MEGA-FISH: Multi-omics Extensible GPU-Accelerated FISH Processing Framework for Huge-Scale Spatial Omics DOI Creative Commons
Yuma Ito, Kosuke Tomimatsu, Masao Nagasaki

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

ABSTRACT Spatial omics enables comprehensive mapping of cell types and states in their spatial context, providing profound insights into cellular communication tissue organization. However, analyzing large sections, especially crucial for clinical applications, remains a significant challenge due to the computational demands current image processing methods. To overcome these limitations, we developed MEGA-FISH, flexible, GPU-accelerated Python framework optimized large-scale analysis. Benchmarking on simulated images demonstrated that MEGA-FISH achieved high accuracy spot detection while significantly reducing times compared with established tools. The framework’s adaptable capabilities optimize resource allocation (e.g., GPU or multi-core CPU) diverse tasks, its scalable architecture integration advanced imaging segmentation techniques. By bridging cutting-edge methods single-cell analysis, provides an efficient platform multi-modal analysis advances research applications at organ organism scales.

Язык: Английский

Towards deciphering the bone marrow microenvironment with spatial multi-omics DOI
Raymond K. H. Yip, Edwin D. Hawkins,

Bowden Rory

и другие.

Seminars in Cell and Developmental Biology, Год журнала: 2025, Номер 167, С. 10 - 21

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0
Seeing more with less: Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data DOI Creative Commons
John G. Lock, Ihuan Gunawan, Felix Kohane

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 13, 2024

Abstract Standard immunofluorescence imaging captures just ~4 molecular markers (‘4-plex’) per cell, limiting dissection of complex biology. Inspired by multimodal omics-based data integration approaches, we propose an Extensible Immunofluorescence (ExIF) framework that transforms carefully designed but easily produced panels 4-plex into a unified dataset with theoretically unlimited marker plexity, using generative deep learning-based virtual labelling. ExIF enables integrated analyses cell biology, exemplified here through interrogation the epithelial-mesenchymal transition (EMT), driving significant improvements in downstream quantitative usually reserved for omics data, including: classification phenotypes; manifold learning phenotype heterogeneity, and; pseudotemporal inference dynamics. Introducing concepts from to microscopy, provides blueprint empowering life scientists use routine methods achieve previously inaccessible high-plex imaging-based single-cell analyses.

Язык: Английский

Процитировано

0
Dexmedetomidine ameliorates diabetic intestinal injury by promoting the polarization of M2 macrophages through the MMP23B pathway DOI Open Access

Man Lu,

Xiaowen Guo, Fang Fang Zhang

и другие.

World Journal of Diabetes, Год журнала: 2024, Номер 15(9), С. 1962 - 1977

Опубликована: Авг. 27, 2024

Diabetes is often associated with gastrointestinal dysfunctions, which can lead to hypoglycemia. Dexmedetomidine (DEX) a commonly used sedative in perioperative diabetic patients and may affect function.

Язык: Английский

Процитировано

0
MEGA-FISH: Multi-omics Extensible GPU-Accelerated FISH Processing Framework for Huge-Scale Spatial Omics DOI Creative Commons
Yuma Ito, Kosuke Tomimatsu, Masao Nagasaki

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

ABSTRACT Spatial omics enables comprehensive mapping of cell types and states in their spatial context, providing profound insights into cellular communication tissue organization. However, analyzing large sections, especially crucial for clinical applications, remains a significant challenge due to the computational demands current image processing methods. To overcome these limitations, we developed MEGA-FISH, flexible, GPU-accelerated Python framework optimized large-scale analysis. Benchmarking on simulated images demonstrated that MEGA-FISH achieved high accuracy spot detection while significantly reducing times compared with established tools. The framework’s adaptable capabilities optimize resource allocation (e.g., GPU or multi-core CPU) diverse tasks, its scalable architecture integration advanced imaging segmentation techniques. By bridging cutting-edge methods single-cell analysis, provides an efficient platform multi-modal analysis advances research applications at organ organism scales.

Язык: Английский

Процитировано

0