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Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed ‘single-cell combinatorial fluidic indexing’ (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation. Combining whole-transcriptome preindexing with standard droplet microfluidics, scifi-RNA-seq enables single-cell RNA-seq with massive throughput and built-in sample multiplexing.

CRISPR-based genetic screens are accelerating biological discovery, but current methods have inherent limitations. Widely used pooled screens are restricted to simple readouts including cell proliferation and sortable marker proteins. Arrayed screens allow for comprehensive molecular readouts such as transcriptome profiling, but at much lower throughput. Here we combine pooled CRISPR screening with single-cell RNA sequencing into a broadly applicable workflow, directly linking guide RNA expression to transcriptome responses in thousands of individual cells. Our method for CRISPR droplet sequencing (CROP-seq) enables pooled CRISPR screens with single-cell transcriptome resolution, which will facilitate high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations.

Epigenome-wide association studies (EWAS) hold promise for the detection of new regulatory mechanisms that may be susceptible to modification by environmental and lifestyle factors affecting susceptibility to disease. Epigenome-wide screening methods cover an increasing number of CpG sites, but the complexity of the data poses a challenge to separating robust signals from noise. Appropriate study design, a detailed a priori analysis plan and validation of results are essential to minimize the danger of false positive results and contribute to a unified approach. Epigenome-wide mapping studies in homogenous cell populations will inform our understanding of normal variation in the methylome that is not associated with disease or aging. Here we review concepts for conducting a stringent and powerful EWAS, including the choice of analyzed tissue, sources of variability and systematic biases, outline analytical solutions to EWAS-specific problems and highlight caveats in interpretation of data generated from samples with cellular heterogeneity.

ChIP-seq combines chromatin immunoprecipitation with sequencing in order tomeasure genome-wide distributions of chromatin proteins. Despite recent efforts tooptimize and improve ChIP-seq, current protocols are still quite tedious, time-consuming, and costly. The development of a hyperactive Tn5 transposase thatenables simultaneous DNA fragmentation and adapter tagging (“tagmentation”)presents an opportunity for faster and cheaper library preparation (Ref. 1). Here, wedemonstrate tagmentation of immunoprecipitated chromatin in a robust one-stepreaction performed directly on bead-bound chromatin (Schmidl et al. 2015 NatureMethods, Ref. 2). This method – which we call ChIPmentation – provides a fast,cheap, low-input ChIP-seq workflow and yields excellent results for both histonemarks and transcription factors. Using ChIPmentation we observed footprints thatresemble those in ATAC-seq data. Moreover, histone ChIPmentation data shows astriking periodicity indicative of nucleosome stability, and an ordered signal aroundnucleosome dyads that might be used for accurate nucleosome positioning.

Reduced representation bisulfite sequencing (RRBS) enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of whole-genome sequencing. The Diagenode Premium RRBS kit makes this technology widely available and provides high coverage (up to 4 million CpGs in human samples). Multiplexing prior to bisulfite conversion allows processing of 96 samples per experiment, enabling studies of large cohorts.

RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de). Supported assays include whole genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays, and any other protocol that produces high-resolution DNA methylation data. Important applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

August 1, 2010, Bisulfite sequencing measures absolute levels of DNA methylation at single-nucleotide resolution, providing a robust platform for molecular diagnostics. Here, we optimize bisulfite sequencing for genome-scale analysis of clinical samples. Specifically, we outline how restriction digestion targets bisulfite sequencing to hotspots of epigenetic regulation; we show that 30ng of DNA are sufficient for genome-scale analysis; we demonstrate that our protocol works well on formalinfixed, paraffin-embedded (FFPE) samples; and we describe a statistical method for assessing significance of altered DNA methylation patterns., National Institutes of Health (U.S.) (Grant R01HG004401), National Institutes of Health (U.S.) (Grant U54HG03067), National Institutes of Health (U.S.) (Grant U01ES017155)