Biology subjects

Roberts, E.

Publications and source records attributed to Roberts, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Superinfection exclusion creates spatially distinct influenza virus populations

Influenza viruses can interact during coinfections, allowing viral fitness to be altered by genome complementation and competition, and increasing population diversity through reassortment. However, opportunities for these interactions are limited, as coinfection is blocked shortly after primary infection by a process known as superinfection exclusion (SIE). We asked whether SIE, which occurs at the level of individual cells, could limit within-host interactions between populations of influenza viruses as they spread across regions of cells. We first created a simplified model of within-host spread by infecting monolayers of cells with two isogenic influenza A viruses, each encoding a different fluorophore, and measuring the proportion of coinfected cells. In this system SIE begins within 2-4 hours of primary infection, with the kinetics of onset defined by the dose of primary virus. We then asked how SIE controls opportunities for coinfection as viruses spread across a monolayer of cells. We observed that viruses spreading from a single coinfected focus continued to coinfect cells as they spread, as all new infections were of cells that had not yet established SIE. In contrast, viruses spreading towards each other from separately infected foci could only establish minimal regions of coinfection before SIE blocked further coinfection. This patterning was recapitulated in the lungs of infected mice and is likely to apply to other viruses that exhibit SIE. It suggests that the kinetics of SIE onset separate a spreading infection into discrete regions, within which interactions between virus populations can occur freely, and between which they are blocked. ImportanceViral fitness and diversity are altered by genome interactions, which occur when multiple viruses coinfect a cell. This has been extensively studied for influenza A viruses (IAV), which use genome reassortment to adapt to new hosts and create pandemic strains, and whose replication can be compromised by the acquisition of defective-interfering RNAs. Coinfection of an individual cell by IAV is restricted by the gradual onset of superinfection exclusion (SIE). Replication of IAVs within host organisms involve the asynchronous replication of viruses as they spread to infect multiple cells. We found that under these circumstances, SIE creates spatially separated sub-populations of IAV, between which there are limited opportunities for genome interactions. Our work suggests SIE will cause many viruses to segregate into distinct subpopulations within their hosts, constraining the effects of genome interactions on their fitness and evolution.

microbiology↗

bioRxiv2022

PP6-regulated Aurora A activity towards NDC80 limits mitotic spindle size to prevent micronucleation

Amplification of the mitotic kinase Aurora A or loss of its regulator protein phosphatase 6 (PP6) have emerged as drivers of genome instability. Cells lacking PPP6C, the catalytic subunit of PP6, have amplified Aurora A activity and as we show here, enlarged mitotic spindles which fail to hold chromosomes tightly together in anaphase, causing defective nuclear structure. Using functional genomics to shed light on the processes underpinning these changes, we discover synthetic lethality between PPP6C and the kinetochore protein NDC80. We find that NDC80 is phosphorylated on multiple N-terminal sites during spindle formation by Aurora A-TPX2, exclusively at checkpoint-silenced, microtubule-attached kinetochores. NDC80 phosphorylation persists until spindle disassembly in telophase, is increased in PPP6C-knockout cells and, and is Aurora B-independent. An Aurora-phosphorylation-deficient NDC80-9A mutant reduces spindle size and suppresses defective nuclear structure in PPP6C-knockout cells. By regulating NDC80 phosphorylation by Aurora A-TPX2, PP6 plays an important role in mitotic spindle formation and size control, and thus the fidelity of cell division.

cell biology↗

bioRxiv2022

MPS1 localizes to microtubule-attached kinetochores and actively promotesmicrotubule release

SummaryIn eukaryotes, the spindle assembly checkpoint protects genome stability in mitosis by preventing anaphase onset until incorrect microtubule-kinetochore attachment geometries have been eliminated and chromosome biorientation has been completed. These error correction and checkpoint processes are linked by two conserved serine/threonine kinases, Aurora B and MPS1 1, 2. In the prevailing model for spindle checkpoint signaling, MPS1 detects microtubule-free kinetochores generated by the Aurora B-dependent error correction pathway, and initiates spindle checkpoint signaling. However, we find that MPS1 initially localizes to microtubule-attached kinetochores in a manner dependent on the relative activities of Aurora B and its counteracting phosphatase PP2A-B56, and then actively promotes microtubule release. Thus, MPS1 is not a passive sensor for the microtubule binding state of kinetochores but actively generates microtubule-free checkpoint signaling kinetochores. MPS1 is thus instrumental for both the initial resolution of incorrect microtubule-kinetochore attachments and the downstream propagation of spindle checkpoint signaling.

cell biology↗

bioRxiv2022

Urine-derived exosomes from individuals with IPF carry pro-fibrotic cargo.

