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Abstract
Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.
Highlights
Single-cell gene expression analysis utilizing high-throughput DNA sequencing has emerged as a powerful tool to investigate complex biological systems[1,2,3,4,5,6,7]
The high-density microchambers on the chips and the vertical flow system reduced the volume of expensive enzymatic reagent per cell by a factor of 2028, reducing the cost of sample preparation compared to reactions performed in tubes
We have demonstrated a method for reliable cell typing based on single-cell gene expression analysis
Summary
Single-cell gene expression analysis utilizing high-throughput DNA sequencing has emerged as a powerful tool to investigate complex biological systems[1,2,3,4,5,6,7]. Cell typing without a priori knowledge provides a foundation for further studies of biological processes, including screening gene markers, the lack of statistical reliability hampers the application of single-cell analysis in discerning the functions of genes in heterogeneous tissues To address this limitation, precise measurement technologies[11,20,22,23,24,25,26,27,28], high-throughput sample preparation technologies[2,11,12,24] and statistical methods for determining cell types[1,11] have recently been developed. Since it is difficult to reduce measurement noise thoroughly by improving the efficiency of sample preparation, the combination of the VFAC-based and statistical method to effectively manage the stochastic behavior of single-cell gene expression can be applied to various fields, including immunology, cancer biology, and developmental biology
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