Abstract
The development of RNA sequencing, or RNAseq, have opened the path of intensive biomarkers research in many areas of biology. The complete information of the transcriptome contained in the output data, allows a bioinformatician to surpass the current knowledge and to access, thanks to advanced computer pipelines, to signatures of new interest. In this thesis, we are showing that these potential markers, classically used in clinical and pathological conditions, can be used to characterize cell types without extensive markers profile. We have studied mesenchymal stem cells, a type of adult multipotent stem cells, strongly used in clinics but without strickly specific positive markers. Our study mainly focuses on the search for non-annotated, long non-coding RNAs. These RNAs, also called "lncRNA", constitute an emerging class of transcripts and are still lightly explored.In addition, this category presents a highly tissue-related specificity. We have developed an optimized RNAseq pipeline for the reconstruction and quantification of non-annotated lncRNAs.Using public data from RNAseq, coming from different sources of MSC and other cell types, we have identified new non-annotated lncRNAs clearly and specifically expressed in MSCs. to complete this project, we developed Kmerator.jl, a bioinformatical tool that allows to decompose a transcript in k-mer, and select specific sub-sequences, in order to search and quantify at a faster rate the signature of our candidates in a large number of RNAseq dataset. After validation of these new biomarkers of MSCs by qPCR, we used several computer tools to predict their potential functions. Finally, we analyzed single-cell RNAseq data to address the heterogeneity of expression within MSC populations.