Limits...
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.


Neural Voxel Puzzling Overview.(A) An example of 6 “neurons” (lines) going through 4 voxels. Each neuron has a unique DNA barcode (here color). (B) These voxels are broken apart. (C) A coincidence matrix, X, is constructed describing which neurons are in which voxels. Gray signifies that a neuron is in a particular voxel. (D) A similarity matrix is constructed describing how many neurons a pair of voxels has in common. This matrix can be calculated as XXT. (E) The voxels are puzzled back together. The reconstruction may be rotated or flipped, as shown here.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g002: Neural Voxel Puzzling Overview.(A) An example of 6 “neurons” (lines) going through 4 voxels. Each neuron has a unique DNA barcode (here color). (B) These voxels are broken apart. (C) A coincidence matrix, X, is constructed describing which neurons are in which voxels. Gray signifies that a neuron is in a particular voxel. (D) A similarity matrix is constructed describing how many neurons a pair of voxels has in common. This matrix can be calculated as XXT. (E) The voxels are puzzled back together. The reconstruction may be rotated or flipped, as shown here.

Mentions: The first step in voxel puzzling is to label each neuron in the brain with a unique DNA or RNA barcode throughout its entire length (Fig 2A). Ongoing research aims to tackle this challenge [2, 11, 12]. Recently, researchers have succeeded in having bacteria generate a large diversity of barcodes in vivo [11]. Next, the brain is shattered into many voxels (Fig 2B; note that voxels are only squares for simplification), and the DNA in each voxel is sequenced, yielding a record of which neurons were in which voxels. This provides us with relative spatial information about voxel placement: voxels that share more neurons will likely be closer to each other. We can use this relative spatial information to puzzle the voxels into their correct locations.


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

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

Neural Voxel Puzzling Overview.(A) An example of 6 “neurons” (lines) going through 4 voxels. Each neuron has a unique DNA barcode (here color). (B) These voxels are broken apart. (C) A coincidence matrix, X, is constructed describing which neurons are in which voxels. Gray signifies that a neuron is in a particular voxel. (D) A similarity matrix is constructed describing how many neurons a pair of voxels has in common. This matrix can be calculated as XXT. (E) The voxels are puzzled back together. The reconstruction may be rotated or flipped, as shown here.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131593.g002: Neural Voxel Puzzling Overview.(A) An example of 6 “neurons” (lines) going through 4 voxels. Each neuron has a unique DNA barcode (here color). (B) These voxels are broken apart. (C) A coincidence matrix, X, is constructed describing which neurons are in which voxels. Gray signifies that a neuron is in a particular voxel. (D) A similarity matrix is constructed describing how many neurons a pair of voxels has in common. This matrix can be calculated as XXT. (E) The voxels are puzzled back together. The reconstruction may be rotated or flipped, as shown here.
Mentions: The first step in voxel puzzling is to label each neuron in the brain with a unique DNA or RNA barcode throughout its entire length (Fig 2A). Ongoing research aims to tackle this challenge [2, 11, 12]. Recently, researchers have succeeded in having bacteria generate a large diversity of barcodes in vivo [11]. Next, the brain is shattered into many voxels (Fig 2B; note that voxels are only squares for simplification), and the DNA in each voxel is sequenced, yielding a record of which neurons were in which voxels. This provides us with relative spatial information about voxel placement: voxels that share more neurons will likely be closer to each other. We can use this relative spatial information to puzzle the voxels into their correct locations.

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.