Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization
This approach could enable imaging from homogenized tissue samples, addressing a bottleneck in high-resolution imaging for biological research, though it is incremental as it builds on existing dimensionality reduction methods.
The paper tackles the problem of imaging spatially scrambled samples by introducing 'puzzle imaging', a technique that pieces together disordered samples using local properties, and demonstrates its theoretical capabilities in biological scenarios such as 3D brain imaging and neural network structure recovery.
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.