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Enthusiasm for patient-specific therapies based on induced pluripotent stem cells (iPSCs) has risen in parallel with rapid advances in genome editing. This Review summarizes the progress in iPSC-based disease modelling over the past decade, with a focus on 3D organoid systems and chimeric models being exploited for new therapeutic approaches.
The functional interpretation of single-cell RNA sequencing (scRNA-seq) data can be enhanced by integrating additional data types beyond RNA-based gene expression. In this Review, Stuart and Satija discuss diverse approaches for integrative single-cell analysis, including experimental methods for profiling multiple omics types from the same cells, analytical approaches for extracting additional layers of information directly from scRNA-seq data and computational integration of omics data collected across different cell samples.
Chromatin accessibility comprises the positions, compaction and dynamics of nucleosomes, as well as the occupancy of DNA by other proteins such as transcription factors. In this Review, the authors discuss diverse methods for characterizing chromatin accessibility, how accessibility is determined and remodelled in cells and the regulatory roles of accessibility in gene expression and development.
Disruption of genomic imprinting can lead to disease. Recent studies suggest that interactions between the genome, the epigenome and the environment in germ cells and early embryos have an impact on developmental outcomes and on the heritability of imprinting disorders.
Comparing the microbiomes of great apes enables an evolutionary perspective on microbial communities. This approach is revealing not only new insights about humans and what differentiates us from our closest relatives but also the factors that influence microbiome composition and the ways in which microbiomes diverge.
Single-cell RNA sequencing (scRNA-seq) enables transcriptome-based characterization of the constituent cell types within a heterogeneous sample. However, reliable analysis and biological interpretation typically require optimal use of clustering algorithms. This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.
