Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.

MotivationThe analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.ResultsIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.Availability and ImplementationThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODESupplementary information Supplementary data are available at Bioinformatics online.

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The remarkable capability of planarian regeneration is mediated by a group of adult stem cells referred to as neoblasts. Although these cells possess many unique cytological characteristics (e.g. they are X-ray sensitive and contain chromatoid bodies), it has been difficult to isolate them after cell dissociation. This is one of the major reasons why planarian regenerative mechanisms have remained elusive for a long time. Here, we describe a new method to isolate the planarian adult stem cells as X-ray-sensitive cell populations by fluorescence-activated cell sorting (FACS). Dissociated cells from whole planarians were labeled with fluorescent dyes prior to fractionation by FACS. We compared the FACS profiles from X-ray-irradiated and non-irradiated planarians, and thereby found two cell fractions which contained X-ray-sensitive cells. These fractions, designated X1 and X2, were subjected to electron microscopic morphological analysis. We concluded that X-ray-sensitive cells in both fractions possessed typical stem cell morphology: an ovoid shape with a large nucleus and scant cytoplasm, and chromatoid bodies in the cytoplasm. This method of isolating X-ray-sensitive cells using FACS may provide a key tool for advancing our understanding of the stem cell system in planarians.

Recent transcriptome analyses have revealed that a large body of noncoding regions of mammalian genomes are actually transcribed into RNAs. Our understanding of the molecular features of these noncoding RNAs is far from complete. We have identified a novel mRNA-like noncoding gene, named Gomafu, which is expressed in a distinct set of neurons in the mouse nervous system. Interestingly, spliced mature Gomafu RNA is localized to the nucleus despite its mRNA-like characteristics, which usually act as potent export signals to the cytoplasm. Within the nucleus, Gomafu RNA is detected as numerous spots that do not colocalize with known nuclear domain markers. Gomafu RNA is extremely insoluble and remains intact after nuclear matrix preparation. Furthermore, heterokaryon assays revealed that Gomafu RNA does not shuttle between the nucleus and cytoplasm, but is retained in the nucleus after its transcription. We propose that Gomafu RNA represents a novel family of mRNA-like noncoding RNA that constitutes a cell-type-specific component of the nuclear matrix.