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A simple microfluidic dispenser for single-microparticle and cell samples.

ABSTRACT: Non-destructive isolation of single-cells has become an important need for many biology research laboratories; however, there is a lack of easily employed and inexpensive tools. Here, we present a single-particle sample delivery approach fabricated from simple, economical components that may address this need. In this, we employ unique flow and timing strategies to bridge the significant force and length scale differences inherent in transitioning from single particle isolation to delivery. Demonstrating this approach, we use an optical trap to isolate individual microparticles and red blood cells that are dispensed within separate 50 ?l droplets off a microfluidic chip for collection into microscope slides or microtiter plates.

SUBMITTER: Kasukurti A

PROVIDER: S-EPMC4229394 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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A simple microfluidic dispenser for single-microparticle and cell samples.

Kasukurti A A Eggleton C D CD Desai S A SA Disharoon D I DI Marr D W M DW

Lab on a chip 20141015 24

Non-destructive isolation of single-cells has become an important need for many biology research laboratories; however, there is a lack of easily employed and inexpensive tools. Here, we present a single-particle sample delivery approach fabricated from simple, economical components that may address this need. In this, we employ unique flow and timing strategies to bridge the significant force and length scale differences inherent in transitioning from single particle isolation to delivery. Demo ...[more]

PMID: 25316326

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Project description:Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next-generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis. We investigated gene expression in mouse embryonic cells using microfluidic-facilitated RNA-Seq to analyze 56 single mouse ES cell (mESC) transcriptomes and 6 single mouse embryonic fibroblast (MEF) transcriptomes. To quantitatively evaluate the sensitivity and precision of our technique, we also sequenced 23 libraries from extracted mESC RNA, representing three sets of technical replicates with varying starting amounts of material.

Dataset Information

A simple microfluidic dispenser for single-microparticle and cell samples.

Publications

A simple microfluidic dispenser for single-microparticle and cell samples.

Similar Datasets

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