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Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

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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.

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Quantitative performance of VFACs.(a) Reaction efficiency from mRNA to molecular counts by sequencing 20 housekeeping genes. The R value is the square root of the residual variance. (b) Mapping of the molecular counts on VFACs for a predetermined position and amount of injected mRNA. (c) Dynamic range of quantification by molecular counting of fragments covering the amount generated at the single-cell level: 1 pg of mRNA. Cross markers indicate background signals corresponding to each gene expression level indicated by the round markers. These data indicate that crosstalk during the RT reaction was less than 1%. (d) Minimum detection limit of this method. (e) Coefficient of variation of molecular counts for various levels of expression.
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f2: Quantitative performance of VFACs.(a) Reaction efficiency from mRNA to molecular counts by sequencing 20 housekeeping genes. The R value is the square root of the residual variance. (b) Mapping of the molecular counts on VFACs for a predetermined position and amount of injected mRNA. (c) Dynamic range of quantification by molecular counting of fragments covering the amount generated at the single-cell level: 1 pg of mRNA. Cross markers indicate background signals corresponding to each gene expression level indicated by the round markers. These data indicate that crosstalk during the RT reaction was less than 1%. (d) Minimum detection limit of this method. (e) Coefficient of variation of molecular counts for various levels of expression.

Mentions: We tested the quantification performance of the VFACs using a calibrated amount of mRNA (Methods). The mRNA solutions were injected into microchambers on the VFACs, and the cDNAs were then synthesized individually on the chips. After PCR amplification, next-generation sequencing (NGS; Ion PGM) was performed. First, the copy numbers of mRNA determined by qPCR were correlated with molecular counts by sequencing (Fig. 2(a)). The copy numbers of mRNA injected into each microchamber on the chips were estimated using a calibration curve of the cDNA and cRNA acquired by bead-based RT-qPCR (Supplementary Figure 4). The efficiency of sample preparation from mRNA to molecular counts was estimated to be greater than 50 ± 16.5% for the injection of more than 15 copies of mRNA per microchamber (Fig. 2(a)). The acquired efficiency was approximately 4 times higher than was obtained by Drop-seq714.Second, we assessed the parallel sample preparation capability of the microchambers when the mRNA solutions were injected. Figure 2(b) presents the mapping of the total expression of 20 housekeeping genes on a VFAC with 10 by 10 micro-reaction chambers. The positions of the peaks matched the positions of the microchambers into which the solutions were injected, confirming the accuracy of the sample position assignments. The quantities of gene expression in Fig. 2(b–e) were indicated as molecular counts using UMIs, where the total number of acquired reads mapped on 20 genes was approximately 1 million for each VFAC. Third, Fig. 2(c) presents calibration curves of the gene expression levels of four housekeeping genes (RPS18, EEF1G, ALDOA, and HMBS). The amount of mRNA in a single cell (1 pg) is within the linear region for all genes. Figure 2(d) presents the lowest copy number detection limit of 15 copies per chamber, at which the detection rate is greater than 90%. Finally, we estimated the coefficient of variation (CV) depending on the copy number of mRNAs injected into individual chambers (Fig. 2(e)). These curves describing the CV values of measurement noise (55% at 15 copies of mRNA) are comparable to the ones reported previously26 as a result of the highly efficient cDNA synthesis. The regression curve was used as a probabilistic model of measurement noise.


Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments
Quantitative performance of VFACs.(a) Reaction efficiency from mRNA to molecular counts by sequencing 20 housekeeping genes. The R value is the square root of the residual variance. (b) Mapping of the molecular counts on VFACs for a predetermined position and amount of injected mRNA. (c) Dynamic range of quantification by molecular counting of fragments covering the amount generated at the single-cell level: 1 pg of mRNA. Cross markers indicate background signals corresponding to each gene expression level indicated by the round markers. These data indicate that crosstalk during the RT reaction was less than 1%. (d) Minimum detection limit of this method. (e) Coefficient of variation of molecular counts for various levels of expression.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120284&req=5

f2: Quantitative performance of VFACs.(a) Reaction efficiency from mRNA to molecular counts by sequencing 20 housekeeping genes. The R value is the square root of the residual variance. (b) Mapping of the molecular counts on VFACs for a predetermined position and amount of injected mRNA. (c) Dynamic range of quantification by molecular counting of fragments covering the amount generated at the single-cell level: 1 pg of mRNA. Cross markers indicate background signals corresponding to each gene expression level indicated by the round markers. These data indicate that crosstalk during the RT reaction was less than 1%. (d) Minimum detection limit of this method. (e) Coefficient of variation of molecular counts for various levels of expression.
Mentions: We tested the quantification performance of the VFACs using a calibrated amount of mRNA (Methods). The mRNA solutions were injected into microchambers on the VFACs, and the cDNAs were then synthesized individually on the chips. After PCR amplification, next-generation sequencing (NGS; Ion PGM) was performed. First, the copy numbers of mRNA determined by qPCR were correlated with molecular counts by sequencing (Fig. 2(a)). The copy numbers of mRNA injected into each microchamber on the chips were estimated using a calibration curve of the cDNA and cRNA acquired by bead-based RT-qPCR (Supplementary Figure 4). The efficiency of sample preparation from mRNA to molecular counts was estimated to be greater than 50 ± 16.5% for the injection of more than 15 copies of mRNA per microchamber (Fig. 2(a)). The acquired efficiency was approximately 4 times higher than was obtained by Drop-seq714.Second, we assessed the parallel sample preparation capability of the microchambers when the mRNA solutions were injected. Figure 2(b) presents the mapping of the total expression of 20 housekeeping genes on a VFAC with 10 by 10 micro-reaction chambers. The positions of the peaks matched the positions of the microchambers into which the solutions were injected, confirming the accuracy of the sample position assignments. The quantities of gene expression in Fig. 2(b–e) were indicated as molecular counts using UMIs, where the total number of acquired reads mapped on 20 genes was approximately 1 million for each VFAC. Third, Fig. 2(c) presents calibration curves of the gene expression levels of four housekeeping genes (RPS18, EEF1G, ALDOA, and HMBS). The amount of mRNA in a single cell (1 pg) is within the linear region for all genes. Figure 2(d) presents the lowest copy number detection limit of 15 copies per chamber, at which the detection rate is greater than 90%. Finally, we estimated the coefficient of variation (CV) depending on the copy number of mRNAs injected into individual chambers (Fig. 2(e)). These curves describing the CV values of measurement noise (55% at 15 copies of mRNA) are comparable to the ones reported previously26 as a result of the highly efficient cDNA synthesis. The regression curve was used as a probabilistic model of measurement noise.

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus