This is an incredible result! Allows you to find memory genes from a static snapshot of single cell analysis. Wow!🤯

Happy to share our paper describing SPACE-seq - a new approach to spatial multiomics and lineage tracing - out today in @PNASNews! 🧵(1/5) https://t.co/bzTm0fqasH

@EMBO workshop on Single cell Lineage Tracing! Sept 18-21 in Sant Feliu, Girona, Spain. Registration is OPEN!!!!! Tons of opportunities for talks are still available (including full length ones). https://t.co/5zyDYe4ZaU #EMBOlineagetracing

Cancer cells often harbor oncogenes outside chromosomes on extrachromosomal DNA (ecDNA), which is unevenly inherited by dividing cells. Today we report in @Nature that collectives of ecDNAs are inherited together by dividing cancer cells. 1/ https://t.co/scC2sc61Gh

Same cell type, same trans environment, same sequence—different expression pattern. Locking in of stochastic differences into epigenetic memory. I just think this is so cool!

Got out of the nucleus! Tested ~30,000 tiles from human RNA-binding proteins, found lots of domains that downregulate RNA&built an inducible synthetic RNA-binding protein for keeping target RNA at constant intermediate levels: https://t.co/g6d7XEZOlY So many follow-up projects!

📢 To maintain proteome homeostasis, cells need to coordinate the production and elimination of proteins. How does it work? Check out our new preprint: https://t.co/PlaMkmUFgu A 🧵

Hello Berlin students, we are looking for a motivated student assistant to support our research team at @berlinnovation and @BIMSB_MDC: https://t.co/4mnSJuUYKz. Please apply by July 20! What we do: https://t.co/6twsZ2hCms and https://t.co/uD1ZdHwF99 Retweets welcome 🙂

Second postdoc opening on our #ERC synergy project with @gagneurlab  and @KevinVerstrepen . If you want to do RNA biology at scale combining genomics, synthetic biology and Machine learning. Join us at @karolinskainst @scilifelab  Deadline 2024-06-15 https://t.co/cJL6dv0oDc

https://t.co/xGlwG6Sanx

@davidsuter_epfl @MTarbier @vpelech Many thanks also to the EPFL platforms involved! Gene Expression Core Facility, @BastienMangeat @Cytometry_EPFL, Biomolecular Screening Facility. And finally thanks to the feedback from numerous people at EPFL and beyond @GioeleLaManno @BartDeplancke

Massive thanks to everyone involved! In particular my supervisor @davidsuter_epfl and my collaborator @MTarbier and @vpelech, as well as the fantastic student Alexia Dormann.

7) GEMLI is available as R package on GitHub: https://t.co/RYx1mnPUVD Check it out! We are happy to help.

6) GEMLI fills a methodological gap left by other lineage tracing techniques as it can identify closely-related cells in human samples! This can also help to refine lineage trees generated by other techniques.

5) …further, GEMLI is great to study cell fate decisions by identifying closely-related cells undergoing various differentiation processes. In human breast cancer GEMLI mapped lineages making the transition to invasiveness- something no other technique can achieve!

4) GEMLI can reconstruct individual multicellular structures as individual organoids, crypts or small metastases ..

3) Leveraging these “memory genes” we developed GEMLI – Gene Expression Memory-based Lineage Inference, a computational tool to annotate lineages in scRNA-seq datasets solely based on gene-expression.

2) We could show that in any cell type we studied, from fibroblasts, to hematopoietic cells, immune cells, breast- prostate- or leukemic- cancer cells, and embryonic stem cells, thousands of “memory genes” maintain their gene expression levels over cell divisions.

1) The lineage tracing techniques with scRNA-seq readout today rely mainly on genetic marks. This restricts their use and time resolution. Could we use gene expression alone to identify lineages in scRNA-seq data? Yes we can! ..

Do you want to annotate lineages in your scRNA-seq datasets solely using gene expression? GEMLI is for you! Work from my postdoc with and thanks to @davidsuter_epfl @EPFL in collaboration with @MTarbier and @pelechanolab from @SciLifelab