Genetic regulation of gene expression is dynamic, as transcription can change during cell differentiation and across cell types. We mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynamics of genetic effects on cell type specific gene expression. We generated time-series RNA-sequencing data, capturing 16 time points from induced pluripotent stem cells to cardiomyocytes, in 19 human cell lines. We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancers of relevant cell types. We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differentiation, and cannot be found by using data from mature tissues. These fleeting genetic associations with gene regulation may represent a new mechanism to explain complex traits and disease. We highlight one example of a nonlinear eQTL that is associated with body mass index.

Changes in gene copy number are important in the setting of precision medicine. Recent studies have established that copy number alterations (CNAs) can be detected in sequencing libraries prepared by hybridization-capture, but there has been comparatively little attention given to CNA assessment in amplicon-based libraries prepared by PCR. In this study, we developed an algorithm for detecting CNAs in amplicon-based sequencing data. CNAs determined from the algorithm mirrored those from a hybridization-capture library. In addition, analysis of 14 pairs of matched normal and breast carcinoma tissues revealed that sequence data pooled from normal samples could be substituted for a matched normal tissue without affecting the detection of clinically relevant CNAs (>j2j copies). Comparison of CNAs identified by array comparative genomic hybridization and amplicon-based libraries across 10 breast carcinoma samples showed an excellent correlation. The CNA algorithm also compared favorably with fluorescence in situ hybridization, with agreement in 33 of 38 assessments across four different genes. Factors that influenced the detection of CNAs included the number of amplicons per gene, the average read depth, and, most important, the proportion of tumor within the sample. Our results show that CNAs can be identified in amplicon-based targeted sequencing data, and that their detection can be optimized by ensuring adequate tumor content and read coverage. (J Mol Diagn 2015, 17: 53e63; http://dx

There is growing demand for routine identification of actionable mutations in clinical cancer specimens. Genotyping platforms must provide rapid turnaround times and work effectively with limited amounts of formalin-fixed, paraffin-embedded (FFPE) tissue specimens that often yield poor quality DNA. We describe semiconductor-based sequencing of DNA from FFPE specimens using a single-tube, multiplexed panel of 190 amplicons targeting 46 cancer genes. With just 10 ng of input DNA, average read depths of 2000× can be obtained in 48 hours, with >95% of the reads on target. A validation set of 45 FFPE tumor specimens containing 53 point mutations previously identified with a mass spectrometry-based genotyping platform, along with 19 indels ranging from 4 to 63 bp, was used to evaluate assay performance. With a mutant allele ratio cutoff of 8%, we were able to achieve 100% sensitivity (95% CI = 97.3% to 100.0%) and 95.1% specificity (95% CI = 91.8% to 98.0%) of point mutation detection. All indels were visible by manual inspection of aligned reads; 6/9 indels ≤12 bp long were detected by the variant caller software either exactly or as mismatched nucleotides within the indel region. The rapid turnaround time and low input DNA requirements make the multiplex PCR and semiconductor-based sequencing approach a viable option for mutation detection in a clinical laboratory.

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.

We have analyzed the transcriptional activity of cellular target sequences for Moloney murine leukemia virus integration in mouse fibroblasts. At least five of the nine random, unselected integration target sequences studied showed direct evidence for transcriptional activity by hybridization to nuclear run-on transcripts prepared from uninfected cells. At least four of the sequences contained multiple recognition sites for several restriction enzymes that cut preferentially in CpG-rich islands, indicating integration into 5' or 3' ends or flanking regions of genes. Assuming that only a minor fraction (<20%) of the genome is transcribed in mammalian cells, we calculated the probability that this association of retroviral integration sites with transcribed sequences is due to chance to be very low (1.6 x 10-2). Thus, our results strongly suggest that transcriptionally active genome regions are preferred targets for retrovirus integration.