Hyobin Kim

Research Professor at Korea University College of Medicine

#SpatialTranscriptomics #scRNAseq #CellCellInteractions #GeneRegulation #BooleanNetworks

[Waggoner Lab @LabWaggoner]

Stromal barriers to therapeutic antibody delivery in human solid tumors @NatureBiotech @GuolanLu
@Stanford @VanderbiltU @EbenRosenthal @GarryPNolan
https://t.co/yL0TX55jSk

[Cell @CellCellPress]

Now online! Uncovering spatially resolved functional genomics with CRISPR screen sequencing https://t.co/EZw03UBnvl

[Dr. Thomas Ichim @exosome]

Today we all lost our jobs.....

Three Nature papers showing that scientists in the conventional sense are obsolete

At least read the first one.... the AI replaced all things that the scientist does ....

https://t.co/zMsRLaaRDU

[Kyoungjae Won @KJWonKJ]

Excited to share our new approach to studying cell-cell interactions from Visium data — without relying on predefined ligand-receptor pairs. The method identifies genes influenced by CCI that were previously difficult to detect. Led by @HyobinKim4.
https://t.co/wrX2ovEWTr

[Nature Methods @naturemethods]

Spatial 5mC-seq offers nonbiased spatiotemporal analysis of the genome-wide methylome at single cell resolution.

https://t.co/YmrtB8hfFK

[Rahul Satija @satijalab]

Interested in single cell genomics but need help getting started? Come to my lab's 10th (!) Single Cell Genomics Day on 6/12

Talks include Aviv Regev @anshulkundaje @junyue_cao @xinjin + (many) more + @ATJCagan illustrations!

Free Youtube livestream at https://t.co/KJpeGwXjxZ

[Lung Cancer Europe @LungCancerEu]

New research in Nature suggests cancer cells can learn to resist therapy, not by mutating, but by reprogramming themselves.

In #lungcancer, resistance to targeted treatments is a significant challenge. Understanding how this happens is an important step in the search for better treatment

🔗 https://t.co/RvSA6FljUj #LCSM

[Nature Methods @naturemethods]

Predictive virtual embryo models integrate single-cell and spatial data with AI, offering a tool for modeling mammalian embryogenesis across scales, as discussed in this Comment.

https://t.co/QYUSHqhWWc

[OpenAI @OpenAI]

Introducing GPT-Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine.

[Fabian Theis @fabian_theis]

1/ Annbatch unlocks terabyte-scale training of biological data in anndata 🚀
Check out our preprint here: https://t.co/vAjSo4S4GJ 📄
Grab the code on GitHub: https://t.co/c1DssS7peF
Massive shoutout to project leads @felix_f0097 and Ilan Gold for driving this home! 🧵👇

[James Zou @james_y_zou]

You can now give your agent deep knowledge of millions of papers in one line with #paperclip!📎

>8 million papers natively indexed for agents.
Much more thorough + often 10x faster than standard deep research.
Just add the paperclip mcp (instruction below).

[Tailin Wu @tailin_wu]

🎉 Excited to share our latest work, “scDFM: Towards Distributional Flow Matching for Single-Cell Perturbation Prediction”, accepted to ICLR 2026 @iclr_conf

scDFM is a generative framework for single-cell perturbation prediction. Instead of relying on the impractical one-to-one cell correspondence assumption, it models perturbation effects as distribution-level generation. Concretely, we combine Conditional Flow Matching with MMD-based distribution alignment to capture population-level shifts without paired cell labels.

For more information:
📦 Code Repository: https://t.co/nBba3LqwqB
📚 Paper Link: https://t.co/F3g8TbG0vH
🧾 OpenReview: https://t.co/GH9MYUMKJu

[Aging Science News @AgingBiology]

Temporal AI model predicts drivers of cell state trajectories across human aging
https://t.co/5oD9nDiWHw

[Mo Lotfollahi @mo_lotfollahi]

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once.

We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program.
We explored this across multiple systems.

In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation.
By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches

Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight.

This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors @mariemoullet, @Tomo_Isobe, @AmirhVahidi, @CarloLeonardi7, and everyone from @roserventotormo's Lab, @HaniffaLab, Nicola Wilson and @BertieGottgens's Lab, bringing together expertise across @SCICambridge, @OpenTargets, @sangerinstitute and @Cambridge_Uni.

@mariemoullet is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at @genentech , I have no doubt she will do amazing work there and continue to make her mark.

paper:https://t.co/jkQagOPNxE
code: https://t.co/2xnQWMcqbA

[Xin Jin, PhD @xinjin]

📢 Preprint: we present a whole-mouse-brain in vivo Perturb-seq atlas, 7.7 million cells, 1947 disease-associated perturbations, moving toward direct readout of how human genetics rewires cell states & circuits in vivo. Grateful for the Team! @NVIDIAHealth https://t.co/01c1KFuLFw

[Fabian Theis @fabian_theis]

Excited to share new preprint with NVIDIA on GPU-accelerated single-cell analysis 🚀

rapids-singlecell brings native GPU support to scverse/AnnData: hours → seconds (1M cells: ~52m → ~25s), scaling to 100M cells. Huge thanks to Severin Dicks 🙌

📄 https://t.co/X75u6Y8RuN

[Nature Methods @naturemethods]

stVCR models and reconstructs single-cell dynamics of cell differentiation, proliferation and migration from time-series spatial transcriptome data.

https://t.co/S3bDTEnJcA

[Saez-Rodriguez Group @saezlab]

🧵 See 👇 our new preprint on shared and organ-specific gene expression programs of fibrotic diseases 🧬

📄 Paper: https://t.co/DK0KRmWXHf

📊 Explore the data: https://t.co/47SXGfj5l5

[Waggoner Lab @LabWaggoner]

Simultaneous spatial transcriptomics and morphology profiling as tools to explore how microglia change with age @NatureAging @StephenQuake @Stanford
https://t.co/h8jSepT4qM

[Chew Wei Leong @ChewWeiLeong]

Spatial perturb-seq: single-cell functional genomics within intact tissue architecture

Scale up your in situ CRISPR screens

Out in @NatureComms