ggtern : Ternary Diagrams Using ggplot2 Hamilton, Nicholas E.; Ferry, Michael
Journal of statistical software,
12/2018, Letnik:
87, Številka:
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This paper presents the ggtern package for R, which has been developed for the rendering of ternary diagrams. Based on the well-established ggplot2 package (Wickham 2009), the present package adopts ...the familiar and convenient programming syntax of its parent. We demonstrate that ggplot2 can be used as the basis for producing specialized plotting packages and, in the present case, a package has been developed specifically for the production of high quality ternary diagrams. In order to produce ggtern, it was necessary to overcome a number of design issues, such as finding a means to modify existing geometries designed for a 2D Cartesian coordinate system and permitting them to function in an environment that requires an additional spatial aesthetic mapping. In the present paper, we provide examples of this package in its most basic form followed by a demonstration of its ease of use, particularly if one is familiar with, and has a predilection towards using ggplot2 on a regular basis.
•The most common skin cancers (BCC, SCC and IEC) are amenable to machine learning methods.•Semantic segmentation allows a network to learn the full context of skin tissue types in an interpretable ...way.•High classification accuracy (>93%) can be achieve when classifying whole tissue sections.•Full-characterisation of the tissue allows for the automation of routine pathologist tasks such as surgical margin clearance assessments.
We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constitute more than 90% of diagnoses, we demonstrate that the majority of dermatopathology work is amenable to automatic machine analysis. A major feature of this work is characterising the tissue by classifying it into 12 meaningful dermatological classes, including hair follicles, sweat glands as well as identifying the well-defined stratified layers of the skin. These provide highly interpretable outputs as the network is trained to represent the problem domain in the same way a pathologist would. While this enables a high accuracy of whole image classification (93.6-97.9%), by characterising the full context of the tissue we can also work towards performing routine pathologist tasks, for instance, orientating sections and automatically assessing and measuring surgical margins. This work seeks to inform ways in which future computer aided diagnosis systems could be applied usefully in a clinical setting with human interpretable outcomes.
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Although kidneys of equal size can vary 10-fold in nephron number at birth, discovering what regulates such variation has been hampered by a lack of quantitative parameters defining kidney ...development. Here we report a comprehensive, quantitative, multiscale analysis of mammalian kidney development in which we measure changes in cell number, compartment volumes, and cellular dynamics across the entirety of organogenesis, focusing on two key nephrogenic progenitor populations: the ureteric epithelium and the cap mesenchyme. In doing so, we describe a discontinuous developmental program governed by dynamic changes in interactions between these key cellular populations occurring within a previously unappreciated structurally stereotypic organ architecture. We also illustrate the application of this approach to the detection of a subtle mutant phenotype. This baseline program of kidney morphogenesis provides a framework for assessing genetic and environmental developmental perturbation and will serve as a gold standard for the analysis of other organs.
•A comprehensive temporospatial and multiscale profile of normal kidney development•Analysis of structural stereotypy and branch form across development•Dynamic changes in the cap and tip progenitor cell niches over time•Application of the approach to identify a subtle mutant phenotype
Short et al. describe a comprehensive, quantitative analysis of shape, cell number, and proliferation rate for key progenitor populations across mammalian kidney development. This reveals a morphogenetic program with structural stereotypy, asynchronous branching, substantial remodeling, and discontinuous nephron induction, elucidating how variation in size and nephron number may arise.
RhoA stimulates cell contractility by recruiting downstream effectors to the cortical plasma membrane. We now show that direct binding by anillin is required for effective signaling: this antagonizes ...the otherwise labile membrane association of GTP-RhoA to promote effector recruitment. However, since its binding to RhoA blocks access by other effectors, we demonstrate that anillin must also concentrate membrane phosphoinositide-4,5-P2 (PIP2) to promote signaling. We propose and test a sequential pathway where GTP-RhoA first binds to anillin and then is retained at the membrane by PIP2 after it disengages from anillin. Importantly, re-binding of membrane GTP-RhoA to anillin, regulated by the cortical density of anillin, creates cycles through this pathway. These cycles repeatedly reset the dissociation kinetics of GTP-RhoA, substantially increasing its dwell time to recruit effectors. Thus, anillin regulates RhoA signaling by a paradigm of kinetic scaffolding that may apply to other signals whose efficacy depends on their cortical dwell times.
