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Siyu He

@siyuhe.bsky.social

Postdoc@stanford | PhD@columbia | AI4Biomedicine | Spatial biology | computational cancer biology | machine learning

58 Followers  |  114 Following  |  12 Posts  |  Joined: 21.11.2024
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Posts by Siyu He (@siyuhe.bsky.social)

Schematic of cell differentiation prediction (a) and drug response prediction (b).

Schematic of cell differentiation prediction (a) and drug response prediction (b).

New paper out in @natmethods.nature.com from @elhamazizi.bsky.social, Kam Leong & @jameszou.bsky.social! The team developed Squidiff, a diffusion #AI model to predict cellular responses to environmental cues and accelerate #PrecisionMedicine.

Learn more: bit.ly/3WRPNsx

10.11.2025 13:59 β€” πŸ‘ 8    πŸ” 5    πŸ’¬ 1    πŸ“Œ 0
Learning single-cell spatial context through integrated spatial multiomics with CORAL - Siyu He
YouTube video by Computational Spatial Biology seminar Learning single-cell spatial context through integrated spatial multiomics with CORAL - Siyu He

Missed last week’s #CompSpatialBio seminar @justjhong.bsky.social with @siyuhe.bsky.social on CORALβ€”a deep graph model for spatial-omics? The recording is now available: youtu.be/BV5soaUVUNA

πŸ“… Stay tuned for the next session! csbseminar.github.io

13.05.2025 14:54 β€” πŸ‘ 3    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0

Thanks @justjhong.bsky.social for the invitation! Looking forward to the discussion.

24.04.2025 17:07 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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1/10 Excited to share our latest - the first whole-body map of both DNA methylation and 3D genome at single-cell resolution.

25.03.2025 15:49 β€” πŸ‘ 8    πŸ” 5    πŸ’¬ 1    πŸ“Œ 1
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Check out our new collaborative work with Dr. Kam Leong @columbiauniversity.bsky.social now out in Advanced Functional Materials! We developed oral nanoparticles to help mitigate hematopoietic acute radiation syndrome, especially in cases of deep space exploration:

t.co/1jQwcU1pDB

24.02.2025 17:40 β€” πŸ‘ 1    πŸ” 2    πŸ’¬ 1    πŸ“Œ 0

Applying #CORAL on paired CODEX and Visium data from HCC, we delineated tissue ecosystems and identified macrophages interacting with CD4+ T cells as key players hindering responses.
For details, check our preprint & GitHub!πŸ˜€
#Immunotherapy #HCC #SpatialOmics #CancerResearch

08.02.2025 16:00 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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#CORAL enables the prediction of cell-cell interactions, as demonstrated in the mouse thymus. The latent variables in the model capture spatial features that transition from the medulla to the cortex, associating with thymic development and T cell maturation. 🧬5/πŸͺΈ

08.02.2025 16:00 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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Given paired spatial proteomics and downsampled ST data (such as Visium), #CORAL performs comparably to #SpatialGlue in detecting functional domains, while also achieving high-res STπŸ”¬4/πŸͺΈ

08.02.2025 16:00 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

By leveraging deep generative model and graph attention mechanisms, #CORAL offers key benefits:
πŸ”Ή Spatial domain detection
πŸ”Ή Single-cell modality imputation
πŸ”Ή Inferring cell-cell interactions
Unlocking new possibilities in analyzing #spatialmultiomics integration! 3/πŸͺΈ

08.02.2025 16:00 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

Big thanks to our collaborators Matthew Bieniosek,
SongDongyuan,zhou_jingtian, benchidester, ZhenqinWu, Joseph Boen, Padmanee Sharma! And a special thanks to my advisors @jameszou.bsky.social and Alex Trevino for their invaluable guidance and support. 2/πŸͺΈ

08.02.2025 15:59 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

Thrilled to introduce #CORALπŸͺΈβ€”a deep probabilistic graphical model that integrates spatial multiomics data!
πŸ”ΉResolves spatial resolution discrepancies across modalities
πŸ”ΉDeconvolutes low-res data to achieve single-cell granularity
πŸ”ΉEnables parallel integrative analysis 1/πŸͺΈ

08.02.2025 15:58 β€” πŸ‘ 14    πŸ” 4    πŸ’¬ 1    πŸ“Œ 0
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We applied Squidiff to model blood vessel #organoids using single-cell RNA sequencing to examine their response to neutron irradiation and pro-regenerative G-CSF treatment, simulating cosmic irradiation anticipated in #deepspace missionsπŸ§‘β€πŸš€. 5/πŸ¦‘

09.12.2024 14:13 β€” πŸ‘ 2    πŸ” 2    πŸ’¬ 1    πŸ“Œ 0

Squidiff effectively predicts non-additive gene perturbation and cell type-specific drug responses, as demonstrated in its application to the analysis of temporal cell states in the glioblastoma dataset in response to novel drug combinations. 4/πŸ¦‘

09.12.2024 14:13 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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Given the scRNA data on day 0 and day 3, can we predict the data on day 1, day 2 and further? Squidiff predicts this differentiation of induced pluripotent stem cells (iPSCs) into mesendoderm and endoderm, guided by stimuli vectors. 3/πŸ¦‘

09.12.2024 14:13 β€” πŸ‘ 4    πŸ” 3    πŸ’¬ 1    πŸ“Œ 0

Thank you so much for the long-term collaboration: Yuefei, @naveedtavakol.bsky.social , Haotian, and Sima. A special thanks to my advisors, James Zou, @elhamazizi.bsky.social , and Kam W. Leong, whose guidance and support have been invaluable. 2/πŸ¦‘

09.12.2024 14:13 β€” πŸ‘ 3    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
Preview
Squidiff: Predicting cellular development and responses to perturbations using a diffusion model Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in respo...

We are thrilled to share #Squidiff πŸ¦‘, a conditional diffusion model, which generates new transcriptomes that represent distinct cellular states, and its application to cell differentiation and drug perturbation www.biorxiv.org/content/10.1... 1/πŸ¦‘

09.12.2024 14:13 β€” πŸ‘ 9    πŸ” 2    πŸ’¬ 1    πŸ“Œ 0