The broken Alzheimer’s disease genome

Gouveia Roque, Cláudio; Phatnani, Hemali; Hengst, Ulrich

doi:10.1016/j.xgen.2024.100555

Cited by 2 publications

(2 citation statements)

References 145 publications

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“…We applied SOFisher to a real dataset of Alzheimer's Disease (AD) obtained using spatially resolved transcript amplicon readout mapping with protein localization and unlimited sequencing (STARmapPLUS) 28 . This advanced spatial technology measures spatially resolved gene expressions along with two neuropathologic hallmarks 29 of AD: amyloid-β plaques (Aβ) and neurofibrillary tangles (p-tau) in brain tissues of an AD mouse model at 8 and 13 months of age. Such single-cell multi-omics spatial co-profiling technology remains inaccessible to most labs, and mainstream spatial technologies can only measure either spatially resolved genes or proteins in a tissue at single-cell resolution.…”

Section: Sampling Strategy For P-tau In Alzheimer's Diseasementioning

confidence: 99%

“…In studying Alzheimer's Disease (AD) mechanisms and progression, scientists often focus on the local cell niche surrounding Aβ and p-tau 29 . We demonstrate that the trained SOFisher policy, as described in the previous section, can design experiments that reveal broader biological insights, including AD-associated differential abundance of cell types, subtypes, and gene programs.…”

Section: Sofisher-designed Experiments Capture Biological Insights In...mentioning

confidence: 99%

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SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Li,

Wu,

Cui

et al. 2024

Preprint

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Spatial omics technologies enable the precise detection of proteins and RNAs at high spatial resolution. Designing spatial omics experiments requires careful consideration of “what” targets to measure and “where” to position the field of views (FOVs). Current FOV sampling strategies often involve acquiring densely sampled FOVs and stitching them together, which is time-consuming, resource-intensive, and sometimes impossible. To optimize FOV sampling strategies, we developed SOFisher, a reinforcement learning-based framework that harnesses the knowledge gained from the sequence of previously sampled FOVs to guide the selection of the next FOV position, to improve the efficiency of capturing more regions of interest. We rigorously evaluated SOFisher’s performance using comprehensive simulations based on real spatial datasets, and our results clearly demonstrated that SOFisher consistently outperformed the conventional approach across various metrics. SOFisher’s robustness and generalizability were further validated through cross-domain generalization tests and its adaptability to varying FOV sizes. On a real Alzheimer’s Disease (AD) dataset, SOFisher successfully guided the selection of FOVs containing neurofibrillary tangles and amyloid-β plaques in both single and dual target tissue landmark scenarios. Remarkably, SOFisher-guided experiment design of spatial single-omics on limited tissue areas yielded insights into AD-related cell states, subtypes, and gene programs previously obtained through extensive spatial multi-omics experiments. SOFisher has the potential to revolutionize the experiment design of spatial biology.

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Section: Sampling Strategy For P-tau In Alzheimer's Diseasementioning

confidence: 99%

Section: Sofisher-designed Experiments Capture Biological Insights In...mentioning

confidence: 99%

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Li,

Wu,

Cui

et al. 2024

Preprint

View full text Add to dashboard Cite

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NicheTrans: Spatial-aware Cross-omics Translation

Wang,

Lin,

Zou

et al. 2024

Preprint

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Spatial omics technologies have revolutionized our studies on tissue architecture and cellular interactions at single-cell resolution. While spatial multi-omics approaches offer unprecedented insights into complex biological systems, their widespread adoption is hindered by technical challenges, specialized requirements, and limited accessibility. To address these limitations, we present NicheTrans, the first spatially-aware cross-omics translation method and a flexible Transformer-based multi-modal deep learning framework. Unlike existing single-cell (non-spatial) translation methods, NicheTrans uniquely incorporates both cellular microenvironment information and flexible integration of multi-modal data, such as morphology and prior knowledge. We validated NicheTrans across diverse biological cases: Parkinson's Disease (PD), Alzheimer's Disease (AD), breast cancer, and lymph nodes. Our approach demonstrated superior performance compared to existing single-cell methods, highlighting the crucial role of spatial and multi-modal information in cross-omics translation. Through NicheTrans, we uncovered spatial multi-omics domains that were not detectable through single-omics analysis alone. Model interpretation revealed key molecular relationships, including gene programs associated with dopamine metabolism and amyloid β-associated cell states. Additionally, using translated protein markers as spatial landmarks, we quantified the spatial organization of key glial cell subtypes in the AD brain. NicheTrans represents a powerful tool for generating comprehensive spatial multi-omics insights from more accessible single-omics measurements, making multi-omics analysis more feasible for the broader research community.

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The broken Alzheimer’s disease genome

Cited by 2 publications

References 145 publications

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

NicheTrans: Spatial-aware Cross-omics Translation

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