High-Resolution Temporal Response Patterns to Influenza Vaccine Reveal a Distinct Human Plasma Cell Gene Signature

Abstract

To identify sources of inter-subject variation in vaccine responses, we performed high-frequency sampling of human peripheral blood cells post-vaccination, followed by a novel systems biology analysis. Functional principal component analysis was used to examine time varying B cell vaccine responses. In subjects vaccinated within the previous three years, 90% of transcriptome variation was explained by a single subject-specific mathematical pattern. Within individual vaccine response patterns, a common subset of 742 genes was strongly correlated with migrating plasma cells. Of these, 366 genes were associated with human plasmablasts differentiating in vitro. Additionally, subject-specific temporal transcriptome patterns in peripheral blood mononuclear cells identified migration of myeloid/dendritic cell lineage cells one day after vaccination. Upstream analyses of transcriptome changes suggested both shared and subject-specific transcription groups underlying larger patterns. With robust statistical methods, time-varying response characteristics of individual subjects were effectively captured along with a shared plasma cell gene signature.

Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging

“…2A). Interestingly, the abnormally expanded "superlineages" continued to make up a similar fraction of the total repertoire at days 7 and 28 after vaccination as they did before vaccination at day 0, indicating that the size of any vaccine response [e.g., release of recalled IgG plasmablasts around day 7 (11,24,25)] was minor compared with the weight of the superlineages. Note that no CDR3 sequences were shared between superlineages, which provides confidence that superlineages did not arise from contamination.…”

hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data

“…5.3. Henn et al (2013) paired data set. Here we illustrate the application of the hmmSeq method to paired data sets with an analysis of a subset of an RNA-seq data set obtained by Henn et al (2013) on immune response to a trivalent influenza vaccine.…”

“…Section 4 uses simulated data to demonstrate the effectiveness of hmmSeq relative to well-known techniques for RNA-seq. The Marioni et al (2008), Zeng et al (2012) and Henn et al (2013) data sets are analyzed in Sections 5.1, 5.2 and 5.3. In all cases, the results are compared and contrasted with those of existing approaches to demonstrate the success of hmmSeq.…”

“…The second data set [Zeng et al (2012)] consists of six biological replicates extracted from two regions, frontal pole and hippocampus, of normal human brains. Finally, the third data set [Henn et al (2013)] consists of paired B-cell samples data of day 0 (before vaccination) and day 7 (post-vaccination) for 3 pre-vaccinated subjects. Therefore, we demonstrate the power and flexibility of the hmmSeq methodology on three types of RNA-seq data: technical replicates, biological replicates and paired samples.…”

“…Data analysis. To illustrate the power and flexibility of our proposed RNA-seq analysis method, we have applied the hmmSeq method to analyze three data sets: Marioni et al (2008) (technical replicates), Zeng et al (2012) (biological replicates) and Henn et al (2013) (paired data). For each of these three data sets, the treatment-specific replicate effects ρ jk are obtained by the upper-quartile normalizing technique of Bullard et al (2010).…”