Identification of the stress granule transcriptome via RNA-editing in single cells and in vivo

Stress granules are phase-separated assemblies formed around RNAs. So far, the techniques available to identify these RNAs are not suitable for single cells and small tissues displaying cell heterogeneity. Here, we used TRIBE (target of RNA-binding proteins identified by editing) to profile stress granule RNAs. We used an RNA-binding protein (FMR1) fused to the catalytic domain of an RNA-editing enzyme (ADAR), which coalesces into stress granules upon oxidative stress. RNAs colocalized with this fusion are edited, producing mutations that are detectable by VASA sequencing. Using single-molecule FISH, we validated that this purification-free method can reliably identify stress granule RNAs in bulk and single S2 cells and in Drosophila neurons. Similar to mammalian cells, we find that stress granule mRNAs encode ATP binding, cell cycle, and transcription factors. This method opens the possibility to identify stress granule RNAs and other RNA-based assemblies in other single cells and tissues

A Systems-Level Study Reveals Regulators of Membrane-less Organelles in Human Cells

Membrane-less organelles (MLOs) are liquid-like subcellular compartments that form through phase separation of proteins and RNA. While their biophysical properties are increasingly understood, their regulation and the consequences of perturbed MLO states for cell physiology are less clear. To study the regulatory networks, we targeted 1,354 human genes and screened for morphological changes of nucleoli, Cajal bodies, splicing speckles, PML nuclear bodies (PML-NBs), cytoplasmic processing bodies, and stress granules. By multivariate analysis of MLO features we identified hundreds of genes that control MLO homeostasis. We discovered regulatory crosstalk between MLOs, and mapped hierarchical interactions between aberrant MLO states and cellular properties. We provide evidence that perturbation of pre-mRNA splicing results in stress granule formation and reveal that PML-NB abundance influences DNA replication rates and that PML-NBs are in turn controlled by HIP kinases. Together, our comprehensive dataset is an unprecedented resource for deciphering the regulation and biological functions of MLOs

Computer vision for image-based transcriptomics

Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods. In addition, image-based transcriptomics allows the study of the spatial organization of the transcriptome in single cells at single-molecule, and, when combined with superresolution microscopy, nanometer resolution. However, in order to unlock the full power of image-based transcriptomics, robust computer vision of single molecules and cells is required. Here, we shortly discuss the setup of the experimental pipeline for image-based transcriptomics, and then describe in detail the algorithms that we developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome. These computer vision algorithms and pipelines can be downloaded from: https://github.com/pelkmanslab/ImageBasedTranscriptomics

Molecular mechanisms of cellular stress responses in cancer and their therapeutic implications

In response to stress, cells can activate a myriad of signalling pathways to bring about a specific cellular outcome, including cell cycle arrest, DNA repair, senescence and apoptosis. This response is pivotal for tumour suppression as all of these outcomes result in restriction of the growth and/or elimination of damaged and pre-malignant cells. Thus, a large number of anti-cancer agents target specific components of stress response signalling pathways with the aim of causing tumour regression by stimulating cell death. However, the efficacy of these agents is often impaired due to mutations in genes that are involved in these stress-responsive signalling pathways and instead the oncogenic potential of a cell is increased leading to the initiation and/or progression of tumourigenesis. Moreover, these genetic defects can increase or contribute to resistance to chemotherapeutic agents and/or radiotherapy. Modulating the outcome of cellular stress responses towards cell death in tumour cells without affecting surrounding normal cells is thus one of the ultimate aims in the development of new cancer therapeutics. To achieve this aim, a detailed understanding of cellular stress response pathways and their aberrations in cancer is required.This Research topic aims to reflect the broadness and complexity of this important area of cancer research

Adaptive trajectories towards the three inverse LacI variants.

