Browsing by Author "Losert, Wolfgang"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    Adenylyl cyclase mRNA localizes to the posterior of polarized DICTYOSTELIUM cells during chemotaxis
    (Springer Nature, 2017-05-25) Das, Satarupa; Parker, Joshua M.; Guven, Can; Wang, Weiye; Kriebel, Paul W.; Losert, Wolfgang; Larson, Daniel R.; Parent, Carole A.
    In Dictyostelium discoideum, vesicular transport of the adenylyl cyclase A (ACA) to the posterior of polarized cells is essential to relay exogenous 3′,5′-cyclic adenosine monophosphate (cAMP) signals during chemotaxis and for the collective migration of cells in head-to-tail arrangements called streams. Using fluorescence in situ hybridization (FISH), we discovered that the ACA mRNA is asymmetrically distributed at the posterior of polarized cells. Using both standard estimators and Monte Carlo simulation methods, we found that the ACA mRNA enrichment depends on the position of the cell within a stream, with the posterior localization of ACA mRNA being strongest for cells at the end of a stream. By monitoring the recovery of ACA-YFP after cycloheximide (CHX) treatment, we observed that ACA mRNA and newly synthesized ACA-YFP first emerge as fluorescent punctae that later accumulate to the posterior of cells. We also found that the ACA mRNA localization requires 3′ ACA cis-acting elements. Together, our findings suggest that the asymmetric distribution of ACA mRNA allows the local translation and accumulation of ACA protein at the posterior of cells. These data represent a novel functional role for localized translation in the relay of chemotactic signal during chemotaxis.
  • No Thumbnail Available
    Item
    Data for "Signaling through polymerization and degradation: Analysis and simulations of T cell activation mediated by Bcl10"
    (2021) Campanello, Leonard; Traver, Maria; Shroff, Hari; Schaefer, Brian; Losert, Wolfgang
    The adaptive immune system serves as a potent and highly specific defense mechanism against pathogen infection. One component of this system, the effector T cell, facilitates pathogen clearance upon detection of specific antigens by the T cell receptor (TCR). A critical process in effector T cell activation is transmission of signals from the TCR to a key transcriptional regulator, NF-κB. The transmission of this signal involves a highly dynamic process in which helical filaments of Bcl10, a key protein constituent of the TCR signaling cascade, undergo competing processes of polymeric assembly and macroautophagy-dependent degradation. Through computational analysis of three-dimensional, super-resolution optical micrographs, we quantitatively characterize TCR-stimulated Bcl10 filament assembly and length dynamics, and demonstrate that filaments become shorter over time. Additionally, we develop an image-based, bootstrap-like resampling method that demonstrates the preferred association between autophagosomes and both Bcl10-filament ends and punctate-Bcl10 structures, implying that autophagosome-driven macroautophagy is directly responsible for Bcl10 filament shortening. We probe Bcl10 polymerization-depolymerization dynamics with a stochastic Monte-Carlo simulation of nucleation-limited filament assembly and degradation, and we show that high probabilities of filament nucleation in response to TCR engagement could provide the observed robust, homogeneous, and tunable response dynamic. Furthermore, we demonstrate that the speed of filament disassembly preferentially at filament ends provides effective regulatory control. Taken together, these data suggest that Bcl10 filament growth and degradation act as an excitable system that provides a digital response mechanism and the reliable timing critical for T cell activation and regulatory processes.
