DBTSS/DBKERO for integrated analysis of transcriptional regulation.

DBTSS (Database of Transcriptional Start Sites)/DBKERO (Database of Kashiwa Encyclopedia for human genome mutations in Regulatory regions and their Omics contexts) is the database originally initiated with the information of transcriptional start sites and their upstream transcriptional regulatory regions. In recent years, we updated the database to assist users to elucidate biological relevance of the human genome variations or somatic mutations in cancers which may affect the transcriptional regulation. In this update, we facilitate interpretations of disease associated genomic variation, using the Japanese population as a model case. We enriched the genomic variation dataset consisting of the 13,368 individuals collected for various genome-wide association studies and the reference epigenome information in the surrounding regions using a total of 455 epigenome datasets (four tissue types from 67 healthy individuals) collected for the International Human Epigenome Consortium (IHEC). The data directly obtained from the clinical samples was associated with that obtained from various model systems, such as the drug perturbation datasets using cultured cancer cells. Furthermore, we incorporated the results obtained using the newly developed analytical methods, Nanopore/10x Genomics long-read sequencing of the human genome and single cell analyses. The database is made publicly accessible at the URL (http://dbtss.hgc.jp/).

A biochemical landscape of A-to-I RNA editing in the human brain transcriptome.

Inosine is an abundant RNA modification in the human transcriptome and is essential for many biological processes in modulating gene expression at the post-transcriptional level. Adenosine deaminases acting on RNA (ADARs) catalyze the hydrolytic deamination of adenosines to inosines (A-to-I editing) in double-stranded regions. We previously established a biochemical method called "inosine chemical erasing" (ICE) to directly identify inosines on RNA strands with high reliability. Here, we have applied the ICE method combined with deep sequencing (ICE-seq) to conduct an unbiased genome-wide screening of A-to-I editing sites in the transcriptome of human adult brain. Taken together with the sites identified by the conventional ICE method, we mapped 19,791 novel sites and newly found 1258 edited mRNAs, including 66 novel sites in coding regions, 41 of which cause altered amino acid assignment. ICE-seq detected novel editing sites in various repeat elements as well as in short hairpins. Gene ontology analysis revealed that these edited mRNAs are associated with transcription, energy metabolism, and neurological disorders, providing new insights into various aspects of human brain functions.

Evaluation of Microplate Handling Accuracy for Applying Robotic Arms in Laboratory Automation.

An inexpensive single-arm robot is widely utilized for recent laboratory automation solutions. The integration of a single-arm robot as a transfer system into a semi-automatic liquid dispenser without a transfer system can be realized as an inexpensive alternative to a fully automated liquid handling system. However, there has been no quantitative investigation of the positional accuracy of robot arms required to transfer microplates. In this study, we constructed a platform comprising aluminum frames and digital gauges to facilitate such measurements. We measured the position repeatability of a robot arm equipped with a custom-made finger by repeatedly transferring microplates. Further, the acceptable misalignment of plate transfer was evaluated by adding an artificial offset to the microplate position using this platform. The results of these experiments are expected to serve as benchmarks for the selection of robot arms for laboratory automation in biology. Furthermore, all information for replicating this device will be made publicly available, thereby allowing many researchers to collaborate and accumulate knowledge, hopefully contributing to advances in this field.

Transcripts of unknown function in multiple-signaling pathways involved in human stem cell differentiation.

Mammalian transcriptome analysis has uncovered tens of thousands of novel transcripts of unknown function (TUFs). Classical and recent examples suggest that the majority of TUFs may underlie vital intracellular functions as non-coding RNAs because of their low coding potentials. However, only a portion of TUFs have been studied to date, and the functional significance of TUFs remains mostly uncharacterized. To increase the repertoire of functional TUFs, we screened for TUFs whose expression is controlled during differentiation of pluripotent human mesenchymal stem cells (hMSCs). The resulting six TUFs, named transcripts related to hMSC differentiation (TMDs), displayed distinct transcriptional kinetics during hMSC adipogenesis and/or osteogenesis. Structural and comparative genomic characterization suggested a wide variety of biologically active structures of these TMDs, including a long nuclear non-coding RNA, a microRNA host gene and a novel small protein gene. Moreover, the transcriptional response to established pathway activators indicated that most of these TMDs were transcriptionally regulated by each of the two key pathways for hMSC differentiation: the Wnt and protein kinase A (PKA) signaling pathways. The present study suggests that not only TMDs but also other human TUFs may in general participate in vital cellular functions with different molecular mechanisms.

