COEXIST: Coordinated single-cell integration of serial multiplexed tissue images DOI Creative Commons

Robert T. Heussner,

Cameron Watson, Christopher Eddy

et al.

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

Published: May 7, 2024

ABSTRACT Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial sectioning to comprehensively profile samples by each section with unique biomarker panels or assays. The dependence on sections is attributed technological limitations of MTI panel size incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities information, which are critical for effectively completing this task. To address problem, we first use Monte-Carlo simulations estimate overlap between 5μm-thick sections. We then introduce COEXIST, novel algorithm that synergistically combines shared molecular profiles information seamlessly integrate level. demonstrate COEXIST necessity performance several applications. These include combining improved profiling, rectification miscalled phenotypes using single panel, comparison platforms resolution. not only elevates platform validation but also overcomes constraints MTI’s limitation full nuclei slide, capturing more intact in consecutive thus enabling deeper lineages functional states.

Language: Английский

The Microanatomy of Human Skin in Aging DOI Creative Commons
Kyu Sang Han, Inbal Sander,

Jacqueline Kumer

et al.

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

Published: April 5, 2024

Abstract Aging is a major driver of diseases in humans. Identifying features associated with aging essential for designing robust intervention strategies and discovering novel biomarkers aging. Extensive studies at both the molecular organ/whole-body physiological scales have helped determined However, lack meso-scale studies, particularly tissue level, limits ability to translate findings made scale impaired functions In this work, we established image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) that leverages deep learning machine vision fully label cellular compartments sections. The mapped images address challenges finding an interpretable feature set quantitatively profile age-related microanatomic changes. We optimized qMAP skin tissues applied it cohort 99 donors aged 14 92. extracted 914 found broad spectrum these features, represented by 10 cores processes, are strongly Our shows microanatomical can predict mean absolute error (MAE) 7.7 years, comparable state-of-the-art epigenetic clocks. study demonstrates tissue-level architectural changes represent category complement markers. results highlight complex underexplored multi-scale relationship between scales.

Language: Английский

Citations

5
Why AI image generators cannot afford to be blind to racial bias DOI Creative Commons
Muhammad Arif, Yoshiyasu Takefuji

AI & Society, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский

Citations

0
CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images DOI Creative Commons

Valentina Matos-Romero,

Jaime Gómez-Becerril,

André Forjaz

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Abstract Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training algorithms requires advanced programming skills intricate workflows, limiting their accessibility many investigators. Here, we present a protocol software automatic images guided by graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies process microanatomical enabling users train highly customizable models without extensive coding expertise. The outlines best practices creating robust datasets, configuring model parameters, optimizing performance across diverse biomedical image modalities. enhances usability CODA algorithm ( Nature Methods , 2022) streamlining parameter configuration, training, evaluation, automatically generating quantitative results comprehensive reports. We expand beyond original implementation serial histology demonstrating numerous modalities biological questions. provide sample data types including histology, magnetic resonance imaging (MRI), computed tomography (CT). demonstrate use this tool applications metastatic burden vivo deconvolution spot-based transcriptomics datasets. is designed researchers interest rapid design basic understanding anatomy.

Language: Английский

Citations

0
COEXIST: Coordinated single-cell integration of serial multiplexed tissue images DOI Creative Commons

Robert T. Heussner,

Cameron Watson, Christopher Eddy

et al.

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

Published: May 7, 2024

ABSTRACT Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial sectioning to comprehensively profile samples by each section with unique biomarker panels or assays. The dependence on sections is attributed technological limitations of MTI panel size incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities information, which are critical for effectively completing this task. To address problem, we first use Monte-Carlo simulations estimate overlap between 5μm-thick sections. We then introduce COEXIST, novel algorithm that synergistically combines shared molecular profiles information seamlessly integrate level. demonstrate COEXIST necessity performance several applications. These include combining improved profiling, rectification miscalled phenotypes using single panel, comparison platforms resolution. not only elevates platform validation but also overcomes constraints MTI’s limitation full nuclei slide, capturing more intact in consecutive thus enabling deeper lineages functional states.

Language: Английский

Citations

1