| 2020 — 2021 |
Stewart, Tessandra |
R33Activity Code Description:
The R33 award is to provide a second phase for the support for innovative exploratory and development research activities initiated under the R21 mechanism. Although only R21 awardees are generally eligible to apply for R33 support, specific program initiatives may establish eligibility criteria under which applications could be accepted from applicants demonstrating progress equivalent to that expected under R33. |
Characterization and Quantification of Cns Cell Specific Extracellular Microvesicles in Blood @ University of Washington
Extracellular microvesicles (EMVs) are small, membrane-bound vesicles released by most cell types, and can be found circulating in the blood and other biofluids. The proteins, miRNAs, and other molecular components they carry as cargo have become a target for the development of novel biomarkers that reflect the EMV parent cell types. In particular, a strategy of targeting cellular markers carried on their membrane surfaces has been used to probe the state of the brain by examining EMVs carrying L1CAM, a relatively CNS-specific neuronal marker. Measurement of cargo proteins in such EMVs has shown particular promise in identifying blood-based biomarkers for neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). Their utility in probing the state of the brain in other pathological conditions, such as after a traumatic injury, remains to be determined. Additional targets are now being developed to identify EMVs from non-neuronal brain cell types, including GLAST and GLT-1 for astrocytes and CNPase for oligodendrocytes. Despite this progress, identification of cell-specific markers remains crude, focused only on markers that tend to be present across a broad cell type. The cells themselves, in contrast, encompass multiple sub-types with different functional niches, likely differentially affected in pathological states. Thus, it should be possible to identify sub- types of EMVs and examine their composition for more specific reflections of brain processes. However, the appropriate surface markers, or combinations of markers, to target have not yet been identified. Despite the promise of EMV-based biomarkers for CNS conditions, serious challenges to their widespread adoption for clinical usage remain. The current strategies for EMV isolation are largely centered on ultracentrifugation, yield vesicle samples with contamination by large protein aggregates, and usually require large sample volumes. The newly developed immunocapture method that has allowed specific measurement of L1CAM EMV cargos is far more specific, as it targets surface markers, but is expensive, time consuming, and tends to have poor yield. Therefore, novel technologies are needed to identify EMVs of interest, isolate them, and quantify cargos. Here, we will address these challenges by developing novel strategies and technologies to better quantify and characterize brain-derived plasma EMVs in the R21 stage, and then validating them in a large cohort of human subjects in the R33 stage. First, we will optimize two new EMV capture and sorting strategies, precipitation using Smart Beads, and sorting using a microfluidics device, to isolate specific categories of EMVs based on surface markers. Second, we will identify new, more specific targets, to isolate sub-populations of EMVs that might better represent disease-relevant cells of interest. Next, in the R33 stage, we will scale up these new techniques to examine large cohorts, and measure cargo proteins of interest within these specific EMV populations as biomarkers of brain dysfunction caused by AD, PD, and traumatic brain injury.
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