[This corrects the article DOI: 10.1371/journal.pone.0295971.].

Despite the development of various in vitro differentiation protocols for the efficient derivation of specific cell types, human induced pluripotent stem cell (hiPSC) lines have varing ability to differentiate into specific lineages. Therefore, surrogate markers for accurately predicting the differentiation propensity of hiPSC lines may facilitate cell-based therapeutic product development and manufacture. We attempted to identify marker genes that could predict the differentiation propensity of hiPSCs into neural stem/progenitor cells (NS/PCs). Using Spearman’s rank correlation coefficients, we investigated genes in the undifferentiated state, the expression levels of which were significantly correlated with the neuronal differentiation propensity of several hiPSC lines. Among genes significantly correlated with NS/PC differentiation (P < 0.01), we identified ROR2 as a novel predictive marker. ROR2 expression in hiPSCs was negatively correlated with NS/PC differentiation tendency, regardless of the differentiation method, whereas its knockdown enhanced differentiation. ROR2 regulates NS/PC differentiation, suggesting that ROR2 is functionally essential for NS/PC differentiation. Selecting cell lines with relatively low ROR2 expression facilitated identification of hiPSCs that can differentiate into NS/PCs. Cells with ROR2 knockdown showed increased efficiency of differentiation into forebrain GABAergic neurons compared to controls. These findings suggest that ROR2 is a surrogate marker for selecting hiPSC lines appropriate for NS/PC and GABAergic neuronal differentiations.

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

ChIA-PET associations between differentially expressed enhancerRNAs (eRNAs) and promoters of annotated genes.

Background: Thymic squamous cell carcinoma and type B3 thymoma are primary neoplasms of the anterior mediastinum that are sometimes difficult to differentiate from one another histologically. However, only a few immunohistochemical markers are available for the differential diagnosis. The purpose of this study was to discover a novel marker for differentiating between thymic squamous cell carcinoma and type B3 thymoma.Methods: We used histological samples of thymic carcinomas (n = 26) and type B3 thymomas (n = 38) which were resected between 1986 and 2017. To search for candidates of differential markers, gene expression levels were evaluated in samples using promoter analysis by cap analysis of gene expression (CAGE) sequencing.Results: Promoter level expression of CALML5 genes was significantly higher in thymic carcinomas than in type B3 thymomas. We further validated the results of the CAGE analysis in all 26 thymic carcinomas and 38 type B3 thymomas by immunohistochemistry (IHC). CALML5 was strongly expressed in the cytoplasm in 19 of 26 cases with thymic carcinoma, whereas positivity at the protein level was shown in two of 38 type B3 thymomas. Thus, the sensitivity (73.1%) and specificity (94.7%) of CALML5 as markers for immunohistochemical diagnosis of thymic carcinoma were extremely high.Conclusion: We identified CALML5 as a potential marker for differentiating thymic squamous cell carcinoma from type B3 thymoma. It is assumed that future clinical use of CALML5 may improve the diagnostic accuracy of differentiating between these two diseases.

Genes located within 100kb from the promoters of differentially expressed long non-coding RNAs.

Differentially expressed promoters.

Primers used for the qRTPCR validations.

Results of promoter level Differential Expression Analysis by EdgeR.

<p>Gene level overlap between differentially expressed lncRNAs from the FANTOM5 analysis and the miTranscriptome study.</p>

Human multipotent mesenchymal stromal/stem cells (MSCs) have been utilized in cell therapy for various diseases and their clinical applications are expected to increase in the future. However, the variation in MSC-based product quality due to the MSC heterogeneity has resulted in significant constraints in the clinical utility of MSCs. Therefore, we hypothesized that it might be important to identify and ensure/enrich suitable cell subpopulations for therapies using MSC-based products. In this study, we aimed to identify functional cell subpopulations to predict the efficacy of angiogenic therapy using bone marrow-derived MSCs (BM-MSCs). To assess its angiogenic potency, we observed various levels of vascular endothelial growth factor (VEGF) secretion among 11 donor-derived BM-MSC lines under in vitro ischemic culture conditions. Next, by clarifying the heterogeneity of BM-MSCs using single-cell RNA-sequencing analysis, we identified a functional cell subpopulation that contributed to the overall VEGF production in BM-MSC lines under ischemic conditions. We also found that leucine-rich repeat-containing 75A (LRRC75A) was more highly expressed in this cell subpopulation than in the others. Importantly, knockdown of LRRC75A using small interfering RNA resulted in significant inhibition of VEGF secretion in ischemic BM-MSCs, indicating that LRRC75A regulates VEGF secretion under ischemic conditions. Therefore, LRRC75A may be a useful biomarker to identify cell subpopulations that contribute to the angiogenic effects of BM-MSCs. Our work provides evidence that a strategy based on single-cell transcriptome profiles is effective for identifying functional cell subpopulations in heterogeneous MSC-based products.

Summary of the enhancerRNAs detected as differentially expressed in the FANTOM5 analysis and those associated with transcript models from miTranscriptome.

The list of genes differentially expressed in TCGAs primary tumors vs. normal tissues.

The overlap between the up regulated genes in FANTOM5 cancer cell lines and TCGA primary tumors, and signatures of pan cancer up-regulated genes reported by Rhodes 2004 and Xu 2007.

Results of EdgeR Differential Expression Analysis.

Figure S1. Methylation and expression levels of genes in lung cancer and normal lung tissues. Figure S2. Expression levels of epi-markers in TCGA LUAD dataset. Figure S3. Expression levels of epi-markers in TCGA LUSC dataset. Figure S4. The signature of 22 epigenetically regulated genes in non-small cell lung cancer. Figure S5. ZENBU genome browser view of RP1-90G24.10 and AL022344.4. Figure S6. ZENBU genome browser view of MYEOV. Figure S7. Higher MYEOV expression is associated with poor prognosis in NSCLC.

The expression changes of cancer related genes that were differentially expressed in FANTOM5 cancer cell lines (S1); Heatmaps showing the spectral count data corresponding to protein levels of selected genes in 90 colorectal cancers and 30 normal samples generated by Zhang et al (S2); Pan-cancer long non-coding RNAs and neighboring cancer associated genes (S3); ChIA-pet data show pan-cancer enhancers are physically associated with genes implicated in cancer (S4); The correlation between expression fold changes and the frequency of overlapping repetitive element (S5).

Description of additional methods and procedures used in the study.

List of all samples from FANTOM5 CAGE data.

Gene level overlap between differentially expressed, protein-coding genes from the FANTOM5 analysis, the TCGA in-house analysis and the TCGA analysis by Cabanski at al. and Zhang at al.