Single-molecule tracking of myelin basic protein during oligodendrocyte differentiation.

This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.

Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on ß2 adrenoceptor.

G protein-coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known ß2AR ligands to multiple ß2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K972.68×67 , F194ECL2 , S2035.42×43 , S2045.43×44 , S2075.46×641 , H2966.58×58 , and K3057.32×31 . Meanwhile, the antagonist ligands made interactions with W2866.48×48 and Y3167.43×42 , both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure-activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.

Quantitative Characterization of Three Carbonic Anhydrase Inhibitors by LESA Mass Spectrometry.

Liquid extraction surface analysis (LESA) coupled to native mass spectrometry (MS) presents unique analytical opportunities due to its sensitivity, speed, and automation. Here, we examine whether this tool can be used to quantitatively probe protein-ligand interactions through calculation of equilibrium dissociation constants (Kd values). We performed native LESA MS analyses for a well-characterized system comprising bovine carbonic anhydrase II and the ligands chlorothiazide, dansylamide, and sulfanilamide, and compared the results with those obtained from direct infusion mass spectrometry and surface plasmon resonance measurements. Two LESA approaches were considered: In one approach, the protein and ligand were premixed in solution before being deposited and dried onto a solid substrate for LESA sampling, and in the second, the protein alone was dried onto the substrate and the ligand was included in the LESA sampling solvent. Good agreement was found between the Kd values derived from direct infusion MS and LESA MS when the protein and ligand were premixed; however, Kd values determined from LESA MS measurements where the ligand was in the sampling solvent were inconsistent. Our results suggest that LESA MS is a suitable tool for quantitative analysis of protein-ligand interactions when the dried sample comprises both protein and ligand.

Native Ambient Mass Spectrometry Imaging of Ligand-Bound and Metal-Bound Proteins in Rat Brain.

Label-free spatial mapping of the noncovalent interactions of proteins in their tissue environment has the potential to revolutionize life sciences research by providing opportunities for the interrogation of disease progression, drug interactions, and structural and molecular biology more broadly. Here, we demonstrate mass spectrometry imaging of endogenous intact noncovalent protein-ligand complexes in rat brain. The spatial distributions of a range of ligand-bound and metal-bound proteins were mapped in thin tissue sections by use of nanospray-desorption electrospray ionization. Proteins were identified directly from the tissue by top-down mass spectrometry. Three GDP-binding proteins (ADP ribosylation factor ARF3, ARF1, and GTPase Ran) were detected, identified, and imaged in their ligand-bound form. The nature of the ligand was confirmed by multiple rounds of tandem mass spectrometry. In addition, the metal-binding proteins parvalbumin-α and carbonic anhydrase 2 were detected, identified, and imaged in their native form, i.e., parvalbumin-α + 2Ca2+ and carbonic anhydrase + Zn2+. GTPase Ran was detected with both GDP and Mg2+ bound. Several natively monomeric proteins displaying distinct spatial distributions were also identified by top-down mass spectrometry. Protein mass spectrometry imaging was achieved at a spatial resolution of 200 µm.

Learning a Model-Driven Variational Network for Deformable Image Registration.

Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.

Targeting structural features of viral genomes with a nano-sized supramolecular drug.

RNA targeting is an exciting frontier for drug design. Intriguing targets include functional RNA structures in structurally-conserved untranslated regions (UTRs) of many lethal viruses. However, computational docking screens, valuable in protein structure targeting, fail for inherently flexible RNA. Herein we harness MD simulations with Markov state modeling to enable nanosize metallo-supramolecular cylinders to explore the dynamic RNA conformational landscape of HIV-1 TAR untranslated region RNA (representative for many viruses) replicating experimental observations. These cylinders are exciting as they have unprecedented nucleic acid binding and are the first supramolecular helicates shown to have anti-viral activity in cellulo: the approach developed in this study provides additional new insight about how such viral UTR structures might be targeted with the cylinder binding into the heart of an RNA-bulge cavity, how that reduces the conformational flexibility of the RNA and molecular details of the insertion mechanism. The approach and understanding developed represents a new roadmap for design of supramolecular drugs to target RNA structural motifs across biology and nucleic acid nanoscience.

Liquid Extraction Surface Analysis Mass Spectrometry of ESKAPE Pathogens.

