Daniel Ramsköld

Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells

Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data

The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out. We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing (RNA-Seq) data across human and mouse tissues and cell lines. We observed that roughly 8,000 protein-coding genes were ubiquitously expressed, contributing to around 75% of all mRNAs by message copy number in most tissues. These mRNAs encoded proteins that were often intracellular, and tended to be involved in metabolism, transcription, RNA processing or translation. In contrast, genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues. The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes. Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3′ untranslated regions (UTRs). Muscle and liver had the least complex transcriptomes, in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes, whereas brain, kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes. mRNAs expressed in brain had unusually long 3′UTRs, and mean 3′UTR length was higher for genes involved in development, morphogenesis and signal transduction, suggesting added complexity of UTR-based regulation for these genes. Our results support a model in which variable exterior components feed into a large, densely connected core composed of ubiquitously expressed intracellular proteins.

Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

Expressing One Allele at a Time Although genetic traits are often dominant or recessive, the impact of the same heterozygous genotype can vary quite a bit between individuals. Deng et al. (p. 193), analyzed global gene expression in hundreds of individual mouse cells and found that a substantial fraction of the genes only expressed one of the alleles, chosen randomly, at any given point in time. Such stochasticity in transcription increases the heterogeneity among cells and likely contributes to the phenotypic variance among individuals of identical genotype. Independent allelic transcription generates fluctuations in the single-cell transcriptome. Expression from both alleles is generally observed in analyses of diploid cell populations, but studies addressing allelic expression patterns genome-wide in single cells are lacking. Here, we present global analyses of allelic expression across individual cells of mouse preimplantation embryos of mixed background (CAST/EiJ × C57BL/6J). We discovered abundant (12 to 24%) monoallelic expression of autosomal genes and that expression of the two alleles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal X chromosome. We conclude that independent and stochastic allelic transcription generates abundant random monoallelic expression in the mammalian cell.

Sequentially acting Sox transcription factors in neural lineage development

Pluripotent embryonic stem (ES) cells can generate all cell types, but how cell lineages are initially specified and maintained during development remains largely unknown. Different classes of Sox transcription factors are expressed during neurogenesis and have been assigned important roles from early lineage specification to neuronal differentiation. Here we characterize the genome-wide binding for Sox2, Sox3, and Sox11, which have vital functions in ES cells, neural precursor cells (NPCs), and maturing neurons, respectively. The data demonstrate that Sox factor binding depends on developmental stage-specific constraints and reveal a remarkable sequential binding of Sox proteins to a common set of neural genes. Interestingly, in ES cells, Sox2 preselects for neural lineage-specific genes destined to be bound and activated by Sox3 in NPCs. In NPCs, Sox3 binds genes that are later bound and activated by Sox11 in differentiating neurons. Genes prebound by Sox proteins are associated with a bivalent chromatin signature, which is resolved into a permissive monovalent state upon binding of activating Sox factors. These data indicate that a single key transcription factor family acts sequentially to coordinate neural gene expression from the early lineage specification in pluripotent cells to later stages of neuronal development.

Transcription factor Nurr1 maintains fiber integrity and nuclear-encoded mitochondrial gene expression in dopamine neurons

Developmental transcription factors important in early neuron specification and differentiation often remain expressed in the adult brain. However, how these transcription factors function to mantain appropriate neuronal identities in adult neurons and how transcription factor dysregulation may contribute to disease remain largely unknown. The transcription factor Nurr1 has been associated with Parkinsons disease and is essential for the development of ventral midbrain dopamine (DA) neurons. We used conditional Nurr1 gene-targeted mice in which Nurr1 is ablated selectively in mature DA neurons by treatment with tamoxifen. We show that Nurr1 ablation results in a progressive pathology associated with reduced striatal DA, impaired motor behaviors, and dystrophic axons and dendrites. We used laser-microdissected DA neurons for RNA extraction and next-generation mRNA sequencing to identify Nurr1-regulated genes. This analysis revealed that Nurr1 functions mainly in transcriptional activation to regulate a battery of genes expressed in DA neurons. Importantly, nuclear-encoded mitochondrial genes were identified as the major functional category of Nurr1-regulated target genes. These studies indicate that Nurr1 has a key function in sustaining high respiratory function in these cells, and that Nurr1 ablation in mice recapitulates early features of Parkinsons disease.

Non-canonical notch signaling activates IL-6/JAK/STAT signaling in breast tumor cells and is controlled by p53 and IKKα/IKKβ

Notch signaling is frequently hyperactivated in breast cancer, but how the enhanced signaling contributes to the tumor process is less well understood. In this report, we identify the proinflammatory cytokine interleukin-6 (IL-6) as a novel Notch target in breast tumor cells. Enhanced Notch signaling upregulated IL-6 expression, leading to activation of autocrine and paracrine Janus kinase/signal transducers and activators of transcription signaling. IL-6 upregulation was mediated by non-canonical Notch signaling, as it could be effectuated by a cytoplasmically localized Notch intracellular domain and was independent of the DNA-binding protein CSL. Instead, Notch-mediated IL-6 upregulation was controlled by two proteins in the nuclear factor (NF)-κB signaling cascade, IKKα and IKKβ (inhibitor of nuclear factor kappa-B kinase subunit alpha and beta, respectively), as well as by p53. Activation of IL-6 by Notch required IKKα/IKKβ function, but interestingly, did not engage canonical NF-κB signaling, in contrast to IL-6 activation by inflammatory agents such as lipopolysaccharide. With regard to p53 status, IL-6 expression was upregulated by Notch when p53 was mutated or lost, and restoring wild-type p53 into p53-mutated or -deficient cells abrogated the IL-6 upregulation. Furthermore, Notch-induced transcriptomes from p53 wild-type and -mutated breast tumor cell lines differed extensively, and for a subset of genes upregulated by Notch in a p53-mutant cell line, this upregulation was reduced by wild-type p53. In conclusion, we identify IL-6 as a novel non-canonical Notch target gene, and reveal roles for p53 and IKKα/IKKβ in non-canonical Notch signaling in breast cancer and in the generation of cell context-dependent diversity in the Notch signaling output.

Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution

Although the locations of promoters and enhancers have been identified in several cell types, we still have limited information on their connectivity. We developed HiCap, which combines a 4-cutter restriction enzyme Hi-C with sequence capture of promoter regions. Applying the method to mouse embryonic stem cells, we identified promoter-anchored interactions involving 15,905 promoters and 71,984 distal regions. The distal regions were enriched for enhancer marks and transcription, and had a mean fragment size of only 699 bp — close to single-enhancer resolution. High-resolution maps of promoter-anchored interactions with HiCap will be important for detailed characterizations of chromatin interaction landscapes.

BMP signaling and its pSMAD1/5 target genes differentially regulate hair follicle stem cell lineages.

Hair follicle stem cells (HFSCs) and their transit amplifying cell (TAC) progeny sense BMPs at defined stages of the hair cycle to control their proliferation and differentiation. Here, we exploit the distinct spatial and temporal localizations of these cells to selectively ablate BMP signaling in each compartment and examine its functional role. We find that BMP signaling is required for HFSC quiescence and to promote TAC differentiation along different lineages as the hair cycle progresses. We also combine in vivo genome-wide chromatin immunoprecipitation and deep-sequencing, transcriptional profiling, and loss-of-function genetics to define BMP-regulated genes. We show that some pSMAD1/5 targets, like Gata3, function specifically in TAC lineage-progression. Others, like Id1 and Id3, function in both HFSCs and TACs, but in distinct ways. Our study therefore illustrates the complex differential roles that a key signaling pathway can play in regulation of closely related stem/progenitor cells within the context of their overall niche.

How to Analyze Gene Expression Using RNA-Sequencing Data

RNA-Seq is arising as a powerful method for transcriptome analyses that will eventually make microarrays obsolete for gene expression analyses. Improvements in high-throughput sequencing and efficient sample barcoding are now enabling tens of samples to be run in a cost-effective manner, competing with microarrays in price, excelling in performance. Still, most studies use microarrays, partly due to the ease of data analyses using programs and modules that quickly turn raw microarray data into spreadsheets of gene expression values and significant differentially expressed genes. Instead RNA-Seq data analyses are still in its infancy and the researchers are facing new challenges and have to combine different tools to carry out an analysis. In this chapter, we provide a tutorial on RNA-Seq data analysis to enable researchers to quantify gene expression, identify splice junctions, and find novel transcripts using publicly available software. We focus on the analyses performed in organisms where a reference genome is available and discuss issues with current methodology that have to be solved before RNA-Seq data can utilize its full potential.

RNA helicase A is a downstream mediator of KIF1Bβ tumor suppressor function in neuroblastoma

UNLABELLED Inherited KIF1B loss-of-function mutations in neuroblastomas and pheochromocytomas implicate the kinesin KIF1B as a 1p36.2 tumor suppressor. However, the mechanism of tumor suppression is unknown. We found that KIF1B isoform β (KIF1Bβ) interacts with RNA helicase A (DHX9), causing nuclear accumulation of DHX9, followed by subsequent induction of the proapoptotic XIAP-associated factor 1 (XAF1) and, consequently, apoptosis. Pheochromocytoma and neuroblastoma arise from neural crest progenitors that compete for growth factors such as nerve growth factor (NGF) during development. KIF1Bβ is required for developmental apoptosis induced by competition for NGF. We show that DHX9 is induced by and required for apoptosis stimulated by NGF deprivation. Moreover, neuroblastomas with chromosomal deletion of 1p36 exhibit loss of KIF1Bβ expression and impaired DHX9 nuclear localization, implicating the loss of DHX9 nuclear activity in neuroblastoma pathogenesis. SIGNIFICANCE KIF1Bβ has neuroblastoma tumor-suppressor properties and promotes and requires nuclear-localized DHX9 for its apoptotic function by activating XAF1 expression. Loss of KIF1Bβ alters subcellular localization of DHX9 and diminishes NGF dependence of sympathetic neurons, leading to reduced culling of neural progenitors, and, therefore, might predispose to tumor formation.