<p>The three inverse LacI variants all contain three mutations. Each mutation is represented by a vector (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003580#pgen-1003580-g002" target="_blank">Figure 2</a>). The axes indicate expression without IPTG in Env₀ and expression with IPTG in Env₁. Expression levels in both environments are normalized to the LacI_wt level. Note that expression along the vertical axis is represented as (Expression)⁻¹, as during inversion the expression level in Env₁ decreases. The inverse, triple mutant, is located in the upper right corner of the plot. A) LacI_inv1: S97P (blue), R207L (green), T258A (red). B) LacI_inv2: S97P (blue), L307H (green), L349P (red). C) LacI_inv3: S97P (blue), G315D (green), P339H (red). The significance of the phenotypic effect of mutations is tested with a <i>t</i>-test with Bonferroni correction for multiple comparisons (<i>P</i><0.05), error-bars are standard deviations, n = 3.</p

Genetic interactions and their environmental dependence.

<p>The genetic interactions are indicated for three inverse LacI variants. Each row details the interactions between two mutations, each indicated by an X, either in a LacI_wt background (denoted by a ○), or a single mutant background (denoted by a •). We consider three types of interactions: M, magnitude epistasis; S, sign epistasis; R, reciprocal sign epistasis. The mutation that changes sign is indicated between brackets. The data shows that most genetic interactions display different types of epistasis in each of the two environments. The significance of the phenotypic effect of mutations in LacI is tested with a <i>t</i>-test in conjunction with a Bonferroni correction for multiple comparisons (<i>P</i><0.05).</p

Functional description and schematic representation of genetic variants in the <i>lac</i> system.

<p>A) Schematic representation of the genetic system in <i>E. coli</i>. The <i>lac</i> repressor, LacI, controls expression of LacZ. The system responds to IPTG. IPTG acts as an inducer in the wild type LacI (blue block-arrow), and as a co-repressor in the phenotypically inverse mutants (red arrow). B) Environmental dependence of the expression level of lacZ. Expression levels are measured in two environments. For the wild type LacI (LacI_wt), LacZ expression level is high in the presence of IPTG (Env₁) and low in its absence (Env₀) (blue line). For the inverse LacI variant (LacI_inv), LacZ expression level is high in the absence of IPTG (Env₀) and low in its presence (Env₁) (red line). We consider mutational trajectories from the wild type to the inverse variant (arrows).</p

Analysis of higher order genotype-environment interactions.

<p>A) Schematic representation of the effect of mutations on phenotype in two environments. Mutations are represented as vectors with the start in the origin of the coordinate system. Mutations are either beneficial in both environments, Env₀ and Env₁ (quadrant I), beneficial in one environment but deleterious in the other (quadrant II or IV) or deleterious in both environments (quadrant III). Classification of interactions between two mutations in two environments: B) Opposite sides of the polygon represent the same mutation in different genetic backgrounds (a to A (red) in background b or B, and b to B in background a or A (blue)). Absence of epistasis or genotype x environment (GxE) interactions. The vectors of opposing sides are positioned in either quadrant I or III, and the polygon is a simple parallelogram, in the absence of magnitude epistasis. C) Genotype x environment interactions. Opposing sides of the parallelogram are located in the same quadrant. At least one pair of opposing sides lies in quadrant II or IV. D) Sign epistasis. Here, mutation b to B changes sign depending on the genetic background (a or A) in both environments. E) Higher-order GxGxE interactions. At least one pair of vectors from opposing sides of the polygon are located in different quadrants of which at least one vector is located in quadrant II or IV. Note however, that the presence of both GxE and GxG interactions not necessarily implies the presence of GxGxE interactions. In the case that one mutation displays sign epistasis, and the other mutation GxE, their combination does not imply GxGxE (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003580#pgen.1003580.s001" target="_blank">Figure S1</a>).</p

Mutational effects on expression in both environments.

<p>Expression along mutational trajectories towards all three LacI_inv variants. A) (Expression)⁻¹ in Env₁ along all trajectories. B) Expression in Env₀ along all trajectories. For all three inverse variants, expression in Env₀ increases for nearly all mutational steps, in contrast to the more erratic pattern in Env₁ .</p