Information optimization of laser scanning microscopes for real-time feedback-driven single particle tracking.

Real-time feedback-driven single particle tracking (RT-FD-SPT) is a class of microscopy techniques that uses measurements of finite excitation/detection volume in a feedback control loop to actuate that volume and track with high spatio-temporal resolution a single particle moving in three dimensions. A variety of methods have been developed, each defined by a set of user-defined choices. Selection of those values is typically done through ad hoc, off-line tuning for the best perceived performance.

Quantitative nanopatterning of fg-scale liquids with dip-pen nanolithography.

The ability to precisely pattern nanoscale amounts of liquids is essential for biotechnology and high-throughput chemistry, but controlling fluid flow on these scales is very challenging. Scanning probe lithography methods such as dip-pen nanolithography (DPN) provide a mechanism to write fluids at the nanoscale, but this is an open loop process as methods to provide feedback while patterning sub-pg features have yet to be reported. Here, we demonstrate a novel method for programmably nanopatterning liquid features at the fg-scale through a combination of ultrafast atomic force microscopy probes, the use of spherical tips, and inertial mass sensing.

Synthetic Stochastic Motion Platform for Testing Single Particle Tracking Microscopes.

We describe the design and implementation of a control system for testing the performance of single particle tracking microscopes with the method of synthetic motion. Single particle tracking (SPT) has become a common and powerful tool in the study of biomolecular transport in cellular biology, providing the ability to track individual biological macromolecules in their native environment. Existing methods for testing SPT techniques rely on physical simulations and there is a clear need for experimental-based schemes for both comparing different approaches and for characterizing the accuracy and precision of techniques on particular experimental setups.

Real-Time Feedback-Driven Single-Particle Tracking: A Survey and Perspective.

Real-time feedback-driven single-particle tracking (RT-FD-SPT) is a class of techniques in the field of single-particle tracking that uses feedback control to keep a particle of interest in a detection volume. These methods provide high spatiotemporal resolution on particle dynamics and allow for concurrent spectroscopic measurements. This review article begins with a survey of existing techniques and of applications where RT-FD-SPT has played an important role.

Analysis of an Extremum Seeking Controller Under Bounded Disturbance.

One of the applications of Extremum Seeking (ES) is to localize the source of a scalar field by using a mobile agent that can measure this field at its current location. While the scientific literature has presented many approaches to this problem, a formal analysis of the behavior of ES controllers for source seeking in the presence of disturbances is still lacking. This paper aims to fill this gap by analyzing a specific version of an ES control algorithm in the presence of source movement and measurement disturbances.

Computationally efficient application of Sequential Monte Carlo expectation maximization to confined single particle tracking.

Single Particle Tracking (SPT) plays a crucial role in biophysics through its ability to reveal dynamic mechanisms and physical properties of biological macromolecules moving inside living cells. Such molecules are often subject to confinement and important information can be revealed by understanding the mobility of the molecules and the size of the domain they are restricted to. In previous work, we introduced a method known as Sequential Monte Carlo-Expectation Maximization (SMC-EM) to simultaneously estimate particle trajectories and model parameters.

Three-dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions.

Confined diffusion is an important model for describing the motion of biological macromolecules moving in the crowded, three-dimensional environment of the cell. In this work we build upon the technique known as sequential Monte Carlo - expectation maximization (SMC-EM) to simultaneously localize the particle and estimate the motion model parameters from single particle tracking data. We extend SMC-EM to handle the double-helix point spread function (DH-PSF) for encoding the three-dimensional position of the particle in the two-dimensional image plane of the camera.

EM-based algorithms for single particle tracking of Ornstein-Uhlenbeck motion from sCMOS camera data.

Single particle tracking plays an important role in studying physical and kinetic properties of biomolecules. In this work, we introduce the application of Expectation Maximization (EM) based algorithms for solving localization and parameter estimation problems in SPT using data captured from scientific complementary metal-oxide semiconductor (sCMOS) camera sensors. Two representative methods are considered for generating the filtered and smoothed distributions needed by EM: Sequential Monte Carlo - EM, and Unscented - EM.

Tracking Multiple Diffusing Particles Using Information Optimal Control.

We study the problem of tracking multiple diffusing particles using a laser scanning fluorescence microscope. The goal is to design trajectories for the laser to maximize the information contained in the measured intensity signal about the particles' trajectories. Our approach consists of a two level scheme: in the lower level we use an extremum seeking controller to track a single particle by first seeking it then orbiting around it.

Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images.