BackgroundMicroRNAs (miRNA) and other components contained in extracellular vesicles may reflect the presence of a disease. Lung tissue, sputum and sera of individuals with idiopathic pulmonary fibrosis (IPF) show alterations in miRNA expression. We designed this study to test whether urine and/or tissue derived exosomal miRNAs from individuals with IPF carry cargo that can promote fibrosis. MethodsExosomes were isolated from urine (U-IPFexo), lung tissue myofibroblasts (MF-IPFexo), serum from individuals with IPF (n=16) and age/sex-matched controls without lung disease (n=10). We analyzed microRNA expression of isolated exosomes and their in vivo bio-distribution. We investigated the effect on ex vivo skin wound healing and in in vivo mouse lung models. ResultsU-IPFexo or MF-IPFexo expressed miR let-7d, miR-29a-5p, miR 181b-3p and miR-199a-3p consistent with previous reports of miRNA expression obtained from lung tissue/sera from patients with IPF. In vivo bio-distribution experiments detected bioluminescent exosomes in the lung of normal C57Bl6 mice within 5 minutes after intravenous infusion, followed by distribution to other organs irrespective of exosome source. Exosomes labeled with gold nanoparticles and imaged by transmission electron microscopy were visualized in alveolar epithelial type I and type II cells. Treatment of human and mouse lung punches obtained from control, non-fibrotic lungs with either U-IPFexo or MF-IPFexo produced a fibrotic phenotype. A fibrotic phenotype was also induced in a human ex vivo skin model and in in vivo lung models. ConclusionsOur results provide evidence of a systemic feature of IPF whereby exosomes contain pro-fibrotic miRNAs when obtained from a fibrotic source and interfere with response to tissue injury as measured in skin and lung models. FundingThis work was supported in part by Lester and Sue Smith Foundation and The Samrick Family Foundation and NIH grants R21 AG060338 (SE and MKG), U01 DK119085 (IP, RS, MTC).

cell biology↗

bioRxiv2022

Mixtures of large-scale dynamic functional brain network modes

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMMs while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMMs assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

neuroscience↗

bioRxiv2021

A spatial stochastic model reveals the role of supercoiling in transcription regulation

In Escherichia coli, translocation of RNA polymerase (RNAP) during transcription introduces supercoiling to DNA, which influences the initiation and elongation behaviors of RNAP. To quantify the role of supercoiling in transcription regulation, we develop a spatially resolved supercoiling model of transcription, describing RNAP-supercoiling interactions, topoisomerase activities, stochastic topological domain formation, and supercoiling diffusion in all transcription stages. This model establishes that transcription-induced supercoiling mediates the cooperation of co-transcribing RNAP molecules in highly expressed genes. It reveals that supercoiling transmits RNAP-accessible information through DNA and enables different RNAP molecules to communicate within and between genes. It thus predicts that a topological domain could serve as a transcription regulator, generating substantial transcription bursting and coordinating communications between adjacent genes in the domain. The model provides a quantitative platform for further theoretical and experimental investigations of how genome organization impacts transcription. Author SummaryDNA mechanics and transcription dynamics are intimately coupled. During transcription, the translocation of RNA polymerase overwinds the DNA ahead and underwinds the DNA behind, rendering the DNA supercoiled. The supercoiled DNA could, in return, influences the behavior of the RNA polymerase, and consequently the amount of mRNA product it makes. Furthermore, supercoils could propagate on the DNA over thousands of base pairs, impacting RNA polymerase molecules at faraway sites. These complicated interplays between supercoiling and RNA polymerase makes supercoiling an important transcription regulator. To quantitatively investigate the role of supercoiling in transcription, we build a spatially resolved model that links transcription with the generation, propagation, and dissipation of supercoiling. Our model reveals that supercoiling mediates transcription at multiple length scales. At a single-gene scale, we show that supercoiling gives rise to the collective motion of co-transcribing RNA polymerase molecules, supporting recent experimental observations. Additionally, large variations in mRNA production of a gene can arise from the constraints of supercoiling diffusion in a topological domain. At a multi-gene scale, we show that supercoiling dynamics allow two adjacent genes influence each others transcription kinetics, thus serving as a transcription regulator.

biophysics↗

bioRxiv2021

Gene regulation network inference using k-nearest neighbor-based mutual information estimation- Revisiting an old DREAM

