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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 Overview.(A) An example area contains a chemical whose concentration is represented by a grayscale value. (B) Pioneer cells (X’s and O’s, each with a different color) are put on the plate. X’s represent F+ cells that can transfer an F-plasmid into an F− cell (O’s). (C) The pioneer cells replicate and spread. Descendants of a pioneer are shown in the same color. (D) When the cell colonies become large enough to contact neighboring colonies, the F+ cells (X’s) will copy the F-plasmid and transfer it to the F− cells (O’s). This is shown as the X’s color filling in the center of the O’s. In the inset (below), the F-plasmid transfer (conjugation) is shown. (E) The DNA is sequenced to determine which pioneer cells are “connected” (which had a conjugative transfer occur between their colonies). A connectivity matrix is made from this data. (F) The matrix of connections doesn’t directly provide accurate information about how close the original cells are to each other because O’s can’t be connected to O’s (and same for X’s). As our similarity matrix, we thus use the matrix of mutual connections, which allows O’s to be connected to O’s. (G) The location of the original cells is estimated from the matrix in panel F. (H) The chemical concentrations at each of the original cells locations is known as the cells’ DNA acts as a chemical sensor. (I) The chemical concentration everywhere is extrapolated based on the chemical concentrations at the known pioneer cells.
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pone.0131593.g006: Chemical Puzzling Overview.(A) An example area contains a chemical whose concentration is represented by a grayscale value. (B) Pioneer cells (X’s and O’s, each with a different color) are put on the plate. X’s represent F+ cells that can transfer an F-plasmid into an F− cell (O’s). (C) The pioneer cells replicate and spread. Descendants of a pioneer are shown in the same color. (D) When the cell colonies become large enough to contact neighboring colonies, the F+ cells (X’s) will copy the F-plasmid and transfer it to the F− cells (O’s). This is shown as the X’s color filling in the center of the O’s. In the inset (below), the F-plasmid transfer (conjugation) is shown. (E) The DNA is sequenced to determine which pioneer cells are “connected” (which had a conjugative transfer occur between their colonies). A connectivity matrix is made from this data. (F) The matrix of connections doesn’t directly provide accurate information about how close the original cells are to each other because O’s can’t be connected to O’s (and same for X’s). As our similarity matrix, we thus use the matrix of mutual connections, which allows O’s to be connected to O’s. (G) The location of the original cells is estimated from the matrix in panel F. (H) The chemical concentrations at each of the original cells locations is known as the cells’ DNA acts as a chemical sensor. (I) The chemical concentration everywhere is extrapolated based on the chemical concentrations at the known pioneer cells.

Mentions: One example of a chemical puzzling assay would consist of the initial spreading of many “pioneer” cells across an environment containing a heterogeneous distribution of a particular chemical (Fig 6A, 6B). These pioneer cells would be endowed with the ability to detect the presence of that chemical and to record its concentration into a nucleotide sequence. This could be done through molecular ticker-tape methods using DNA polymerases [3, 19, 20], similar strategies using RNA polymerases, or other mechanisms [4, 5, 21, 22], for example involving chemically-induced methylation or recombination.


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

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

Chemical Puzzling Overview.(A) An example area contains a chemical whose concentration is represented by a grayscale value. (B) Pioneer cells (X’s and O’s, each with a different color) are put on the plate. X’s represent F+ cells that can transfer an F-plasmid into an F− cell (O’s). (C) The pioneer cells replicate and spread. Descendants of a pioneer are shown in the same color. (D) When the cell colonies become large enough to contact neighboring colonies, the F+ cells (X’s) will copy the F-plasmid and transfer it to the F− cells (O’s). This is shown as the X’s color filling in the center of the O’s. In the inset (below), the F-plasmid transfer (conjugation) is shown. (E) The DNA is sequenced to determine which pioneer cells are “connected” (which had a conjugative transfer occur between their colonies). A connectivity matrix is made from this data. (F) The matrix of connections doesn’t directly provide accurate information about how close the original cells are to each other because O’s can’t be connected to O’s (and same for X’s). As our similarity matrix, we thus use the matrix of mutual connections, which allows O’s to be connected to O’s. (G) The location of the original cells is estimated from the matrix in panel F. (H) The chemical concentrations at each of the original cells locations is known as the cells’ DNA acts as a chemical sensor. (I) The chemical concentration everywhere is extrapolated based on the chemical concentrations at the known pioneer cells.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g006: Chemical Puzzling Overview.(A) An example area contains a chemical whose concentration is represented by a grayscale value. (B) Pioneer cells (X’s and O’s, each with a different color) are put on the plate. X’s represent F+ cells that can transfer an F-plasmid into an F− cell (O’s). (C) The pioneer cells replicate and spread. Descendants of a pioneer are shown in the same color. (D) When the cell colonies become large enough to contact neighboring colonies, the F+ cells (X’s) will copy the F-plasmid and transfer it to the F− cells (O’s). This is shown as the X’s color filling in the center of the O’s. In the inset (below), the F-plasmid transfer (conjugation) is shown. (E) The DNA is sequenced to determine which pioneer cells are “connected” (which had a conjugative transfer occur between their colonies). A connectivity matrix is made from this data. (F) The matrix of connections doesn’t directly provide accurate information about how close the original cells are to each other because O’s can’t be connected to O’s (and same for X’s). As our similarity matrix, we thus use the matrix of mutual connections, which allows O’s to be connected to O’s. (G) The location of the original cells is estimated from the matrix in panel F. (H) The chemical concentrations at each of the original cells locations is known as the cells’ DNA acts as a chemical sensor. (I) The chemical concentration everywhere is extrapolated based on the chemical concentrations at the known pioneer cells.
Mentions: One example of a chemical puzzling assay would consist of the initial spreading of many “pioneer” cells across an environment containing a heterogeneous distribution of a particular chemical (Fig 6A, 6B). These pioneer cells would be endowed with the ability to detect the presence of that chemical and to record its concentration into a nucleotide sequence. This could be done through molecular ticker-tape methods using DNA polymerases [3, 19, 20], similar strategies using RNA polymerases, or other mechanisms [4, 5, 21, 22], for example involving chemically-induced methylation or recombination.

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.