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A diffusion-based microfluidic device for single-cell RNA-seq.

ABSTRACT: Microfluidic devices provide a low-input and efficient platform for single-cell RNA-seq (scRNA-seq). Existing microfluidic devices have a complicated multi-chambered structure for handling the multi-step process involved in RNA-seq and dilution between steps is used to negate the inhibitory effects among reagents. This makes the device difficult to fabricate and operate. Here we present microfluidic diffusion-based RNA-seq (MID-RNA-seq) for conducting scRNA-seq with a diffusion-based reagent swapping scheme. This device incorporates cell trapping, lysis, reverse transcription and PCR amplification all in one simple microfluidic device. MID-RNA-seq provides high data quality that is comparable to existing scRNA-seq methods while implementing a simple device design that permits multiplexing. The robustness and scalability of the MID-RNA-seq device will be important for transcriptomic studies of scarce cell samples.

SUBMITTER: Sarma M

PROVIDER: S-EPMC6459606 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A diffusion-based microfluidic device for single-cell RNA-seq.

Sarma Mimosa M Lee Jiyoung J Ma Sai S Li Song S Lu Chang C

Lab on a chip 20190301 7

Microfluidic devices provide a low-input and efficient platform for single-cell RNA-seq (scRNA-seq). Existing microfluidic devices have a complicated multi-chambered structure for handling the multi-step process involved in RNA-seq and dilution between steps is used to negate the inhibitory effects among reagents. This makes the device difficult to fabricate and operate. Here we present microfluidic diffusion-based RNA-seq (MID-RNA-seq) for conducting scRNA-seq with a diffusion-based reagent swa ...[more]

PMID: 30815639

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A diffusion-based microfluidic device for single-cell RNA-seq

Project description:Microfluidic devices provide a low-input and efficient platform for single-cell RNA-seq (scRNA-Seq). Here we present microfluidic diffusion-based RNA-seq (MID-RNA-seq) for conducting scRNA-seq with a diffusion-based reagent swapping scheme. This device incorporates cell trapping, lysis, reverse transcription and PCR amplification all in one microfluidic chamber. MID-RNA-Seq provides high data quality that is comparable to existing scRNA-seq methods while implementing a simple device design that permits multiplexing. The robustness and scalability of MID-RNA-Seq device will be important for transcriptomic studies of scarce cell samples.

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Project description:Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

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Project description:Single-cell RNA sequencing is a powerful technology for obtaining transcriptomes at single-cell resolutions. However, it suffers from dropout events (i.e., excess zero counts) since only a small fraction of transcripts get sequenced in each cell during the sequencing process. This inherent sparsity of expression profiles hinders further characterizations at cell/gene-level such as cell type identification and downstream analysis. To alleviate this dropout issue we introduce a network-based method, netImpute, by leveraging the hidden information in gene co-expression networks to recover real signals. netImpute employs Random Walk with Restart (RWR) to adjust the gene expression level in a given cell by borrowing information from its neighbors in a gene co-expression network. Performance evaluation and comparison with existing tools on simulated data and seven real datasets show that netImpute substantially enhances clustering accuracy and data visualization clarity, thanks to its effective treatment of dropouts. While the idea of netImpute is general and can be applied with other types of networks such as cell co-expression network or protein-protein interaction (PPI) network, evaluation results show that gene co-expression network is consistently more beneficial, presumably because PPI network usually lacks cell type context, while cell co-expression network can cause information loss for rare cell types. Evaluation results on several biological datasets show that netImpute can more effectively recover missing transcripts in scRNA-seq data and enhance the identification and visualization of heterogeneous cell types than existing methods.

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