Finally, we epigenetically profiled a collection of genetically diverse hiPSCs to identify sequence determinants and candidate transcription factors that may be responsible for establishing the primed enhancer state at key enhancers for human neural development.

…suggesting that while enhancer priming is established early in human and mouse development, it has functional consequences that manifest through remarkably long developmental periods along cell specification trajectories.

Epigenetic priming of enhancers is associated with the lineage-specific regulation of key developmental gene networks, and occurs at enhancer elements specific for both imminent and surprisingly late developmental stages…

Check out Wolf speaking at the recent @codexworld Top 50 Innovators event! 🧪 youtu.be/KPzCXAmSeR8?si=pf1p… #T50 #CodexTalks

Lab members are having a great time at #ISSCR2023, despite getting a bit soggy in an unexpected storm! @AliceSantam @ikafetzo @BrendanTerry8

Photos by talented @tomek_criters

Thank you especially @vonMeyenn, @ChristelKrueger, @joannebrock1969, and @cdws100 for making it happen!

It was truly wonderful to see so many close friends, great colleagues and amazing mentors virtually and in person at the Reik Lab & Friends Reunion in Cambridge! A beautiful day full of inspiring science, moving memories and lots of fun 🐑🐁🧬🧫🔬👩‍🔬⛷️🍷🎂🎉!

Next, using the transcriptome data we assigned celltypes then asked if different lineages age at a different rate. Indeed, B-cells were predicated on average to be older than T-cells.

We wondered if this increased variance reflected biological variability in the epigenetic-age of individual cells. To test this we compared with simulated single-cell data finding 19% of cells deviated from expected technical variance.

Using our model to predict the ages of single blood cells, we find good agreement between the median predicted age and the age of the animal. Variance increases with age .

We developed a new modelling approach which makes a number of improvements over previous work. 1) we generalised it to work on both bulk and single-cell data, 2) we used a larger number of reference samples for training data, and 3) we optimised feature selection and modelling.

To address this we generated a new dataset using our scM&T-seq protocol to obtain parallel single-cell methylomes and transcriptomes. We profiled >1000 cells from peripheral blood of mice spanning 4 ages.

For example, two blood TF genes, Klf1 and Lyl1, share a number of primed enhancers. These enhancers and the interactions between them were clearly perturbed in Tet TKO epiblast-like cells, potentially explaining why the genes aren’t upregulated upon differentiation.