications

(227 citation statements)

References 21 publications

(40 reference statements)

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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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“…The reservoir system is based on a previously reported design in which water-filled channels served to reduce sample evaporation in adjacent channels. 34 The reservoir is located above the storage area of a microfluidic device, separated from the flow layer by a thin PDMS membrane. The reservoir consists of a set of microfluidic channels that are back-filled with water.…”

Section: Sample Evaporationmentioning

confidence: 99%

Digital microfluidics using soft lithography

et al. 2006

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Although microfluidic chips have demonstrated basic functionality for single applications, performing varied and complex experiments on a single device is still technically challenging. While many groups have implemented control software to drive the pumps, valves, and electrodes used to manipulate fluids in microfluidic devices, a new level of programmability is needed for end users to orchestrate their own unique experiments on a given device. This paper presents an approach for programmable and scalable control of discrete fluid samples in a polydimethylsiloxane (PDMS) microfluidic system using multiphase flows. An immiscible fluid phase is utilized to separate aqueous samples from one another, and a novel ''microfluidic latch'' is used to precisely align a sample after it has been transported a long distance through the flow channels. To demonstrate the scalability of the approach, this paper introduces a ''generalpurpose'' microfluidic chip containing a rotary mixer and addressable storage cells. The system is general purpose in that all operations on the chip operate in terms of unit-sized aqueous samples; using the underlying mechanisms for sample transport and storage, additional sensors and actuators can be integrated in a scalable manner. A novel high-level software library allows users to specify experiments in terms of variables (i.e., fluids) and operations (i.e., mixes) without the need for detailed knowledge about the underlying device architecture. This research represents a first step to provide a programmable interface to the microfluidic realm, with the aim of enabling a new level of scalability and flexibility for lab-on-a-chip experiments.

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“…This design provides the basis for diagnostic systems in paper that are more complex than the simplest dip-stick assays (in the sense that multiple assays can be performed on a small array with ~5-20 μL of blood, urine, or other fluid), but simpler and more affordable than highertechnology microfluidic assays. [20][21][22][23] An advantage of using PDMS to pattern the paper rather than SU-8 or PMMA is that PDMS is an elastomer. Since PDMS in paper is significantly more flexible than is photoresist in paper, a paper device printed with PDMS can be folded without destroying the channel.…”

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confidence: 99%

Low-Cost Printing of Poly(dimethylsiloxane) Barriers To Define Microchannels in Paper

2008

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This paper describes the use of a modified x,y-plotter to generate hydrophilic channels by printing a solution of hydrophobic polymer (poly(dimethyl siloxane; PDMS) dissolved in hexanes onto filter paper. The PDMS penetrates the depth of the paper, and forms a hydrophobic wall that aqueous solutions cannot cross. The minimum size of printed features is approximately 1 mm; this resolution is adequate for the rapid prototyping of hand-held, visually read, diagnostic assays (and other microfluidic systems) based on paper. After curing the printed PDMS, the paper-based devices can be bent or folded to generate three-dimensional (3D) systems of channels. Capillary action pulls aqueous samples into the paper channels. Colorimetric assays for the presence of glucose and protein are demonstrated in the printed devices; spots of Bromothymol Blue distinguished samples with slightly basic pH (6.5) from samples with slightly acidic pH (8.0). The work also describes using printed devices that can be loaded using multipipettes, and printed flexible, foldable channels in paper over areas larger than 100 cm 2 . This paper describes the use of a modified desktop plotter to fabricate simple patterns of hydrophilic microchannels in paper. We defined the boundaries of the microchannels by printing a solution of poly(dimethyl siloxane) (PDMS) in hexanes onto filter paper, using an x,y-plotter as a print-engine. Because PDMS is an elastomer, the paper could be bent and folded, without destroying the integrity of the channels. The channels have a minimum width of 1 mm, and the minimum spacing between two channels can be as small as 1 mm. These dimensions are large relative to those normally encountered in microfluidic systems, but they are the right size for the basic analytical and diagnostic devices that are our primary objectives, since readout of these devices will often involve visual observations of unmagnified spots of analytes. 1 We believe that this low-cost system for printing hydrophobic polymers and other materials on paper will be useful for prototyping simple paper-based diagnostics assays.A variety of simple diagnostic tests (for example, "dip-stick" tests) rely on paper-based assays. [2][3][4][5][6][7][8][9][10][11][12][13][14][15] Some of these tests analyze environmental conditions, 16,17 or detect illness (often in the developing world). [18][19][20] We have recently patterned paper into hydrophilic regions demarcated by hydrophobic walls using photolithography and conventional photoresists (SU-8; and poly(methyl methacrylate), or PMMA). 1 The hydrophilic regions act as microfluidic channels, in which capillary action wicks aqueous samples into the device. This design provides the basis for diagnostic systems in paper that are more complex than the simplest dip-stick assays (in the sense that multiple assays can be performed on a small array with ~5-20 μL of blood, urine, or other fluid), but simpler and more affordable than highertechnology microfluidic assays. 20 An advantage of using PDMS to pattern the p...

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Solving the “World-to-Chip” Interface Problem with a Microfluidic Matrix

Cited by 259 publications

References 21 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

Digital microfluidics using soft lithography

Low-Cost Printing of Poly(dimethylsiloxane) Barriers To Define Microchannels in Paper

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