Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data.

Internalin B-mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. Here, we propose a method based on hidden Markov modeling and explainable artificial intelligence that machine-learns the key differences in MET mobility between internalin B-treated and -untreated cells from single-particle tracking data. Our method assigns receptor mobility to three diffusion modes (immobile, slow, and fast). It discriminates between internalin B-treated and -untreated cells with a balanced accuracy of >99% and identifies three parameters that are most affected by internalin B treatment: a decrease in the mobility of slow molecules (1) and a depopulation of the fast mode (2) caused by an increased transition of fast molecules to the slow mode (3). Our approach is based entirely on free software and is readily applicable to the analysis of other membrane receptors.

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP).

The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non-data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).

Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling).

MOTIVATION: The size of today's biomedical data sets pushes computer equipment to its limits, even for seemingly standard analysis tasks such as data projection or clustering. Reducing large biomedical data by downsampling is therefore a common early step in data processing, often performed as random uniform class-proportional downsampling. In this report, we hypothesized that this can be optimized to obtain samples that better reflect the entire data set than those obtained using the current standard method. RESULTS: By repeating the random sampling and comparing the distribution of the drawn sample with the distribution of the original data, it was possible to establish a method for obtaining subsets of data that better reflect the entire data set than taking only the first randomly selected subsample, as is the current standard. Experiments on artificial and real biomedical data sets showed that the reconstruction of the remaining data from the original data set from the downsampled data improved significantly. This was observed with both principal component analysis and autoencoding neural networks. The fidelity was dependent on both the number of cases drawn from the original and the number of samples drawn. CONCLUSIONS: Optimal distribution-preserving class-proportional downsampling yields data subsets that reflect the structure of the entire data better than those obtained with the standard method. By using distributional similarity as the only selection criterion, the proposed method does not in any way affect the results of a later planned analysis.

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis.

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain.

The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient's pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all "pain genes" would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called "pain genes" derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

Quantitative single-molecule imaging of TNFR1 reveals zafirlukast as antagonist of TNFR1 clustering and TNFα-induced NF-ĸB signaling.

TNFR1 is a crucial regulator of NF-ĸB-mediated proinflammatory cell survival responses and programmed cell death (PCD). Deregulation of TNFα- and TNFR1-controlled NF-ĸB signaling underlies major diseases, like cancer, inflammation, and autoimmune diseases. Therefore, although being routinely used, antagonists of TNFα might also affect TNFR2-mediated processes, so that alternative approaches to directly antagonize TNFR1 are beneficial. Here, we apply quantitative single-molecule localization microscopy (SMLM) of TNFR1 in physiologic cellular settings to validate and characterize TNFR1 inhibitory substances, exemplified by the recently described TNFR1 antagonist zafirlukast. Treatment of TNFR1-mEos2 reconstituted TNFR1/2 knockout mouse embryonic fibroblasts (MEFs) with zafirlukast inhibited both ligand-independent preligand assembly domain (PLAD)-mediated TNFR1 dimerization as well as TNFα-induced TNFR1 oligomerization. In addition, zafirlukast-mediated inhibition of TNFR1 clustering was accompanied by deregulation of acute and prolonged NF-ĸB signaling in reconstituted TNFR1-mEos2 MEFs and human cervical carcinoma cells. These findings reveal the necessity of PLAD-mediated, ligand-independent TNFR1 dimerization for NF-ĸB activation, highlight the PLAD as central regulator of TNFα-induced TNFR1 oligomerization, and demonstrate that TNFR1-mEos2 MEFs can be used to investigate TNFR1-antagonizing compounds employing single-molecule quantification and functional NF-ĸB assays at physiologic conditions.

Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI).

BACKGROUND: In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster. METHODS: Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub-symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100-fold cross-validation. RESULTS: The importance of the variables of the data set (6 pain-related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub-symbolic classifiers. A generalized post-hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy. CONCLUSIONS: Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human-understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy.

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes.

Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

Single-Molecule Super-Resolution Microscopy Reveals Heteromeric Complexes of MET and EGFR upon Ligand Activation.

Receptor tyrosine kinases (RTKs) orchestrate cell motility and differentiation. Deregulated RTKs may promote cancer and are prime targets for specific inhibitors. Increasing evidence indicates that resistance to inhibitor treatment involves receptor cross-interactions circumventing inhibition of one RTK by activating alternative signaling pathways. Here, we used single-molecule super-resolution microscopy to simultaneously visualize single MET and epidermal growth factor receptor (EGFR) clusters in two cancer cell lines, HeLa and BT-20, in fixed and living cells. We found heteromeric receptor clusters of EGFR and MET in both cell types, promoted by ligand activation. Single-protein tracking experiments in living cells revealed that both MET and EGFR respond to their cognate as well as non-cognate ligands by slower diffusion. In summary, for the first time, we present static as well as dynamic evidence of the presence of heteromeric clusters of MET and EGFR on the cell membrane that correlates with the relative surface expression levels of the two receptors.

Single-molecule imaging reveals the oligomeric state of functional TNFα-induced plasma membrane TNFR1 clusters in cells.

Ligand-induced tumor necrosis factor receptor 1 (TNFR1) activation controls nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) signaling, cell proliferation, programmed cell death, and survival and is crucially involved in inflammation, autoimmune disorders, and cancer progression. Despite the relevance of TNFR1 clustering for signaling, oligomerization of ligand-free and ligand-activated TNFR1 remains controversial. At present, models range from ligand-independent receptor predimerization to ligand-induced oligomerization. Here, we used quantitative, single-molecule superresolution microscopy to study TNFR1 assembly directly in native cellular settings and at physiological cell surface abundance. In the absence of its ligand TNFα, TNFR1 assembled into monomeric and dimeric receptor units. Upon binding of TNFα, TNFR1 clustered predominantly not only into trimers but also into higher-order oligomers. A functional mutation in the preligand assembly domain of TNFR1 resulted in only monomeric TNFR1, which exhibited impaired ligand binding. In contrast, a form of TNFR1 with a mutation in the ligand-binding CRD2 subdomain retained the monomer-to-dimer ratio of the unliganded wild-type TNFR1 but exhibited no ligand binding. These results underscore the importance of ligand-independent TNFR1 dimerization in NF-κB signaling.

Super-resolution imaging and estimation of protein copy numbers at single synapses with DNA-point accumulation for imaging in nanoscale topography.

In the brain, the strength of each individual synapse is defined by the complement of proteins present or the "local proteome." Activity-dependent changes in synaptic strength are the result of changes in this local proteome and posttranslational protein modifications. Although most synaptic proteins have been identified, we still know little about protein copy numbers in individual synapses and variations between synapses. We use DNA-point accumulation for imaging in nanoscale topography as a single-molecule super-resolution imaging technique to visualize and quantify protein copy numbers in single synapses. The imaging technique provides near-molecular spatial resolution, is unaffected by photobleaching, enables imaging of large field of views, and provides quantitative molecular information. We demonstrate these benefits by accessing copy numbers of surface AMPA-type receptors at single synapses of rat hippocampal neurons along dendritic segments.

Correlative Single-Molecule FRET and DNA-PAINT Imaging.

DNA-PAINT is an optical super-resolution microscopy method that can visualize nanoscale protein arrangements and provide spectrally unlimited multiplexing capabilities. However, current multiplexing implementations based on, for example, DNA exchange (such as Exchange-PAINT) achieves multitarget detection by sequential imaging, limiting throughput. Here, we combine DNA-PAINT with single-molecule FRET and use the FRET efficiency as parameter for multiplexed imaging with high specificity. We demonstrate correlated single-molecule FRET and super-resolution on DNA origami structures, which are equipped with binding sequences that are targeted by pairs of dye-labeled oligonucleotides generating the FRET signal. We futher extract FRET values from single binding sites that are spaced just ∼55 nm apart, demonstrating super-resolution FRET imaging. This combination of FRET and DNA-PAINT allows for multiplexed super-resolution imaging with low background and opens the door for accurate distance readout in the 1-10 nm range.

