StemBase: a resource for the analysis of stem cell gene expression data

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Web resources for stem cell research

In this short review, we have presented a brief overview on major web resources relevant to stem cell research. To facilitate more efficient use of these resources, we have provided a preliminary rating based on our own user experience of the overall quality for each resource. We plan to update the information on an annual basis.

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.

ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells

High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse high-throughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers. The web-interface also includes tools to predict the effects of combinatorial KDs by additive effects controlled by sliders, or through simulation software implemented in MATLAB. Overall, the Embryonic Stem Cell Atlas from Pluripotency Evidence database is a comprehensive resource for the stem cell systems biology community. Database URL: http://www.maayanlab.net/ESCAPE

Snail regulates MyoD binding-site occupancy to direct enhancer switching and differentiation-specific transcription in myogenesis

In skeletal myogenesis, the transcription factor MyoD activates distinct transcriptional programs in progenitors compared to terminally differentiated cells. Using ChIP-Seq and gene expression analyses, we show that in primary myoblasts, Snail-HDAC1/2 repressive complex binds and excludes MyoD from its targets. Notably, Snail binds E box motifs that are G/C rich in their central dinucleotides, and such sites are almost exclusively associated with genes expressed during differentiation. By contrast, Snail does not bind the A/T-rich E boxes associated with MyoD targets in myoblasts. Thus, Snai1-HDAC1/2 prevent MyoD occupancy on differentiation-specific regulatory elements, and the change from Snail to MyoD binding often results in enhancer switching during differentiation. Furthermore, we show that a regulatory network involving myogenic regulatory factors (MRFs), Snai1/2, miR-30a, and miR-206 acts as a molecular switch that controls entry into myogenic differentiation. Together, these results reveal a regulatory paradigm that directs distinct gene expression programs in progenitors versus terminally differentiated cells.

Identification of metabolic changes in genetically unstable stem cells by using model analysis of gene expression

Stem-cell research seeks to address many different questions related to fundamental stem-cell function with the ultimate goal of being able to control and utilize stem cells for a broad range of therapeutic needs. While a large amount of work is focused on discovering and controlling differentiation mechanisms in stem cells, an equally interesting and important area of work is to understand the basics of stem-cell propagation and self-renewal. With high-throughput genomics and transcriptomic information on hand, it is becoming possible to address some of the detailed mechanistic processes occurring in stem cells, though interpretation of these data is often difficult. In this work, stem cells with genetic abnormalities were compared to genetically normal stem cells using gene-expression array data integrated with a large-scale metabolic model to help interpret changes in metabolism resulting in the identification of several metabolic pathways that were different in the normal and abnormal cells.

The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons

Mounting evidence suggests that malignant tumors are initiated and maintained by a subpopulation of cancerous cells with biological properties similar to those of normal stem cells. However, descriptions of stem-like gene and pathway signatures in cancers are inconsistent across experimental systems. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), we have developed the Stem Cell Discovery Engine (SCDE)-an online database of curated CSC experiments coupled to the Galaxy analytical framework. The SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. Our initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. The SCDE is available at http://discovery.hsci.harvard.edu.

Functional genomics: new insights into the 'function' of low levels of gene expression in stem cells

Understanding the global gene expression profile of stem cells and their multilineage differentiation will be essential for their ultimate therapeutic application. Efforts to characterize stem cells have relied on analyzing the genome-wide expression profiles that are biased towards the identification of genes that display the most pronounced differential expression. Rather than being viewed as a “blank” state, recent studies suggest that stem cells express low levels of multiple lineage specific genes prior to differentiation, a phenomenon known as “lineage priming.” It is not likely that low levels of lineage-specific genes produce sufficient amounts of differentiation factors, but rather to provide rapid transcription to a wide range of lineage programs prior to differentiation. Thus, stem cell differentiation may involve the elimination of other potential pathways and the activation of a specific lineage program.

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.

SCLD: a Stem Cell Lineage Database for the annotation of cell types and developmental lineages

Stem cell biology has experienced explosive growth over the past decade as researchers attempt to generate therapeutically relevant cell types in the laboratory. Recapitulation of endogenous developmental trajectories is a dominant paradigm in the design of directed differentiation protocols, and attempts to guide stem cell differentiation are often based explicitly on knowledge of in vivo development. Therefore, when designing protocols, stem cell biologists rely heavily upon information including (i) cell type-specific gene expression profiles, (ii) anatomical and developmental relationships between cells and tissues and (iii) signals important for progression from progenitors to target cell types. Here, we present the Stem Cell Lineage Database (SCLD) (http://scld.mcb.uconn.edu) that aims to unify this information into a single resource where users can easily store and access information about cell type gene expression, cell lineage maps and stem cell differentiation protocols for both human and mouse stem cells and endogenous developmental lineages. By establishing the SCLD, we provide scientists with a centralized location to organize access and share data, dispute and resolve contentious relationships between cell types and within lineages, uncover discriminating cell type marker panels and design directed differentiation protocols.

