A genetically encoded fluorescent sensor for in vivo imaging of GABA, bioRxiv, 2018-05-15
AbstractCurrent techniques for monitoring GABA, the primary inhibitory neurotransmitter in vertebrates, cannot follow ephemeral transients in intact neural circuits. We applied the design principles used to create iGluSnFR, a fluorescent reporter of synaptic glutamate, to develop a GABA sensor using a protein derived from a previously unsequenced Pseudomonas fluorescens strain. Structure-guided mutagenesis and library screening led to a usable iGABASnFR (ΔFFmax ~ 2.5, Kd ~ 9 μM, good specificity, adequate kinetics). iGABASnFR is genetically encoded, detects single action potential-evoked GABA release events in culture, and produces readily detectable fluorescence increases in vivo in mice and zebrafish. iGABASnFR enabled tracking of (1) mitochondrial GABA content and its modulation by an anticonvulsant; (2) swimming-evoked GABAergic transmission in zebrafish cerebellum; (3) GABA release events during inter-ictal spikes and seizures in awake mice; and (4) GABAergic tone decreases during isoflurane anesthesia. iGABASnFR will permit high spatiotemporal resolution of GABA signaling in intact preparations.
biorxiv neuroscience 100-200-users 2018Optimization of Golden Gate assembly through application of ligation sequence-dependent fidelity and bias profiling, bioRxiv, 2018-05-15
ABSTRACTModern synthetic biology depends on the manufacture of large DNA constructs from libraries of genes, regulatory elements or other genetic parts. Type IIS restriction enzyme-dependent DNA assembly methods (e.g., Golden Gate) enable rapid one-pot, ordered, multi-fragment DNA assembly, facilitating the generation of high-complexity constructs. The order of assembly of genetic parts is determined by the ligation of flanking Watson-Crick base-paired overhangs. The ligation of mismatched overhangs leads to erroneous assembly, and the need to avoid such pairings has typically been accomplished by using small sets of empirically vetted junction pairs, limiting the number of parts that can be joined in a single reaction. Here, we report the use of a comprehensive method for profiling end-joining ligation fidelity and bias to predict highly accurate sets of connections for ligation-based DNA assembly methods. This data set allows quantification of sequence-dependent ligation efficiency and identification of mismatch-prone pairings. The ligation profile accurately predicted junction fidelity in ten-fragment Golden Gate assembly reactions, and enabled efficient assembly of a lac cassette from up to 24-fragments in a single reaction. Application of the ligation fidelity profile to inform choice of junctions thus enables highly flexible assembly design, with >20 fragments in a single reaction.
biorxiv synthetic-biology 0-100-users 2018The Repertoire of Mutational Signatures in Human Cancer, bioRxiv, 2018-05-15
ABSTRACTSomatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair andor replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.
biorxiv cancer-biology 100-200-users 2018Automating multimodal microscopy with NanoJ-Fluidics, bioRxiv, 2018-05-14
AbstractFluorescence microscopy can reveal all aspects of cellular mechanisms, from molecular details to dynamics, thanks to approaches such as super-resolution and live-cell imaging. Each of its modalities requires specific sample preparation and imaging conditions to obtain high-quality, artefact-free images, ultimately providing complementary information. Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows involving multiple sample preparation steps. We present a robust fluidics approach to automate complex sequences of treatment, labelling and imaging of live and fixed cells. Our open-source NanoJ-Fluidics system is based on low-cost LEGO hardware controlled by ImageJ-based software and can be directly adapted to any microscope, providing easy-to-implement high-content, multimodal imaging with high reproducibility. We demonstrate its capacity to carry out complex sequences of experiments such as super-resolved live-to-fixed imaging to study actin dynamics; highly-multiplexed STORM and DNA-PAINT acquisitions of multiple targets; and event-driven fixation microscopy to study the role of adhesion contacts in mitosis.
biorxiv cell-biology 200-500-users 2018Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism, bioRxiv, 2018-05-11
AbstractWe address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts specific regulatory effects and deleterious disease impact of genetic variants. Applying this framework to 1,790 Autism Spectrum Disorder (ASD) simplex families reveals autism disease causality of noncoding mutations by demonstrating that ASD probands harbor transcriptional (TRDs) and post-transcriptional (RRDs) regulation-disrupting mutations of significantly higher functional impact than unaffected siblings. Importantly, we detect this significant noncoding contribution at each level, transcriptional and post-transcriptional, independently and after multiple hypothesis correction. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development, and reveals a convergent genetic landscape of coding and noncoding (TRD and RRD) de novo mutations in ASD. We demonstrate that sequences carrying prioritized proband de novo mutations possess transcriptional regulatory activity and drive expression differentially, and highlight a link between noncoding mutations and IQ heterogeneity in ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD, prioritizes high impact transcriptional and post-transcriptional regulatory mutations for further study, and is broadly applicable to complex human diseases.
biorxiv genomics 100-200-users 2018Massive single-cell RNA-seq analysis and imputation via deep learning, bioRxiv, 2018-05-06
Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
biorxiv bioinformatics 0-100-users 2018