Calibrating optical tweezers with Bayesian inference.

We present a new method for calibrating an optical-tweezer setup that does not depend on input parameters and is less affected by systematic errors like drift of the setup. It is based on an inference approach that uses Bayesian probability to infer the diffusion coefficient and the potential felt by a bead trapped in an optical or magnetic trap. It exploits a much larger amount of the information stored in the recorded bead trajectory than standard calibration approaches. We demonstrate that this method outperforms the equipartition method and the power-spectrum method in input information required (bead radius and trajectory length) and in output accuracy.

[1]  Cees Dekker,et al.  Recent advances in magnetic tweezers. , 2012, Annual review of biophysics.

[2]  Christoph F. Schmidt,et al.  Moving into the cell: single-molecule studies of molecular motors in complex environments , 2011, Nature Reviews Molecular Cell Biology.

[3]  M. Rief,et al.  Auto- and cross-power spectral analysis of dual trap optical tweezer experiments using Bayesian inference. , 2012, The Review of scientific instruments.

[4]  Mincheng Zhong,et al.  Trapping red blood cells in living animals using optical tweezers , 2013, Nature Communications.

[5]  Jonathan Leach,et al.  Aberration correction in holographic optical tweezers. , 2006, Optics express.

[6]  H. Flyvbjerg,et al.  Power spectrum analysis for optical tweezers , 2004 .

[7]  Shenghua Xu,et al.  Axial deviation of an optically trapped particle in trapping force calibration using the drag force method , 2007 .

[8]  Andrew C. Richardson,et al.  Non-harmonic potential of a single beam optical trap. , 2008, Optics express.

[9]  Yoh Ogawa,et al.  Optical trapping of small particles using a 1.3-μm compact InGaAsP diode laser , 1991 .

[10]  Jaime Castillo-León,et al.  Micro and nano techniques for the handling of biological samples , 2011 .

[11]  A. Sasso,et al.  Measurements of trapping efficiency and stiffness in optical tweezers , 2002 .

[12]  H. Flyvbjerg,et al.  MatLab program for precision calibration of optical tweezers , 2004 .

[13]  Jonathan Leach,et al.  Aberration correction in holographic optical tweezers. , 2006, Optics express.

[14]  C. Bustamante,et al.  Overstretching B-DNA: The Elastic Response of Individual Double-Stranded and Single-Stranded DNA Molecules , 1996, Science.

[15]  Michelle D. Wang,et al.  Stretching DNA with optical tweezers. , 1997, Biophysical journal.

[16]  Maximilian U. Richly,et al.  Probing Membrane Protein Interactions with Their Lipid Raft Environment Using Single-Molecule Tracking and Bayesian Inference Analysis , 2013, PloS one.

[18]  Charlie Gosse,et al.  Magnetic tweezers: micromanipulation and force measurement at the molecular level. , 2002, Biophysical journal.

[19]  Matthias Rief,et al.  Myosin-V is a processive actin-based motor , 1999, Nature.

[20]  S. Chu,et al.  Observation of a single-beam gradient force optical trap for dielectric particles. , 1986, Optics letters.

[21]  E. Peterman,et al.  Quantifying how DNA stretches, melts and changes twist under tension , 2011 .

[22]  Kirstine Berg-Sørensen,et al.  tweezercalib 2.0: Faster version of MatLab package for precise calibration of optical tweezers , 2006, Comput. Phys. Commun..

[23]  Christoph F Schmidt,et al.  Optical trap stiffness in the presence and absence of spherical aberrations. , 2006, Applied optics.

[24]  S. Smith,et al.  Folding-unfolding transitions in single titin molecules characterized with laser tweezers. , 1997, Science.

[25]  Alessandro Borgia,et al.  Single-molecule studies of protein folding. , 2008, Annual review of biochemistry.

[26]  Jonathon Howard,et al.  Surface forces and drag coefficients of microspheres near a plane surface measured with optical tweezers. , 2007, Langmuir : the ACS journal of surfaces and colloids.

[27]  S. Block,et al.  Construction of multiple-beam optical traps with nanometer-resolution position sensing , 1996 .

[28]  Carlos Bustamante,et al.  Recent advances in optical tweezers. , 2008, Annual review of biochemistry.

[29]  Jean-Baptiste Masson,et al.  A Bayesian inference scheme to extract diffusivity and potential fields from confined single-molecule trajectories. , 2012, Biophysical journal.

[30]  Andrew C. Richardson,et al.  Quantitative determination of optical trapping strength and viscoelastic moduli inside living cells , 2013, Physical biology.

[31]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[32]  D. Grier.  A revolution in optical manipulation , 2003, Nature.

[33]  V. Lubchenko,et al.  The use of dynamic light scattering and brownian microscopy to characterize protein aggregation. , 2011, The Review of scientific instruments.

[34]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[35]  Carlos Bustamante,et al.  Direct Observation of the Three-State Folding of a Single Protein Molecule , 2005, Science.

[36]  K. Svoboda,et al.  Biological applications of optical forces. , 1994, Annual review of biophysics and biomolecular structure.

[37]  M Vergassola,et al.  Inferring maps of forces inside cell membrane microdomains. , 2009, Physical review letters.

[38]  Thierry Gacoin,et al.  Observing the confinement potential of bacterial pore-forming toxin receptors inside rafts with nonblinking Eu(3+)-doped oxide nanoparticles. , 2012, Biophysical journal.