ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable 'gears' between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

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Publication Details

Journal
Nature Communications
Published
2024-09-06
DOI
https://doi.org/10.1038/s41467-024-51935-0
Citations
8
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
10.39

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article

ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

Junfu Guo, Bowen Ma, Lulu Zuo, Yunjia Zhang et al.
8 citations
Nature Communications
Robotics and Sensor-Based Localization
10.39
article

ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

Junfu Guo, Bowen Ma, Lulu Zuo, Yunjia Zhang, Tianyi Xia, Mengyang Xu, Mei Li, Lu Qin, Shuangsang Fang, Lei Cao, Bohan Zhang, Luni Hu, Yuxiang Li, Yong Zhang, Chuan Chen, Lei Zhang, Shi Chang, Taotao Pan, Chao Liu
article en
8 citations

Abstract

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable 'gears' between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

Nature CommunicationsVol. 15(1)
BGI Group (China) (CN), Beijing Graphene Institute (CN), BGI Research (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Robotics and Sensor-Based Localization
10.39
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