BackgroundA cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. ResultsIn this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoogbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. ConclusionsUsing three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.

bioinformatics↗

bioRxiv2021

Efemp1 modulates elastic fiber formation and mechanics of the extrahepatic bile duct

EGF-Containing Fibulin Extracellular Matrix Protein 1 (EFEMP1, also called fibulin 3) is an extracellular matrix protein linked in a genome-wide association study to biliary atresia, a fibro-inflammatory disease of the neonatal extrahepatic bile duct. EFEMP1 is expressed in most tissues and Efemp1 null mice have decreased elastic fibers in visceral fascia; however, in contrast to other short fibulins (fibulins 4 and 5), EFEMP1 does not have a role in the development of large elastic fibers, and its overall function remains unclear. We demonstrated that EFEMP1 is expressed in the submucosa of both neonatal and adult mouse and human extrahepatic bile ducts and that, in adult Efemp1+/- mice, elastin organization into fibers is decreased. We used pressure myography, a technique developed to study the mechanics of the vasculature, to show that Efemp1+/- extrahepatic bile ducts are more compliant to luminal pressure, leading to increased circumferential stretch. We conclude that EFEMP1 has an important role in the formation of elastic fibers and mechanical properties of the extrahepatic bile duct. These data suggest that altered expression of EFEMP1 in the extrahepatic bile duct leads to an abnormal response to mechanical stress such as obstruction, potentially explaining the role of EFEMP1 in biliary atresia.

physiology↗

bioRxiv2021

RAGE antagonist peptide mitigates AGE-mediated endothelial hyperpermeability and accumulation of glycoxidation products in human ascending aortas and in a murine model of aortic aneurysm

BackgroundAortic dissection and aneurysm are the result of altered biomechanical forces associated with structural weakening of the aortic wall caused by genetic or acquired factors. Current guidelines recommend replacement of the ascending aorta when the diameter is >5.5 cm in tricuspid aortic valve patients. Aortopathies are associated with altered wall stress and stiffness as well as endothelial cell dysfunction and synthetic vascular smooth muscle cell (VSMC) phenotype. We reported that these mechanisms are mediated by glycoxidation products [Reactive oxygen species (ROS) and Advance Glycation End products (AGE)]. This study addresses the role of glycoxidation on endothelial function and AGE-mediated aortic stiffness. Hypothesis and aimsHere we investigate how circulating glycation products infiltrate the aortic wall via AGE-mediated endothelial hyperpermeability and contribute to both VSMC synthetic phenotype and extracellular matrix (ECM) remodeling in vivo and ex vivo. We also study how RAGE antagonist peptide (RAP) can rescue the effect of AGEs in vitro and in vivo in eNOS-/- vs WT mice. Methods and resultsHuman ascending aortas (n=30) were analyzed for AGE, ROS, and ECM markers. In vitro glycation was obtained by treating VSMC or human and murine aortas with glyoxal. Endothelial permeability was measured under glycation treatment. Vascular stiffness was measured by a pressure myograph comparing wild-type mice {+/-} glyoxal. eNOS-/- mice, a model of increased endothelial permeability, were treated for 28 days with hyperlipidemic diet {+/-} Angiotensin II (1000ng/kg/min) with or without anti-glycation treatment (RAP 20mg/kg). Echo data of aortic diameter were collected. Murine vascular stiffness was measured by a pressure myograph (n=5/group). Glycoxidation products were detected in all human aortas independently of aortic diameter, with stronger accumulation on the lumen and the adventitia layer. AGEs increased endothelial permeability, induce synthetic phenotypic switch in human VSMCs, and inhibit cell migration. RAP pre-treatment rescue the effect of glyoxal on endothelial cells. Ex vivo glycation treatment of murine arteries impacted on ECM and increased stiffness. Aortic stiffness was higher in eNOS-/- vs WT mice. Ang II-mediated aortopathies results in aortic dilation, and AGE/ROS accumulation, which is rescued by RAGE antagonist peptide treatment of eNOS-/- mice. ConclusionsGlycoxidation reaction mediate EC permeability, VSMCs phenotype, and ECM remodeling leading to dysfunctional microstructure of the ascending aorta, altered vascular stiffness and increasing aortic susceptibility to dilation and rupture. Moreover, we show that RAP can mitigate AGE-mediated endothelial hyper-permeability in vitro and impact on ascending aneurysm in vivo

physiology↗

bioRxiv2021

Identification of SARS-CoV-2 Antiviral Compounds by Screening for Small Molecule Inhibitors of the nsp14 RNA Cap Methyltransferase

