Literature DB >> 28416035

Measuring the Autocorrelation Function of Nanoscale Three-Dimensional Density Distribution in Individual Cells Using Scanning Transmission Electron Microscopy, Atomic Force Microscopy, and a New Deconvolution Algorithm.

Yue Li1, Di Zhang2, Ilker Capoglu2, Karl A Hujsak3, Dhwanil Damania2, Lusik Cherkezyan2, Eric Roth3, Reiner Bleher3, Jinsong S Wu3, Hariharan Subramanian2, Vinayak P Dravid3, Vadim Backman2.   

Abstract

Essentially all biological processes are highly dependent on the nanoscale architecture of the cellular components where these processes take place. Statistical measures, such as the autocorrelation function (ACF) of the three-dimensional (3D) mass-density distribution, are widely used to characterize cellular nanostructure. However, conventional methods of reconstruction of the deterministic 3D mass-density distribution, from which these statistical measures can be calculated, have been inadequate for thick biological structures, such as whole cells, due to the conflict between the need for nanoscale resolution and its inverse relationship with thickness after conventional tomographic reconstruction. To tackle the problem, we have developed a robust method to calculate the ACF of the 3D mass-density distribution without tomography. Assuming the biological mass distribution is isotropic, our method allows for accurate statistical characterization of the 3D mass-density distribution by ACF with two data sets: a single projection image by scanning transmission electron microscopy and a thickness map by atomic force microscopy. Here we present validation of the ACF reconstruction algorithm, as well as its application to calculate the statistics of the 3D distribution of mass-density in a region containing the nucleus of an entire mammalian cell. This method may provide important insights into architectural changes that accompany cellular processes.

Entities:  

Keywords:  3D autocorrelation function; AFM; STEM; algorithm; cellular mass–density distribution

Mesh:

Year:  2017        PMID: 28416035      PMCID: PMC5790320          DOI: 10.1017/S1431927617000447

Source DB:  PubMed          Journal:  Microsc Microanal        ISSN: 1431-9276            Impact factor:   4.127


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