Intermediate-term hematopoietic stem cells with extended but time-limited reconstitution potential

Sustained blood cell production depends on divisions by hematopoietic stem cells (HSCs) that yield both differentiating progeny as well as new HSCs via self-renewal. Differentiating progeny remain capable of self-renewal, but only HSCs sustain self-renewal through successive divisions securely enough to maintain clones that persist life-long. Until recently, the first identified next stage consisted of "short-term" reconstituting cells able to sustain clones of differentiating cells for only 4-6 weeks. Here we expand evidence for a numerically dominant "intermediate-term" multipotent HSC stage in mice whose clones persist for 6-8 months before becoming extinct and that are separable from both short-term as well as permanently reconstituting "long-term" HSCs. The findings suggest that the first step in stem cell differentiation consists not in loss of initial capacity for serial self-renewal divisions, but rather in loss of mechanisms that stabilize self-renewing behavior throughout successive future stem cell divisions.

Computational Epigenetics: the new scientific paradigm

Epigenetics has recently emerged as a critical field for studying how non-gene factors can influence the traits and functions of an organism. At the core of this new wave of research is the use of computational tools that play critical roles not only in directing the selection of key experiments, but also in formulating new testable hypotheses through detailed analysis of complex genomic information that is not achievable using traditional approaches alone. Epigenomics, which combines traditional genomics with computer science, mathematics, chemistry, biochemistry and proteomics for the large-scale analysis of heritable changes in phenotype, gene function or gene expression that are not dependent on gene sequence, offers new opportunities to further our understanding of transcriptional regulation, nuclear organization, development and disease. This article examines existing computational strategies for the study of epigenetic factors. The most important databases and bioinformatic tools in this rapidly growing field have been reviewed.

Genomics Portals: integrative web-platform for mining genomics data

Background

A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems.

Results

Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis.

Conclusion

The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.

The FunGenES database: a genomics resource for mouse embryonic stem cell differentiation

Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types. To investigate gene networks operating in pluripotent ES cells and their derivatives, the “Functional Genomics in Embryonic Stem Cells” consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have organized the results in an interactive database with a number of features and tools. Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in “Expression Waves” and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the biology of ES cells.

The Characterization Tool: A knowledge-based stem cell, differentiated cell, and tissue database with a web-based analysis front-end

In the rapidly growing field of stem cell research, there is a need for universal databases and web-based applications that provide a common knowledge base on the characteristics of stem cells, differentiated cells, and tissues by collecting, processing, and making available diverse types of characterization data. The Characterization Tool is such a novel knowledge database that allows the storage of various characteristics of cells, cell lines, and tissues across different species as well as the analysis of associated marker profiles. Its broad ontology-based framework facilitates the integration of characterization data on the morphological, molecular, and functional level acquired in vivo and in vitro including published marker expressions, cross-references to other databases, text descriptions, information on characterization experiments, and image storage. Data input and modification are recorded on the basis of a secure user management. By means of several easy-to-use data mining tools, marker profiles can be searched and analyzed. The Characterization Tool will aid in the establishment of standards for cell characterization, needed, for example, for stem cell isolation, propagation, and differentiation. The Characterization Tool is available at http://characterizationtool.cellnet.org. It currently holds more than 7000 marker expressions for different human embryonic stem cell lines, adult stem cells, and differentiated cells.

A genome-wide RNAi screen identifies a new transcriptional module required for self-renewal

We performed a genome-wide siRNA screen in mouse embryonic stem (ES) cells to identify genes essential for self-renewal, and found 148 genes whose down-regulation caused differentiation. Many of the identified genes function in gene regulation and/or development, and are highly expressed in ES cells and embryonic tissues. We further identified target genes of two transcription regulators Cnot3 and Trim28. We discovered that Cnot3 and Trim28 co-occupy many putative gene promoters with c-Myc and Zfx, but not other pluripotency-associated transcription factors. They form a unique module in the self-renewal transcription network, separate from the core module formed by Nanog, Oct4, and Sox2. The transcriptional targets of this module are enriched for genes involved in cell cycle, cell death, and cancer. This supports the idea that regulatory networks controlling self-renewal in stem cells may also be active in certain cancers and may represent novel anti-cancer targets. Our screen has implicated over 100 new genes in ES cell self-renewal, and illustrates the power of RNAi and forward genetics for the systematic study of self-renewal.

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.

BloodExpress: a database of gene expression in mouse haematopoiesis

Haematopoiesis is the process whereby blood stem cells give rise to at least fourteen functionally distinct mature cell types, and represents the best characterized mammalian adult stem cell system. Here we introduce the BloodExpress database, the first public resource integrating mouse blood cell expression profiles. BloodExpress enables the searching of data from individual studies in a single database accessible through a user-friendly web interface. Microarray datasets have been processed uniformly to allow their comparison on the BloodExpress platform. BloodExpress covers the majority of murine blood cell types, including both progenitors and terminally differentiated cells. This allows for the identification of dynamic changes in gene expression as cells differentiate down the well-defined haematopoietic hierarchy. A gene-centric interface returns haematopoietic expression patterns together with functional annotation and a list of other genes with similar expression patterns. A cell type-centric interface allows the identification of genes expressed at specific points of blood development, with the additional and useful capability of filtering by specific gene functional categories. BloodExpress thus constitutes a platform for the discovery of novel gene functions across the haematopoietic tree. BloodExpress is freely accessible at http://hscl.cimr.cam.ac.uk/bloodexpress/.