Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell transcriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions.

INTRODUCTION: Conventional Illumina RNA-Seq does not have the resolution to decode the complex eukaryote transcriptome due to the lack of RNA polarity information. Strand-specific RNA sequencing (ssRNA-Seq) can overcome these limitations and as such is better suited for genome annotation, de novo transcriptome assembly, and accurate digital gene expression analysis. This protocol describes a simple and robust method to generate ssRNA-Seq libraries for the Illumina sequencing platform. It has significantly increased the throughput to 96 libraries in a two-day preparation while simultaneously lowering the reagent costs to below ten dollars per library. It is compatible with both single-read and paired-end multiplex sequencing and, most importantly, its data can also be used with existing conventional RNA-Seq data. This is a significant advantage, because it enables researchers to switch to ssRNA-Seq even if a large amount of data has already been generated by the nonstrand specific methods.

Summary: Solanum lycopersicum (tomato) and its wild relatives harbor genetic diversity that yields heritable variation in fruit chemistry that could be exploited to identify genes regulating their synthesis and accumulation. Carotenoids, for example, are essential in plant and animal nutrition, and are the visual indicators of ripening for many fruits, including tomato. Whereas carotenoid synthesis is well characterized, factors regulating flux through the pathway are poorly understood at the molecular level. To exploit the impact of tomato genetic diversity on carotenoids, Solanum pennellii introgression lines were used as a source of defined natural variation and as a resource for the identification of candidate regulatory genes. Ripe fruits were analyzed for numerous fruit metabolites and transcriptome profiles generated using a 12 000 unigene oligoarray. Correlation analysis between carotenoid content and gene expression profiles revealed 953 carotenoid‐correlated genes. To narrow the pool, subnetwork analysis of carotenoid‐correlated transcription revealed 38 candidates. One candidate for impact on trans‐lycopene and β‐carotene accumulation was functionally charaterized, SlERF6, revealing that it indeed influences carotenoid biosynthesis and additional ripening phenotypes. Reduced expression of SlERF6 by RNAi enhanced both carotenoid and ethylene levels during fruit ripening, demonstrating an important role for SlERF6 in ripening, integrating the ethylene and carotenoid synthesis pathways.

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay.