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

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 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.

Language: Английский

A point cloud segmentation framework for image-based spatial transcriptomics DOI Creative Commons
Thomas Defard, Hugo Laporte, Mallick Ayan

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: July 6, 2024

Abstract Recent progress in image-based spatial RNA profiling enables to spatially resolve tens hundreds of distinct species with high resolution. It presents new avenues for comprehending tissue organization. In this context, the ability assign detected transcripts individual cells is crucial downstream analyses, such as in-situ cell type calling. Yet, accurate segmentation can be challenging data, particular absence a high-quality membrane marker. To address issue, we introduce ComSeg, algorithm that operates directly on single positions and does not come implicit or explicit priors shape. ComSeg applicable complex tissues arbitrary shapes. Through comprehensive evaluations simulated experimental datasets, show outperforms existing state-of-the-art methods single-cell available documented open source pip package at https://github.com/fish-quant/ComSeg .

Language: Английский

Citations

6
Trem2-expressing multinucleated giant macrophages are a biomarker of good prognosis in head and neck squamous cell carcinoma DOI Creative Commons
Grégoire Gessain,

Ahmed-Amine Anzali,

Marvin Lerousseau

et al.

Cancer Discovery, Journal Year: 2024, Volume and Issue: 14(12), P. 2352 - 2366

Published: Sept. 13, 2024

Abstract Patients with head and neck squamous cell carcinomas (HNSCC) often have poor outcomes due to suboptimal risk management treatment strategies; yet integrating novel prognostic biomarkers into clinical practice is challenging. Here, we report the presence of multinucleated giant cells (MGC)—a type macrophages—in tumors from patients HNSCC, which are associated a favorable prognosis in treatment-naive preoperative chemotherapy–treated patients. Importantly, MGC density increased following therapy, suggesting role these antitumoral response. To enable translation as marker, developed deep-learning model automate its quantification on routinely stained pathological whole slide images. Finally, used spatial transcriptomic proteomic approaches describe MGC-related tumor microenvironment observed an increase central memory CD4 T cells. We defined MGC-specific signature resembling TREM2-expressing mononuclear tumor-associated macrophages, colocalized keratin niches. Significance: Novel individual needed guide therapeutic decisions for cancer. first time, granulomas macrophages keratin-rich niches, biomarker slides.

Language: Английский

Citations

4
Towards deciphering the bone marrow microenvironment with spatial multi-omics DOI
Raymond K. H. Yip, Edwin D. Hawkins,

Bowden Rory

et al.

Seminars in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 167, P. 10 - 21

Published: Jan. 30, 2025

Language: Английский

Citations

0
SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with single-cell spatial omics data DOI Creative Commons
Shunsuke A. Sakai, Ryosuke Nomura, Satoi Nagasawa

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(2), P. e1012854 - e1012854

Published: Feb. 18, 2025

Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment using bioinformatic methods is limited by the need to detect histological morphology extend it surrounding area. In this study, we developed SpatialKNifeY (SKNY), an image-processing-based toolkit that detects domains potentially reflect histology extends these Using transcriptomic data from breast cancer, applied SKNY algorithm identify tumor domains, followed clustering trajectory estimation, extension (TME). The results estimation were consistent with known cancer progression. We observed vascularization immunodeficiency at mid- late-stage progression TME. Furthermore, integrate cluster 14 patients metastatic colorectal clusters divided based on TME characteristics. conclusion, facilitates determination cataloguing features.

Language: Английский

Citations

0
spacedeconv: deconvolution of tissue architecture from spatial transcriptomics DOI
Constantin Zackl, Maria Zopoglou, Reto Stauffer

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 4, 2024

Abstract Investigating tissue architecture is key to understanding function in health and disease. While spatial omics technologies enable the study of cell transcriptomes within their native context, they often lack single-cell resolution. Deconvolution methods can computationally infer composition from transcriptomics data, but differences workflows complicate use comparison. We developed spacedeconv, a unified interface different deconvolution that additionally supports data preprocessing, visualization, analysis communication multimodal data. Here, we demonstrate how spacedeconv streamlines investigation cellular molecular underpinnings organisms contexts.

Language: Английский

Citations

1
An integrative spatial multi-omic workflow for unified analysis of tumor tissue DOI Creative Commons
Jurgen Kriel, Joel J.D. Moffet, T.C. Lu

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 18, 2024

Abstract Combining molecular profiling with imaging techniques has advanced the field of spatial biology, offering new insights into complex biological processes. Focusing on diffuse IDH -mutated low-grade glioma, this study presents a workflow for Spatial Multi-omics Integration, SMINT, specifically combining transcriptomics and metabolomics. Our incorporates both existing custom-developed computational tools to enable cell segmentation registration coordinates from modalities common coordinate framework. During our investigation strategies, we found that nuclei-only segmentation, while containing only 40% segmented transcripts, enables accurate type annotation, but does not account multinucleated cells. integrative including cell-morphology identified distinct cellular neighborhoods at infiltrating edge gliomas, which were enriched in oligodendrocyte-lineage tumor cells, may drive invasion normal cortical layers brain. Highlights Alignment integrated analysis transcriptomic metabolomic data Nuclei-only segmentations are concordant annotation Spatially regions conserved datasets Multi-omic exploration glioma leading identifies novel features

Language: Английский

Citations

1
Deep learning pipeline for automated cell profiling from cyclic imaging DOI Creative Commons
Christian Landeros, Juhyun Oh, Ralph Weissleder

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 9, 2024

Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis these datasets can time-prohibitive due the sheer volume data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pre-processes image files, quickly corrects translation errors between imaging cycles, leverages pre-trained neural network segment individual cells generate single-cell molecular profiles. We applied CycloNET dataset 22 human samples from head neck squamous carcinoma patients trained immune cells. efficiently processed large-scale (17 fields view per cycle 13 staining cycles specimen) 10 min, delivering insights at resolution facilitating identification rare clusters. expect that rapid will serve as powerful tool understand complex biological systems cellular level, with potential facilitate breakthroughs areas such developmental biology, disease pathology, personalized medicine.

Language: Английский

Citations

0
mxfda: A comprehensive toolkit for functional data analysis of single-cell spatial data DOI Creative Commons
Julia Wrobel, Alex C. Soupir, Mitchell Hayes

et al.

Bioinformatics Advances, Journal Year: 2024, Volume and Issue: 4(1)

Published: Jan. 1, 2024

Abstract Summary Technologies that produce spatial single-cell (SC) data have revolutionized the study of tissue microstructures and promise to advance personalized treatment cancer by revealing new insights about tumor microenvironment. Functional analysis (FDA) is an ideal analytic framework for connecting cell relationships patient outcomes, but can be challenging implement. To address this need, we present mxfda, R package end-to-end SC using FDA. mxfda implements a suite methods facilitate imaging FDA techniques. Availability implementation The freely available at https://cran.r-project.org/package=mxfda has detailed documentation, including four vignettes, http://juliawrobel.com/mxfda/.

Language: Английский

Citations

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

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 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.

Language: Английский

Citations

0