Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0938-8) contains supplementary material, which is available to authorized users.

DNA methylation is a fundamental epigenetic mark that governs gene expression and chromatin organization, thus providing a window into cellular identity and developmental processes1. Current datasets typically include only a fraction of methylation sites and are often based either on cell lines that underwent massive changes in culture or on tissues containing unspecified mixtures of cells2–5. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing, allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 205 healthy tissue samples. Replicates of the same cell type are more than 99.5% identical, demonstrating the robustness of cell identity programmes to environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny and identifies methylation patterns retained since embryonic development. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hypermethylated loci are rare and are enriched for CpG islands, Polycomb targets and CTCF binding sites, suggesting a new role in shaping cell-type-specific chromatin looping. The atlas provides an essential resource for study of gene regulation and disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies.

Cellular senescence is thought to contribute to age-associated deterioration of tissue physiology. The senescence effector p16Ink4a is expressed in pancreatic beta cells during aging and limits their proliferative potential; however, its effects on beta cell function are poorly characterized. We found that beta cell-specific activation of p16Ink4a in transgenic mice enhances glucose-stimulated insulin secretion (GSIS). In mice with diabetes, this leads to improved glucose homeostasis, providing an unexpected functional benefit. Expression of p16Ink4a in beta cells induces hallmarks of senescence—including cell enlargement, and greater glucose uptake and mitochondrial activity—which promote increased insulin secretion. GSIS increases during the normal aging of mice and is driven by elevated p16Ink4a activity. We found that islets from human adults contain p16Ink4a-expressing senescent beta cells and that senescence induced by p16Ink4a in a human beta cell line increases insulin secretion in a manner dependent, in part, on the activity of the mechanistic target of rapamycin (mTOR) and the peroxisome proliferator-activated receptor (PPAR)-γ proteins. Our findings reveal a novel role for p16Ink4a and cellular senescence in promoting insulin secretion by beta cells and in regulating normal functional tissue maturation with age.

Introduction: Testing for active SARS-CoV-2 infection is a fundamental tool in the public health measures taken to control the COVID-19 pandemic. Because of the overwhelming use of SARS-CoV-2 reverse transcription (RT)-PCR tests worldwide, the availability of test kits has become a major bottleneck and the need to increase testing throughput is rising. We aim to overcome these challenges by pooling samples together, and performing RNA extraction and RT-PCR in pools. Methods: We tested the efficiency and sensitivity of pooling strategies for RNA extraction and RT-PCR detection of SARS-CoV-2. We tested 184 samples both individually and in pools to estimate the effects of pooling. We further implemented Dorfman pooling with a pool size of eight samples in large-scale clinical tests. Results: We demonstrated pooling strategies that increase testing throughput while maintaining high sensitivity. A comparison of 184 samples tested individually and in pools of eight samples showed that test results were not significantly affected. Implementing the eight-sample Dorfman pooling to test 26 576 samples from asymptomatic individuals, we identified 31 (0.12%) SARS-CoV-2 positive samples, achieving a 7.3-fold increase in throughput. Discussion: Pooling approaches for SARS-CoV-2 testing allow a drastic increase in throughput while maintaining clinical sensitivity. We report the successful large-scale pooled screening of asymptomatic