The COVID-19 vaccine study that took up most of my 2022 is out 😁 The most data types I’ve integrated in a single cohort - RNASeq, proteomics, lipidomics, cytokines, immune cell counts, & antigen specific antibody + T cells. But what did we find? 🧵 1/X cell.com/cell-reports-m…
First this was a big effort from everyone in the @davidjohnlynn team @sahmriAU @Flinders @StevensWithAPhD @StephenBlakeAU @SaoirseBenson with great collabs including @Dr_Banika and others 2/X
A week after dose 1 of ChAdOx1-S we saw what looks *a lot* like pre-existing memory to the adenovirus vector, which was correlated with levels of coagulation proteins in blood. Potential insights into mechanisms under rare but serious adverse events linked to ChAdOx1-S 3/X
We didn’t detect any association between antibodies and side effects after vaccination (E.g. pain, fatigue, headache). But we did with T-cell responses (like 👇) which we now know are super important for robust protection against COVID-19 (T cells FTW) 4/X 
The blood transcriptome / immune status pre-vaccination was correlated with antibody & T cell responses months after vaccination. Such as B cell transcriptional signals pre vax being negatively associated with T cell responses post vax 👇5/X
The early innate immune response induced in the days following vaccination is remarkably similar irrespective of dose or vaccine type (at least for those we tested). Some RNASeq data below but held up across multiple data types. 6/X
Expression of RNF115 in blood ~48 hours post 2nd dose is strongly predictive of spike specific T cell response one month later. Years of working with multi omics data and I almost never see a relationship this strong between two different data types. 7/X
If you’ve gotten this far just go read the paper. It’s #OpenAccess and these were just some of my favourite bits. Lots more there 😁 8/8
• • •
Missing some Tweet in this thread? You can try to
force a refresh
