8/ With SPLONGGET we were able to figure out the exact molecular mechanisms underpinning CAR T-cell therapy resistance. At relapse, we identify an ~8 MB deletion encompassing CD19 as well as 4 unique SNVs in conserved splice site motives that lead to intron retention and non-functional transcripts.

7/ We further explore the effect of CNAs on gene expression and chromatin accessibility. For example, we can observe changes between D0 and Q1, when isochromosome 7 is formed (p-arm lost and q-arm gained).

6/ SPLONGGET also allowed us to comprehensively detect different types of variation including SNP and SVs across time points. Importantly, we identify various CNVs from this data, including the ones which are only present at later time points. We can also look at CN profiles of individual cells.

5/ With the SPLONGGET transcriptome and accessibility data we identified different tumour subclones and immune populations. To gain more insight into gene regulatory changes over time, we ran SCENIC+ from our collaborators at @steinaerts.bsky.social lab and identified key regulators, including ERG.

4/ With our collaborators, @jancools.bsky.social, Margo Aertgeerts, and Heidi Segers we applied SPLONGGET to longitudinal samples from a case of B-ALL to follow its evolution from diagnosis through therapy and ultimately resistance to anti-CD19 CAR T-cell therapy.

2/ We adapted @10xgenomics.bsky.social Multiome to retain all tagmentation fragments (not just short ATAC ones) and intact cDNA. Sequencing these libraries on @nanoporetech.com simultaneous full-length transcriptome, open chromatin and whole-genome data from 1000’s of cells.

1/ First preprint from @jdemeul.bsky.social lab 🥳! We present our new multi-modal single-cell long-read method SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome)! www.biorxiv.org/content/10.1...