Generation and Profiling of 2,135 Human ESC Lines for the Systematic Analyses of Cell States Perturbed by Inducing Single Transcription Factors.

Transcription factors (TFs) play a pivotal role in determining cell states, yet our understanding of the causative relationship between TFs and cell states is limited. Here, we systematically examine the state changes of human pluripotent embryonic stem cells (hESCs) by the large-scale manipulation of single TFs. We establish 2,135 hESC lines, representing three clones each of 714 doxycycline (Dox)-inducible genes including 481 TFs, and obtain 26,998 microscopic cell images and 2,174 transcriptome datasets-RNA sequencing (RNA-seq) or microarrays-48 h after the presence or absence of Dox. Interestingly, the expression of essentially all the genes, including genes located in heterochromatin regions, are perturbed by these TFs. TFs are also characterized by their ability to induce differentiation of hESCs into specific cell lineages. These analyses help to provide a way of classifying TFs and identifying specific sets of TFs for directing hESC differentiation into desired cell types.

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.

Distribution of human single-nucleotide polymorphisms is approximated by the power law and represents a fractal structure.

Single-nucleotide polymorphisms (SNPs) are one of the main causes of evolution. The distribution of human SNPs, which were examined in detail genomewide, was analyzed. Three discrete databases of human SNPs were used for this analysis, and similar results were obtained from these databases. It was found that the distribution of the distance between SNPs was approximated by the power law, and the shape of the regions including SNPs had the so-called fractal structure. Although the reason why the distribution of SNPs obeys such a certain law of physics is unclear, a speculation was attempted in connection with the three-dimensional structure of human chromatin which has a fractal structure.