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 Connectomics Puzzling Overview.(A) An example of 9 connected neurons (circles). Lines signify connections. (B) After the brain is homogenized, the only remaining information is a record of the connections. Connections are shown here as adjacent circles. (C) A connectivity matrix is constructed describing the connections between neurons. Gray signifies a connection. Since connections are correlated with how close neurons are to one another, this connectivity matrix can be treated as the similarity matrix. (D) The neurons are puzzled back together. The formation may be rotated or flipped, as shown here.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131593.g004: Neural Connectomics Puzzling Overview.(A) An example of 9 connected neurons (circles). Lines signify connections. (B) After the brain is homogenized, the only remaining information is a record of the connections. Connections are shown here as adjacent circles. (C) A connectivity matrix is constructed describing the connections between neurons. Gray signifies a connection. Since connections are correlated with how close neurons are to one another, this connectivity matrix can be treated as the similarity matrix. (D) The neurons are puzzled back together. The formation may be rotated or flipped, as shown here.

Mentions: The first steps in connectomics puzzling are to label each neuron in the brain with a unique DNA barcode and label neural connections via the pairing of barcodes (Fig 4A). One proposed method would be to have viruses shuttle the barcodes across synapses, where they can be integrated [2]. Other techniques for creating, pairing, and transporting barcodes have been proposed [10, 16]. Next, the brain is homogenized, and the DNA barcode pairs are sequenced, yielding a record of which neurons are connected (Fig 4B).


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

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

Neural Connectomics Puzzling Overview.(A) An example of 9 connected neurons (circles). Lines signify connections. (B) After the brain is homogenized, the only remaining information is a record of the connections. Connections are shown here as adjacent circles. (C) A connectivity matrix is constructed describing the connections between neurons. Gray signifies a connection. Since connections are correlated with how close neurons are to one another, this connectivity matrix can be treated as the similarity matrix. (D) The neurons are puzzled back together. The formation 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.g004: Neural Connectomics Puzzling Overview.(A) An example of 9 connected neurons (circles). Lines signify connections. (B) After the brain is homogenized, the only remaining information is a record of the connections. Connections are shown here as adjacent circles. (C) A connectivity matrix is constructed describing the connections between neurons. Gray signifies a connection. Since connections are correlated with how close neurons are to one another, this connectivity matrix can be treated as the similarity matrix. (D) The neurons are puzzled back together. The formation may be rotated or flipped, as shown here.
Mentions: The first steps in connectomics puzzling are to label each neuron in the brain with a unique DNA barcode and label neural connections via the pairing of barcodes (Fig 4A). One proposed method would be to have viruses shuttle the barcodes across synapses, where they can be integrated [2]. Other techniques for creating, pairing, and transporting barcodes have been proposed [10, 16]. Next, the brain is homogenized, and the DNA barcode pairs are sequenced, yielding a record of which neurons are connected (Fig 4B).

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.