Autologous fecal transplant (AFT, transfer of ileostomy feces to the colon) for diversion colitis (DC) has not yet been described in children or adolescents. We performed serial AFTs in a 16-year-old with Crohn’s disease and DC. As evidenced by endoscopic, histologic, laboratory, and multiomic profiling of the colon, the first AFT led to interval improvement in DC, whereas the second AFT reactivated Crohn’s colitis. These findings ultimately guided shared decision-making towards total colectomy.

Introduction: Inflammation plays a key role in the development of atherosclerosis, yet targeted therapies are lacking. Previous trials have demonstrated that IL-1b is a potential target, albeit with adverse effects on immune suppression. IL-6 has emerged as another mediator of coronary artery disease. It clear that a strategy to identify a biomarker customized for inflammatory inhibition is needed. Mass cytometry shows promise as a technology for customized biomarker identification. Hypothesis: A mass cytometry panel including markers to identify circulating immune subtypes in humans and phosphorylation of key transcription factors driving downstream inflammation can identify cell subtypes most responsive to cytokine stimulation. Methods: Peripheral blood mononuclear cells (PBMCs) were isolated from patients undergoing coronary computed tomography angiography (CCTA). Using PBMCs from 12 pairs of matched subjects, we stimulated cells with vehicle, IL-6, and IL-1b. These samples were analyzed using a custom mass cytometry panel. Results: Mass cytometry analysis of PMBCs after 15 minute cytokine stimulation identified specific lymphocyte and monocyte subtypes that were particularly responsive to IL-1b and IL-6. Notably, there was clear heterogeneity in this response suggesting this approach may identify those most impacted by cytokine inhibition or those with most severe disease. Conclusions: High dimensional mass cytometry has the potential to better identify individuals at risk and those that may respond to cytokine inhibition. We are in the process of performing advanced bioinformatic analysis including machine learning to leverage this novel technology for precision medicine.