Single molecule transcription profiling with AFM.

Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA 90095, USA.
Nanotechnology (Impact Factor: 3.67). 05/2007; 18(4):44032. DOI: 10.1088/0957-4484/18/4/044032
Source: PubMed

ABSTRACT Established techniques for global gene expression profiling, such as microarrays, face fundamental sensitivity constraints. Due to greatly increasing interest in examining minute samples from micro-dissected tissues, including single cells, unorthodox approaches, including molecular nanotechnologies, are being explored in this application. Here, we examine the use of single molecule, ordered restriction mapping, combined with AFM, to measure gene transcription levels from very low abundance samples. We frame the problem mathematically, using coding theory, and present an analysis of the critical error sources that may serve as a guide to designing future studies. We follow with experiments detailing the construction of high density, single molecule, ordered restriction maps from plasmids and from cDNA molecules, using two different enzymes, a result not previously reported. We discuss these results in the context of our calculations.

1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a method for the characterization of the local intrinsic curvature of adsorbed DNA molecules. It relies on a novel statistical chain descriptor, namely the ensemble averaged product of curvatures for two nanosized segments, symmetrically placed on the contour of atomic force microscopy imaged chains. We demonstrate by theoretical arguments and experimental investigation of representative samples that the fine mapping of the average product along the molecular backbone generates a characteristic pattern of variation that effectively highlights all pairs of DNA tracts with large intrinsic curvature. The centrosymmetric character of the chain descriptor enables targetting strands with unknown orientation. This overcomes a remarkable limitation of the current experimental strategies that estimate curvature maps solely from the trajectories of end-labeled molecules or palindromes. As a consequence our approach paves the way for a reliable, unbiased, label-free comparative analysis of bent duplexes, aimed to detect local conformational changes of physical or biological relevance in large sample numbers. Notably, such an assay is virtually inaccessible to the automated intrinsic curvature computation algorithms proposed so far. We foresee several challenging applications, including the validation of DNA adsorption and bending models by experiments and the discrimination of specimens for genetic screening purposes.
    Nucleic Acids Research 03/2012; 40(11):e84. DOI:10.1093/nar/gks210 · 8.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: There are many examples of problems in pattern analysis for which it is often possible to obtain systematic characterizations, if in addition a small number of useful features or parameters of the image are known a priori or can be estimated reasonably well. Often the relevant features of a particular pattern analysis problem are easy to enumerate, as when statistical structures of the patterns are well understood from the knowledge of the domain. We study a problem from molecular image analysis, where such a domain-dependent understanding may be lacking to some degree and the features must be inferred via machine-learning techniques. In this paper, we propose a rigorous, fully-automated technique for this problem. We are motivated by an application of atomic force microscopy (AFM) image processing needed to solve a central problem in molecular biology, aimed at obtaining the complete transcription profile of a single cell, a snapshot that shows which genes are being expressed and to what degree. Reed et al (Single molecule transcription profiling with AFM, Nanotechnology, 18:4, 2007) showed the transcription profiling problem reduces to making high-precision measurements of biomolecule backbone lengths, correct to within 20-25 bp (6-7.5 nm). Here we present an image processing and length estimation pipeline using AFM that comes close to achieving these measurement tolerances. In particular, we develop a biased length estimator on trained coefficients of a simple linear regression model, biweighted by a Beaton-Tukey function, whose feature universe is constrained by James-Stein shrinkage to avoid overfitting. In terms of extensibility and addressing the model selection problem, this formulation subsumes the models we studied.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 06/2012; 16(6). DOI:10.1109/TITB.2012.2206819 · 2.07 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Atomic force microscopy (AFM) was invented in 1986 [1]. By using a compliant flexure probe, such as a microcantilever beam with a sharp tip at one end, the interaction forces between atoms on the probe-tip and atoms on the material surface can be measured (see Figure 1). Since its invention, the simple strategy of using a beam with a sharp tip is now being employed to measure many diverse properties of matter at the nanometer scale including electrical, magnetic, chemical, and mechanical properties [2]. Many different operational modes have evolved that have demonstrated the versatility of the basic underlying principle [3]. AFM has led to many seminal insights in science such as obtained in the recent imaging of pentacene molecules with subatomic resolution [4].
    IEEE control systems 12/2013; 33(6):106-118. DOI:10.1109/MCS.2013.2279475 · 3.39 Impact Factor