Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session A5: Tracking, Localization and Inference: Methods and ApplicationsFocus
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Sponsoring Units: DBIO Chair: Steve Presse, IUPUI Room: 264 |
Monday, March 13, 2017 8:00AM - 8:36AM |
A5.00001: Leveraging Time Series Analysis and Machine Learning to Quantify Intra and Inter Trajectory Heterogeneity in Particle Tracking Experiments Invited Speaker: Christopher Calderon Microscopy hardware is now capable of producing high accuracy position vs. time data characterizing fluorescently tagged molecules in live cells. However, analytical methods for efficiently quantifying molecular motion parameters from the raw 3D (or 2D) single particle tracking (SPT) data are underdeveloped. "Downstream" trajectory analysis methods have only begun to efficiently and reliably harness the wealth of statistical kinetic information buried in SPT time series. The lack of analytical methods is due in part to the numerous challenges facing the translation the noisy position measurement information encoded in image stacks into unambiguous and readily interpretable biophysical information quantities (e.g., instantaneous effective diffusivity, forces, molecular friction, etc.). Some of these challenges are caused by: the inherently stochastic (and often nonlinear) nature of the dynamics of molecules in live cells, the highly crowded and heterogeneous time changing micro-environment of live cells influencing the dynamics of tagged molecules, and artifacts induced by the measurement device (e.g.localization error and motion blur). This talk will demonstrate how the merging of ideas from high frequency financial time series analysis, machine learning, and nonparametric Bayesian statistics can address these challenges, overcome limitations inherent in classic SPT methods, and provide insight into various single particle tracking experiments. We will describe and illustrate the new SPT trajectory analysis methods and discuss how the methods can be used to more reliably estimate data-driven and physically interpretable models. [Preview Abstract] |
Monday, March 13, 2017 8:36AM - 8:48AM |
A5.00002: Single molecule analysis of B cell receptor motion during signaling activation Ivan Rey Suarez, Peter Koo, Shu Zhou, Brittany Wheatley, Wenxia Song, Simon Mochrie, Arpita Upadhyaya B cells are an essential part of the adaptive immune system. They patrol the body for signs of infection in the form of antigen on the surface of antigen presenting cells. B cell receptor (BCR) binding to antigen induces a signaling cascade that leads to B cell activation and spreading. During activation, BCR form signaling microclusters that later coalesce as the cell contracts. We have studied the dynamics of BCRs on activated murine primary B cells using single particle tracking. The tracks are analyzed using perturbation expectation-maximization (pEM), a systems-level analysis, which allows identification of different short-time diffusive states from single molecule tracks. We identified four dominant diffusive states, two of which correspond to BCRs interacting with signaling molecules. For wild-type cells, the number of BCR in signaling states increases as the cell spreads and then decreases during cell contraction. In contrast, cells lacking the actin regulatory protein, N-WASP, are unable to contract and BCRs remain in the signaling states for longer times. These observations indicate that actin cytoskeleton dynamics modulate BCR diffusion and clustering. Our results provide novel information regarding the timescale of interaction between BCR and signaling molecules. [Preview Abstract] |
Monday, March 13, 2017 8:48AM - 9:00AM |
A5.00003: 3D Tracking of individual growth factor receptors on polarized cells James Werner, Dominik Stich, Cedric Cleyrat, Mary Phipps, Angela Wadinger-Ness, Bridget Wilson We have been developing methods for following 3D motion of selected biomolecular species throughout mammalian cells. Our approach exploits a custom designed confocal microscope that uses a unique spatial filter geometry and active feedback 200 times/second to follow fast 3D motion. By exploiting new non-blinking quantum dots as fluorescence labels, individual molecular trajectories can be observed for several minutes. We also will discuss recent instrument upgrades, including the ability to perform spinning disk fluorescence microscopy on the whole mammalian cell performed simultaneously with 3D molecular tracking experiments. These instrument upgrades were used to quantify 3D heterogeneous transport of individual growth factor receptors (EGFR) on live human renal cortical epithelial cells. [Preview Abstract] |
Monday, March 13, 2017 9:00AM - 9:36AM |
A5.00004: Single molecule transcription factor dynamics in the syncytial Drosophila embryo Invited Speaker: Xavier Darzacq During early development in the Drosophila embryo, cell fates are determined over the course of just 2 hours with exquisite spatio-temoral precision. One of the key regulators of this process is the transcription factor Bicoid which forms a concentration gradient across the long axis of the embryo. Although Bicoids' primary role is activation at the anterior, where concentrations are highest, it is also known to play a role in the posterior where there are only 100s of molecules per nucleus. Understanding how Bicoid can find its target at such low concentrations has remained intractable, largely due to the inability to perform single molecule imaging in the context of the developing embryo. Here we use lattice light sheet microscopy to overcome the technical barriers of sample thickness and auto-fluorescence to characterize the single molecule dynamics of Bicoid. We find that off-rates do not vary across the embryo and that instead the on-rates are modulated through the formation of clusters that enrich local concentration. This data is contrary to the current concentration dependent model of Bicoid function since \textit{local} concentration within the nucleus is now a regulated parameter and suggests a previously unknown mechanism for regulation at extremely low concentrations. [Preview Abstract] |
