Identification of novel stem cell markers using gap analysis of gene expression data

Longitudinal single-cell RNA-seq of hESCs-derived retinal organoids

Human retina development involves multiple well-studied signaling pathways that promote the genesis of a wide arrange of different cell types in a complex architectural structure. Human embryonic stem cells (hESCs)-derived retinal organoids could recapitulate the human retinal development. We performed single-cell RNA-seq of retinal organoids from 5 time points (D36, D66, D96, D126, D186) and identified 9 distinct populations of cells. In addition, we analyzed the molecular characteristics of each main population and followed them from genesis to maturity by pseudotime analysis and characterized the cell-cell interactions between different cell types. Interestingly, we identified insulin receptor (INSR) as a specifically expressed receptor involved in the genesis of photoreceptors, and pleiothropin (PTN)-protein tyrosine phosphatase receptor type Z1 (PTPRZ1) as a mediator of a previously unknown interaction between Müller and retinal progenitor cells. Taken together, these findings provide a rich transcriptome-based lineage map for studying human retinal development and modeling developmental disorders in retinal organoids.

Therapeutic Implications of Newly Identified Stem Cell Populations From the Skin Dermis

Skin, the largest organ of the body, is a promising reservoir for adult stem cells. The epidermal stem cells and hair follicle stem cells have been well studied for their important roles in homeostasis, regeneration, and repair of the epidermis and appendages for decades. However, stem cells residing in dermis were not identified until the year 2001, when a variety of stem cell subpopulations have been isolated and identified from the dermis of mammalian skin such as neural crest stem cells, mesenchymal stem cell-like dermal stem cells, and dermal hematopoietic cells. These stem cell subpopulations exhibited capabilities of self-renewing, multipotent differentiating, and immunosuppressive properties. Hence, the dermis-derived stem cells showed extensive potential applications in regenerative medicine, especially for wound healing/tissue repair, neural repair, and hematopoietic recovery. Here we summarized current research on the stem cell subpopulations derived from the dermis and aimed to provide a comprehensive review on their isolation, specific markers, differentiation capacity, and the functional activities in homeostasis, regeneration, and tissue repair.

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

BACKGROUND

Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.

RESULTS

We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% 'precision' (accuracy of prediction), 97% 'recall' (sensitivity)] in predicting 'true-positive' targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low 'recall' values. Score optimizations increased the 'recall' to only 70% (corresponding 'precision': 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly 'precise' predictions. The large number of 'false negative' predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.

CONCLUSION

Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

CellFinder: a cell data repository

CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians.

MicroRNA-regulated gene networks during mammary cell differentiation are associated with breast cancer

MicroRNAs (miRNAs) play pivotal roles in stem cell biology, differentiation and oncogenesis and are of high interest as potential breast cancer therapeutics. However, their expression and function during normal mammary differentiation and in breast cancer remain to be elucidated. In order to identify which miRNAs are involved in mammary differentiation, we thoroughly investigated miRNA expression during functional differentiation of undifferentiated, stem cell-like, murine mammary cells using two different large-scale approaches followed by qPCR. Significant changes in expression of 21 miRNAs were observed in repeated rounds of mammary cell differentiation. The majority, including the miR-200 family and known tumor suppressor miRNAs, was upregulated during differentiation. Only four miRNAs, including oncomiR miR-17, were downregulated. Pathway analysis indicated complex interactions between regulated miRNA clusters and major pathways involved in differentiation, proliferation and stem cell maintenance. Comparisons with human breast cancer tumors showed the gene profile from the undifferentiated, stem-like stage clustered with that of poor-prognosis breast cancer. A common nominator in these groups was the E2F pathway, which was overrepresented among genes targeted by the differentiation-induced miRNAs. A subset of miRNAs could further discriminate between human non-cancer and breast cancer cell lines, and miR-200a/miR-200b, miR-146b and miR-148a were specifically downregulated in triple-negative breast cancer cells. We show that miR-200a/miR-200b can inhibit epithelial-mesenchymal transition (EMT)-characteristic morphological changes in undifferentiated, non-tumorigenic mammary cells. Our studies propose EphA2 as a novel and important target gene for miR-200a. In conclusion, we present evidentiary data on how miRNAs are involved in mammary cell differentiation and indicate their related roles in breast cancer.

Preimplantation development regulatory pathway construction through a text-mining approach

Background

The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms.

Results

In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements.

Conclusions

The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.

Gene expression in murine mammary epithelial stem cell-like cells shows similarities to human breast cancer gene expression

Introduction

Mammary stem cells are bipotential and suggested to be the origin of breast cancer development, but are elusive and vaguely characterized. Breast tumors can be divided into subgroups, each one requiring specific treatment. To determine a possible association between mammary stem cells and breast cancer, a detailed characterization of the transcriptome in mammary stem cells is essential.

Methods

We have used a murine mammary epithelial stem-like cell line (HC11) and made a thorough investigation of global gene-expression changes during stepwise differentiation using dual-color comparative microarray technique. Subsequently, we have performed a cross-species comparison to reveal conserved gene expression between stem cells and subtype-specific and prognosis gene signatures, and correlated gene expression to in vivo mammary gland development.

Results

Our analysis of mammary stem-like and stepwise cell differentiation, and an in-depth description of our findings in a breast cancer perspective provide a unique map of the transcriptomic changes and a number of novel mammary stem cell markers. We correlate the alterations to in vivo mammary gland differentiation, and describe novel changes in nuclear receptor gene expression. Interestingly, our comparisons show that specific subtypes of breast cancers with poor prognosis and metastasizing capabilities show resemblance to stem-like gene expression.

Conclusions

The transcriptional characterization of these mammary stem-like cells and their differentiation-induced gene expression patterns is here made widely accessible and provides a basis for research on mammary stem-like cells. Our comparisons suggest that some tumors are more stem-like than others, with a corresponding worse prognosis. This information would, if established, be important for treatment decisions. We also suggest several marker candidates valuable to investigate further.

Recent developments in StemBase: a tool to study gene expression in human and murine stem cells

BACKGROUND

Currently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation.

FINDINGS

Since its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments.

CONCLUSION

StemBase can be used to study gene expression in human and murine stem cells and is available at http://www.stembase.ca.