Single Molecule Imaging and Tracking for High-Throughput Screening Single Molecule Imaging and Tracking for High-Throughput Screening Greg Bashford Dept. of Biological Systems Engineering

Outline  Proposal overview and goals  NIH review  Recent progress

HTS and Drug Discovery  High-throughput screening (HTS) methods have been an area of growing interest for the discovery and characterization of new drugs.  The development rate of new pharmaceutical compounds in recent years has greatly accelerated.  Thus, there is a large backlog of potential compounds needing to be screened for their therapeutic potential.  Therefore, an obvious need exists for developing new and improved HTS techniques to mitigate this backlog.

Goals and Objectives  Long-term goal: Create novel (and accelerate conventional) rapid bioanalysis methods by capitalizing on image analysis Create novel (and accelerate conventional) rapid bioanalysis methods by capitalizing on image analysis  Objective of this application: Use computer modeling to determine the expected effects of using single molecule imaging and tracking for applications such as HTS for pharmaceuticals Use computer modeling to determine the expected effects of using single molecule imaging and tracking for applications such as HTS for pharmaceuticals

Background  Fluorescence Correlation Spectroscopy Pictures from Stowers Institute for Medical Research The size of the compound affects its diffusion coefficient Binding is detected by a larger compound size

An “Extension” of FCS  Instead of point detection – image over a larger field of view Laser waist Imaging area Microfluidics flowcell Multiple observations of single molecules are made simultaneously

Single Particle Tracking (SPT)  Molecules are tracked across multiple image frames  Assumption: within each frame, any particle doesn’t move “much” (else blurring) Frame 1 Frame 2 Frame 3

In Contrast to SPT…  Molecules are driven through the field of view by forced flow Pressure-driven, EOF Pressure-driven, EOF  Molecules move “fast” with respect to one image integration time  Results in blurring, or a particle “streak” Horizontal: diffusion Horizontal: diffusion Hypothesis: we can back-calculate diffusion information from the image streak Forced flow

A Computer Simulation of SMD/SMI Obj CCD Detector em Flow: Pressure, Eph, EOF Through-objective TIR Molecule transport Flowcell interaction Photophysics Optics CCD Detection Noise

Specific Aim 1  Refine and optimize a computer model of single fluorescent molecules imaged within a microfluidics flowcell To add: To add: Molecule adsorption to flowcell wallMolecule adsorption to flowcell wall TIR intensity enhancementTIR intensity enhancement BlinkingBlinking Readout blurReadout blur Updated objective, camera specificationsUpdated objective, camera specifications Compare with model system – DNA/SfiI complex Compare with model system – DNA/SfiI complex

Specific Aim 2  Develop image-analysis based algorithms for measuring molecular interactions of ligand-receptor pairs from CCD images First, determine the “best” way to measure diffusion First, determine the “best” way to measure diffusion

Specific Aim 2  Develop image-analysis based algorithms for measuring molecular interactions of ligand-receptor pairs from CCD images Next, determine how best to discriminate between species of differing diffusion Next, determine how best to discriminate between species of differing diffusion

Specific Aim 2  Develop image-analysis based algorithms for measuring molecular interactions of ligand-receptor pairs from CCD images Finally, determine the limits of measuring diffusion Finally, determine the limits of measuring diffusion

Specific Aim 2  Also, test the limits of feature identification How many molecules visible in this frame? How many molecules visible in this frame?

Specific Aim 3  Develop protocols in which the algorithms from Specific Aim 2 can be used to increase throughput and information content (compared to FCS) For example: Consider a sample composed of a mixture of two different types of single molecules that have different diffusion constants - the goal of the measurement is to determine the fraction of each species For example: Consider a sample composed of a mixture of two different types of single molecules that have different diffusion constants - the goal of the measurement is to determine the fraction of each species

Specific Aim 3  Develop protocols in which the algorithms from Specific Aim 2 can be used to increase throughput and information content (compared to FCS) Parameters to study: Parameters to study: Bulk flowBulk flow Ratio of diffusion coefficientsRatio of diffusion coefficients Concentration ratio of two speciesConcentration ratio of two species Number of frames used in analysisNumber of frames used in analysis

NIH Review  Significance: “This project, if successful, could greatly increase the rate of high-throughput screening and improve its efficiency and potentially its success rate … The techniques could also have broader applications for the study of the interaction of ligands with intact cells.”  Innovation: “This is a very innovative approach that will build a model for single molecule imaging that can improve screening and analysis of molecular interactions.”  Investigator: “The project investigator is highly skilled and has the resources to complete this project.”  Environment: “The environment is excellent, with a good mentoring program and the resources to perform the development. There is good complementarity to other COBRE projects.”  Section Score: Outstanding (No changes recommended)

Current Work  Starting on Specific Aim 1 (refine model)  Hosted visit from single-molecule detection consultant (Dr. Lloyd Davis) Visited with Dr. Lyubchenko at UNMC Visited with Dr. Lyubchenko at UNMC  Goals for Spring 2009 Incorporate changes to model to allow for diffusion measurement Incorporate changes to model to allow for diffusion measurement Submit publication with Dr. Davis Submit publication with Dr. Davis