Meletiou, A. | bioRxiv Science

bioRxiv2019

MuGVRE. A virtual research environment for 3D/4D genomics

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available at https://vre.multiscalegenomics.eu and documentation and general information at https://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

bioinformatics↗

bioRxiv2019

Detecting nanoscale distribution of protein pairs by proximity-dependent super-resolution microscopy

Interactions between biomolecules such as proteins underlie most cellular processes. It is crucial to visualize these molecular-interaction complexes directly within the cell, to show precisely where these interactions occur and thus improve our understanding of cellular regulation. Currently available proximity-sensitive assays for in-situ imaging of such interactions produce diffraction-limited signals and therefore preclude information on the nanometer-scale distribution of interaction complexes. By contrast, optical super-resolution imaging provides information about molecular distributions with nanometer resolution which has greatly advanced our understanding of cell biology. However, current co-localization analysis of super-resolution fluorescence imaging is prone to false positive signals as the detection of protein proximity is directly dependent on the local optical resolution. Here we present Proximity-Dependent PAINT (PD-PAINT), a method for sub-diffraction imaging of protein pairs, in which proximity detection is decoupled from optical resolution. Proximity is detected via the highly distance-dependent interaction of two DNA labels anchored to the target species. Labeled protein pairs are then imaged with high contrast and nanoscale resolution using the super-resolution approach of DNA-PAINT. The mechanisms underlying the new technique are analyzed by means of coarse-grained molecular simulations and experimentally demonstrated by imaging DNA-origami tiles and epitopes of cardiac proteins in isolated cardiomyocytes. We show that PD-PAINT can be straightforwardly integrated in a multiplexed super-resolution imaging protocol and benefits from advantages of DNA-based super-resolution localization microscopy, such as high specificity, high resolution and the ability to image quantitatively.

molecular biology↗