Monday, March 13, 2017 9:36AM - 9:48AM |
A5.00005: Transmembrane protein CD93 diffuses by a continuous time random walk. Maria Goiko, John de Bruyn, Bryan Heit Molecular motion within the cell membrane is a poorly-defined process. In this study, we characterized the diffusion of the transmembrane protein CD93. By careful analysis of the dependence of the ensemble-averaged mean squared displacement (EA-MSD, $r^2$) on time $t$ and the ensemble-averaged, time-averaged MSD (EA-TAMSD, $\delta^2$) on lag time $\tau$ and total measurement time $T$, we showed that the motion of CD93 is well-described by a continuous-time random walk (CTRW). CD93 tracks were acquired using single particle tracking. The tracks were classified as confined or free, and the behavior of the MSD analyzed. EA-MSDs of both populations grew non-linearly with $t$, indicative of anomalous diffusion. Their EA-TAMSDs were found to depend on both $\tau$ and $T$, indicating non-ergodicity. Free molecules had $r^2\propto t^\alpha$ and $\delta^2\propto(\tau/T^{1-\alpha})$, with $\alpha\approx0.5$, consistent with a CTRW. Mean maximal excursion analysis supported this result. Confined CD93 had $r^2\propto t^0$ and $\delta^2\propto(\tau/T)^\alpha$, with $\alpha\approx0.3$, consistent with a confined CTRW. CTRWs are described by a series of random jumps interspersed with power-law distributed waiting times, and may arise due to the interactions of CD93 with the endocytic machinery. [Preview Abstract] |
Monday, March 13, 2017 9:48AM - 10:00AM |
A5.00006: Analyzing Single Molecule Measurements With Bayesian Non-Parametric Methods Ioannis Sgouralis, Steve Presse Single molecule measurements are commonly modeled and analyzed by means of Bayesian statistics. Despite their popularity, the traditional Bayesian methods can lead to overfitting mainly because they require the number of different states the molecule attains to be pre-specified and fixed. In the talk, I will present methods that lift this requirement and thus that avoid overfitting. These methods utilize novel concepts from Bayesian non-parametric statistics and allow full posterior inference without assuming a pre-specified or fixed number of molecular states. This characteristic makes them ideal for the analysis of biophysical data, especially as alternatives to the existing methods which are based on model selection and information criteria. [Preview Abstract] |
Monday, March 13, 2017 10:00AM - 10:36AM |
A5.00007: Determination of in vivo regulation kinetics of small non-coding RNA in bacteria Invited Speaker: Jingyi Fei Small RNAs (sRNAs) play important roles in regulating gene expression through a variety of mechanisms. As one of the most common strategies, sRNA induced target messenger RNA (mRNA) includes two major steps: target search by base-pairing interactions with the and downstream execution by modulating translation or the stability of the mRNA. Here we describe a new imaging and analysis platform based on super-resolution fluorescence microscopy, which enabled the first in vivo kinetic measurement of sRNA-mediated gene regulation. Specifically, this platform was used to investigate a sugar-phosphate stress-induced bacterial sRNA that induces the degradation of target mRNAs. The data reveal that the sRNA binds to a primary target mRNA in a reversible and dynamic fashion, and that formation of the sRNA-mRNA complexes is the rate-limiting step, dictating the overall efficiency of regulation in vivo; whereas the downstream co-degradation of sRNA-mRNA complex can kinetically compete with the fast complex disassembly. Examination of a secondary target of this sRNA indicated that differences in the target search kinetics contribute to setting the regulation priority among different target mRNAs. This super-resolution imaging and analysis approach provides a conceptual framework that can be generalized to other sRNA systems and other target search processes. [Preview Abstract] |
Monday, March 13, 2017 10:36AM - 10:48AM |
A5.00008: The effect of propofol on plasma membrane ultrastructure in the intact cells. Weixiang Jin, Arnd Pralle The mechanism of general anesthesia is still unknown. One drug used for human anesthesia, propofol, has been shown to interact with some ligand gated ion-channels, but also easily dissolves in the lipid bilayer and alters fluidity. Which mechanism dominates or even how anesthesia arises are unclear. We study the influence of propofol on plasma membrane (PM) ultrastructure in intact cells. In the PM, transient submicroscopic nanodomains form by interactions between lipid-acyl-chains or lipid head groups, stabilized by cholesterol. In addition, membrane cytoskeleton further regulates the nanodomains, which then regulate signaling. We study transient propofol effects on these domains from low to clinically relevant propofol concentrations by analyzing diffusion of GFP-tagged outer leaflet/inner leaflet membrane proteins. Using bimFCS we measure diffusion on multiple length scales simultaneously. We observe that at low propofol concentrations, the nanodomains trap GPI-mGFP less, consistent with studies showing that propofol decreases the phase transition temperature of membrane derived vesicles. Interestingly, at clinical relevant concentrations of propofol, the nanodomains trap GPI-mGFP more strongly. This is only observed at 37C. By inhibiting myosin activity or actin filaments (de-)polymerization, we find that the activity of actin filaments further alters the behavior of cholesterol nanodomains due to propofol. We compare the effect of propofol and its analog confirming specificity. [Preview Abstract] |
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