Leveraging the Host Response to Materials to Monitor Autoimmunity

Introduction:: Autoimmune diseases are caused by a breakdown in self-tolerance and are often characterized by relapses and remissions which has made the ability to monitor disease progression and treatment responsiveness using biomarkers an elusive goal of the field. With early detection, therapeutics could likely be employed in a proactive as opposed to reactive manner, to intervene and prevent relapse and disease progression. We hypothesized that alterations in systemic inflammatory responses in autoimmunity could be interrogated by perturbing the system and measuring immune function at distal inflamed sites. To investigate this hypothesis, we leveraged the host response, also called the foreign body response (FBR), to implanted porous biomaterials to serve as an immunological niche (IN) in a mouse model of MS (experimental autoimmune encephalomyelitis). We explore how alterations of material properties of the IN can alter the host response in favorable ways for monitoring autoimmunity. The IN platform provides a tunable tool for monitoring the onset, progression, and treatment of diseases of immune dysfunction.

Materials and Methods:: INs, made of biomaterial scaffolds (PCL or PEG) were custom fabricated using sacrificial porogens to tune pore size and polymer composition. We first characterized cell infiltration into the INs using a combination of histology and flow cytometry to monitor immune populations after subcutaneous implantation in healthy mice. Subsequently, INs were implanted into mice (SJL or C57/BL6J) two weeks to form an immunological niche before inducing EAE by adoptive transfer of encephalitogenic T-cells (reactive to myelin) to induce disease, or T-cells reactive to OVA_323-339 as a control. Scaffolds were excised at various times during disease development (depending on the model used) and analyzed by a combination of single cell RNA sequencing, bulk sequencing, qPCR panels, and flow cytometry to measure alterations in disease. Single cell sequencing was processed in R Studio using the Seurat package. Flow cytometry was analyzed in FlowJo.

Results, Conclusions, and Discussions:: Via scRNAseq, alterations in both innate and adaptive immune cell numbers and phenotypes within the biomaterial implants were clear in disease (Fig 1a-b). These alterations were dynamic with the phase of disease (Fig 1b-c). Furthermore, the biomaterial recruits a variety of stromal cells (not present in blood-based analyses) that enable interrogation of tissue-level alterations (Fig 1c). Leveraging biomaterial platforms for single cell analysis in autoimmunity will enable longitudinal measurements in a single animal, something which is unachievable by tissue biopsy or analysis of blood. Beyond the scRNAseq data, bulk sequencing and qCR panels illustrated that hundreds of genes were differentially expressed and could be readily measured by qPCR. We successfully built panels of genes and paired computational algorithms that enabled prediction and monitoring of disease and responses to therapy by calculating a “niche score” (Fig 1d). The niche score is an aggregate score composed via machine learning that leverages gene expression data across many targets and effectively monitors phase of disease (Fig 1d). An example gene that changes in disease, COX2 is expressed in scaffolds from control animals at roughly three times the levels of asymptomatic EAE animals (pre-symptomatic and remission), and at 16 times the levels in symptomatic animals (peak disease and relapse). Ongoing work is exploring differential cell recruitment to the material by altering material properties. We expect to tune the host response to engineer further improved surrogate systems for monitoring diseases of immune dysfunction. These immunological niches add to the toolbox of techniques for investigating immune dysfunction because they provide an in vivo microphysiological model of an inflammatory site that can be molecularly dissected, which is simply not possible in many native tissues.

Acknowledgements (Optional): : National Institutes of Health R00 EB028840

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