Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model
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Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model

Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model

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12

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing

Eric D. SunOlivia Y. ZhouMax HauptscheinNimrod RappoportLucy Xu10
Nature
2024
2024/12/18
00 p.1-12
Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain ageing is complex and is accompanied by many cellular changes2. Furthermore, the influence that aged cells have on neighbouring cells and how this contributes to tissue decline is unk...
AgeingCognitive ageingMachine learning
10.1038/S41586-024-08334-8
ISSN:0028-0836

MENDER: fast and scalable tissue structure identification in spatial omics data

Zhiyuan Yuan
Nature Communications
2024
2024/1/5
Vol.15 No.1 p.1-17
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question i...
Classification and taxonomyComputational modelsData integrationRNA sequencingSoftware
10.1038/S41467-023-44367-9
ISSN:2041-1723

Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN

Xinming ZhangZhuohan YuGaoyang HaoQi YaoYanmei Hu10
Nature Communications
2026
2026/1/17
Vol.17 No.1 p.17920
Spatial omics links molecular measurements to their positions in tissue, revealing cellular organization and interactions. Yet most computational tools highlight common cell types and overlook rare populations that can drive disease. Here we show GARDEN, a computational framework that identifies and...
Gene expression profilingHigh-throughput screening
10.1038/S41467-026-68500-6
ISSN:2041-1723

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope

Xiaomeng WanJiashun XiaoSindy Sing Ting TamMingxuan CaiRyohichi Sugimura10
Nature Communications
2023
2023/11/29
Vol.14 No.1 p.1-22
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approac...
BioinformaticsComputational modelsMachine learningSoftwareStatistical methods
10.1038/S41467-023-43629-W
ISSN:2041-1723

Network model for alignment, stitching and slice-to-volume 3D reconstruction of large-scale spatially resolved slices

Yu WangZaiyi LiuXiaoke Ma
Nature Communications
2026
2026/3/20
0
Advances in spatially resolved technologies enable the characterization of tissues at molecular resolution by preserving spatial information. However, integrating and aligning spatial-omics data across different platforms and modalities remains challenging. Flexible tools for slice alignment, stitch...
Data miningMachine learningTranscriptomics
10.1038/S41467-026-71042-6
ISSN:2041-1723

Search and match across spatial omics samples at single-cell resolution

Zefang TangShuchen LuoHu ZengJiahao HuangXin Sui7
Nature Methods
2024
2024/9/18
Vol.21 No.10 p.1818-1829
Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of ...
Computational modelsData integrationGene expression profilingTranscriptomicsTranslation
10.1038/S41592-024-02410-7
ISSN:1548-7091

Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease

Zhang XinyiWang XiaoShivashankar G. V.Uhler Caroline
Nature Communications
2022
2022/12/3
Vol.13 No.1 p.1-17
Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a c...
Data integrationFluorescence imagingFluorescence in situ hybridizationMachine learningPrognostic markers
10.1038/S41467-022-35233-1
ISSN:2041-1723

Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues using STARmap PLUS, RIBOmap and TEMPOmap

Jingyi RenHu ZengJiahao HuangJiakun TianMorgan Wu12
Nature Protocols
2025
2025/9/30
00 p.1-33
Controlled gene expression programs have a crucial role in shaping cellular functions and activities. At the core of this process lies the RNA life cycle, ensuring protein products are synthesized in the right place at the right time. Here we detail an integrated protocol for imaging-based highly mu...
Gene expression profilingRNASingle-cell imagingTranscriptomics
10.1038/S41596-025-01248-3
ISSN:1754-2189

SANTO: a coarse-to-fine alignment and stitching method for spatial omics

Haoyang LiYingxin LinWenjia HeWenkai HanXiaopeng Xu9
Nature Communications
2024
2024/7/18
Vol.15 No.1 p.1-12
With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extre...
Computational modelsData integrationData processingMachine learningRNA sequencing
10.1038/S41467-024-50308-X
ISSN:2041-1723

Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease

Zeng HuHuang JiahaoZhou HaowenMeilandt William J.Dejanovic Borislav15
Nature Neuroscience
2023
2023/2/2
Vol.26 No.3 p.430-446
Complex diseases are characterized by spatiotemporal cellular and molecular changes that may be difficult to comprehensively capture. However, understanding the spatiotemporal dynamics underlying pathology can shed light on disease mechanisms and progression. Here we introduce STARmap PLUS, a method...
Alzheimer's diseaseFluorescence imagingGene expression profilingRNA sequencingTranscriptomics
10.1038/S41593-022-01251-X
ISSN:1097-6256

3d-OT: a deep geometry-aware framework for heterogeneous slices alignment of spatial multi-omics

Bingjie DaiLitai YiPeizhuo WangHanshuang LiPengwei Hu10
Nature Methods
2026
2026/3/27
Vol.23 No.4 p.760-771
The rapid advancement of spatial multi-omics technologies has unveiled opportunities for deciphering the intricate spatial heterogeneity; however, current computational approaches struggle to comprehensively integrate diverse molecular and spatial information. Here we propose 3d-OT, a deep geometry-...
Computational modelsMachine learning
10.1038/S41592-026-03034-9
ISSN:1548-7091

NicheTrans: spatial-aware cross-omics translation

Zhikang WangQi ZouSenlin LinSijie LiYan Cui13
Nature Methods
2026
2026/7/9
00 p.1-12
While spatial multiomics offers insights into complex biological systems, its widespread adoption is hindered by technical challenges, specialized requirements and limited accessibility. Here we present NicheTrans, a spatially aware cross-omics translation method and a flexible Transformer-based mul...
Computational modelsData integration
10.1038/S41592-026-03153-3
ISSN:1548-7091