Outline Detecting Response at the Cellular Level • Introduction and Motivation Diego Rubén Barrettino, Ph.D. Senior Research Scientist Integrated Systems Laboratory École Polytechnique Fédéral de Lausanne (EPFL) E-mail: diego.barrettino@ep fl .ch General Strategy for Systems Biology “New directions in science are launched by new tools much more often than by new concepts.” BIOLOGY “The effect of a concept-driven revolution is to explain old things in new ways.” “The effect of a tool-driven revolution is to COMPUTATION TECHNOLOGY discover new things that have to be explained.” Freeman Dyson, Imagined Worlds Courtesy of Lee Hood, Institute for Systems Biology, Seattle, USA Courtesy of Lee Hood, Institute for Systems Biology, Seattle, USA Single Cell Facts Why Single Cells? Typical mammalian cell Averaged populations do not distinguish between these two very different cases 10- � m diameter 500 fL volume 350 pg water 75 pg protein 2 fmol total protein • Understanding cellular response mechanisms requires ~200 zmol (10 -21 mol) average protein measurements at the single cell level. • Gene expression occurs within cells. • Increasing recognition of cellular heterogeneity in populations. 1
Challenges of Single-Cell Outline Measurements • Contain a single cell in a device comparable to it’s • Technology own volume. • Automatically array and isolate hundreds of individual cells. • Selectively deliver reagents to each cell. • Detect nucleic acids and proteins from each cell. High-Throughput Biology Tools for understanding the interconnected pathways from genes to complex cellular processes Miniaturization • Passive measurements provide genome-level expression information, e.g., Parallelization – Microarrays – 2D protein gels Integration Automation • Dynamic measurements provide temporal and spatial patterns of expression under speci fi c metabolic response regimes, e.g., – Single cell dynamic analyses with fl uorescent reporters (e.g. GFP Technologies: variants) – New methods and instruments are under development. • Micro fl uidics (devices to do chemistry on the nanoliter scale) • Microelectronics • Microtechnology (MEMS) Flow Cytometry Flow Cytometry Three Stages: – Fluidics Control: Positioning of cell sample stream by hydrodynamic or De fi nition: A technique of rapidly measuring physical electrokinetic focusing and chemical characteristics of cells as they – Optical Detection: Analysis of scattering effects and fl uorescence fl ow in single fi le through a sensing region. emitted after illumination by light beam – Cell Sorting: Aerosol droplet sorting using electrokinetics 2
Microfabricated Flow Cytometer Single-Cell Studies Measurement Procedure 1. Manipulation (Trapping). Figure from Microfabrication Lab, 580.495 Movie clip: http://www.ece.jhu.edu/faculty/andreou/495/ Optical Trapping Single-Cell Manipulation • Optical trapping (Optical Tweezers ). � Ashkin et al. discovered optical trapping in 1969 when he found • Dielectrophoresis (DEP) trapping. he could manipulate biological particles using infrared (IR) light. � � The principle of optical trapping utilizes the property of radiation • Magnetic trapping. pressure which involves focusing one or more laser beams on a particle and thereby trapping it. • Hydrodynamic trapping. � Fig. 1 shows manipulation of microscopic particles using tweezers Figure 1: Manipulation of yeast cells • Structural trapping. DEP Trapping Optical Tweezer Setup Moving Neutral Particles: Dielectrophoresis Modern Optical Tweezers: In practice, optical tweezers are very expensive, custom-built instruments. High power infrared laser beams are often used to achieve high trapping stiffness with minimal photo-damage to biological samples. Precise steering of the optical trap is accomplished with lenses, mirrors, and acousto/electro-optical devices that can be controlled via computer. Fig. 5 is meant to give an idea of the number of elements in such a system. In short, optical tweezers require a working knowledge of microscopy, optics, and laser techniques. Modern Optical tweezers setup N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. 3