Model-based identification of TNFα-induced IKKß-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds.

Drug-induced liver injury (DILI) has become a major problem for patients and for clinicians, academics and the pharmaceutical industry. To date, existing hepatotoxicity test systems are only poorly predictive and the underlying mechanisms are still unclear. One of the factors known to amplify hepatotoxicity is the tumor necrosis factor alpha (TNFα), especially due to its synergy with commonly used drugs such as diclofenac. However, the exact mechanism of how diclofenac in combination with TNFα induces liver injury remains elusive. Here, we combined time-resolved immunoblotting and live-cell imaging data of HepG2 cells and primary human hepatocytes (PHH) with dynamic pathway modeling using ordinary differential equations (ODEs) to describe the complex structure of TNFα-induced NFκB signal transduction and integrated the perturbations of the pathway caused by diclofenac. The resulting mathematical model was used to systematically identify parameters affected by diclofenac. These analyses showed that more than one regulatory module of TNFα-induced NFκB signal transduction is affected by diclofenac, suggesting that hepatotoxicity is the integrated consequence of multiple changes in hepatocytes and that multiple factors define toxicity thresholds. Applying our mathematical modeling approach to other DILI-causing compounds representing different putative DILI mechanism classes enabled us to quantify their impact on pathway activation, highlighting the potential of the dynamic pathway model as a quantitative tool for the analysis of DILI compounds.

Quantitative Single-Molecule Localization Microscopy (qSMLM) of Membrane Proteins Based on Kinetic Analysis of Fluorophore Blinking Cycles.

Photoswitchable or photoactivatable fluorophores are the key in single-molecule localization microscopy. Next to providing fluorescence images with subdiffraction spatial resolution, additional information is available from observing single fluorophores over time. This includes the characteristic photophysical phenomenon of "blinking" that is exhibited by single fluorescent proteins or fluorophores and follows well-defined kinetic laws. Analyzing the kinetics of "blinking" allows determining the number of fluorophores in a multi-molecular complex. As such, quantitative information at the molecular level can be extracted, representing a tremendously useful extension of single-molecule super-resolution microscopy. This concept is in particular useful to study homo- and heterooligomeric signaling protein complexes in the plasma membrane of an intact cell with molecular resolution. Here, we provide an experimental framework for deciphering the stoichiometry of membrane proteins on the basis of SMLM and photoswitching statistics.

Extracting quantitative information from single-molecule super-resolution imaging data with LAMA - LocAlization Microscopy Analyzer.

Super-resolution fluorescence microscopy revolutionizes cell biology research and provides novel insights on how proteins are organized at the nanoscale and in the cellular context. In order to extract a maximum of information, specialized tools for image analysis are necessary. Here, we introduce the LocAlization Microscopy Analyzer (LAMA), a comprehensive software tool that extracts quantitative information from single-molecule super-resolution imaging data. LAMA allows characterizing cellular structures by their size, shape, intensity, distribution, as well as the degree of colocalization with other structures. LAMA is freely available, platform-independent and designed to provide direct access to individual analysis of super-resolution data.

Single-particle tracking uncovers dynamics of glutamate-induced retrograde transport of NF-κB p65 in living neurons.

Retrograde transport of NF-κB from the synapse to the nucleus in neurons is mediated by the dynein/dynactin motor complex and can be triggered by synaptic activation. The caliber of axons is highly variable ranging down to 100 nm, aggravating the investigation of transport processes in neurites of living neurons using conventional light microscopy. We quantified for the first time the transport of the NF-κB subunit p65 using high-density single-particle tracking in combination with photoactivatable fluorescent proteins in living mouse hippocampal neurons. We detected an increase of the mean diffusion coefficient ([Formula: see text]) in neurites from [Formula: see text] to [Formula: see text] after stimulation with glutamate. We further observed that the relative amount of retrogradely transported p65 molecules is increased after stimulation. Glutamate treatment resulted in an increase of the mean retrograde velocity from [Formula: see text] to [Formula: see text], whereas a velocity increase from [Formula: see text] to [Formula: see text] was observed for anterogradely transported p65. This study demonstrates for the first time that glutamate stimulation leads to an increased mobility of single NF-κB p65 molecules in neurites of living hippocampal neurons.

