CellFinder: a cell data repository

Cell Taxonomy: a curated repository of cell types with multifaceted characterization

Single-cell studies have delineated cellular diversity and uncovered increasing numbers of previously uncharacterized cell types in complex tissues. Thus, synthesizing growing knowledge of cellular characteristics is critical for dissecting cellular heterogeneity, developmental processes and tumorigenesis at single-cell resolution. Here, we present Cell Taxonomy (https://ngdc.cncb.ac.cn/celltaxonomy), a comprehensive and curated repository of cell types and associated cell markers encompassing a wide range of species, tissues and conditions. Combined with literature curation and data integration, the current version of Cell Taxonomy establishes a well-structured taxonomy for 3,143 cell types and houses a comprehensive collection of 26,613 associated cell markers in 257 conditions and 387 tissues across 34 species. Based on 4,299 publications and single-cell transcriptomic profiles of ∼3.5 million cells, Cell Taxonomy features multifaceted characterization for cell types and cell markers, involving quality assessment of cell markers and cell clusters, cross-species comparison, cell composition of tissues and cellular similarity based on markers. Taken together, Cell Taxonomy represents a fundamentally useful reference to systematically and accurately characterize cell types and thus lays an important foundation for deeply understanding and exploring cellular biology in diverse species.

A Survey of Biological Data in a Big Data Perspective

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Cellular Aquaculture: Prospects and Challenges

Aquaculture plays an important role as one of the fastest-growing food-producing sectors in global food and nutritional security. Demand for animal protein in the form of fish has been increasing tremendously. Aquaculture faces many challenges to produce quality fish for the burgeoning world population. Cellular aquaculture can provide an alternative, climate-resilient food production system to produce quality fish. Potential applications of fish muscle cell lines in cellular aquaculture have raised the importance of developing and characterizing these cell lines. In vitro models, such as the mouse C2C12 cell line, have been extremely useful for expanding knowledge about molecular mechanisms of muscle growth and differentiation in mammals. Such studies are in an infancy stage in teleost due to the unavailability of equivalent permanent muscle cell lines, except a few fish muscle cell lines that have not yet been used for cellular aquaculture. The Prospect of cell-based aquaculture relies on the development of appropriate muscle cells, optimization of cell conditions, and mass production of cells in bioreactors. Hence, it is required to develop and characterize fish muscle cell lines along with their cryopreservation in cell line repositories and production of ideal mass cells in suitably designed bioreactors to overcome current cellular aquaculture challenges.

CellMeSH: probabilistic cell-type identification using indexed literature

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledge.

RESULTS

Here, we introduce CellMeSH-a new automated approach to identifying cell types for clusters based on prior literature. CellMeSH combines a database of gene-cell-type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell-type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and is easily updated with new data. The probabilistic query method enables reliable information retrieval even though the gene-cell-type associations extracted from the literature are noisy. CellMeSH is also able to optionally utilize prior knowledge about tissues or cells for further annotation improvement. CellMeSH achieves top-one and top-three accuracies on a number of mouse and human datasets that are consistently better than existing approaches.

AVAILABILITY AND IMPLEMENTATION

Web server at https://uncurl.cs.washington.edu/db_query and API at https://github.com/shunfumao/cellmesh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions

In vitro differentiation or expansion of stem and progenitor cells under chemical stimulation or genetic manipulation is used for understanding the molecular mechanisms of cell differentiation and self-renewal. However, concerns around the cell identity of in vitro-cultured cells exist. Bioinformatics methods, which rely heavily on signatures of cell types, have been developed to estimate cell types in bulk samples. The Tabula Muris Senis project provides an important basis for the comprehensive identification of signatures for different cell types. Here, we identified 46 cell type-specific (CTS) gene clusters for 83 mouse cell types. We conducted Gene Ontology term enrichment analysis on the gene clusters and revealed the specific functions of the relevant cell types. Next, we proposed a simple method, named CTSFinder, to identify different cell types between bulk RNA-Seq samples using the 46 CTS gene clusters. We applied CTSFinder on bulk RNA-Seq data from 17 organs and from developing mouse liver over different stages. We successfully identified the specific cell types between organs and captured the dynamics of different cell types during liver development. We applied CTSFinder with bulk RNA-Seq data from a growth factor-induced neural progenitor cell culture system and identified the dynamics of brain immune cells and nonimmune cells during the long-time cell culture. We also applied CTSFinder with bulk RNA-Seq data from reprogramming induced pluripotent stem cells and identified the stage when those cells were massively induced. Finally, we applied CTSFinder with bulk RNA-Seq data from in vivo and in vitro developing mouse retina and captured the dynamics of different cell types in the two development systems. The CTS gene clusters and CTSFinder method could thus serve as promising toolkits for assessing the cell identity of in vitro culture systems.