The COVID-19 pandemic has presented itself as one of the most critical public health challenges of the century, with SARS-CoV-2 being the third member of the Coronaviridae family to cause fatal disease in humans. There is currently only one antiviral compound, remdesivir, that can be used for the treatment of COVID-19. In order to identify additional potential therapeutics, we investigated the enzymatic proteins encoded in the SARS-CoV-2 genome. In this study, we focussed on the viral RNA cap methyltransferases, which play a key role in enabling viral protein translation and facilitating viral escape from the immune system. We expressed and purified both the guanine-N7 methyltransferase nsp14, and the nsp16 2-O-methyltransferase with its activating cofactor, nsp10. We performed an in vitro high-throughput screen for inhibitors of nsp14 using a custom compound library of over 5,000 pharmaceutical compounds that have previously been characterised in either clinical or basic research. We identified 4 compounds as potential inhibitors of nsp14, all of which also show antiviral capacity in a cell based model of SARS-CoV-2 infection. Three of the 4 compounds also exhibited synergistic effects on viral replication with remdesivir.

bioengineering↗

bioRxiv2021

Identifying SARS-CoV-2 Antiviral Compounds by Screening for Small Molecule Inhibitors of Nsp14/nsp10 Exoribonuclease

SARS-CoV-2 is a coronavirus that emerged in 2019 and rapidly spread across the world causing a deadly pandemic with tremendous social and economic costs. Healthcare systems worldwide are under great pressure, and there is urgent need for effective antiviral treatments. The only currently approved antiviral treatment for COVID-19 is remdesivir, an inhibitor of viral genome replication. SARS-CoV-2 proliferation relies on the enzymatic activities of the non-structural proteins (nsp), which makes them interesting targets for the development of new antiviral treatments. With the aim to identify novel SARS-CoV-2 antivirals, we have purified the exoribonuclease/methyltransferase (nsp14) and its cofactor (nsp10) and developed biochemical assays compatible with high-throughput approaches to screen for exoribonuclease inhibitors. We have screened a library of over 5000 commercial compounds and identified patulin and aurintricarboxylic acid (ATA) as inhibitors of nsp14 exoribonuclease in vitro. We found that patulin and ATA inhibit replication of SARS-CoV-2 in a VERO E6 cell-culture model. These two new antiviral compounds will be valuable tools for further coronavirus research as well as potentially contributing to new therapeutic opportunities for COVID-19.

biochemistry↗

bioRxiv2021

Measurement of Klebsiella Intestinal Colonization Density to Assess Infection Risk

BackgroundKlebsiella pneumoniae and closely related species K. variicola and K. quasipneumoniae are common causes of healthcare-associated infections, and patients frequently become infected with their intestinal colonizing strain. To assess the association between Klebsiella colonization density and subsequent infections, a case-control study was performed. MethodsA multiplex qPCR assay was developed and validated to quantify Klebsiella (K. pneumoniae, K. variicola, and K. quasipneumoniae combined) relative to total bacterial DNA copies in rectal swabs. Cases of Klebsiella infection were identified based on clinical definitions and having a clinical culture isolate and preceding or co-incident colonization isolate with the same wzi capsular sequence type. Controls were colonized patients without subsequent infection and were matched 2:1 to cases based on age, sex, and rectal swab collection date. Quantitative PCR (qPCR) from rectal swab samples was used to measure the association between relative abundance (RA) of Klebsiella and subsequent infections. ResultsKlebsiella RA by qPCR highly correlated with 16S sequencing ({rho}=0.79; P <.001). The median Klebsiella RA in the study group was 2.6% (interquartile range (IQR) 0.1-22.5, n=238), and was higher in cases (15.7%, IQR 0.93-52.6%, n=83) than controls (1.01%, IQR 0.02-12.8%; n=155; P <0.0001). After adjusting for multiple clinical covariates using inverse probability of treatment weighting, subjects with a Klebsiella RA > 22% had a 2.87-fold (1.64-5.03, P =0.0003) increased odds of infection compared to those with lower colonization density levels. ConclusionsMeasurement of colonization density by qPCR could represent a novel approach to identify hospitalized patients at risk for Klebsiella infection. ImportanceColonization by bacterial pathogens often precedes infection, and offers a window of opportunity to prevent these infections. Klebsiella colonization is significantly and reproducibly associated with subsequent infection, however factors that enhance or mitigate this risk in individual patients are unclear. This study developed an assay to measure the density of Klebsiella colonization, relative to total fecal bacteria, in rectal swabs from hospitalized patients. Applying this assay to 238 colonized patients, high Klebsiella density defined as >22% of total bacteria, was significantly associated with subsequent infection. Based on widely available polymerase chain reaction (PCR) technology, this type of assay could be deployed in clinical laboratories to identify patients at increased risk of Klebsiella infections. As novel therapeutics are developed to eliminate pathogens from the gut microbiome, a rapid Klebsiella colonization density assay could identify patients who would benefit from this type of infection prevention interventions.