Correlative light- and electron microscopy with chemical tags.

Correlative microscopy incorporates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Several approaches exist for correlative microscopy, most of which have used the green fluorescent protein (GFP) as the label for light microscopy. Here we use chemical tagging and synthetic fluorophores instead, in order to achieve protein-specific labeling, and to perform multicolor imaging. We show that synthetic fluorophores preserve their post-embedding fluorescence in the presence of uranyl acetate. Post-embedding fluorescence is of such quality that the specimen can be prepared with identical protocols for scanning electron microscopy (SEM) and transmission electron microscopy (TEM); this is particularly valuable when singular or otherwise difficult samples are examined. We show that synthetic fluorophores give bright, well-resolved signals in super-resolution light microscopy, enabling us to superimpose light microscopic images with a precision of up to 25 nm in the x-y plane on electron micrographs. To exemplify the preservation quality of our new method we visualize the molecular arrangement of cadherins in adherens junctions of mouse epithelial cells.

Coordinate-based co-localization-mediated analysis of arrestin clustering upon stimulation of the C-C chemokine receptor 5 with RANTES/CCL5 analogues.

G protein-coupled receptor activation and desensitization leads to recruitment of arrestin proteins from cytosolic pools to the cell membrane where they form clusters difficult to characterize due to their small size and further mediate receptor internalization. We quantitatively investigated clustering of arrestin 3 induced by potent anti-HIV analogues of the chemokine RANTES after stimulation of the C-C chemokine receptor 5 using single-molecule localization-based super-resolution microscopy. We determined arrestin 3 cluster sizes and relative fractions of arrestin 3 molecules in each cluster through image-based analysis of the localization data by adapting a method originally developed for co-localization analysis from molecular coordinates. We found that only classical agonists in the set of tested ligands were able to efficiently recruit arrestin 3 to clusters mostly larger than 150 nm in size and compare our results with existing data on arrestin 2 clustering induced by the same chemokine analogues.

A simple method to estimate the average localization precision of a single-molecule localization microscopy experiment.

The localization precision is a crucial and important parameter for single-molecule localization microscopy (SMLM) and directly influences the achievable spatial resolution. It primarily depends on experimental imaging conditions and the registration potency of the algorithm used. We propose a new and simple routine to estimate the average experimental localization precision in SMLM, based on the nearest neighbor analysis. By exploring different experimental and simulated targets, we show that this approach can be generally used for any 2D or 3D SMLM data and that reliable values for the localization precision σ SMLM are obtained. Knowing σ SMLM is a prerequisite for consistent visualization or any quantitative structural analysis, e.g., cluster analysis or colocalization studies.

Quantitative single-molecule localization microscopy combined with rule-based modeling reveals ligand-induced TNF-R1 reorganization toward higher-order oligomers.

We report on the assembly of tumor necrosis factor receptor 1 (TNF-R1) prior to ligand activation and its ligand-induced reorganization at the cell membrane. We apply single-molecule localization microscopy to obtain quantitative information on receptor cluster sizes and copy numbers. Our data suggest a dimeric pre-assembly of TNF-R1, as well as receptor reorganization toward higher oligomeric states with stable populations comprising three to six TNF-R1. Our experimental results directly serve as input parameters for computational modeling of the ligand-receptor interaction. Simulations corroborate the experimental finding of higher-order oligomeric states. This work is a first demonstration how quantitative, super-resolution and advanced microscopy can be used for systems biology approaches at the single-molecule and single-cell level.