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Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

ABSTRACT: Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

SUBMITTER: Glaser JI

PROVIDER: S-EPMC4507868 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

Glaser Joshua I JI Zamft Bradley M BM Church George M GM Kording Konrad P KP

PloS one 20150720 7

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical c ...[more]

PMID: 26192446

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