The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae) represent clinically important bacterial species that are responsible for most hospital-acquired drug-resistant infections; hence, the need for rapid identification is of high importance. Previous work has demonstrated the suitability of liquid extraction surface analysis mass spectrometry (LESA MS) for the direct analysis of colonies of two of the ESKAPE pathogens (Staphylococcus aureus and Pseudomonas aeruginosa) growing on agar. Here, we apply LESA MS to the remaining four ESKAPE species (E. faecium E745, K. pneumoniae KP257, A. baumannii AYE, and E. cloacae S11) as well as E. faecalis V583 (a close relative of E. faecium) and a clinical isolate of A. baumannii AC02 using an optimized solvent sampling system. In each case, top-down LESA MS/MS was employed for protein identification. In total, 24 proteins were identified from 37 MS/MS spectra by searching against protein databases for the individual species. The MS/MS spectra for the identified proteins were subsequently searched against multiple databases from multiple species in an automated data analysis workflow with a view to determining the accuracy of identification of unknowns. Out of 24 proteins, 19 were correctly assigned at the protein and species level, corresponding to an identification success rate of 79%.

An adaptable analysis workflow for characterization of platelet spreading and morphology.

The assessment of platelet spreading through light microscopy, and the subsequent quantification of parameters such as surface area and circularity, is a key assay for many platelet biologists. Here we present an analysis workflow which robustly segments individual platelets to facilitate the analysis of large numbers of cells while minimizing user bias. Image segmentation is performed by interactive learning and touching platelets are separated with an efficient semi-automated protocol. We also use machine learning methods to robustly automate the classification of platelets into different subtypes. These adaptable and reproducible workflows are made freely available and are implemented using the open-source software KNIME and ilastik.

Direct identification of bacterial and human proteins from infected wounds in living 3D skin models.

Trauma is one of the leading causes of death in people under the age of 49 and complications due to wound infection are the primary cause of death in the first few days after injury. The ESKAPE pathogens are a group of bacteria that are a leading cause of hospital-acquired infections and a major concern in terms of antibiotic resistance. Here, we demonstrate a novel and highly accurate approach for the rapid identification of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) directly from infected wounds in 3D in vitro skin models. Wounded skin models were inoculated with bacteria and left to incubate. Bacterial proteins were identified within minutes, directly from the wound, by liquid extraction surface analysis mass spectrometry. This approach was able to distinguish closely related strains and, unlike genomic approaches, can be modified to provide dynamic information about pathogen behaviour at the wound site. In addition, since human skin proteins were also identified, this method offers the opportunity to analyse both host and pathogen biomarkers during wound infection in near real-time.

Native LESA TWIMS-MSI: Spatial, Conformational, and Mass Analysis of Proteins and Protein Complexes.

We have previously demonstrated native liquid extraction surface analysis (LESA) mass spectrometry imaging of small intact proteins in thin tissue sections. We also showed calculation of collision cross sections for specific proteins extracted from discrete locations in tissue by LESA traveling wave ion mobility spectrometry (TWIMS). Here, we demonstrate an integrated native LESA TWIMS mass spectrometry imaging (MSI) workflow, in which ion mobility separation is central to the imaging experiment and which provides spatial, conformational, and mass information on endogenous proteins in a single experiment. The approach was applied to MSI of a thin tissue section of mouse kidney. The results show that the benefits of integration of TWIMS include improved specificity of the ion images and the capacity to calculate collision cross sections for any protein or protein complex detected in any pixel (without a priori knowledge of the presence of the protein).

Comprehensive LESA Mass Spectrometry Imaging of Intact Proteins by Integration of Cylindrical FAIMS.

The benefits of high field asymmetric waveform ion mobility spectrometry (FAIMS) for mass spectrometry imaging of intact proteins in thin tissue sections have been demonstrated previously. In those works, a planar FAIMS device coupled with a Thermo Elite mass spectrometer was employed. Here, we have evaluated a newly introduced cylindrical FAIMS device (the FAIMS Pro) coupled with a Thermo Fusion Lumos mass spectrometer for liquid extraction surface analysis mass spectrometry imaging of intact proteins in thin tissue sections from rat testes, kidney, and brain. The method makes use of multiple FAIMS compensation values at each location (pixel) of the imaging array. A total of 975 nonredundant protein species were detected in the testes imaging dataset, 981 in the kidney dataset, and 249 in the brain dataset. These numbers represent a 7-fold (brain) and over 10-fold (testes, kidney) improvement on the numbers of proteins previously detected in LESA FAIMS imaging, and a 10-fold to over 20-fold improvement on the numbers detected without FAIMS on this higher performance mass spectrometer, approaching the same order of magnitude as those obtained in top-down proteomics of cell lines. Nevertheless, high throughput identification within the LESA FAIMS imaging workflow remains a challenge.

