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.

A hierarchical map of regulatory genetic interactions in membrane trafficking.

Endocytosis is critical for cellular physiology and thus is highly regulated. To identify regulatory interactions controlling the endocytic membrane system, we conducted 13 RNAi screens on multiple endocytic activities and their downstream organelles. Combined with image analysis of thousands of single cells per perturbation and their cell-to-cell variability, this created a high-quality and cross-comparable quantitative data set. Unbiased analysis revealed emergent properties of the endocytic membrane system and how its complexity evolved and distinct programs of regulatory control that coregulate specific subsets of endocytic uptake routes and organelle abundances. We show that these subset effects allow the mapping of functional regulatory interactions and their interaction motifs between kinases, membrane-trafficking machinery, and the cytoskeleton at a large scale, some of which we further characterize. Our work presents a powerful approach to identify regulatory interactions in complex cellular systems from parallel single-gene or double-gene perturbation screens in human cells and yeast.

Dual specificity kinase DYRK3 couples stress granule condensation/dissolution to mTORC1 signaling.

Cytosolic compartmentalization through liquid-liquid unmixing, such as the formation of RNA granules, is involved in many cellular processes and might be used to regulate signal transduction. However, specific molecular mechanisms by which liquid-liquid unmixing and signal transduction are coupled remain unknown. Here, we show that during cellular stress the dual specificity kinase DYRK3 regulates the stability of P-granule-like structures and mTORC1 signaling. DYRK3 displays a cyclic partitioning mechanism between stress granules and the cytosol via a low-complexity domain in its N terminus and its kinase activity. When DYRK3 is inactive, it prevents stress granule dissolution and the release of sequestered mTORC1. When DYRK3 is active, it allows stress granule dissolution, releasing mTORC1 for signaling and promoting its activity by directly phosphorylating the mTORC1 inhibitor PRAS40. This mechanism links cytoplasmic compartmentalization via liquid phase transitions with cellular signaling.

Multiscale chromatin dynamics and high entropy in plant iPSC ancestors.

Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.

Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.

Learning single-cell perturbation responses using neural optimal transport.

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-ß and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.

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.

Kinase-controlled phase transition of membraneless organelles in mitosis.

Liquid-liquid phase separation has been shown to underlie the formation and disassembly of membraneless organelles in cells, but the cellular mechanisms that control this phenomenon are poorly understood. A prominent example of regulated and reversible segregation of liquid phases may occur during mitosis, when membraneless organelles disappear upon nuclear-envelope breakdown and reappear as mitosis is completed. Here we show that the dual-specificity kinase DYRK3 acts as a central dissolvase of several types of membraneless organelle during mitosis. DYRK3 kinase activity is essential to prevent the unmixing of the mitotic cytoplasm into aberrant liquid-like hybrid organelles and the over-nucleation of spindle bodies. Our work supports a mechanism in which the dilution of phase-separating proteins during nuclear-envelope breakdown and the DYRK3-dependent degree of their solubility combine to allow cells to dissolve and condense several membraneless organelles during mitosis.

Post-transcriptional control of executioner caspases by RNA-binding proteins.

Caspases are key components of apoptotic pathways. Regulation of caspases occurs at several levels, including transcription, proteolytic processing, inhibition of enzymatic function, and protein degradation. In contrast, little is known about the extent of post-transcriptional control of caspases. Here, we describe four conserved RNA-binding proteins (RBPs)-PUF-8, MEX-3, GLD-1, and CGH-1-that sequentially repress the CED-3 caspase in distinct regions of the Caenorhabditis elegans germline. We demonstrate that GLD-1 represses ced-3 mRNA translation via two binding sites in its 3' untranslated region (UTR), thereby ensuring a dual control of unwanted cell death: at the level of p53/CEP-1 and at the executioner caspase level. Moreover, we identified seven RBPs that regulate human caspase-3 expression and/or activation, including human PUF-8, GLD-1, and CGH-1 homologs PUM1, QKI, and DDX6. Given the presence of unusually long executioner caspase 3' UTRs in many metazoans, translational control of executioner caspases by RBPs might be a strategy used widely across the animal kingdom to control apoptosis.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Origins of regulated cell-to-cell variability.

Single-cell measurements and lineage-tracing experiments are revealing that phenotypic cell-to-cell variability is often the result of deterministic processes, despite the existence of intrinsic noise in molecular networks. In most cases, this determinism represents largely uncharacterized molecular regulatory mechanisms, which places the study of cell-to-cell variability in the realm of molecular cell biology. Further research in the field will be important to advance quantitative cell biology because it will provide new insights into the mechanisms by which cells coordinate their intracellular activities in the spatiotemporal context of the multicellular environment.

KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens.

Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFß and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.

Single-cell and multivariate approaches in genetic perturbation screens.

Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.

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.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9.

Modifiers of prion protein biogenesis and recycling identified by a highly parallel endocytosis kinetics assay.

The cellular prion protein, PrPC, is attached by a glycosylphosphatidylinositol anchor to the outer leaflet of the plasma membrane. Its misfolded isoform PrPSc is the causative agent of prion diseases. Conversion of PrPC into PrPSc is thought to take place at the cell surface or in endolysosomal organelles. Understanding the intracellular trafficking of PrPC may, therefore, help elucidate the conversion process. Here we describe a time-resolved fluorescence energy transfer (FRET) assay reporting membrane expression and real-time internalization rates of PrPC The assay is suitable for high-throughput genetic and pharmaceutical screens for modulators of PrPC trafficking. Simultaneous administration of FRET donor and acceptor anti-PrPC antibodies to living cells yielded a measure of PrPC surface density, whereas sequential addition of each antibody visualized the internalization rate of PrPC (Z' factor >0.5). RNA interference assays showed that suppression of AP2M1 (AP-2 adaptor protein), RAB5A, VPS35 (vacuolar protein sorting 35 homolog), and M6PR (mannose 6-phosphate receptor) blocked PrPC internalization, whereas down-regulation of GIT2 and VPS28 increased PrPC internalization. PrPC cell-surface expression was reduced by down-regulation of RAB5A, VPS28, and VPS35 and enhanced by silencing EHD1. These data identify a network of proteins implicated in PrPC trafficking and demonstrate the power of this assay for identifying modulators of PrPC trafficking.

Trajectories of cell-cycle progression from fixed cell populations.

An accurate dissection of sources of cell-to-cell variability is crucial for quantitative biology at the single-cell level but has been challenging for the cell cycle. We present Cycler, a robust method that constructs a continuous trajectory of cell-cycle progression from images of fixed cells. Cycler handles heterogeneous microenvironments and does not require perturbations or genetic markers, making it generally applicable to quantifying multiple sources of cell-to-cell variability in mammalian cells.

Wnt directs the endosomal flux of LDL-derived cholesterol and lipid droplet homeostasis.

The Wnt pathway, which controls crucial steps of the development and differentiation programs, has been proposed to influence lipid storage and homeostasis. In this paper, using an unbiased strategy based on high-content genome-wide RNAi screens that monitored lipid distribution and amounts, we find that Wnt3a regulates cellular cholesterol. We show that Wnt3a stimulates the production of lipid droplets and that this stimulation strictly depends on endocytosed, LDL-derived cholesterol and on functional early and late endosomes. We also show that Wnt signaling itself controls cholesterol endocytosis and flux along the endosomal pathway, which in turn modulates cellular lipid homeostasis. These results underscore the importance of endosome functions for LD formation and reveal a previously unknown regulatory mechanism of the cellular programs controlling lipid storage and endosome transport under the control of Wnt signaling.