Single Particle Tracking (SPT) is a well known class of tools for studying the dynamics of biological macromolecules moving inside living cells. In this paper, we focus on the problem of localization and parameter estimation given a sequence of segmented images. In the standard paradigm, the location of the emitter inside each frame of a sequence of camera images is estimated using, for example, Gaussian fitting (GF), and these locations are linked to provide an estimate of the trajectory.

Closed-Loop Nanopatterning of Liquids with Dip-Pen Nanolithography.

The ability to reliably manipulate small quantities of liquids is the backbone of high-throughput chemistry, but the continual drive for miniaturization necessitates creativity in how nanoscale samples of liquids are handled. Here, we describe a closed-loop method for patterning liquid samples on pL to sub-fL scales using scanning probe lithography. Specifically, we employ tipless scanning probes and identify liquid properties that enable probe-sample transport that is readily tuned using probe withdrawal speed.

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters.

Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment.

Model Segmentation in Single Particle Tracking.

In this paper, we implement and compare two different change detection techniques applied to determining the time points in Single Particle Tracking (SPT) data where the particle changes the dynamic model of motion. The goal is to use this change detection to segment the data in order to estimate the relevant parameters of such models. We consider two well-known statistics commonly used for change detection: the likelihood ratio test (LRT) and the Kullback-Leibler divergence (KLD).

Information Optimal Control for Single Particle Tracking Microscopy.

We consider the problem of designing a control policy for a laser scanning microscope (LSM) which will minimize the estimation uncertainty when identifying the state and motion model of a fluorescent biological particle. Using the information optimal design framework we pose an optimization problem which seeks to maximize the Fisher information of the particle's state. We then apply optimal control methods to determine the laser trajectory that maximizes a criterion based on the Fisher information.

Joint Estimation of Trajectory and Model Parameters for Single Particle Tracking of 3D Confined Diffusion Using the Double-Helix Point Spread Function.

Single particle tracking plays a significant role in biophysics through its ability to reveal dynamic mechanisms and physical properties of biological macromolecules inside living cells. The motion of these molecules can often be modeled as a confined diffusion. The standard paradigm in the biophysics community is to first estimate the trajectory of a particle and then use a technique such as the Mean Square Displacement or the Maximum Likelihood Estimation (MLE) to determine model parameters.

High-resolution measurement of atomic force microscope cantilever resonance frequency.

The atomic force microscope (AFM) is widely used in a wide range of applications due to its high scanning resolution and diverse scanning modes. In many applications, there is a need for accurate and precise measurement of the vibrational resonance frequency of a cantilever. These frequency shifts can be related to changes in mass of the cantilever arising from, e.

Monte Carlo Simulation of Brownian Motion using a Piezo-Actuated Microscope Stage.

Single particle tracking is a powerful tool for studying and understanding the motions of biological macromolecules integral to cellular processes. In the past three decades there has been continuous and rapid development of these techniques in both optical microscope design and in algorithms to estimate the statistics and positions of the molecule's trajectory. Although there has been great progress, comparison between different microscope configurations and estimation algorithms has been difficult beyond simulated data.

Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM.

Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop an algorithm for jointly estimating both particle trajectory and motion model parameters from the data.

A time-varying approach to single particle tracking with a nonlinear observation model.

Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop a local time-varying estimation algorithm for estimating motion model parameters from the data considering nonlinear observations.

Estimation of general time-varying single particle tracking linear models using local likelihood.

In this work, we study a general approach to the estimation of single particle tracking models with time-varying parameters. The main idea is to use local Maximum Likelihood (ML), applying a sliding window over the data and estimating the model parameters in each window. We combine local ML with Expectation Maximization to iteratively find the ML estimate in each window, an approach that is amenable to generalization to nonlinear models.

Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is that of accurate image reconstruction from a limited number of measurements. In this work, we present a deep neural network (DNN) approach to reconstruct μ-path sub-sampled AFM images.

Feedforward Control for Single Particle Tracking Synthetic Motion.

Single particle tracking (SPT) is a method to study the transport of biomolecules with nanometer resolution. Unfortunately, recent reports show that systematic errors in position localization and uncertainty in model parameter estimates limits the utility of these techniques in studying biological processes. There is a need for an experimental method with a known ground-truth that tests the total SPT system (sample, microscope, algorithm) on both localization and estimation of model parameters.

OBSERVER-BASED CONTROL OF A DUAL-STAGE PIEZOELECTRIC SCANNER.

Despite its proven success in a wide variety of applications, the atomic force microscope (AFM) remains limited by its slow imaging rate. One approach to overcome this challenge is to rely on algorithmic approaches that reduce the imaging time not by scanning faster but by scanning less. Such schemes are particularly useful on older instruments as they can provide significant gains despite the existing (slow) hardware.