ArticlePloS one2015
Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Scalable spatial transcriptomics through computational array reconstruction.Nature biotechnology · 2026Article
- Spatial coherence in DNA barcode networks.Patterns (New York, N.Y.) · 2025Article
- Scalable imaging-free spatial genomics through computational reconstruction.bioRxiv : the preprint server for biology · 2024Article
- Article
- Bringing Microscopy-By-Sequencing into View.Trends in biotechnology · 2020Review
- Nucleotide-time alignment for molecular recorders.PLoS computational biology · 2017Article
- The Development and Analysis of Integrated Neuroscience Data.Frontiers in computational neuroscience · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.