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.

Detection of condition-specific marker genes from RNA-seq data with MGFR

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR’s predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

Toward mapping the human body at a cellular resolution

The adult human body is composed of nearly 37 trillion cells, each with potentially unique molecular characteristics. This Perspective describes some of the challenges and opportunities faced in mapping the molecular characteristics of these cells in specific regions of the body and highlights areas for international collaboration toward the broader goal of comprehensively mapping the human body with cellular resolution.

The Cellosaurus, a Cell-Line Knowledge Resource

The Cellosaurus is a knowledge resource on cell lines. It aims to describe all cell lines used in biomedical research. Its scope encompasses both vertebrates and invertebrates. Currently, information for >100,000 cell lines is provided. For each cell line, it provides a wealth of information, cross-references, and literature citations. The Cellosaurus is available on the ExPASy server (https://web.expasy.org/cellosaurus/) and can be downloaded in a variety of formats. Among its many uses, the Cellosaurus is a key resource to help researchers identify potentially contaminated/misidentified cell lines, thus contributing to improving the quality of research in the life sciences.

Unifying cancer and normal RNA sequencing data from different sources

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare.

Usage of cell nomenclature in biomedical literature

BACKGROUND

Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell nomenclature in publications at a large scale for understanding the level of uptake of cell nomenclature in literature by scientists. In this study, we analyse the usage of cell nomenclature, both in Vivo, and in Vitro in biomedical literature by using text mining methods and present our results.

RESULTS

We identified 59% of the cell type classes in the Cell Ontology and 13% of the cell line classes in the Cell Line Ontology in the literature. Our analysis showed that cell line nomenclature is much more ambiguous compared to the cell type nomenclature. However, trends indicate that standardised nomenclature for cell lines and cell types are being increasingly used in publications by the scientists.

CONCLUSIONS

Our findings provide an insight to understand how experimental cells are described in publications and may allow for an improved standardisation of cell type and cell line nomenclature as well as can be utilised to develop efficient text mining applications on cell types and cell lines. All data generated in this study is available at https://github.com/shenay/CellNomenclatureStudy.

Genomic and molecular control of cell type and cell type conversions

Organisms are made of a limited number of cell types that combine to form higher order tissues and organs. Cell types have traditionally been defined by their morphologies or biological activity, yet the underlying molecular controls of cell type remain unclear. The onset of single cell technologies, and more recently genomics (particularly single cell genomics), has substantially increased the understanding of the concept of cell type, but has also increased the complexity of this understanding. These new technologies have added a new genome wide molecular dimension to the description of cell type, with genome-wide expression and epigenetic data acting as a cell type ‘fingerprint’ to describe the cell state. Using these genomic fingerprints cell types are being increasingly defined based on specific genomic and molecular criteria, without necessarily a distinct biological function. In this review, we will discuss the molecular definitions of cell types and cell type control, and particularly how endogenous and exogenous transcription factors can control cell types and cell type conversions.