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•Anillin enhances RhoA signaling by countering its labile cortical association•Anillin concentrates PI(4,5)P2 to retain membrane GTP-RhoA for effector recruitment•Re-binding of RhoA to anillin creates cycles that increase the dwell time of active RhoA•Kinetic scaffolding is sufficient for anillin to support cell contractility
Anillin promotes cell contractility by inhibiting the cortical dissociation of active RhoA. Budnar et al. explain this by a kinetic scaffolding model where anillin also concentrates membrane PI(4,5)P2. Cyclic binding and unbinding of RhoA to anillin allows free RhoA to be retained by PI(4,5)P2, increasing its dwell time for signaling.
Stem cell-based tracheal replacement represents an emerging therapeutic option for patients with otherwise untreatable airway diseases including long-segment congenital tracheal stenosis and upper ...airway tumors. Clinical experience demonstrates that restoration of mucociliary clearance in the lungs after transplantation of tissue-engineered grafts is critical, with preclinical studies showing that seeding scaffolds with autologous mucosa improves regeneration. High epithelial cell-seeding densities are required in regenerative medicine, and existing techniques are inadequate to achieve coverage of clinically suitable grafts.
To define a scalable cell culture system to deliver airway epithelium to clinical grafts.
Human respiratory epithelial cells derived from endobronchial biopsies were cultured using a combination of mitotically inactivated fibroblasts and Rho-associated protein kinase (ROCK) inhibition using Y-27632 (3T3+Y). Cells were analyzed by immunofluorescence, quantitative polymerase chain reaction, and flow cytometry to assess airway stem cell marker expression. Karyotyping and multiplex ligation-dependent probe amplification were performed to assess cell safety. Differentiation capacity was tested in three-dimensional tracheospheres, organotypic cultures, air-liquid interface cultures, and an in vivo tracheal xenograft model. Ciliary function was assessed in air-liquid interface cultures.
3T3-J2 feeder cells and ROCK inhibition allowed rapid expansion of airway basal cells. These cells were capable of multipotent differentiation in vitro, generating both ciliated and goblet cell lineages. Cilia were functional with normal beat frequency and pattern. Cultured cells repopulated tracheal scaffolds in a heterotopic transplantation xenograft model.
Our method generates large numbers of functional airway basal epithelial cells with the efficiency demanded by clinical transplantation, suggesting its suitability for use in tracheal reconstruction.
The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a ...protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping.
Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively.
Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches.
Ionizable lipid-containing lipid nanoparticles (LNPs) have enabled the delivery of RNA for a range of therapeutic applications. In order to optimize safe, targeted, and effective LNP-based RNA ...delivery platforms, an understanding of the role of composition and pH in their structural properties and self-assembly is crucial, yet there have been few computational studies of such phenomena. Here we present a coarse-grained model of ionizable lipid and mRNA-containing LNPs. Our model allows access to the large length- and time-scales necessary for LNP self-assembly and is mapped and parametrized with reference to all-atom structures and simulations of the corresponding components at compositions typical of LNPs used for mRNA delivery. Our simulations reveal insights into the dynamics of self-assembly of such mRNA-encapsulating LNPs, as well as the subsequent pH change-driven LNP morphology and release of mRNA.
