Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open source, multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. This model outperforms other current methods when applied to Gene ST data and could potentially be used to enrich the >10,000 Affymetrix Gene ST blood gene expression profiles currently available on GEO.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. We have previously shown that protective antibody levels could be elicited in a subset of recipients with only a single dose of the hepatitis B virus (HBV) vaccine. Why some, but not all, recipients responded in this way was not clear. Using single cell RNA sequencing of sorted innate immune cell subsets, we identified two distinct myeloid dendritic cell subsets (NDRG1 expressing mDC2 and CDKN1C expressing mDC4), the ratio of which at baseline (prevaccination) predicted immune response to a single dose of HBV vaccine. Our results suggest that the participants in our vaccine study were in one of two different dendritic cell dispositional states at baseline: an NDRG2 mDC2 state in which the vaccine elicited an antibody response after a single immunization or a CDKN1C mDC4 state in which the vaccine required two or three doses for induction of antibody responses. Genes expressed in these mDC subsets were used as an approach for feature selection prior to the construction of a predictive model using supervised canonical correlation machine learning. The resulting model showed an improved ability to predict serum antibody titers in response to vaccination. Taken together, these results suggest that the propensity of circulating dendritic cells toward either activation or suppression, their dispositional endotype, could dictate response to vaccination. The fact that these mDCs could be modulated via TLR stimulation could guide progress towards design of effective single dose vaccination strategies.