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy.

MOTIVATION: Localization microscopy data is represented by a set of spatial coordinates, each corresponding to a single detection, that form a point cloud. This can be analyzed either by rendering an image from these coordinates, or by analyzing the point cloud directly. Analysis of this type has focused on clustering detections into distinct groups which produces measurements such as cluster area, but has limited capacity to quantify complex molecular organization and nano-structure. RESULTS: We present a segmentation protocol which, through the application of persistence-based clustering, is capable of probing densely packed structures which vary in scale. An increase in segmentation performance over state-of-the-art methods is demonstrated. Moreover we employ persistent homology to move beyond clustering, and quantify the topological structure within data. This provides new information about the preserved shapes formed by molecular architecture. Our methods are flexible and we demonstrate this by applying them to receptor clustering in platelets, nuclear pore components, endocytic proteins and microtubule networks. Both 2D and 3D implementations are provided within RSMLM, an R package for pointillist-based analysis and batch processing of localization microscopy data. AVAILABILITY AND IMPLEMENTATION: RSMLM has been released under the GNU General Public License v3.0 and is available at https://github.com/JeremyPike/RSMLM. Tutorials for this library implemented as Binder ready Jupyter notebooks are available at https://github.com/JeremyPike/RSMLM-tutorials. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural Analysis of 14-3-3-ζ-Derived Phosphopeptides Using Electron Capture Dissociation Mass Spectrometry, Traveling Wave Ion Mobility Spectrometry, and Molecular Modeling.

Previously, we have demonstrated the effect of salt bridges on the electron capture dissociation mass spectrometry behavior of synthetic model phosphopeptides and applied an ion mobility spectrometry/molecular modeling approach to rationalize the findings in terms of peptide ion structure. Here, we develop and apply the approach to a biologically derived phosphopeptide. Specifically, we have investigated variants of a 15-mer phosphopeptide VVGARRSsWRVVSSI (s denotes phosphorylated Ser) derived from Akt1 substrate 14-3-3-ζ, which contains the phosphorylation motif RRSsWR. Variants were generated by successive arginine-to-leucine substitutions within the phosphorylation motif. ECD fragmentation patterns for the eight phosphopeptide variants show greater sequence coverage with successive R → L substitutions. Peptides with two or more basic residues had regions with no sequence coverage, while full sequence coverage was observed for peptides with one or no basic residues. For three of the peptide variants, low-abundance fragments were observed between the phosphoserine and a basic residue, possibly due to the presence of multiple conformers with and without noncovalent interactions between these residues. For the five variants whose dissociation behavior suggested the presence of intramolecular noncovalent interactions, we employed ion mobility spectrometry and molecular modeling to probe the nature of these interactions. Our workflow allowed us to propose candidate structures whose noncovalent interactions were consistent with the ECD data for all of the peptides modeled. Additionally, the AMBER parameter sets created for and validated by this work are presented and made available online ( http://www.biosciences-labs.bham.ac.uk/cooper/datasets.php ).

New nonlocal forward model for diffuse optical tomography.

The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerically discretized by the classical finite element method (FEM). We propose a nonlocal diffusion equation (NDE) as a new forward model for DOT, the discretization of which is carried out with an efficient graph-based numerical method (GNM). To quantitatively evaluate the new forward model, we first conduct experiments on a homogeneous slab, where the numerical accuracy of both NDE and DE is compared against the existing analytical solution. We further evaluate NDE by comparing its image reconstruction performance (inverse problem) to that of DE. Our experiments show that NDE is quantitatively comparable to DE and is up to 64% faster due to the efficient graph-based representation that can be implemented identically for geometries in different dimensions. The proposed discretization method can be easily applied to other imaging techniques like diffuse correlation spectroscopy which are normally modeled by the diffusion equation.

Quantitative Imaging of Proteins in Tissue by Stable Isotope Labeled Mimetic Liquid Extraction Surface Analysis Mass Spectrometry.