With recent advances in microscopy, recordings of cell behaviour can result in terabyte-size datasets. The lattice light sheet microscope (LLSM) images cells at high speed and high 3D resolution, ...accumulating data at 100 frames/second over hours, presenting a major challenge for interrogating these datasets. The surfaces of vertebrate cells can rapidly deform to create projections that interact with the microenvironment. Such surface projections include spike-like filopodia and wave-like ruffles on the surface of macrophages as they engage in immune surveillance. LLSM imaging has provided new insights into the complex surface behaviours of immune cells, including revealing new types of ruffles. However, full use of these data requires systematic and quantitative analysis of thousands of projections over hundreds of time steps, and an effective system for analysis of individual structures at this scale requires efficient and robust methods with minimal user intervention. We present LLAMA, a platform to enable systematic analysis of terabyte-scale 4D microscopy datasets. We use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, generating detailed object level statistics. Our system is designed to run on high-performance computing to achieve high throughput, with outputs suitable for visualisation and statistical analysis. Advanced visualisation is a key element of LLAMA: we provide a specialised tool which supports interactive quality control, optimisation, and output visualisation processes to complement the processing pipeline. LLAMA is demonstrated in an analysis of macrophage surface projections, in which it is used to i) discriminate ruffles induced by lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1) and ii) determine the autonomy of ruffle morphologies. LLAMA provides an effective open source tool for running a cell microscopy analysis pipeline based on semantic segmentation, object analysis and tracking. Detailed numerical and visual outputs enable effective statistical analysis, identifying distinct patterns of increased activity under the two interventions considered in our example analysis. Our system provides the capacity to screen large datasets for specific structural configurations. LLAMA identified distinct features of LPS and CSF-1 induced ruffles and it identified a continuity of behaviour between tent pole ruffling, wave-like ruffling and filopodia deployment.
Establishing how polymeric vectors such as polyethylenimine (PEI) bind and package their nucleic acid cargo is vital toward developing more efficacious and cost-effective gene therapies. To develop a ...molecular-level picture of DNA binding, we examined how the Raman spectra of PEIs report on their local chemical environment. We find that the intense Raman bands located in the 1400–1500 cm–1 region derive from vibrations with significant CH2 scissoring and NH bending character. The Raman bands that derive from these vibrations show profound intensity changes that depend on both the local dielectric environment and hydrogen bonding interactions with the secondary amine groups on the polymer. We use these bands as spectroscopic markers to assess the binding between low molecular weight PEIs and single-stranded DNA (ssDNA). Analysis of the Raman spectra suggest that PEI primarily binds via electrostatic interactions to the phosphate backbone, which induces the condensation of the ssDNA. We additionally confirm this finding by conducting molecular dynamics simulations. We expect that the spectral correlations determined here will enable future studies to investigate important gene delivery activities, including how PEI interacts with cellular membranes to facilitate cargo internalization into cells.
A genetic mutation in the Vps35 subunit of retromer has recently been linked to late onset Parkinson's disease. We observed that the distribution and maturation of Vps35 D620N positive endosomes are ...altered. While Vps35 D620N containing retromer still binds CI‐M6PR, its trafficking is perturbed as shown by secretion of its ligand cathepsin D. As cathepsin D is involved in processing of α‐synuclein, a well‐established causative agent of Parkinson's disease, its altered trafficking may therefore represent the underlying cause of disease.
The retromer is a trimeric cargo‐recognition protein complex composed of Vps26, Vps29 and Vps35 associated with protein trafficking within endosomes. Recently, a pathogenic point mutation within the Vps35 subunit (D620N) was linked to the manifestation of Parkinson's disease (PD). Here, we investigated details underlying the molecular mechanism by which the D620N mutation in Vps35 modulates retromer function, including examination of retromer's subcellular localization and its capacity to sort cargo. We show that expression of the PD‐linked Vps35 D620N mutant redistributes retromer‐positive endosomes to a perinuclear subcellular localization and that these endosomes are enlarged in both model cell lines and fibroblasts isolated from a PD patient. Vps35 D620N is correctly folded and binds Vps29 and Vps26A with the same affinity as wild‐type Vps35. While PD‐linked point mutant Vps35 D620N interacts with the cation‐independent mannose‐6‐phosphate receptor (CI‐M6PR), a known retromer cargo, we find that its expression disrupts the trafficking of cathepsin D, a CI‐M6PR ligand and protease responsible for degradation of α‐synuclein, a causative agent of PD. In summary, we find that the expression of Vps35 D620N leads to endosomal alterations and trafficking defects that may partly explain its action in PD.
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