Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization

TL;DR

Puzzle imaging is a novel technique that reconstructs spatially scrambled samples into high-resolution images using dimensionality reduction, enabling applications like brain imaging, neural network structure recovery, and chemical mapping from DNA data.

Contribution

Introduces puzzle imaging, a new method for reconstructing images from disordered samples using local properties and dimensionality reduction algorithms.

Findings

3D brain imaging is feasible with puzzle imaging.

Neural network structure can be recovered from connectivity data.

Chemical maps can be reconstructed using bacteria with chemosensitive DNA.

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.