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

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

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.

No MeSH data available.


Chemical Puzzling Performance.(A) The chemical concentration across the plate. It is described by the letter “P,” with the concentration decreasing moving outwards from the center, and a constant background concentration. (B) A simulation is done with an initial cell density of 1%. (C) A simulation is done with an initial cell density of 0.1%. For panels B and C, the top row shows the initial locations of the pioneer cells. They are color-coded by location. The second row shows example reconstructed locations of the pioneer cells. The third row shows the reconstructed chemical concentrations when 50 base pairs are used to detect the concentration. The bottom row shows the reconstructed chemical concentrations when 2 base pairs are used to detect the concentration. Note that the black border represents regions of unknown concentration.
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pone.0131593.g007: Chemical Puzzling Performance.(A) The chemical concentration across the plate. It is described by the letter “P,” with the concentration decreasing moving outwards from the center, and a constant background concentration. (B) A simulation is done with an initial cell density of 1%. (C) A simulation is done with an initial cell density of 0.1%. For panels B and C, the top row shows the initial locations of the pioneer cells. They are color-coded by location. The second row shows example reconstructed locations of the pioneer cells. The third row shows the reconstructed chemical concentrations when 50 base pairs are used to detect the concentration. The bottom row shows the reconstructed chemical concentrations when 2 base pairs are used to detect the concentration. Note that the black border represents regions of unknown concentration.

Mentions: Performance. To further demonstrate the potential for chemical puzzling, we performed a simulation of the chemical puzzling problem. We used a complex chemical concentration described by the letter “P” (for Puzzle Imaging), with the concentration also decreasing when moving outwards from the center, and a constant background concentration (Fig 7A). The image size is 1000 x 1000 pixels, and each pixel is 1 μm2 (about the size of a cell [25]). The corresponding size of the letter, then, is about 600 μm x 800 μm. Each pioneer cell is randomly placed on a single pixel.


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

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Chemical Puzzling Performance.(A) The chemical concentration across the plate. It is described by the letter “P,” with the concentration decreasing moving outwards from the center, and a constant background concentration. (B) A simulation is done with an initial cell density of 1%. (C) A simulation is done with an initial cell density of 0.1%. For panels B and C, the top row shows the initial locations of the pioneer cells. They are color-coded by location. The second row shows example reconstructed locations of the pioneer cells. The third row shows the reconstructed chemical concentrations when 50 base pairs are used to detect the concentration. The bottom row shows the reconstructed chemical concentrations when 2 base pairs are used to detect the concentration. Note that the black border represents regions of unknown concentration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g007: Chemical Puzzling Performance.(A) The chemical concentration across the plate. It is described by the letter “P,” with the concentration decreasing moving outwards from the center, and a constant background concentration. (B) A simulation is done with an initial cell density of 1%. (C) A simulation is done with an initial cell density of 0.1%. For panels B and C, the top row shows the initial locations of the pioneer cells. They are color-coded by location. The second row shows example reconstructed locations of the pioneer cells. The third row shows the reconstructed chemical concentrations when 50 base pairs are used to detect the concentration. The bottom row shows the reconstructed chemical concentrations when 2 base pairs are used to detect the concentration. Note that the black border represents regions of unknown concentration.
Mentions: Performance. To further demonstrate the potential for chemical puzzling, we performed a simulation of the chemical puzzling problem. We used a complex chemical concentration described by the letter “P” (for Puzzle Imaging), with the concentration also decreasing when moving outwards from the center, and a constant background concentration (Fig 7A). The image size is 1000 x 1000 pixels, and each pixel is 1 μm2 (about the size of a cell [25]). The corresponding size of the letter, then, is about 600 μm x 800 μm. Each pioneer cell is randomly placed on a single pixel.

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

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.

No MeSH data available.