Long non-coding RNA exploration for mesenchymal stem cell characterisation

Abstract

Background The development of RNA sequencing (RNAseq) and the corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Therefore, we tested the biomarker potential of lncRNAs on Mesenchymal Stem Cells (MSCs), a complex type of adult multipotent stem cells of diverse tissue origins, that is frequently used in clinics but which is lacking extensive characterization. Results We developed a dedicated bioinformatics pipeline for the purpose of building a cell-specific catalogue of unannotated lncRNAs. The pipeline performs ab initio transcript identification, pseudoalignment and uses new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data. We next applied it on MSCs, and our pipeline was able to highlight novel lncRNAs with high cell specificity. Furthermore, with original and efficient approaches for functional prediction, we demonstrated that each candidate represents one specific state of MSCs biology. Conclusions We showed that our approach can be employed to harness lncRNAs as cell markers. More specifically, our results suggest different candidates as potential actors in MSCs biology and propose promising directions for future experimental investigations.

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets

“…Hence, for lncRNA candidates, following their discovery in a tissue/disease type, their specificity could be easily evaluated through quantification in a wide range of RNA-seq data including normal and pathological conditions as recently described by Riquier et al. ( 40 ).…”

Gene Expression Signature Associated with Clinical Outcome in ALK-Positive Anaplastic Large Cell Lymphoma

“…We finally retained the Wald Test’s most extensive list for further analysis because it gives a ‘beta’ value (size effect) that can be compared to logFC. Thus, finally, 168 genes having an absolute log2 FC between relapse and no-relapse groups greater than 0.5 and a p -value lower than 0.02 [ 34 ] were selected ( Figure S3 ). After intersecting these 168 DE genes with the 47 significantly discriminating genes previously found with the microarray technique ( p value < 0.05), 20 common genes were highlighted ( ANTXR1, CTHRC1, DCN, FAM179A/TOGARAM2, FAP, FN1, FRMD6, GLT8D2, INHBA, IRS1, ITGB7, MYO1F, NT5E, PLAU, PBXIP1, PTPN22, SLC39A14, SULF1, THBS2, and LTBP2 ) (beta value/WT “log2FC” estimator greater than 0.5 and p value < 0.02; Table 2 : green columns, red lines) including 5 and 15 genes overexpressed in relapse and no-relapse groups, respectively ( Figures S4 and S5 ).…”

Tratamento Regenerativo da Osteoartrite de Joelho – Uso de Células-Tronco Mesenquimais e Plasma Rico em Plaquetas

“…Dependendo do estímulo, elas podem se diferenciar em múltiplos tecidos, como adiposo, osso e cartilaginoso, apresentando potencial para o tratamento de diversas doenças. 19 Além disso, as células mesenquimais desempenham papel importante na homeostase, atuando diretamente na regeneração tecidual, cicatrização de feridas, comunicação intercelular e imunomodulação. 20 Elas foram amplamente estudadas devido à sua capacidade antiinflamatória e de regeneração tecidual.…”