Itoshi Nikaido

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity

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.

Context-Dependent Wiring of Sox2 Regulatory Networks for Self-Renewal of Embryonic and Trophoblast Stem Cells

Sox2 is a transcription factor required for the maintenance of pluripotency. It also plays an essential role in different types of multipotent stem cells, raising the possibility that Sox2 governs the common stemness phenotype. Here we show that Sox2 is a critical downstream target of fibroblast growth factor (FGF) signaling, which mediates self-renewal of trophoblast stem cells (TSCs). Sustained expression of Sox2 together with Esrrb or Tfap2c can replace FGF dependency. By comparing genome-wide binding sites of Sox2 in embryonic stem cells (ESCs) and TSCs combined with inducible knockout systems, we found that, despite the common role in safeguarding the stem cell state, Sox2 regulates distinct sets of genes with unique functions in these two different yet developmentally related types of stem cells. Our findings provide insights into the functional versatility of transcription factors during embryogenesis, during which they can be recursively utilized in a variable manner within discrete network structures.

Id4, a New Candidate Gene for Senile Osteoporosis, Acts as a Molecular Switch Promoting Osteoblast Differentiation

Excessive accumulation of bone marrow adipocytes observed in senile osteoporosis or age-related osteopenia is caused by the unbalanced differentiation of MSCs into bone marrow adipocytes or osteoblasts. Several transcription factors are known to regulate the balance between adipocyte and osteoblast differentiation. However, the molecular mechanisms that regulate the balance between adipocyte and osteoblast differentiation in the bone marrow have yet to be elucidated. To identify candidate genes associated with senile osteoporosis, we performed genome-wide expression analyses of differentiating osteoblasts and adipocytes. Among transcription factors that were enriched in the early phase of differentiation, Id4 was identified as a key molecule affecting the differentiation of both cell types. Experiments using bone marrow-derived stromal cell line ST2 and Id4-deficient mice showed that lack of Id4 drastically reduces osteoblast differentiation and drives differentiation toward adipocytes. On the other hand knockdown of Id4 in adipogenic-induced ST2 cells increased the expression of Pparγ2, a master regulator of adipocyte differentiation. Similar results were observed in bone marrow cells of femur and tibia of Id4-deficient mice. However the effect of Id4 on Pparγ2 and adipocyte differentiation is unlikely to be of direct nature. The mechanism of Id4 promoting osteoblast differentiation is associated with the Id4-mediated release of Hes1 from Hes1-Hey2 complexes. Hes1 increases the stability and transcriptional activity of Runx2, a key molecule of osteoblast differentiation, which results in an enhanced osteoblast-specific gene expression. The new role of Id4 in promoting osteoblast differentiation renders it a target for preventing the onset of senile osteoporosis.

Quantitative Expression Profile of Distinct Functional Regions in the Adult Mouse Brain

The adult mammalian brain is composed of distinct regions with specialized roles including regulation of circadian clocks, feeding, sleep/awake, and seasonal rhythms. To find quantitative differences of expression among such various brain regions, we conducted the BrainStars (B*) project, in which we profiled the genome-wide expression of ∼50 small brain regions, including sensory centers, and centers for motion, time, memory, fear, and feeding. To avoid confounds from temporal differences in gene expression, we sampled each region every 4 hours for 24 hours, and pooled the samples for DNA-microarray assays. Therefore, we focused on spatial differences in gene expression. We used informatics to identify candidate genes with expression changes showing high or low expression in specific regions. We also identified candidate genes with stable expression across brain regions that can be used as new internal control genes, and ligand-receptor interactions of neurohormones and neurotransmitters. Through these analyses, we found 8,159 multi-state genes, 2,212 regional marker gene candidates for 44 small brain regions, 915 internal control gene candidates, and 23,864 inferred ligand-receptor interactions. We also found that these sets include well-known genes as well as novel candidate genes that might be related to specific functions in brain regions. We used our findings to develop an integrated database (http://brainstars.org/) for exploring genome-wide expression in the adult mouse brain, and have made this database openly accessible. These new resources will help accelerate the functional analysis of the mammalian brain and the elucidation of its regulatory network systems.

Identification of novel PPARγ target genes by integrated analysis of ChIP-on-chip and microarray expression data during adipocyte differentiation

PPARgamma (peroxisome proliferator-activated receptor gamma) acts as a key molecule of adipocyte differentiation, and transactivates multiple target genes involved in lipid metabolic pathways. Identification of PPARgamma target genes will facilitate to predict the extent to which the drugs can affect and also to understand the molecular basis of lipid metabolism. Here, we have identified five target genes regulated directly by PPARgamma during adipocyte differentiation in 3T3-L1 cells using integrated analyses of ChIP-on-chip and expression microarray. We have confirmed the direct PPARgamma regulation of five genes by luciferase reporter assay in NIH-3T3 cells. Of these five genes Hp, Tmem143 and 1100001G20Rik are novel PPARgamma targets. We have also detected PPREs (PPAR response elements) sequences in the promoter region of the five genes computationally. Unexpectedly, most of the PPREs detected proved to be atypical, suggesting the existence of more atypical PPREs than previously thought in the promoter region of PPARgamma regulated genes.

An automated system for high-throughput single cell-based breeding

When establishing the most appropriate cells from the huge numbers of a cell library for practical use of cells in regenerative medicine and production of various biopharmaceuticals, cell heterogeneity often found in an isogenic cell population limits the refinement of clonal cell culture. Here, we demonstrated high-throughput screening of the most suitable cells in a cell library by an automated undisruptive single-cell analysis and isolation system, followed by expansion of isolated single cells. This system enabled establishment of the most suitable cells, such as embryonic stem cells with the highest expression of the pluripotency marker Rex1 and hybridomas with the highest antibody secretion, which could not be achieved by conventional high-throughput cell screening systems (e.g., a fluorescence-activated cell sorter). This single cell-based breeding system may be a powerful tool to analyze stochastic fluctuations and delineate their molecular mechanisms.

EICO (Expression‐based Imprint Candidate Organizer): finding disease‐related imprinted genes

We have developed an integrated database that is specialized for the study of imprinted disease genes. The database contains novel candidate imprinted genes identified by the RIKEN full-length mouse cDNA microarray study, information on validated single nucleotide polymorphisms (SNPs) to confirm imprinting using reciprocal mouse crosses and the predicted physical position of imprinting-related disease loci in the mouse and human genomes. It has two user-friendly search interfaces: the SNP-central view (MuSCAT: MoUse SNP CATalog) and the candidate gene-central view (CITE: Candidate Imprinted Transcripts by Expression). The database, EICO (Expression-based Imprint Candidate Organizer), can be accessed via the World Wide Web (http://fantom2.gsc.riken.jp/EICODB/) and the DAS client software. These data and interfaces facilitate understanding of the mechanism of imprinting in mammalian inherited traits.

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

Motivation: The 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. Results: In 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 Implementation: The R source code of SCODE is available at https://github.com/hmatsu1226/SCODE Contact: hirotaka.matsumoto@riken.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space

Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington’s disease gene and Bdnf. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30–50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.