Collective intercellular communication through ultra-fast hydrodynamic trigger waves, bioRxiv, 2018-09-27
The biophysical relationships between sensors and actuators have been fundamental to the development of complex life forms; abundant flows are generated and persist in aquatic environments by swimming organisms, while responding promptly to external stimuli is key to survival. Here, akin to a chain reaction, we present the discovery of hydrodynamic trigger waves in cellular communities of the protist Spirostomum ambiguum, propagating hundreds of times faster than the swimming speed. Coiling its cytoskeleton, Spirostomum can contract its long body by 50% within milliseconds, with accelerations reaching 14g-forces. Surprisingly, a single cellular contraction (transmitter) is shown to generate long-ranged vortex flows at intermedi- ate Reynolds numbers, which can trigger neighbouring cells, in turn. To measure the sensitivity to hydrodynamic signals (receiver), we further present a high-throughput suction-flow device to probe mechanosensitive ion channel gating by back-calculating the microscopic forces on the cell mem- brane. These ultra-fast hydrodynamic trigger waves are analysed and modelled quantitatively in a universal framework of antenna and percolation theory. A phase transition is revealed, requiring a critical colony density to sustain collective communication. Our results suggest that this signalling could help organise cohabiting communities over large distances, influencing long-term behaviour through gene expression, comparable to quorum sensing. More immediately, as contractions release toxins, synchronised discharges could also facilitate the repulsion of large predators, or conversely immobilise large prey. We postulate that beyond protists numerous other freshwater and marine organisms could coordinate with variations of hydrodynamic trigger waves.
biorxiv biophysics 200-500-users 2018Liposome-based transfection enhances RNAi and CRISPR-mediated mutagenesis in non-model nematode systems, bioRxiv, 2018-09-27
AbstractNematodes belong to one of the most diverse animal phyla. However, functional genomic studies in nematodes, other than in a few species, have often been limited in their reliability and success. Here we report that by combining liposome-based technology with microinjection, we were able to establish a wide range of genomic techniques in the newly described nematode genus Auanema. The method also allowed heritable changes in dauer larvae of Auanema, despite the immaturity of the gonad at the time of the microinjection. As proof of concept for potential functional studies in other nematode species, we also induced RNAi in the free-living nematode Pristionchus pacificus and targeted the human parasite Strongyloides stercoralis.
biorxiv developmental-biology 0-100-users 2018Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy, bioRxiv, 2018-09-27
AbstractWe report an overfitting issue in recent machine learning formulations of the enhancer-promoter interaction problem arising from the fact that many enhancer-promoter pairs share features. Cross- fold validation schemes which do not correctly separate these feature sharing enhancer-promoter pairs into one test set report high accuracy, which is actually due to overfitting. Cross-fold validation schemes which properly segregate pairs with shared features show markedly reduced ability to predict enhancer-promoter interactions from epigenomic state. Parameter scans with multiple models indicate that local epigenomic features of individual pairs of enhancers and promoters cannot distinguish those pairs that interact from those which do with high accuracy, suggesting that additional information is required to predict enhancer-promoter interactions.
biorxiv genomics 0-100-users 2018PlotsOfData – a web app for visualizing data together with its summaries, bioRxiv, 2018-09-27
AbstractReporting of the actual data in graphs and plots increases transparency and enables independent evaluation. On the other hand, data summaries are often used in graphs since they aid interpretation. State-of-the art data visualizations can be made with the ggplot2 package, which uses the ideas of a ‘grammar of graphics’ to generate a graphic from multiple layers of data. However, ggplot2 requires coding skills and an understanding of the tidy data structure. To democratize state-of-the-art data visualization of raw data with a selection of statistical summaries, a web app was written using Rshiny that uses the ggplot2 package for generating plots. A multilayered approach together with adjustable transparency offers a unique flexibility, enabling users can to choose how to display the data and which of the data summaries to add. Four data summaries are provided, mean, median, boxplot, violinplot, to accommodate several types of data distributions. In addition, 95% confidence intervals can be added for visual inferences. By adjusting the transparency of the layers, the visualization of the raw data together with the summary can be tuned for optimal presentation and interpretation. The app is dubbed PlotsOfData and is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpshuygens.science.uva.nlPlotsOfData>httpshuygens.science.uva.nlPlotsOfData<jatsext-link><jatsfig id=ufig1 position=float orientation=portrait fig-type=figure><jatsgraphic xmlnsxlink=httpwww.w3.org1999xlink xlinkhref=426767v3_ufig1 position=float orientation=portrait >
biorxiv scientific-communication-and-education 0-100-users 2018Towards inferring causal gene regulatory networks from single cell expression Measurements, bioRxiv, 2018-09-27
AbstractSingle-cell transcriptome sequencing now routinely samples thousands of cells, potentially providing enough data to reconstruct causal gene regulatory networks from observational data. Here, we present Scribe, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs Restricted Directed Information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for pseudotime” ordered single-cell data compared to true time series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses therefore highlight an important shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and point the way towards overcoming it.
biorxiv genomics 100-200-users 2018beditor A computational workflow for designing libraries of guide RNAs for CRISPR-mediated base editing, bioRxiv, 2018-09-26
ABSTRACTCRISPR-mediated base editors have opened unique avenues for scar-free genome-wide mutagenesis. Here, we describe a comprehensive computational workflow called beditor that can be broadly adapted for designing guide RNA libraries with a range of CRISPR-mediated base editors, PAM recognition sequences and genomes of many species. Additionally, in order to assist users in selecting the best sets of guide RNAs for their experiments, a priori estimates, called beditor scores are calculated. These beditor scores are intended to select guide RNAs that conform to requirements for optimal base editing the editable base falls within maximum activity window of the CRISPR-mediated base editor and produces non-confounding mutational effects with minimal predicted off-target effects. We demonstrate the utility of the software by designing guide RNAs for base-editing to create or remove thousands of clinically important human disease mutations.
biorxiv synthetic-biology 0-100-users 2018