Absolute quantification of proteins in tissue is important for numerous fields of study. Liquid chromatography-mass spectrometry (LC-MS) methods are the norm but typically involve lengthy sample preparation including tissue homogenization, which results in the loss of information relating to spatial distribution. Here, we propose liquid extraction surface analysis (LESA) mass spectrometry (MS) of stable isotope labeled mimetic tissue models for the spatially resolved quantification of intact ubiquitin in rat and mouse brain tissue. Measured ubiquitin concentrations are in agreement with values found in the literature. Images of rat and mouse brain tissue demonstrate spatial variation in the concentration of ubiquitin and demonstrate the utility of spatially resolved quantitative measurement of proteins in tissue. Although we have focused on ubiquitin, the method has the potential for broader application to the absolute quantitation of any endogenous protein or protein-based drug in tissue.

Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography.

Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DOT) image reconstruction (inverse problem) due to unstructured discretization of complex geometries, non-linearity of the data fitting and regularization terms, and non-differentiability of the regularization term. We develop several approaches to overcome these difficulties by: i) defining discrete differential operators for TV regularization using both finite element and graph representations; ii) developing an optimization algorithm based on the alternating direction method of multipliers (ADMM) for the non-differentiable and non-linear minimization problem; iii) investigating isotropic and anisotropic variants of TV regularization, and comparing their finite element (FEM)- and graph-based implementations. These approaches are evaluated on experiments on simulated data and real data acquired from a tissue phantom. Our results show that both FEM and graph-based TV regularization is able to accurately reconstruct both sparse and non-sparse distributions without the over-smoothing effect of Tikhonov regularization and the over-sparsifying effect of L1 regularization. The graph representation was found to out-perform the FEM method for low-resolution meshes, and the FEM method was found to be more accurate for high-resolution meshes.

DNA barcodes for rapid, whole genome, single-molecule analyses.

We report an approach for visualizing DNA sequence and using these 'DNA barcodes' to search complex mixtures of genomic material for DNA molecules of interest. We demonstrate three applications of this methodology; identifying specific molecules of interest from a dataset containing gigabasepairs of genome; identification of a bacterium from such a dataset and, finally, by locating infecting virus molecules in a background of human genomic material. As a result of the dense fluorescent labelling of the DNA, individual barcodes of the order 40 kb pairs in length can be reliably identified. This means DNA can be prepared for imaging using standard handling and purification techniques. The recorded dataset provides stable physical and electronic records of the total genomic content of a sample that can be readily searched for a molecule or region of interest.

L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography.

Spectrally constrained diffuse optical tomography (SCDOT) is known to improve reconstruction in diffuse optical imaging; constraining the reconstruction by coupling the optical properties across multiple wavelengths suppresses artefacts in the resulting reconstructed images. In other work, L1-norm regularization has been shown to improve certain types of image reconstruction problems as its sparsity-promoting properties render it robust against noise and enable the preservation of edges in images, but because the L1-norm is non-differentiable, it is not always simple to implement. In this work, we show how to incorporate L1 regularization into SCDOT. Three popular algorithms for L1 regularization are assessed for application in SCDOT: iteratively reweighted least square algorithm (IRLS), alternating directional method of multipliers (ADMM), and fast iterative shrinkage-thresholding algorithm (FISTA). We introduce an objective procedure for determining the regularization parameter in these algorithms and compare their performance in simulated experiments, and in real data acquired from a tissue phantom. Our results show that L1 regularization consistently outperforms Tikhonov regularization in this application, particularly in the presence of noise.

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

Quantifying receptor trafficking and colocalization with confocal microscopy.

Confocal microscopy is a powerful tool for the study of cellular receptor trafficking and endocytosis. Unbiased and robust image analysis workflows are required for the identification, and study, of aberrant trafficking. After a brief review of related strategies, identifying both good and bad practice, custom workflows for the analysis of live cell 3D time-lapse data are presented. Strategies for data pre-processing, including denoising and background subtraction are considered. We use a 3D level set protocol to accurately segment cells using only the signal from fluorescently labelled receptor. A protocol for the quantification of changes to subcellular receptor distribution over time is then presented. As an example, ligand stimulated trafficking of epidermal growth factor receptor (EGFR) is shown to be significantly reduced in both AG1478 and Dynasore treated cells. Protocols for the quantitative analysis of colocalization between receptor and endosomes are also introduced, including strategies for signal isolation and statistical testing. By calculating the Manders and Pearson coefficients, both co-occurrence and correlation can be assessed. A statistically significant decrease in the level of ligand induced co-occurrence between EGFR and rab5 positive endosomes is demonstrated for both the AG1478 and Dynasore treated cells relative to a control. Finally, a strategy for the visualisation of co-occurrence is presented, which provides an unbiased alternative to colour overlays.