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Sub-pixel correlation length neutron imaging: Spatially resolved scattering information of microstructures on a macroscopic scale.

ABSTRACT: Neutron imaging and scattering give data of significantly different nature and traditional methods leave a gap of accessible structure sizes at around 10 micrometers. Only in recent years overlap in the probed size ranges could be achieved by independent application of high resolution scattering and imaging methods, however without providing full structural information when microstructures vary on a macroscopic scale. In this study we show how quantitative neutron dark-field imaging with a novel experimental approach provides both sub-pixel resolution with respect to microscopic correlation lengths and imaging of macroscopic variations of the microstructure. Thus it provides combined information on multiple length scales. A dispersion of micrometer sized polystyrene colloids was chosen as a model system to study gravity induced crystallisation of microspheres on a macro scale, including the identification of ordered as well as unordered phases. Our results pave the way to study heterogeneous systems locally in a previously impossible manner.

SUBMITTER: Harti RP

PROVIDER: S-EPMC5355987 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Sub-pixel correlation length neutron imaging: Spatially resolved scattering information of microstructures on a macroscopic scale.

Harti Ralph P RP Strobl Markus M Betz Benedikt B Jefimovs Konstantins K Kagias Matias M Grünzweig Christian C

Scientific reports 20170317

Neutron imaging and scattering give data of significantly different nature and traditional methods leave a gap of accessible structure sizes at around 10 micrometers. Only in recent years overlap in the probed size ranges could be achieved by independent application of high resolution scattering and imaging methods, however without providing full structural information when microstructures vary on a macroscopic scale. In this study we show how quantitative neutron dark-field imaging with a novel ...[more]

PMID: 28303923

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Project description:MotivationUnveiling the heterogeneity in the tissues is crucial to explore cell-cell interactions and cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis. Therefore, deconvolution of ST has been an indispensable step and a technical challenge toward the higher-resolution panorama of tissues.ResultsHere, we propose a novel ST deconvolution method called SD2 integrating spatial information of ST data and embracing an important characteristic, dropout, which is traditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-based genes as informative features from ST and scRNA-seq data by fitting a Michaelis-Menten function. After synthesizing pseudo-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover low-dimensional and non-linear representation of the real- and pseudo-ST spots. Next, we create a graph containing embedded profiles as nodes, and edges determined by transcriptional similarity and spatial relationship. Given the graph, a graph convolutional neural network is used to predict the cell-type compositions for real-ST spots. We benchmark the performance of SD2 on the simulated seqFISH+ dataset with different resolutions and measurements which show superior performance compared with the state-of-the-art methods. SD2 is further validated on three real-world datasets with different ST technologies and demonstrates the capability to localize cell-type composition accurately with quantitative evidence. Finally, ablation study is conducted to verify the contribution of different modules proposed in SD2.Availability and implementationThe SD2 is freely available in github (https://github.com/leihouyeung/SD2) and Zenodo (https://doi.org/10.5281/zenodo.7024684).Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Sub-pixel correlation length neutron imaging: Spatially resolved scattering information of microstructures on a macroscopic scale.

Publications

Sub-pixel correlation length neutron imaging: Spatially resolved scattering information of microstructures on a macroscopic scale.

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