microbiology↗

bioRxiv2020

Diverse molecular mechanisms contribute to differential expression of human duplicated genes

Emerging evidence links genes within human-specific segmental duplications (HSDs) to traits and diseases unique to our species. Strikingly, despite being nearly identical by sequence (>98.5%), paralogous HSD genes are differentially expressed across human cell and tissue types, though the underlying mechanisms have not been examined. We compared cross-tissue mRNA levels of 75 HSD genes from 30 families between humans and chimpanzees and found expression patterns consistent with pseudo- or neofunctionalization. In general, ancestral paralogs exhibited greatest expression conservation with chimpanzee orthologs, though exceptions suggest certain derived paralogs may retain or supplant ancestral functions. Concordantly, analysis of long-read isoform sequencing datasets from diverse human tissues and cell lines found that about half of derived paralogs exhibited globally lower expression. To understand mechanisms underlying these differences, we leveraged data from human lymphoblastoid cell lines (LCLs) and found no relationship between paralogous expression divergence and post- transcriptional regulation, sequence divergence, or copy number variation. Considering cis-regulation, we reanalyzed ENCODE data and recovered hundreds of previously unidentified candidate CREs in HSDs. We also generated large-insert ChIP-sequencing data for active chromatin features in an LCL to better distinguish paralogous regions. Some duplicated CREs were sufficient to drive differential reporter activity, suggesting they may contribute to divergent cis-regulation of paralogous genes. This work provides evidence that cis-regulatory divergence contributes to novel expression patterns of recent gene duplicates in humans.

genomics↗

bioRxiv2020

Learned adaptive multiphoton illumination microscopy

Multiphoton microscopy is a powerful technique for deep in vivo imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to maintain signal while minimizing photodamage. We show that cells with identical fluorescent labels imaged in situ can be used to train a physics-based machine learning model that solves this problem. After this training has been performed, the correct illumination power can be predicted and adaptively adjusted at each point in a 3D volume on subsequent samples as a function of the samples shape, without the need for specialized fluorescent labelling. We use this technique for in vivo imaging of immune responses in mouse lymph nodes following vaccination, with imaging volumes 2-3 orders of magnitude larger than previously reported. We achieve visualization of physiologically realistic numbers of antigen-specific T cells for the first time, and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response.

immunology↗

bioRxiv2019

Reconstructing an epigenetic landscape using a genetic ‘pulling’ approach

Cells use genetic switches to shift between alternate stable gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Conceptually, these stable phenotypes can be considered as attractive states on an epigenetic landscape with phenotypic changes being transitions between states. Measuring these transitions is challenging because they are both very rare in the absence of appropriate signals and very fast. As such, it has proven difficult to experimentally map the epigenetic landscapes that are widely believed to underly developmental networks. Here, we introduce a new nonequilibrium perturbation method to help reconstruct a regulatory networks epigenetic landscape. We derive the mathematical theory needed and then use the method on simulated data to reconstruct the landscapes. Our results show that with a relatively small number of perturbation experiments it is possible to recover an accurate representation of the true epigenetic landscape. We propose that our theory provides a general method by which epigenetic landscapes can be studied. Finally, our theory suggests that the total perturbation impulse required to induce a switch between metastable states is a fundamental quantity in developmental dynamics.

biophysics↗

bioRxiv2019

A Pairwise Distance Distribution Correction (DDC) algorithm for blinking-free super-resolution microscopy

