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.

Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies.

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates using relatively homogeneous cell populations. However, to determine these rates during complex dynamical processes, for instance during cellular differentiation, single-cell resolution is required. Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory strategies. These strategies have distinct consequences for controlling the dynamic range and precision of gene expression. These findings advance our understanding of how individual cells in heterogeneous populations shape their gene expression dynamics.

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.

Passive Noise Filtering by Cellular Compartmentalization.

Chemical reactions contain an inherent element of randomness, which presents itself as noise that interferes with cellular processes and communication. Here we discuss the ability of the spatial partitioning of molecular systems to filter and, thus, remove noise, while preserving regulated and predictable differences between single living cells. In contrast to active noise filtering by network motifs, cellular compartmentalization is highly effective and easily scales to numerous systems without requiring a substantial usage of cellular energy. We will use passive noise filtering by the eukaryotic cell nucleus as an example of how this increases predictability of transcriptional output, with possible implications for the evolution of complex multicellularity.

Control of Transcript Variability in Single Mammalian Cells.

A central question in biology is whether variability between genetically identical cells exposed to the same culture conditions is largely stochastic or deterministic. Using image-based transcriptomics in millions of single human cells, we find that while variability of cytoplasmic transcript abundance is large, it is for most genes minimally stochastic and can be predicted with multivariate models of the phenotypic state and population context of single cells. Computational multiplexing of these predictive signatures across hundreds of genes revealed a complex regulatory system that controls the observed variability of transcript abundance between individual cells. Mathematical modeling and experimental validation show that nuclear retention and transport of transcripts between the nucleus and the cytoplasm is central to buffering stochastic transcriptional fluctuations in mammalian gene expression. Our work indicates that cellular compartmentalization confines transcriptional noise to the nucleus, thereby preventing it from interfering with the control of single-cell transcript abundance in the cytoplasm.

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.

Cell-intrinsic adaptation of lipid composition to local crowding drives social behaviour.

Cells sense the context in which they grow to adapt their phenotype and allow multicellular patterning by mechanisms of autocrine and paracrine signalling. However, patterns also form in cell populations exposed to the same signalling molecules and substratum, which often correlate with specific features of the population context of single cells, such as local cell crowding. Here we reveal a cell-intrinsic molecular mechanism that allows multicellular patterning without requiring specific communication between cells. It acts by sensing the local crowding of a single cell through its ability to spread and activate focal adhesion kinase (FAK, also known as PTK2), resulting in adaptation of genes controlling membrane homeostasis. In cells experiencing low crowding, FAK suppresses transcription of the ABC transporter A1 (ABCA1) by inhibiting FOXO3 and TAL1. Agent-based computational modelling and experimental confirmation identified membrane-based signalling and feedback control as crucial for the emergence of population patterns of ABCA1 expression, which adapts membrane lipid composition to cell crowding and affects multiple signalling activities, including the suppression of ABCA1 expression itself. The simple design of this cell-intrinsic system and its broad impact on the signalling state of mammalian single cells suggests a fundamental role for a tunable membrane lipid composition in collective cell behaviour.

Image-based transcriptomics in thousands of single human cells at single-molecule resolution.

Fluorescence in situ hybridization (FISH) is widely used to obtain information about transcript copy number and subcellular localization in single cells. However, current approaches do not readily scale to the analysis of whole transcriptomes. Here we show that branched DNA technology combined with automated liquid handling, high-content imaging and quantitative image analysis allows highly reproducible quantification of transcript abundance in thousands of single cells at single-molecule resolution. In addition, it allows extraction of a multivariate feature set quantifying subcellular patterning and spatial properties of transcripts and their cell-to-cell variability. This has multiple implications for the functional interpretation of cell-to-cell variability in gene expression and enables the unbiased identification of functionally relevant in situ signatures of the transcriptome without the need for perturbations. Because this method can be incorporated in a wide variety of high-throughput image-based approaches, we expect it to be broadly applicable.

Environmental dependence of genetic constraint.

The epistatic interactions that underlie evolutionary constraint have mainly been studied for constant external conditions. However, environmental changes may modulate epistasis and hence affect genetic constraints. Here we investigate genetic constraints in the adaptive evolution of a novel regulatory function in variable environments, using the lac repressor, LacI, as a model system. We have systematically reconstructed mutational trajectories from wild type LacI to three different variants that each exhibit an inverse response to the inducing ligand IPTG, and analyzed the higher-order interactions between genetic and environmental changes. We find epistasis to depend strongly on the environment. As a result, mutational steps essential to inversion but inaccessible by positive selection in one environment, become accessible in another. We present a graphical method to analyze the observed complex higher-order interactions between multiple mutations and environmental change, and show how the interactions can be explained by a combination of mutational effects on allostery and thermodynamic stability. This dependency of genetic constraint on the environment should fundamentally affect evolutionary dynamics and affects the interpretation of phylogenetic data.