Thread by @MPMule

New @medrxivpreprint from work over 5 years with @tsanglab!
Our framework for integrating human population & single cell variations identified a "naturally adjuvanted" baseline immune system setpoint linked to more robust & immunogenic vaccine responses.
medrxiv.org/content/10.110…

We used mixed effects models to decompose variation in every gene attributable to cell type (defined by surface protein), individual, age, sex, and vaccination effects. Individual differences often explain substantial variation, highlighting the need for hierarchical models.

After accounting for human population variation, we defined vaccine-induced transcriptome perturbations in each cell type, then integrated these statistical model results with "bottom up" computational reconstructions of transcriptional dynamics.

The approach also identified molecular states induced specifically by AS03, a vaccine adjuvant which increases antibody level & diversity through unknown mechanisms. In addition to innate cell activation patterns we found a apoptosis suppression signature in naive B cells...

including downregulation of apoptosis gene NOXA. In flu vaccination & infection NOXA-/- mice B cells outcompete WT cells for GC entry leading to more diverse antibody generation. Human Naïve B cell pools post AS03 vacc may thus phenocopy post vacc NOXA-/- mouse B cells.

In previous work @TsangLab untangled effects of pre-existing immunity, age, sex, and ethnicity on vaccination responses. They found high antibody responders to seasonal flu vaccination could be predicted by the status of their baseline immune system alone. doi.org/10.1016/j.cell…

Over the last decade, more work has indicated that pre-vaccination states can influence vaccine response, although bulk blood profiling has made translating these biomarker signatures into biological insights challenging...

With statistical models and a network analysis approach we revealed the multicellular signatures and their interconnectedness that defined the baseline setpoint of high responders.

Specific molecular phenotypes elevated at baseline in high responders were themselves induced by influenza vaccination and SARS-Cov2 mRNA vaccination within the same cell types.

We then asked whether cell states specifically induced by AS03 adjuvant were phenocopied by the baseline of non-adjuvanted vaccine high responders. Indeed, these high responders to the non-adjuvanted vaccine had elevated AS03-specific innate response phenotypes at baseline.

By using in vitro stimulation experiments, we further dug into innate immune cells to test if the naturally adjuvanted baseline statuses also reflect cell-intrinsic differences in the response capacity of these cells. These data showed enhanced response capacity to TLR agonism.

The R package created for this project can serve as a starting point for using mixed effects models to define cell type specific phenotypes linked to any variables, including time, perturbation type, etc.: github.com/MattPM/scglmmr.

For further details, the code to reproduce the entire analysis and all figures is provided here: github.com/niaid/fsc

We hope this work and the quantitative analysis framework presented will help future sample multiplexed single cell studies integrate human and single cell variations over space and time, and in response to perturbations. Our approach can be applied across disciplines.

This work was made possible by teamwork with many awesome colleagues in John's lab and the CHI. John Tsang provided years of sage mentorship related to many aspects of this project and science in general. John always encouraged me to keep digging deeper. Thank you @TsangLab!

Many thanks to Pam Schwartzberg, my co-mentor Ken Smith, and others from Cambridge for feedback including Eoin Mckinney, Paul Lyons, and @GosiaTrynka. Finally, many thanks to my thesis examiners @BrodinPetter and Sarah Teichmann @teichlab for their careful reading of this work.

@SingleCellMedia @NIAIDNews @NIH #rstats #influenza #vaccine #humanimmunology #SystemsImmunology #singlecellbiology #citeseq #cytof #immunology