No abstract

Long noncoding RNAs have been implicated in oligodendrocyte myelination and oligodendrocyte maturation, but their roles in normal oligodendrocyte differentiation are not fully defined. Here, we report a novel nonprotein-coding RNA, named lnc158, discovered in mouse oligodendrocytes identified in subependymal ventricular zone tissue by single-cell RNA sequencing. Lnc158 is an endogenous antisense transcript of nuclear factor-IB (NFIB) and complementary to 3' untranslated region of NFIB mRNA. NFIB is a member of the nuclear factor-I family and is essential in the development of many organs such as brains and lungs. We found that lnc158 transcripts serve a biological function by regulating the transcription level of the NFIB coding gene in neural stem cells. Overexpression of lnc158 increased the expression of NFIB mRNA and knockdown of lnc158 decreased the expression of NFIB mRNA, suggesting that NFIB is regulated positively by lnc158. Further analyses showed that overexpression of lnc158 in neural stem cells induced a modest increase in CNP, MBP, MAG, and OSP mRNA level, and enhanced induction of differentiation along the lineage of oligodendrocytes. These results together imply that lnc158 positively modulates the transcription level of NFIB mRNA, leading to the enhanced induction of oligodendrocytes.

Background Human naïve pluripotency state cells can be derived from direct isolation of inner cell mass or primed-to-naïve resetting of human embryonic stem cells (hESCs) through different combinations of transcription factors, small molecular inhibitors, and growth factors. Long noncoding RNAs (lncRNAs) have been identified to be crucial in diverse biological processes, including pluripotency regulatory circuit of mouse pluripotent stem cells (PSCs), but few are involved in human PSCs’ regulation of pluripotency and naïve pluripotency derivation. This study initially planned to discover more lncRNAs possibly playing significant roles in the regulation of human PSCs’ pluripotency, but accidently identified a lncRNA whose knockdown in human PSCs induced naïve-like pluripotency conversion. Methods Candidate lncRNAs tightly correlated with human pluripotency were screened from 55 RNA-seq data containing human ESC, human induced pluripotent stem cell (iPSC), and somatic tissue samples. Then loss-of-function experiments in human PSCs were performed to investigate the function of these candidate lncRNAs. The naïve-like pluripotency conversion caused by CCDC144NL-AS1 knockdown (KD) was characterized by quantitative real-time PCR, immunofluorescence staining, western blotting, differentiation of hESCs in vitro and in vivo, RNA-seq, and chromatin immunoprecipitation. Finally, the signaling pathways in CCDC144NL-AS1-KD human PSCs were examined through western blotting and analysis of RNA-seq data. Results The results indicated that knockdown of CCDC144NL-AS1 induces naïve-like state conversion of human PSCs in the absence of additional transcription factors or small molecular inhibitors. CCDC144NL-AS1-KD human PSCs reveal naïve-like pluripotency features, such as elevated expression of naïve pluripotency-associated genes, increased developmental capacity, analogous transcriptional profiles to human naïve PSCs, and global reduction of repressive chromatin modification marks. Furthermore, CCDC144NL-AS1-KD human PSCs display inhibition of MAPK (ERK), accumulation of active β-catenin, and upregulation of some LIF/STAT3 target genes, and all of these are concordant with previously reported traits of human naïve PSCs. Conclusions Our study unveils an unexpected role of a lncRNA, CCDC144NL-AS1 , in the naïve-like state conversion of human PSCs, providing a new perspective to further understand the regulation process of human early pluripotency states conversion. It is suggested that CCDC144NL-AS1 can be potentially valuable for future research on deriving higher quality naïve state human PSCs and promoting their therapeutic applications. Electronic supplementary material The online version of this article (10.1186/s13287-019-1323-9) contains supplementary material, which is available to authorized users.

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.