Jinmiao Chen Lab

Chen Jinmiao Lab for single cell Immunology and AI

[Jinmiao Chen Lab @CJM_lab]

Huge thanks to the incredible team and collaborators who made this possible!

[Jinmiao Chen Lab @CJM_lab]

Excited to share our latest work in @NatureGenetics (https://t.co/ID3uRacigt). We introduce SpaMosaic, a new computational method designed to advance spatial omics by mosaic integration of diverse datasets with partially overlapping modalities.

[Jinmiao Chen Lab @CJM_lab]

It provides a versatile framework for unifying the rapidly accumulating heterogeneous spatial omics data into comprehensive tissue atlases.

[Jinmiao Chen Lab @CJM_lab]

Very excited to share our recent paper titled 'Single-nucleus transcriptomics reveals a distinct microglial state and increased MSR1-mediated phagocytosis as common features across dementia subtypes' published in @GenomeMedicine https://t.co/xO39konjBq

[Jinmiao Chen Lab @CJM_lab]

Happy to announce that our Nature Methods paper 'SpatialGlue' was recently featured in A*STAR Research: https://t.co/5aeusV8oQR

[Jinmiao Chen Lab @CJM_lab]

We would like to announce that disco has moved to https://t.co/peN7guc0El. Thank you for your support ☺️

[Jinmiao Chen Lab @CJM_lab]

Congratulations to Azimuth 2. Delighted that Disco is contributing to this initiative 😊

[Lynn van Olst @LynnOlst]

Excited to share our latest @NatureMedicine study from our new center @ARC_Neuro! 🧠✨ Using spatial transcriptomics & scRNA-seq, we uncover how Aβ-targeting immunotherapies shape microglial responses in AD patient brains.

A thread with our key findings is below! 🧵(1/15)

[Jinmiao Chen Lab @CJM_lab]

Congratulations and very nice study @DGateLab 🙂

[David Gate @DGateLab]

More excellent work from the brilliant @LynnOlst. I’m a very proud PI today. More to come from @ARC_Neuro!

[Nature Medicine @NatureMedicine]

Spatial transcriptomics reveals distinct microglial mechanisms driving amyloid-β clearance in both passive and actively immunized #Alzheimer’s disease patients @NUFeinbergMed
https://t.co/DirVOwGV5r

[Jinmiao Chen Lab @CJM_lab]

https://t.co/g59481OESy

[Jinmiao Chen Lab @CJM_lab]

@RongFan8 @LongYahui Happy new year Dr Fan ☺️😋😇

[Jinmiao Chen Lab @CJM_lab]

Lab Gathering Lunch and Chinese New Year celebration 🎉🎉🥳 Also all the best to @LongYahui and chengwei as they embark on their new journeys 😊

[10x Genomics @10xGenomics]

🎉 Congrats to the Singapore research team on ezSingleCell! 🎉 A game-changer for #singlecell & #spatialbiology, it simplifies data analysis from platforms like Chromium, Visium & Xenium without programming expertise! 🚀 Learn more: https://t.co/PnbtoVFFHt

[DISCO @HSCRM2]

🚀 DISCO December Update 🚀

📊 8,529,538 cells from 1,345 samples across 94 projects added in this month's update! 🎉

Dive in and explore the data that matters to you! 🧬 Check it out here: https://t.co/hOE59oMcfp

#Bioinformatics #SingleCell #LLM #DISCO

[DISCO @HSCRM2]

🎉 Excited to share that our paper DISCO: a database of Deeply Integrated human Single-Cell Omics data has reached 100 citations! 🚀 Grateful for all the researchers utilizing and building upon DISCO. Here's to advancing single-cell biology together! 💡📊
#SingleCell #Bioinformatics

https://t.co/8dcut73g1C

[ENABLE Conference @ENABLE_conf]

@FEBSnews @FEBS_JS @FEBSOpenBio @FEBSJournal Our keynote of Career Day is a FEBS-powered breakout lecture by Dr. Jinmiao Chen (@CJM_lab)! 🌟

She introduces the fundamentals of spatial omics and shares her lab's cutting-edge AI-driven analysis method, STAMP (Zhong et al., 2024). 🚀

[DISCO @HSCRM2]

🚀 scEnrichment Update!
The scEnrichment tool now features an updated reference geneset with 524 cell type 🧬 and phenotype 🔬-related genesets!
🎯 Input your geneset to uncover enriched phenotypes or cell types with ease.

🔗 Try it here https://t.co/2JvRwMG0rQ

#scRNAseq #Bioinformatics #Genomics 🧑‍🔬✨

[DISCO @HSCRM2]

Rediscovering publicly available single-cell data with the DISCO platform https://t.co/LA1KkCaT25

[DISCO @HSCRM2]

🚀 Exciting update! DISCO now supports batch downloads—access data from 113,277,088 cells across 18,402 samples with ease. Perfect for training your LLMs or fueling other applications! 🌐📊 #SingleCellData #Bioinformatics #AI @LLM
https://t.co/16Yjn2gSUa