In single-molecule localization based super-resolution microscopy (SMLM), a fluorophore stochastically switches between fluorescent- and dark-states, leading to intermittent emission of fluorescence, a phenomenon known as blinking. Intermittent emissions create multiple localizations belonging to the same molecule, resulting in blinking-artifacts within SMLM images. These artifacts are often interpreted as true biological assemblies, confounding quantitative analyses and interpretations. Multiple methods have been developed to eliminate these artifacts, but they either require additional experiments, arbitrary thresholds, or specific photo-kinetic models. Here we present a method, termed Distance Distribution Correction (DDC), to eliminate blinking-caused repeat localizations without any additional calibrations. The approach relies on the finding that the true pairwise distance distribution of different fluorophores in an SMLM image can be naturally obtained from the imaging sequence by using distances between localizations separated by a time much longer than the average fluorescence survival time. We show that using the true pairwise distribution we can define and then maximize the likelihood of obtaining a particular set of localizations void of blinking-artifacts, generating an accurate reconstruction of the underlying cellular structure. Using both simulated and experimental data, we show that DDC surpasses all previous existing blinking-artifact correction methodologies, resulting in drastic improvements in obtaining the closest estimate of the true spatial organization and number of fluorescent emitters in a wide range of applications. The simplicity and robustness of DDC will allow it to become the field standard in SMLM imaging, enabling the most accurate reconstruction and quantification of SMLM images to date.

biophysics↗

bioRxiv2019

Modeling binary and graded cone cell fate patterning in the mouse retina

Nervous systems are incredibly diverse, with myriad neuronal subtypes defined by gene expression. How binary and graded fate characteristics are patterned across tissues is poorly understood. Expression of opsin photopigments in the cone photoreceptors of the mouse retina provides an excellent model to address this question. Individual cones express S-opsin only, M-opsin, or both S-opsin and M-opsin. These cell populations are patterned along the dorsal-ventral axis, with greater M-opsin expression in the dorsal region and greater S-opsin expression in the ventral region. Thyroid hormone signaling plays a critical role in activating M-opsin and repressing S-opsin. Here, we developed an image analysis approach to identify individual cone cells and evaluate their opsin expression from immunofluorescence imaging tiles spanning roughly 6 mm along the D-V axis of the mouse retina. From analyzing the opsin expression of [~]250,000 cells, we found that cones make a binary decision between S-opsin only and co-expression competent fates. Co-expression competent cells express graded levels of S- and M-opsins, depending nonlinearly on their position in the dorsal-ventral axis. M- and S-opsin expression display differential, inverse patterns. Using these single-cell data we developed a quantitative, stochastic model of cone cell decisions in the retinal tissue based on thyroid hormone signaling activity. The model recovers the probability distribution for cone fate patterning in the mouse retina and describes a minimal set of interactions that are necessary to reproduce the observed cell fates. Our study provides a paradigm describing how differential responses to regulatory inputs generate complex patterns of binary and graded cell fates.\n\nAuthor SummaryThe development of a cell in a mammalian tissue is governed by a complex regulatory network that responds to many input signals to give the cell a distinct identity, a process referred to as cell-fate specification. Some of these cell fates have binary on-or-off gene expression patterns, while others have graded gene expression that changes across the tissue. Differentiation of the photoreceptor cells that sense light in the mouse retina provides a good example of this process. Here, we explore how complex patterns of cell fates are specified in the mouse retina by building a computational model based on analysis of a large number of photoreceptor cells from microscopy images of whole retinas. We use the data and the model to study what exactly it means for a cell to have a binary or graded cell fate and how these cell fates can be distinguished from each other. Our study shows how tens-of-thousands of individual photoreceptor cells can be patterned across a complex tissue by a regulatory network, creating a different outcome depending upon the received inputs.

developmental biology↗

bioRxiv2018

WRN Helicase is a Synthetic Lethal Target in Microsatellite Unstable Cancers

Summary paragraphSynthetic lethality, an interaction whereby the co-occurrence of two or more genetic events lead to cell death but one event alone does not, can be exploited to develop novel cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets since many cancers exhibit an impaired DNA repair pathway, which can lead these cells to become dependent on specific repair proteins2. The success of poly (ADP-ribose) polymerase 1 (PARP-1) inhibitors in homologous recombination-deficient cancers highlights the potential of this approach in clinical oncology3,4. Hypothesizing that other DNA repair defects would give rise to alternative synthetic lethal relationships, we asked if there are specific dependencies in cancers with microsatellite instability (MSI), which results from impaired DNA mismatch repair (MMR). Here we analyzed data from large-scale CRISPR/Cas9 knockout and RNA interference (RNAi) silencing screens and found that the RecQ DNA helicase WRN was selectively essential in MSI cell lines, yet dispensable in microsatellite stable (MSS) cell lines. WRN depletion induced double-strand DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models specifically required the helicase activity, but not the exonuclease activity of WRN. These findings expose WRN as a synthetic lethal vulnerability and promising drug target in MSI cancers.

cancer biology↗