📢We are recruiting!!
Spatial-Cell-ID is looking for a postdoctoral fellow or research engineer in image analysis applied to spatial transcriptomics.
Come join us in Lyon to analyze our beautiful MERFISH data!
https://t.co/9nPy4koBV7
🚨Preprint alert!
https://t.co/VF3WRkr5ye
Did you ever dream of generating spatial maps of #enhancer activity at #singlecell resolution in a multicellular organism using massively parallel enhancer-reporter assays?
Then spatial-scERA is the method for you!
Join us in Lyon for the annual Spatial-Cell-ID day!
We will welcome @DenisSchapiro (@UniHeidelberg) and Marco Grillo (@MatsNilssonLab@scilifelab) as keynote speakers.
Full program available here:
https://t.co/3jHRg5zMct
🔔Proud and excited to share our latest preprint!
We asked this simple question: do TADs really act as strong boundaries or can functional enhancer-promoter interactions be established across TAD boundaries?
https://t.co/aRXOA3hlgY
A 🧵on our main conclusions: (1/n)
Using transcriptomic similarity and spatial coordinates, PASTE allows aligning and integrating spatial transcriptomics data generated from adjacent tissue slices. @RonZeira@benjraphael
https://t.co/2igB2Mas73
It's great to see our article about the multimodal omics analysis framework muon out!
Many thanks to everyone who provided feedback along the way. 🙌
See a few highlights in the thread below.
New Resource online now at Dev Cell: A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants - https://t.co/gT81YpEIhX
"Spatial components of molecular tissue biology", review wrote with @davidsebfischer@fabian_theis and Aviv Regev is out in @NatureBiotech. We outline biological questions and computational challenges in spatial analysis of tissues. 1/N https://t.co/HM81gle3ks
How to efficiently analyse & process large-scale spatial transcriptomics data from different techs?
We've developed Spacemake that does this and much more
https://t.co/HKAqVf7En0
Spatially resolved transcriptomics and its applications | Nice review from Prof. Mingyao Li's group @DrMingyaoLi Statistical and machine learning methods for spatially resolved transcriptomics with histology https://t.co/uWjoQLateL