DEP Trapping DEP Trapping N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. DEP Trapping DEP Trapping N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. DEP Trapping - Applications Magnetic Trapping Magnetic bead Bead-bound cell N. Manaresi, et al., IEEE Journal of Solid-State Circuits, Vol. 38, Issue 12, pp. 2297 - 2305, 2003. 4
Magnetic Trapping Magnetic Trapping • Cell Preparation Magnetic bead Cell B Cells with engulfed magnetic beads Microcoils H. Lee, et al. , Applied Physics Letters, Vol. 85, pp. 1063-1065, 2004. Magnetic Trapping Magnetic Trapping Magnetic field magnitude on the surface H. Lee, et al. , Applied Physics Letters, Vol. 85, pp. 1063-1065, 2004. H. Lee, et al. , Applied Physics Letters, Vol. 85, pp. 1063-1065, 2004. Magnetic Trapping Magnetic Trapping Microcoil arrays Control electronics Control electronics Chip size 1 mm � 4 mm Microcoil array Control electronics H. Lee, et al. , Applied Physics Letters, Vol. 85, pp. 1063-1065, 2004. 5
Hydrodynamic Trapping Magnetic Trapping steady flow fluid oscillation cylinders: 100 μ m diameter fluid oscillation microspheres: + 20 μ m diameter steady flow 100 μ m Structural Trapping Single-Cell Studies Measurement Procedure 2. Sensing (Biosensing). A. Wheeler, et al., Analytical Chemistry, Vol. 75, pp. 3581-3586, 2003 Biosensors Biosensors Some applications for biosensors… Success story: Glucose Monitoring Data From a drop of blood Implantable Interrogate INFORMATION Capture Condition OUTPUT SAMPLE Amplifify - Storage Reduce - Handling Process - Preparation Store BIOTRANSDUCER - Delivery SIGNAL PROCESSING www.cozmore.com 6
Biosensors Biosensors Classification of Biosensors: For example… • Biosensors based on the type of biorecognition layer • Biosensors based on the type of biorecognition layer used in the biotransducer. used in the biotransducer - Enzyme (glucose oxidase), DNA/RNA (gene), organelle - Enzyme (glucose oxidase) (mitochondria), whole-cell, tissue slice (liver, heart). • Biosensors based on electrochemical (redox reactions) • Biosensors based on the nature of the solid-state transducer. - Conductimetric (change in electrical conductivity) - Optical (light), electrochemical (redox reactions), electronic (I/V characteristics), gravimetric (mass), pyroelectric (heat), • Biosensor based on placement piezoelectric (force-voltage) - In-vitro • Biosensors based on the placement. - In-vivo , in-vitro , etc. An enzyme-conductimetric biosensor for glucose Automated 2-D electrophoresis Single-Cell Studies Comprehensive capillary electrophoresis Measurement Procedure MW samples taken at junction for separation micellar electrophoresis in capillary 2 molecular weight separation separation Detector Capillary 1 Capillary 2 3. Proteomics. Power-supply 1 Power-supply 2 Automated 2-D electrophoresis Automated 2-D electrophoresis Comprehensive capillary electrophoresis Measurements Interface Capillary 1 Capillary 2 7
Cell Modeling and Simulation Outline • Modeling and Simulation Virtual Cell Virtual Cell • Developed by the National Resource for Cell Analysis and Modeling supported by the National Center for Research Resources (NCRR), at the The software is composed of three main components: National Institutes of Health (NIH) 1. Modeling Framework 2. Mathematics Framework 3. WWW Interface-Biological Oriented Interface with Integrated Math Editor Different approaches are used: 1. Ordinary differential equations (ODE), 2. � -calculus formal language, 3. Hybrid functional Petri nets (HFPN), etc . http://www.nrcam.uchc.edu/ http://www.nrcam.uchc.edu/ E-Cell E-Cell A model of a hypothetical, minimal cell, based on the gene set of Mycoplasma genitalium , the self-replicating organism having the smallest known genome was constructed. Its gene set was reduced to only those • E-CELL is a modeling and simulation environment for genes that are required for what was defined as a minimal cellular metabolism. simulation with GUI, based on ODE. • Biochemical reactions are represented as a systems of ODEs. • For reactions which cannot be represented with ODEs, it employs ad-hoc user defined C++ programs. http://www.e-cell.org http://www.e-cell.org 8
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