This was a long, fun project, which took the effort of many talented people (Sara Lobato, Umut Yildiz, @anniquec.bsky.social et al.) from the Zaugg and Noh labs! @embl.org @unibas.ch

A detailed protocol is in the works for release. For now, check out the paper!

Constructing temporal GRNs revealed keys TFs at different stages of development and how perturbation of key lineage TFs shift developmental trajectories of cells (8/)

Lastly, SUM-seq is an ideal match for arrayed screens. We show this in a CRISPRi and CRISPRa, modulating key lineage TFs (GATA2/SOX17/NR4A2) in hiPSCs over a time course (0,4,12, and 18 DIV) of spontaneous differentiation (7/)

To demonstrate use of SUM-seq in primary samples, we profiled PBMC-derived naive CD4+ T cells differentiated into Th0, iTregs, Th2, Th1, Th17 and IFN-β-activated subsets. With this, we provide insight on the TF landscape driving T cell subset differentiation (6/)

Can the GRN help understand disease biology? Yes! GRN genomic regions were enriched for heritability of immune-related diseases (incl. IBD/UC/CD). Taking a step further, we link an intronic SNP in CD40, putatively targeted by ISGF3, to CD40, PLTP, NEURL2 and SLC35C2 (5/)

With SUM-seq, we first define temporal GRN dynamics of mac. M1/M2 polarization. One of the notable insights made was how STAT1 transitions from its homodimer-driven chromatin remodelling functions during early M1 polarization to an ISGF3-driven response at later phases (4/)

The method is optimised to mitigate possible cross modality (ATAC <-> RNA) and within modality (sample <-> sample) hopping, while still providing high quality data (3/)

To address this, we present SUM-seq - embedding two-step combinatorial indexing to snATAC+RNA library construction. Combining sample-specific indexes with 10X droplet-barcoding permits significant scaling of number of samples and cells assayed in a single lane! (2/)

Single-cell multiomics lets us infer cell type-specific GRNs: key to deciphering cell function in health/disease. GRNs however are dynamic, and inference demands data capturing a spectrum of cell states. But current multiomic assays are limited by scalability or data quality (1/)