Therapeutic Complement Targeting in ANCA-Associated Vasculitides and Thrombotic Microangiopathy

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) are a group of systemic autoimmune disorders characterized by necrotizing inflammation of medium-to-small vessels, a relative paucity of immune deposits, and an association with detectable circulating ANCAs. AAVs include granulomatosis with polyangiitis (renamed from Wegener's granulomatosis), microscopic polyangiitis, and eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome). Until recently, AAVs have not been viewed as complement-mediated disorders. However, recent findings predominantly from animal studies demonstrated a crucial role of the complement system in the pathogenesis of AAVs. Complement activation or defects in its regulation have been described in an increasing number of acquired or genetically driven forms of thrombotic microangiopathy. Coinciding with this expanding spectrum of complement-mediated diseases, the question arises as to which AAV patients might benefit from a complement-targeted therapy. Therapies directed against the complement system point to the necessity of a genetic workup of genes of complement components and regulators in patients with AAV. Genetic testing together with pluripotent stem cells and bioinformatics tools may broaden our approach to the treatment of patients with aggressive forms of AAV.

hPSCreg--the human pluripotent stem cell registry

The human pluripotent stem cell registry (hPSCreg), accessible at http://hpscreg.eu, is a public registry and data portal for human embryonic and induced pluripotent stem cell lines (hESC and hiPSC). Since their first isolation the number of hESC lines has steadily increased to over 3000 and new iPSC lines are generated in a rapidly growing number of laboratories as a result of their potentially broad applicability in biomedicine and drug testing. Many of these lines are deposited in stem cell banks, which are globally established to store tens of thousands of lines from healthy and diseased donors. The Registry provides comprehensive and standardized biological and legal information as well as tools to search and compare information from multiple hPSC sources and hence addresses a translational research need. To facilitate unambiguous identification over different resources, hPSCreg automatically creates a unique standardized name for each cell line registered. In addition to biological information, hPSCreg stores extensive data about ethical standards regarding cell sourcing and conditions for application and privacy protection. hPSCreg is the first global registry that holds both, manually validated scientific and ethical information on hPSC lines, and provides access by means of a user-friendly, mobile-ready web application.

MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data

Background

Identification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases.

Results

We have developed a new bioinformatics tool called MGFM (Marker Gene Finder in Microarray data) to predict marker genes from microarray gene expression data. Marker genes are identified through the grouping of samples of the same type with similar marker gene expression levels. We verified our approach using two microarray data sets from the NCBI’s Gene Expression Omnibus public repository encompassing samples for similar sets of five human tissues (brain, heart, kidney, liver, and lung). Comparison with another tool for tissue-specific gene identification and validation with literature-derived established tissue markers established functionality, accuracy and simplicity of our tool. Furthermore, top ranked marker genes were experimentally validated by reverse transcriptase-polymerase chain reaction (RT-PCR). The sets of predicted marker genes associated with the five selected tissues comprised well-known genes of particular importance in these tissues. The tool is freely available from the Bioconductor web site, and it is also provided as an online application integrated into the CellFinder platform (http://cellfinder.org/analysis/marker).

Conclusions

MGFM is a useful tool to predict tissue/cell type marker genes using microarray gene expression data. The implementation of the tool as an R-package as well as an application within CellFinder facilitates its use.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1785-9) contains supplementary material, which is available to authorized users.

Regulatory insight into the European human pluripotent stem cell registry

The European pluripotent stem cell registry aims at listing qualified pluripotent stem cell (PSC) lines that are available globally together with relevant information for each cell line. Specific emphasis is being put on documenting ethical procurement of the cells and providing evidence of pluripotency. The report discusses the tasks and challenges for a global PSC registry as an instrument to develop collaboration, to access cells from diverse resources and banks, and to implement standards, and as a means to follow up usage of cells and support adherence to regulatory and scientific standards and transparency for stakeholders.

Semantic Body Browser: graphical exploration of an organism and spatially resolved expression data visualization

Advancing technologies generate large amounts of molecular and phenotypic data on cells, tissues and organisms, leading to an ever-growing detail and complexity while information retrieval and analysis becomes increasingly time-consuming. The Semantic Body Browser is a web application for intuitively exploring the body of an organism from the organ to the subcellular level and visualising expression profiles by means of semantically annotated anatomical illustrations. It is used to comprehend biological and medical data related to the different body structures while relying on the strong pattern recognition capabilities of human users.

AVAILABILITY AND IMPLEMENTATION

The Semantic Body Browser is a JavaScript web application that is freely available at http://sbb.cellfinder.org. The source code is provided on https://github.com/flekschas/sbb.