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The amount of input DNA available to prepare next-generation sequencing (NGS) libraries is often limited, which can lead to GC content bias and enrichment of specific genomic regions with currently available protocols. In this study, we used breath capture technology to incorporate sequencing adapters into DNA to develop a novel cost-effective protocol for the preparation of genomic DNA libraries. We performed a benchmarking experiment comparing our protocol with common commercially available kits for genomic DNA library preparation with input DNA amount in the range of 1 to 50 ng. Our protocol can generate high-quality genomic sequence data with a marked improvement in coverage breadth and low GC bias, in contrast to standard protocols. Further, our protocol reduces sample handling time and reagent costs, and requires comparatively fewer enzymatic steps relative to other protocols, making it suitable for a range of genomics applications.

SummaryMedullary thymic epithelial cells (mTECs) are critical for self-tolerance induction in T cells via promiscuous expression of tissue-specific antigens (TSAs), which are controlled by transcriptional regulator AIRE. Whereas AIRE-expressing (Aire+) mTECs undergo constant turnover in the adult thymus, mechanisms underlying differentiation of postnatal mTECs remain to be discovered. Integrative analysis of single-cell assays for transposase accessible chromatin (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) suggested the presence of proliferating mTECs with a specific chromatin structure, which express high levels of Aire and co-stimulatory molecules CD80 (Aire+CD80hi). Proliferating Aire+CD80hi mTECs detected by using Fucci technology express a minimal level of Aire-dependent TSAs and are converted into quiescent Aire+CD80hi mTECs expressing high levels of TSAs after a transit amplification. These data provide evidence for the existence of transit amplifying Aire+mTEC precursors during Aire+mTEC differentiation process of the postnatal thymus.

SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.