Sizun Jiang

EBV-driven remodeling of the classic Hodgkin lymphoma microenvironment, including CD8 T cell terminal exhaustion linked to LMP1 and MHC class I on rare malignant Hodgkin and Reed-Sternberg cells cell.com/cell-reports-m… @CellRepMed @SizunJ @harvardmed

Now online in @CD_AACR: Same-Slide Spatial Multi-Omics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment - by Stephanie Pei Tung Yiu, @Yuzhou_Chang, @yeoyaoyu, Huaying Qiu, Wenrui Wu, @SizunJ, and colleagues

@Zihai @OhioStatePIIO @BIDMChealth @harvardmed Found the seminar room!

@OhioStatePIIO welcomes @SizunJ from @BIDMChealth @harvardmed for a seminar on decoding new biological principles using the latest spatial technology.

For more info, check out the paper arxiv.org/abs/2506.03373 And our blog posts: Technical: linkedin.com/feed/update/ur… Applications: linkedin.com/pulse/spatial-… Github: github.com/mahmoodlab/KRO…

We are incredibly thankful for all the support and funding agencies that enabled this work, and our home institutions @harvardmed @MassGeneralNews @BIDMChealth @broadinstitute @DanaFarber. These are stressful times we live in, but its exciting work from the lab that gets us up

Chuffed to share KRONOS: A foundation model for Spatial Proteomics! arxiv.org/abs/2506.03373 Exciting work with the indomitable @AI4Pathology, led by the amazing @mshaban_ai, @Yuzhou_Chang, with Huaying, @yeoyaoyu, @GuillaumeJaume, Andrew Song, and our collaborators

👉📣Introducing KRONOS, a panel-agnostic foundation model purpose-built for multiplex spatial proteomics! Access the pre-print here: arxiv.org/pdf/2506.03373 Trained self-supervised on 47 million single-marker patches spanning 175 protein markers, 16 tissue types, 8 imaging platforms and 5 institutions. Its architecture couples a shared channel-wise stem with sinusoidal marker-identity embeddings, making it natively compatible with high-dimensional multiplex data. 🔬 Biology at scale: label-efficient cell phenotyping, unsupervised tissue phenotyping, tissue/region classification, artefact detection, patient stratification, and spatial biomarker discovery. 🔎 Spatial search engine: drop in any cell, patch, or region of interest and KRONOS retrieves morphologically or immunologically similar areas across multi-cohort databases—supporting both exploratory and hypothesis-driven queries. 💻 Code, pretrained models & tutorials: github.com/mahmoodlab/KRO… Exciting collaboration with my buddy @SizunJ's lab. Congratulations to @mshaban_ai, Yuzhou Chang and all the other co-authors. Read the blog post from Sizun: lnkd.in/dwrZpYkP Read the blog post from Shaban: lnkd.in/dwBsnX7u

Check out our recent tool using deep learning to better discover immune cells in tissues! Always a pleasure to work with this great team: @ZongmingMa, @SizunJ, @GarryPNolan, @ironmathmo (Sheng Gao), @ShuxiaoC, @shaleklab and the rest who may or may not be on this platform!