BackgroundGenome-wide quantification of enhancer activity in the human genome has proven to be a challenging problem. Recent efforts have led to the development of powerful tools for enhancer quantification. However, because of genome size and complexity, these tools have yet to be applied to the whole human genome.Results In the current study, we use a human prostate cancer cell line, LNCaP as a model to perform whole human genome STARR-seq (WHG-STARR-seq) to reliably obtain an assessment of enhancer activity. This approach builds upon previously developed STARR-seq in the fly genome and CapSTARR-seq techniques in targeted human genomic regions. With an improved library preparation strategy, our approach greatly increases the library complexity per unit of starting material, which makes it feasible and cost-effective to explore the landscape of regulatory activity in the much larger human genome. In addition to our ability to identify active, accessible enhancers located in open chromatin regions, we can also detect sequences with the potential for enhancer activity that are located in inaccessible, closed chromatin regions. When treated with the histone deacetylase inhibitor, Trichostatin A, genes nearby this latter class of enhancers are up-regulated, demonstrating the potential for endogenous functionality of these regulatory elements.ConclusionWHG-STARR-seq provides an improved approach to current pipelines for analysis of high complexity genomes to gain a better understanding of the intricacies of transcriptional regulation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1345-5) contains supplementary material, which is available to authorized users.

Highlights d Ovarian tumors separate into two groups: high T and low T cell infiltration groups d Granulysin-expressing CD4 + T cells are present in the high T infiltration cell group d MKI67-expressing plasmablasts are identified in high T cell infiltration tumors d Correlation of CD8 and Tox in this study with immunoreactive subtype in TCGA data

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. Highthroughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3′ RnA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long non-coding RNAs (lncRNAs) within ten SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk. We used RNA-Seq transcriptomics data derived from postmortem brain tissue from control individuals without psychiatric disease as part of the PsychENCODE BrainGVEX and Developmental Capstone projects. We carried out weighted gene coexpression network analysis (WGCNA) to identify protein-coding genes coexpressed with CNV-lncRNAs in the human brain. We identified one neuronal function-related coexpression module shared by both data sets. This module contained a lncRNA within the 22q11.2 CNV region called DGCR5, which was identified as a hub gene. Protein-coding genes associated with SCZ GWAS signals, de novo mutations or differential expression were also contained in this neuronal module. Using DGCR5 knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for DGCR5 in regulating certain SCZ-related genes.

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