Most of what makes cells functionally distinct happens at the post-transcriptional level, but we spent a decade obsessing over transcriptomics.

scRNAseq is powerful precisely because it's incomplete.

It forces us to think about what it doesn't capture.

Great work by Hengxin Pan presenting at #abacbs2025 from @nadia-davidson.bsky.social lab @wehi-research.bsky.social CONCERTDR: an R package with a comprehensive suite of tools for drug response data analysis
github.com/DavidsonGrou...

Nature research paper: Semantic design of functional de novo genes from a genomic language model

go.nature.com/48uEnAn

scRNAseq forced us to realize that cell types we'd carefully defined in textbooks for decades were meaningless—they were just convenient categories drawn around the densest clusters in transcriptional space. Nature doesn't actually sort cells that way.

Single-cell RNA-seq is younger than the iPhone. 🤯 The first bona fide scRNA-seq transcriptomes were published in 2009 by Tang et al., who sequenced individual mouse cells and showed you could capture a whole transcriptome from a single cell. From “sci-fi” to standard tool in just over a decade...

We have started the course on #scRNAseq using R and @bioconductor.bsky.social with Jacques Serizay @almeidasilvaf.bsky.social & an international cohort of attendees working on very different cool projects.

#singlecell #RNAseq #Genomics #Bioinformatics

1/  Mapping quantitative data to color www.nature.com/articles/nm...