A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data

RNA Helicase DDX5 in Association With IFI16 and the Polycomb Repressive Complex 2 Silences Transcription of the Hepatitis B Virus by Interferon

RNA helicase DDX5 is a host restriction factor for hepatitis B virus (HBV) biosynthesis. Mass spectrometry (LC-MS/MS) identified significant DDX5-interacting partners, including interferon-inducible protein 16 (IFI16) and RBBP4/7, an auxiliary subunit of polycomb repressive complex 2 (PRC2). DDX5 co-eluted with IFI16, RBBP4/7, and core PRC2 subunits in size exclusion chromatography fractions derived from native nuclear extracts. Native gel electrophoresis of DDX5 immunoprecipitants revealed a 750 kDa DDX5/IFI16/PRC2 complex, validated by nanoscale co-localization via super-resolution microscopy. Prior studies demonstrated that IFI16 suppresses HBV transcription by binding to the interferon-sensitive response element of covalently closed circular DNA (cccDNA), reducing H3 acetylation and increasing H3K27me3 levels by an unknown mechanism. Herein, we demonstrate that ectopic expression of IFI16 inhibited HBV transcription from recombinant rcccDNA, correlating with increased IFI16 binding to rcccDNA, reduced H3 acetylation, and elevated H3K27me3, determined by chromatin immunoprecipitation. Importantly, the inhibitory effect of ectopic IFI16 on HBV transcription was reversed by siRNA-mediated knockdown of DDX5 and EZH2, the methyltransferase subunit of PRC2. This reversal was associated with decreased IFI16 binding to rcccDNA, enhanced H3 acetylation, and reduced H3K27me3. Similarly, endogenous IFI16 induced by interferon-α inhibited HBV rcccDNA transcription in a DDX5- and PRC2-dependent manner. In HBV-infected HepG2-NTCP cells, the antiviral effect of interferon-α was abrogated upon knockdown of DDX5 and EZH2, underscoring the crucial role of the DDX5 complex in IFI16-mediated antiviral response. In conclusion, in response to interferon, DDX5 partners with IFI16 to bind cccDNA, directing PRC2 to epigenetically silence cccDNA chromatin, thereby regulating immune signaling and HBV transcription.

Substrate stiffness modulates the emergence and magnitude of senescence phenotypes in dermal fibroblasts

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes after exposure to senescence inducers. Using a primary dermal fibroblast model, we show that decreased substrate stiffness accelerates senescence-associated cell-cycle arrest and regulates the expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21WAF1/CIP1 and p16INK4a are mechanosensitive and are in-part regulated by myosin contractility through focal adhesion kinase (FAK)-ROCK signaling. Interestingly, at the protein level senescence-induced dermal fibroblasts on soft substrates (0.5 kPa) do not express p21WAF1/CIP1 and p16INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. Moreover, when cells are transferred from soft to stiff substrates, senescent cells recover an elevated expression of p21WAF1/CIP1 and p16INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotype. Together, our results indicate that the emergent senescence phenotype depends critically on the local mechanical environments of cells and that senescent cells actively respond to changing mechanical cues.

Identifying Intermolecular Interactions in Single-Molecule Localization Microscopy

Intermolecular interactions underlie all cellular functions, yet visualizing these interactions at the single-molecule level remains challenging. Single-molecule localization microscopy (SMLM) offers a potential solution. Given a nanoscale map of two putative interaction partners, it should be possible to assign molecules either to the class of coupled pairs or to the class of non-coupled bystanders. Here, we developed a probabilistic algorithm that allows accurate determination of both the absolute number and the proportion of molecules that form coupled pairs. The algorithm calculates interaction probabilities for all possible pairs of localized molecules, selects the most likely interaction set, and corrects for any spurious colocalizations. Benchmarking this approach across a set of simulated molecular localization maps with varying densities (up to ∼ 50 molecules µm - 2 ) and localization precisions (5 to 50 nm) showed typical errors in the identification of correct pairs of only a few percent. At molecular densities of ∼ 5-10 molecules µm - 2 and localization precisions of 20-30 nm, which are typical parameters for SMLM imaging, the recall was ∼ 90%. The algorithm was effective at differentiating between non-interacting and coupled molecules both in simulations and experiments. Finally, it correctly inferred the number of coupled pairs over time in a simulated reaction-diffusion system, enabling determination of the underlying rate constants. The proposed approach promises to enable direct visualization and quantification of intermolecular interactions using SMLM.

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range

Over the last decade, single-molecule localization microscopy (SMLM) has revolutionized cell biology, making it possible to monitor molecular organization and dynamics with spatial resolution of a few nanometers. Despite being a relatively recent field, SMLM has witnessed the development of dozens of analysis methods for problems as diverse as segmentation, clustering, tracking or colocalization. Among those, Voronoi-based methods have achieved a prominent position for 2D analysis as robust and efficient implementations were available for generating 2D Voronoi diagrams. Unfortunately, this was not the case for 3D Voronoi diagrams, and existing methods were therefore extremely time-consuming. In this work, we present a new hybrid CPU-GPU algorithm for the rapid generation of 3D Voronoi diagrams. Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function.