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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
Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling)
(2021)
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.
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.
Physical Biology is a field of life sciences dealing with the extraction of quantitative data from biophysical or molecular biological experiments with different levels of complexity. Such data are further used as parameters for mathematical models of the biological system. These models allow to predict reactions on external stimuli by describing the relevant molecular interactions and are therefore used for example to generate a deeper comprehension of complex human diseases. An essential technique in biophysical research on human diseases is fluorescence microscopy. This is a constantly developed toolbox comprising a large number of specific labeling strategies, as well as a broad spectrum of fluorescent probes. It is further minimal invasive and therefore suitable for measurements in living cells or organisms. The sensitivity of modern photo-detectors even allows for the detection of a single fluorescent probe with an accuracy of approximately 10 nm.
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The model-prediction was further verified by two color SMLM experiments. In this work the development and application of imaging-systems are described which provide quantitative data with single-molecule resolution for systems biological model approaches with a low degree of abstractness. In the near future, the impact of mathematical models in the research field of complex human diseases will increase. The predictions of these models will be more exact, the more detailed and accurate the input parameters will become. This work gives an impression of how quantitative data obtained by SMLM may serve as input parameters for mathematical models at the single-cell level.
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.
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.
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.
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.
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 (Dmean) in neurites from 0.12±0.05 to 0.61±0.03 μm2/s 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 10.9±1.9 to 15±4.9 μm/s, whereas a velocity increase from 9±1.3 to 14±3 μm/s 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.
Motivation: Gaussian mixture models (GMMs) are probabilistic models commonly used in biomedical research to detect subgroup structures in data sets with one-dimensional information. Reliable model parameterization requires that the number of modes, i.e., states of the generating process, is known. However, this is rarely the case for empirically measured biomedical data. Several implementations are available that estimate GMM parameters differently. This work aims to provide a comparative evaluation of automated GMM fitting methods.
Results and conclusions: The performance of commonly used algorithms for automatic parameterization and mode number determination was compared with respect to reproducing the ground truth of generated data derived from multiple normal distributions. Four main variants of Gaussian mode number detection algorithms and five variants of GMM parameter estimation methods were tested in a combinatory scenario. The combination of best performing mode number determination algorithms and GMM parameter estimation methods was then tested on artificial and real-live data sets known to display a GMM structure. None of the tested methods correctly determined the underlying data structure consistently. The likelihood ratio test had the best performance in identifying the mode number associated with the best GMM fit of the data distribution while the Markov chain Monte Carlo (MCMC) algorithm was best for GMM parameter estimation while. The combination of the two methods of number determination algorithms and GMM parameter estimation was consistently among the best and overall outperformed the available implementations.
Implementation: An automated tool for the detection of GMM based structures in (biomedical) datasets was created based on the present results and made freely available in the R library “opGMMassessment” at https://cran.r-project.org/package=opGMMassessment.