Mesenchymal stromal cells (MSC) from JAK2+ myeloproliferative neoplasms differ from normal MSC and contribute to the maintenance of neoplastic hematopoiesis.

There is evidence of continuous bidirectional cross-talk between malignant cells and bone marrow-derived mesenchymal stromal cells (BM-MSC), which favors the emergence and progression of myeloproliferative neoplastic (MPN) diseases. In the current work we have compared the function and gene expression profile of BM-MSC from healthy donors (HD-MSC) and patients with MPN (JAK2V617F), showing no differences in the morphology, proliferation and differentiation capacity between both groups. However, BM-MSC from MPN expressed higher mean fluorescence intensity (MIF) of CD73, CD44 and CD90, whereas CD105 was lower when compared to controls. Gene expression profile of BM-MSC showed a total of 169 genes that were differentially expressed in BM-MSC from MPN patients compared to HD-MSC. In addition, we studied the ability of BM-MSC to support the growth and survival of hematopoietic stem/progenitor cells (HSPC), showing a significant increase in the number of CFU-GM colonies when MPN-HSPC were co-cultured with MPN-MSC. Furthermore, MPN-MSC showed alteration in the expression of genes associated to the maintenance of hematopoiesis, with an overexpression of SPP1 and NF-kB, and a downregulation of ANGPT1 and THPO. Our results suggest that BM-MSC from JAK2+ patients differ from their normal counterparts and favor the maintenance of malignant clonal hematopoietic cells.

Insights into the human mesenchymal stromal/stem cell identity through integrative transcriptomic profiling.

BACKGROUND: Mesenchymal Stromal/Stem Cells (MSCs), isolated under the criteria established by the ISCT, still have a poorly characterized phenotype that is difficult to distinguish from similar cell populations. Although the field of transcriptomics and functional genomics has quickly grown in the last decade, a deep comparative analysis of human MSCs expression profiles in a meaningful cellular context has not been yet performed. There is also a need to find a well-defined MSCs gene-signature because many recent biomedical studies show that key cellular interaction processes (i.e. inmuno-modulation, cellular cross-talk, cellular maintenance, differentiation, epithelial-mesenchymal transition) are dependent on the mesenchymal stem cells within the stromal niche. RESULTS: In this work we define a core mesenchymal lineage signature of 489 genes based on a deep comparative analysis of multiple transcriptomic expression data series that comprise: (i) MSCs of different tissue origins; (ii) MSCs in different states of commitment; (iii) other related non-mesenchymal human cell types. The work integrates several public datasets, as well as de-novo produced microarray and RNA-Seq datasets. The results present tissue-specific signatures for adipose tissue, chorionic placenta, and bone marrow MSCs, as well as for dermal fibroblasts; providing a better definition of the relationship between fibroblasts and MSCs. Finally, novel CD marker patterns and cytokine-receptor profiles are unravelled, especially for BM-MSCs; with MCAM (CD146) revealed as a prevalent marker in this subtype of MSCs. CONCLUSIONS: The improved biomolecular characterization and the released genome-wide expression signatures of human MSCs provide a comprehensive new resource that can drive further functional studies and redesigned cell therapy applications.

Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.

BACKGROUND: Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes. RESULTS: We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype. CONCLUSIONS: The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.

Transcriptomic portrait of human Mesenchymal Stromal/Stem Cells isolated from bone marrow and placenta.

BACKGROUND: Human Mesenchymal Stromal/Stem Cells (MSCs) are adult multipotent cells that behave in a highly plastic manner, inhabiting the stroma of several tissues. The potential utility of MSCs is nowadays strongly investigated in the field of regenerative medicine and cell therapy, although many questions about their molecular identity remain uncertain. RESULTS: MSC primary cultures from human bone marrow (BM) and placenta (PL) were derived and verified by their immunophenotype standard pattern and trilineage differentiation potential. Then, a broad characterization of the transcriptome of these MSCs was performed using RNA deep sequencing (RNA-Seq). Quantitative analysis of these data rendered an extensive expression footprint that includes 5,271 protein-coding genes. Flow cytometry assays of canonical MSC CD-markers were congruent with their expression levels detected by the RNA-Seq. Expression of other recently proposed MSC markers (CD146, Nestin and CD271) was tested in the placenta samples, finding only CD146 and Nestin. Functional analysis revealed enrichment in stem cell related genes and mesenchymal regulatory transcription factors (TFs). Analysis of TF binding sites (TFBSs) identified 11 meta-regulators, including factors KLF4 and MYC among them. Epigenetically, hypomethylated promoter patterns supported the active expression of the MSC TFs found. An interaction network of these TFs was built to show up their links and relations. Assessment of dissimilarities between cell origins (BM versus PL) disclosed two hundred differentially expressed genes enrolled in microenvironment processes related to the cellular niche, as regulation of bone formation and blood vessel morphogenesis for the case of BM-MSCs. By contrast genes overexpressed in PL-MSCs showed functional enrichment on mitosis, negative regulation of cell-death and embryonic morphogenesis that supported the higher growth rates observed in the cultures of these fetal cells and their closer links with development processes. CONCLUSIONS: The results present a transcriptomic portrait of the human MSCs isolated from bone marrow and placenta. The data are released as a cell-specific resource, providing a comprehensive expression footprint of the MSCs useful to better understand their cellular and molecular biology and for further investigations on the isolation and biomedical use of these multipotent cells.

A robust estimation of exon expression to identify alternative spliced genes applied to human tissues and cancer samples.

BACKGROUND: Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms. RESULTS: Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change. CONCLUSIONS: The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.