Adipose-derived mesenchymal stem cells differentiate into heterogeneous cancer-associated fibroblasts in a stroma-rich xenograft model.

Cancer-associated fibroblasts (CAFs) are the key components of the densely proliferated stroma in pancreatic ductal adenocarcinoma (PDAC) and contribute to tumor progression and drug resistance. CAFs comprise heterogeneous subpopulations playing unique and vital roles. However, the commonly used mouse models have not been able to fully reproduce the histological and functional characteristics of clinical human CAF. Here, we generated a human cell-derived stroma-rich CDX (Sr-CDX) model, to reproduce the clinical tumor microenvironment. By co-transplanting human adipose-derived mesenchymal stem cells (AD-MSCs) and a human PDAC cell line (Capan-1) into mice, the Sr-CDX model recapitulated the characteristics of clinical pancreatic cancer, such as accelerated tumor growth, abundant stromal proliferation, chemoresistance, and dense stroma formed from the heterogeneous CAFs. Global RNA sequencing, single-cell based RNA sequencing, and histological analysis of CAFs in the Sr-CDX model revealed that the CAFs of the Sr-CDX mice were derived from the transplanted AD-MSCs and composed of heterogeneous subpopulations of CAF, including known and unknown subtypes. These lines of evidences suggest that our new tumor-bearing mouse model has the potential to address an open question in CAF research, that is the mechanism of CAF differentiation.

Machine learning approach for discrimination of genotypes based on bright-field cellular images.

Morphological profiling is a combination of established optical microscopes and cutting-edge machine vision technologies, which stacks up successful applications in high-throughput phenotyping. One major question is how much information can be extracted from an image to identify genetic differences between cells. While fluorescent microscopy images of specific organelles have been broadly used for single-cell profiling, the potential ability of bright-field (BF) microscopy images of label-free cells remains to be tested. Here, we examine whether single-gene perturbation can be discriminated based on BF images of label-free cells using a machine learning approach. We acquired hundreds of BF images of single-gene mutant cells, quantified single-cell profiles consisting of texture features of cellular regions, and constructed a machine learning model to discriminate mutant cells from wild-type cells. Interestingly, the mutants were successfully discriminated from the wild type (area under the receiver operating characteristic curve = 0.773). The features that contributed to the discrimination were identified, and they included those related to the morphology of structures that appeared within cellular regions. Furthermore, functionally close gene pairs showed similar feature profiles of the mutant cells. Our study reveals that single-gene mutant cells can be discriminated from wild-type cells based on BF images, suggesting the potential as a useful tool for mutant cell profiling.

Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential.

Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data.

Comprehensive epigenome characterization reveals diverse transcriptional regulation across human vascular endothelial cells.

BACKGROUND: Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood. RESULTS: To characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We developed a robust procedure for comparative epigenome analysis that circumvents variations at the level of the individual and technical noise derived from sample preparation under various conditions. Through this approach, we identified 3765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. We found that the nine EC types can be divided into two subgroups, corresponding to those with upper-body origins and lower-body origins, based on their epigenomic landscape. Epigenomic variations were highly correlated with gene expression patterns, but also provided unique information. Most of the deferentially expressed genes and enhancers were cooperatively enriched in more than one EC type, suggesting that the distinct combinations of multiple genes play key roles in the diverse phenotypes across EC types. Notably, many homeobox genes were differentially expressed across EC types, and their expression was correlated with the relative position of each organ in the body. This reflects the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing. CONCLUSIONS: This comprehensive analysis of epigenome characterization of EC types reveals diverse transcriptional regulation across human vascular systems. These datasets provide a valuable resource for understanding the vascular system and associated diseases.