Standardized whole blood stimulation improves immunomonitoring of induced immune responses in multi-center study.

Functional immune responses are increasingly important for clinical studies, providing in depth biomarker information to assess immunotherapy or vaccination. Incorporating functional immune assays into routine clinical practice has remained limited due to challenges in standardizing sample preparation. We recently described the use of a whole blood syringe-based system, TruCulture®, which permits point-of-care standardized immune stimulation. Here, we report on a multi-center clinical study in seven FOCIS Centers of Excellence to directly compare TruCulture to conventional PBMC methods. Whole blood and PBMCs from healthy donors were exposed to LPS, anti-CD3 anti-CD28 antibodies, or media alone. 55 protein analytes were analyzed centrally by Luminex multi-analyte profiling in a CLIA-certified laboratory. TruCulture responses showed greater reproducibility and improved the statistical power for monitoring differential immune response activation. The use of TruCulture addresses a major unmet need through a robust and flexible method for immunomonitoring that can be reproducibly applied in multi-center clinical studies. ONE SENTENCE SUMMARY: A multi-center study revealed greater reproducibility from whole blood stimulation systems as compared to PBMC stimulation for studying induced immune responses.

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses.

Systems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes.