Eduardo A. Torre

Structural imprints in vivo decode RNA regulatory mechanisms

Visualizing the physical basis for molecular behaviour inside living cells is a great challenge for biology. RNAs are central to biological regulation, and the ability of RNA to adopt specific structures intimately controls every step of the gene expression program. However, our understanding of physiological RNA structures is limited; current in vivo RNA structure profiles include only two of the four nucleotides that make up RNA. Here we present a novel biochemical approach, in vivo click selective 2′-hydroxyl acylation and profiling experiment (icSHAPE), which enables the first global view, to our knowledge, of RNA secondary structures in living cells for all four bases. icSHAPE of the mouse embryonic stem cell transcriptome versus purified RNA folded in vitro shows that the structural dynamics of RNA in the cellular environment distinguish different classes of RNAs and regulatory elements. Structural signatures at translational start sites and ribosome pause sites are conserved from in vitro conditions, suggesting that these RNA elements are programmed by sequence. In contrast, focal structural rearrangements in vivo reveal precise interfaces of RNA with RNA-binding proteins or RNA-modification sites that are consistent with atomic-resolution structural data. Such dynamic structural footprints enable accurate prediction of RNA–protein interactions and N6-methyladenosine (m6A) modification genome wide. These results open the door for structural genomics of RNA in living cells and reveal key physiological structures controlling gene expression.

Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

Therapies that target signalling molecules that are mutated in cancers can often have substantial short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures. Resistance can result from secondary mutations, but in other cases there is no clear genetic cause, raising the possibility of non-genetic rare cell variability. Here we show that human melanoma cells can display profound transcriptional variability at the single-cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins with a loss of SOX10-mediated differentiation followed by activation of new signalling pathways, partially mediated by the activity of the transcription factors JUN and/or AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general program in which expression is displayed in rare subpopulations of cells.

RNA structural analysis by evolving SHAPE chemistry

RNA is central to the flow of biological information. From transcription to splicing, RNA localization, translation, and decay, RNA is intimately involved in regulating every step of the gene expression program, and is thus essential for health and understanding disease. RNA has the unique ability to base‐pair with itself and other nucleic acids to form complex structures. Hence the information content in RNA is not simply its linear sequence of bases, but is also encoded in complex folding of RNA molecules. A general chemical functionality that all RNAs have is a 2′‐hydroxyl group in the ribose ring, and the reactivity of the 2′‐hydroxyl in RNA is gated by local nucleotide flexibility. In other words, the 2′‐hydroxyl is reactive at single‐stranded and conformationally flexible positions but is unreactive at nucleotides constrained by base‐pairing. Recent efforts have been focused on developing reagents that modify RNA as a function of RNA 2′ hydroxyl group reactivity. Such RNA structure probing techniques can be read out by primer extension in experiments termed RNA SHAPE (selective 2′‐ hydroxyl acylation and primer extension). Herein, we describe the efforts devoted to the design and utilization of SHAPE probes for characterizing RNA structure. We also describe current technological advances that are being applied to utilize SHAPE chemistry with deep sequencing to probe many RNAs in parallel. The merging of chemistry with genomics is sure to open the door to genome‐wide exploration of RNA structure and function. WIREs RNA 2014, 5:867–881. doi: 10.1002/wrna.1253

DNA Topoisomerase II Modulates Insulator Function in Drosophila

Insulators are DNA sequences thought to be important for the establishment and maintenance of cell-type specific nuclear architecture. In Drosophila there are several classes of insulators that appear to have unique roles in gene expression. The mechanisms involved in determining and regulating the specific roles of these insulator classes are not understood. Here we report that DNA Topoisomerase II modulates the activity of the Su(Hw) insulator. Downregulation of Topo II by RNAi or mutations in the Top2 gene result in disruption of Su(Hw) insulator function. This effect is mediated by the Mod(mdg4)2.2 protein, which is a unique component of the Su(Hw) insulator complex. Co-immunoprecipitation and yeast two-hybrid experiments show that Topo II and Mod(mdg4)2.2 proteins directly interact. In addition, mutations in Top2 cause a slight decrease of Mod(mdg4)2.2 transcript but have a dramatic effect on Mod(mdg4)2.2 protein levels. In the presence of proteasome inhibitors, normal levels of Mod(mdg4)2.2 protein and its binding to polytene chromosomes are restored. Thus, Topo II is required to prevent Mod(mdg4)2.2 degradation and, consequently, to stabilize Su(Hw) insulator-mediated chromatin organization.

