Mesenchymal Stromal Cells (MSC) are multipotent cells characterized by self-renewal, multilineage differentiation, and immunomodulatory properties. To obtain a gene regulatory profile of human MSCs, we generated a compendium of more than two hundred cell samples with genome-wide expression data, including a homogeneous set of 93 samples of five related primary cell types: bone marrow mesenchymal stem cells (BM-MSC), hematopoietic stem cells (HSC), lymphocytes (LYM), fibroblasts (FIB), and osteoblasts (OSTB). All these samples were integrated to generate a regulatory gene network using the algorithm ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks; based on mutual information), that finds regulons (groups of target genes regulated by transcription factors) and regulators (i.e., transcription factors, TFs). Furtherly, the algorithm VIPER (Algorithm for Virtual Inference of Protein-activity by Enriched Regulon analysis) was used to inference protein activity and to identify the most significant TF regulators, which control the expression profile of the studied cells. Applying these algorithms, a footprint of candidate master regulators of BM-MSCs was defined, including the genes EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3, that presented consistent upregulation and hypomethylation in BM-MSCs. These TFs regulate the activation of the genes in the bone marrow MSC lineage and are involved in development, morphogenesis, cell differentiation, regulation of cell adhesion, and cell structure.

Summary: Since the inception of single-cell level measuring techniques, identification of distinct cell stages, phenotypes and populations has been a challenge. Cell clustering and dimensionality reduction methods are the most popular approaches to identify heterogeneity of single-cell data. But, as public repositories continue to grow in number, integrative analyses and merging of large pools of samples from different and heterogeneous datasets becomes a difficult challenge, which showcases the impossibility of scalability of some of the existing methods. Here we present ClusterFoldSimilarity , an R package that calculates a measure of similarity between clusters from different datasets/batches, without the need of correcting for batch effect or normalizing and merging the data, thus avoiding artifacts and the loss of information derived from these kinds of techniques. The similarity metric is based on the average vector module and sign of the product of logarithmic fold-changes. ClusterFoldSimilarity compares every single pair of clusters from any number of different samples/datasets, including different number of clusters for each sample. Additionally, the algorithm is able to select the top genes which contribute the most to the similarity of two specific clusters, serving also as a feature selection tool. Availability and implementation: The algorithm is freely available as an R package at: https://github.com/OscarGVelasco/ClusterFoldSimilarity Contact: oscargvelasco@gmail.com

In this paper, we present a comparative analysis of the transcriptomic profile of three different human cell types: hematopoietic stem cells (HSCs), bone marrow-derived mesenchymal stem cells (MSCs) and fibroblasts (FIBs). The work aims to identify unique genes that are differentially expressed as specific markers of bone marrow-derived MSCs, and to achieve this undertakes a detailed analysis of three independent datasets that include quantification of the global gene expression profiles of three primary cell types: HSCs, MSCs and FIBs. A robust bioinformatics method, called GlobalTest, is used to assess the specific association between one or more genes expressed in a sample and the outcome variable, that is, the ‘cell type’ provided as a single univariate response. This outcome variable is predicted for each sample tested, based on the expression profile of the specific genes that are used as input to the test. The precision of the tests is calculated along with the statistical sensitivity and specificity for each gene in each dataset, yielding four genes that mark MSCs with high accuracy. Among these, the best performer is the protein-coding gene Transgelin (TAGLN, Gene ID: 6876) (with a Positive Predictive Value > 0.96 and FDR < 0.001), which identifies MSCs better than any of the currently used standard markers: ENG (CD105), THY1 (CD90) or NT5E (CD73). The results are validated by RT-qPCR, providing novel gene biomarkers specific for human MSCs.

There are many different cells that perform highly specialized functions in the human hematological and immune systems. Due to the relevance of their activity, in this work we investigated the cell types and subtypes that form this complex system, using single-cell RNA sequencing (scRNA-seq) to dissect and assess the markers that best define each cell population. We first developed an optimized computational workflow for analyzing large scRNA-seq datasets. We then used it to find gene markers of the different cell types present in bone marrow (BM) and peripheral blood (PB). We analyzed three different single-cell datasets to find specific cell markers using this strategy: first, we searched in the CD marker genes and then in the genes encoding membrane proteins and finally in all detected protein-coding genes. This allowed us not only to confirm known CDs that best mark some cell types (e.g., monocytes, B cells, NK cells, etc.) but also to test the ability of new genes to distinguish specific cell types. Finally, we applied a machine learning method (Random Forest) to test the accuracy of the different markers we found. As a result of all this work, we have found and propose specific and robust gene signatures to identify different types and subtypes of hematological and immune cells.