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

Scaling success: Linking public breeding with private enterprise

<p>The known Downstream Promoter Element and Initiator site motifs are shown in boldface.</p

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

[Background] Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. [Results] This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. [Conclusions] eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations

NCBI GEO: mining millions of expression profiles—database and tools

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30 000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo

Integrated Collection of Stem Cell Bank Data, a Data Portal for Standardized Stem Cell Information

世界中で樹立されたiPS細胞の数や疾患の種類が明らかに. 京都大学プレスリリース. 2021-03-19.The past decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed the Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16, 000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1, 000 diseases, including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals

Human iPS cell-derived cartilaginous tissue spatially and functionally replaces nucleus pulposus

The loss of nucleus pulposus (NP) precedes the intervertebral disk (IVD) degeneration that causes back pain. Here, we demonstrate that the implantation of human iPS cell-derived cartilaginous tissue (hiPS-Cart) restores this loss by replacing lost NP spatially and functionally. NP cells consist of notochordal NP cells and chondrocyte-like NP cells. Single cell RNA sequencing (scRNA-seq) analysis revealed that cells in hiPS-Cart corresponded to chondrocyte-like NP cells but not to notochordal NP cells. The implantation of hiPS-Cart into a nuclectomized space of IVD in nude rats prevented the degeneration of the IVD and preserved its mechanical properties. hiPS-Cart survived and occupied the nuclectomized space for at least six months after implantation, indicating spatial and functional replacement of lost NP by hiPS-Cart. Further scRNA-seq analysis revealed that hiPS-Cart cells changed their profile after implantation, differentiating into two lineages that are metabolically distinct from each other. However, post-implanted hiPS-Cart cells corresponded to chondrocyte-like NP cells only and did not develop into notochordal NP cells, suggesting that chondrocyte-like NP cells are nearly sufficient for NP function. The data collectively indicate that hiPS-Cart is a candidate implant for regenerating NP spatially and functionally and preventing IVD degeneration.Kamatani T., Hagizawa H., Yarimitsu S., et al. Human iPS cell-derived cartilaginous tissue spatially and functionally replaces nucleus pulposus. Biomaterials 284, 121491 (2022); https://doi.org/10.1016/j.biomaterials.2022.121491

PeakRegressor Identifies Composite Sequence Motifs Responsible for STAT1 Binding Sites and Their Potential rSNPs

How to identify true transcription factor binding sites on the basis of sequence motif information (e.g., motif pattern, location, combination, etc.) is an important question in bioinformatics. We present “PeakRegressor,” a system that identifies binding motifs by combining DNA-sequence data and ChIP-Seq data. PeakRegressor uses L1-norm log linear regression in order to predict peak values from binding motif candidates. Our approach successfully predicts the peak values of STAT1 and RNA Polymerase II with correlation coefficients as high as 0.65 and 0.66, respectively. Using PeakRegressor, we could identify composite motifs for STAT1, as well as potential regulatory SNPs (rSNPs) involved in the regulation of transcription levels of neighboring genes. In addition, we show that among five regression methods, L1-norm log linear regression achieves the best performance with respect to binding motif identification, biological interpretability and computational efficiency

Human AK2 links intracellular bioenergetic redistribution to the fate of hematopoietic progenitors

AK2 is an adenylate phosphotransferase that localizes at the intermembrane spaces of the mitochondria, and its mutations cause a severe combined immunodeficiency with neutrophil maturation arrest named reticular dysgenesis (RD). Although the dysfunction of hematopoietic stem cells (HSCs) has been implicated, earlier developmental events that affect the fate of HSCs and/or hematopoietic progenitors have not been reported. Here, we used RD-patient-derived induced pluripotent stem cells (iPSCs) as a model of AK2-deficient human cells. Hematopoietic differentiation from RD-iPSCs was profoundly impaired. RD-iPSC-derived hemoangiogenic progenitor cells (HAPCs) showed decreased ATP distribution in the nucleus and altered global transcriptional profiles. Thus, AK2 has a stage-specific role in maintaining the ATP supply to the nucleus during hematopoietic differentiation, which affects the transcriptional profiles necessary for controlling the fate of multipotential HAPCs. Our data suggest that maintaining the appropriate energy level of each organelle by the intracellular redistribution of ATP is important for controlling the fate of progenitor cells

Classification of heterogeneous microarray data by maximum entropy kernel

<p>Abstract</p> <p>Background</p> <p>There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets.</p> <p>Results</p> <p>We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noise-introduced leukemia, and heterogeneous oral cavity carcinoma metastasis data. The results clearly show that the ME kernel is very robust for heterogeneous data containing missing values and high-noise, and gives higher prediction accuracies than the standard kernels, namely, linear, polynomial and RBF.</p> <p>Conclusion</p> <p>The results demonstrate its utility in effectively analyzing promiscuous microarray data of rare specimens, e.g., minor diseases or species, that present difficulty in compiling homogeneous data in a single laboratory.</p