Defining the identity of mouse embryonic dermal fibroblasts

Embryonic dermal fibroblasts in the skin have the exceptional ability to initiate hair follicle morphogenesis and contribute to scarless wound healing. Activation of the Wnt signaling pathway is critical for dermal fibroblast fate selection and hair follicle induction. In humans, mutations in Wnt pathway components and target genes lead to congenital focal dermal hypoplasias with diminished hair. The gene expression signature of embryonic dermal fibroblasts during differentiation and its dependence on Wnt signaling is unknown. Here we applied Shannon entropy analysis to identify the gene expression signature of mouse embryonic dermal fibroblasts. We used available human DNase‐seq and histone modification ChiP‐seq data on various cell‐types to demonstrate that genes in the fibroblast cell identity signature can be epigenetically repressed in other cell‐types. We found a subset of the signature genes whose expression is dependent on Wnt/β‐catenin activity in vivo. With our approach, we have defined and validated a statistically derived gene expression signature that may mediate dermal fibroblast identity and function in development and disease. genesis 54:415–430, 2016.

SAVER: gene expression recovery for single-cell RNA sequencing

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.SAVER accurately recovers expression values in single-cell RNA-sequencing data to improve downstream analysis.

Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Although single-cell RNA sequencing can reliably detect large-scale transcriptional programs, it is unclear whether it accurately captures the behavior of individual genes, especially those that express only in rare cells. Here, we use single-molecule RNA fluorescence in situ hybridization as a gold standard to assess trade-offs in single-cell RNA-sequencing data for detecting rare cell expression variability. We quantified the gene expression distribution for 26 genes that range from ubiquitous to rarely expressed and found that the correspondence between estimates across platforms improved with both transcriptome coverage and increased number of cells analyzed. Further, by characterizing the trade-off between transcriptome coverage and number of cells analyzed, we show that when the number of genes required to answer a given biological question is small, then greater transcriptome coverage is more important than analyzing large numbers of cells. More generally, our report provides guidelines for selecting quality thresholds for single-cell RNA-sequencing experiments aimed at rare cell analyses.

SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

Rapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

Identification and Characterization of Proteins Involved in Nuclear Organization Using Drosophila GFP Protein Trap Lines

Background Strains from a collection of Drosophila GFP protein trap lines express GFP in the normal tissues where the endogenous protein is present. This collection can be used to screen for proteins distributed in the nucleus in a non-uniform pattern. Methodology/Principal Findings We analyzed four lines that show peripheral or punctate nuclear staining. One of these lines affects an uncharacterized gene named CG11138. The CG11138 protein shows a punctate distribution in the nuclear periphery similar to that of Drosophila insulator proteins but does not co-localize with known insulators. Interestingly, mutations in Lamin proteins result in alterations in CG11138 localization, suggesting that this protein may be a novel component of the nuclear lamina. A second line affects the Decondensation factor 31 (Df31) gene, which encodes a protein with a unique nuclear distribution that appears to segment the nucleus into four different compartments. The X-chromosome of males is confined to one of these compartments. We also find that Drosophila Nucleoplasmin (dNlp) is present in regions of active transcription. Heat shock leads to loss of dNlp from previously transcribed regions of polytene chromosome without redistribution to the heat shock genes. Analysis of Stonewall (Stwl), a protein previously found to be necessary for the maintenance of germline stem cells, shows that Stwl is present in a punctate pattern in the nucleus that partially overlaps with that of known insulator proteins. Finally we show that Stwl, dNlp, and Df31 form part of a highly interactive network. The properties of other components of this network may help understand the role of these proteins in nuclear biology. Conclusions/Significance These results establish screening of GFP protein trap alleles as a strategy to identify factors with novel cellular functions. Information gained from the analysis of CG11138 Stwl, dNlp, and Df31 sets the stage for future studies of these proteins.

A Comparison Between Single Cell RNA Sequencing And Single Molecule RNA FISH For Rare Cell Analysis

The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.