  • Thumbnail Image
    Item
    Data from: Inferring single cell behavior from large-scale epithelial sheet migration patterns
    (2017) Lee, Rachel M.; Yue, Haicen; Rappel, Wouter-Jan; Losert, Wolfgang
    Cell migration plays an important role in a wide variety of biological processes and can incorporate both individual cell motion and collective behavior. The emergent properties of collective migration are receiving increasing attention as collective motion’s role in diseases such as metastatic cancer becomes clear. Yet, how individual cell behavior influences large-scale, multi-cell collective motion remains unclear. In our study, we provided insight into the mechanisms behind collective migration by studying cell migration in a spreading monolayer of epithelial MCF10A cells. We quantify migration using particle image velocimetry and find that cell groups have features of motion that span multiple length scales. Comparing our experimental results to a model of collective cell migration, we find that cell migration within the monolayer can be affected in qualitatively different ways by cell motion at the boundary, yet it is not necessary to introduce leader cells at the boundary or specify other large-scale features to recapitulate this large-scale phenotype in simulations. Instead, in our model, collective motion can be enhanced by increasing the overall activity of the cells or by giving the cells a stronger coupling between their motion and polarity. This suggests that investigating the activity and polarity persistence of individual cells will add insight into the collective migration phenotypes observed during development and disease. This dataset provides microscopy images and analysis to support the article in the Journal of the Royal Society Interface (doi 10.1098/rsif.2017.0147) describing these migration behaviors.
  • Thumbnail Image
    Item
    Detecting heterogeneity in and between breast cancer cell lines
    (Springer Nature, 2020-02-03) Shen, Yang; Schmidt, B. U. Sebastian; Kubitschke, Hans; Morawetz, Erik W.; Wolf, Benjamin; Käs, Josef A.; Losert, Wolfgang
    Cellular heterogeneity in tumor cells is a well-established phenomenon. Genetic and phenotypic cell-to-cell variability have been observed in numerous studies both within the same type of cancer cells and across different types of cancers. Another known fact for metastatic tumor cells is that they tend to be softer than their normal or non-metastatic counterparts. However, the heterogeneity of mechanical properties in tumor cells are not widely studied. Here we analyzed single-cell optical stretcher data with machine learning algorithms on three different breast tumor cell lines and show that similar heterogeneity can also be seen in mechanical properties of cells both within and between breast tumor cell lines. We identified two clusters within MDA-MB-231 cells, with cells in one cluster being softer than in the other. In addition, we show that MDA-MB-231 cells and MDA-MB-436 cells which are both epithelial breast cancer cell lines with a mesenchymal-like phenotype derived from metastatic cancers are mechanically more different from each other than from non-malignant epithelial MCF-10A cells. Since stiffness of tumor cells can be an indicator of metastatic potential, this result suggests that metastatic abilities could vary within the same monoclonal tumor cell line.
  • Thumbnail Image
    Item
    RefCell: multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’
    (Springer Nature, 2018-11-16) Shen, Yang; Kubben, Nard; Candia, Julián; Morozov, Alexandre V.; Misteli, Tom; Losert, Wolfgang
    Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the “curse of dimensionality” and non-standardized outputs. Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these “typical cells” as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample. We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.
  • Thumbnail Image
    Item
    Spontaneous Polarization and Cell Guidance on Asymmetric Nanotopography
    (Springer Nature, 2022-05-11) Herr, Corey; Winkler, Benjamin; Ziebert, Falko; Aranson, Igor S.; Fourkas, John T.; Losert, Wolfgang
    Asymmetric nanotopography with sub-cellular dimensions has recently demonstrated the ability to provide a unidirectional bias in cell migration. The details of this guidance depend on the type of cell studied and the design of the nanotopography. This behavior is not yet well understood, so there is a need for a predictive description of cell migration on such nanotopography that captures both the initiation of migration, and the way cell migration evolves. Here, we employ a three-dimensional, physics-based model to study cell guidance on asymmetric nanosawteeth. In agreement with experimental data, our model predicts that asymmetric sawteeth lead to spontaneous motion. Our model demonstrates that the nanosawteeth induce a unidirectional bias in guidance direction that is dependent upon actin polymerization rate and sawtooth dimensions. Motivated by this model, an analysis of previously reported experimental data indicates that the degree of guidance by asymmetric nanosawteeth increases with the cell velocity.
  • Thumbnail Image
    Item
    svclassify: a method to establish benchmark structural variant calls
    (Springer Nature, 2016-01-16) Parikh, Hemang; Mohiyuddin, Marghoob; Lam, Hugo Y. K.; Iyer, Hariharan; Chen, Desu; Pratt, Mark; Bartha, Gabor; Spies, Noah; Losert, Wolfgang; Zook, Justin M.; Salit, Marc
    The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives. We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz. We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.