BACKGROUND: Prediction of poststroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. METHODS: We intended to incorporate heterogeneity into our retrospective study to broaden the applicability of our findings and prediction tools. We therefore analyzed the effect of 30, 45, and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were coregistered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. RESULTS: Variance of stroke volumes was increased by 45 minutes of arterial occlusion compared with 60 minutes. The inclusion of various genotypes enhanced heterogeneity further. We detected both a subacute and residual motor-functional deficit after stroke in mice and different recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomic regions within the ischemic lesion had particular impact on the prediction of long-term outcomes. Prediction accuracy depended on the degree of functional impairment. CONCLUSIONS: For the first time, we developed and validated a robust tool for the prediction of functional outcomes after experimental stroke in mice using a large and genetically heterogeneous cohort. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.

Cell-free microRNAs in body fluids have emerged as promising biomarker candidates in neurodegenerative diseases. While several studies have identified dysregulated miRNAs in sporadic Parkinson’s disease, it remains unclear whether distinguishable alterations of cell-free miRNAs occur in genetic forms of the disease, such as those associated with the LRRK2 G2019S mutation. In this proof-of-concept study, we used a human induced pluripotent stem cell-derived dopaminergic neuron model to investigate whether the LRRK2 G2019S mutation induces detectable changes in the intra- and extracellular miRNAome, and whether miRNA signatures identified in vitro can be validated in patient-derived cerebrospinal fluid. We differentiated dopaminergic neurons from induced pluripotent stem cells carrying the LRRK2 G2019S mutation and an isogenic gene-corrected control. Extracellular vesicles were isolated from the culture medium and used as a source of cell-free miRNA. Next, small RNA libraries were generated and analyzed. Differentially expressed microRNAs were validated in an independent batch using RT-qPCR. We further quantified candidate microRNAs in cerebrospinal fluid samples from five LRRK2 G2019S patients and matching healthy controls. The patient cohort included the fibroblast donor from whom the stem cells were originally derived. We successfully isolated extracellular vesicles from induced pluripotent stem cell-derived human dopaminergic neurons. We identified a distinct set of differentially expressed miRNAs in cellular and cell-free RNA, among which let-7g-5p and miR-21-5p were consistently upregulated and validated across independent replicates. These alterations were reflected in the cerebrospinal fluid of the original donor and partially reproduced in additional LRRK2 patients, supporting the concept of patient-specific signatures. A strong correlation between intra- and extracellular miRNA expression was observed. Our findings demonstrate that induced pluripotent stem cell-derived dopaminergic neurons can serve as a model to identify individualized, cell-free microRNA signatures associated with the LRRK2 G2019S mutation. The dysregulated miRNAs detected in vitro were mirrored in patient cerebrospinal fluid, supporting their potential as accessible molecular readouts. These results lay the groundwork for personalized biomarker strategies in genetic forms of Parkinson’s disease and warrant further validation in larger patient cohorts.

Background: Mutations in the Leucine Rich Repeat Kinase 2 gene are highly relevant in both sporadic and familial cases of Parkinson’s disease. Specific therapies are entering clinical trials but patient stratification remains challenging. Dysregulated microRNA expression levels have been proposed as biomarker candidates in sporadic Parkinson’s disease. Objective: In this proof-of concept study we evaluate the potential of extracellular miRNA signatures to identify LRRK2-driven molecular patterns in Parkinson’s disease. Methods: We measured expression levels of 91 miRNAs via RT-qPCR in ten individuals with sporadic Parkinson’s disease, ten LRRK2 mutation carriers and eleven healthy controls using both plasma and cerebrospinal fluid. We compared miRNA signatures using heatmaps and t-tests. Next, we applied group sorting algorithms and tested sensitivity and specificity of their group predictions. Results: miR-29c-3p was differentially expressed between LRRK2 mutation carriers and sporadic cases, with miR-425-5p trending towards significance. Individuals clustered in principal component analysis along mutation status. Group affiliation was predicted with high accuracy in the prediction models (sensitivity up to 89%, specificity up to 70%). miRs-128-3p, 29c-3p, 223-3p and 424-5p were identified as promising discriminators among all analyses. Conclusions: LRRK2 mutation status impacts the extracellular miRNA signature measured in plasma and separates mutation carriers from sporadic Parkinson’s disease patients. Monitoring LRRK2 miRNA signatures could be an interesting approach to test drug efficacy of LRRK2-targeting therapies. In light of small sample size, the suggested approach needs to be validated in larger cohorts.

Background and objective The discovery of cell-free micro-RNAs in body fluids has made them a promising biomarker target in the field of neurodegenerative diseases. Although they have been reported to be differentially expressed in biofluids and tissues from sporadic Parkinson’s disease patients, it remains unclear whether similar observations can be made in patients with genetic forms of the disease and if miRNA profiles reflect mutation-specific pathogenic pathways. Since induced pluripotent stem cell-derived neurons represent a widely used research model for both sporadic and familial Parkinson’s disease, we sought to assess the usability of this model for the identification of differentially expressed cell-free micro-RNAs in the context of the Parkinson’s disease-related LRRK2 G2019S mutation in a proof-of-concept study. Materials and methods We isolated extracellular vesicles carrying cell-free RNA from patient-derived induced pluripotent stem cells carrying the LRRK2 G2019S mutation and their gene-corrected isogenic controls. After the generation of small-RNA libraries and differential expression analysis, we quantified expression levels of fourteen micro-RNAs in an independent batch of cell-free and cellular RNA via RT-qPCR. Finally, we quantified pRab10 levels as a proxy of LRRK2 activity and correlated observable changes to the miRNA expression levels. Results We successfully isolated extracellular vesicles from induced pluripotent stem cell-derived human dopaminergic neurons. We detected over 2000 different micro-RNAs of which 56 were differentially expressed. Dysregulation of four micro-RNAs was confirmed in an independent batch of cell-free RNA. We discovered a high correlation between changes in the cell-free and cellular micro-RNAomes. Finally, we showed poor correlation between LRRK2 expression or activity and miRNA expression levels. Conclusions Our results suggest that patients carrying the LRRK2 G2019S mutation display alterations in cellular and cell-free micro-RNA expression levels. Notably, the miRNA changes observed in this study did not follow a linear relationship with LRRK2 expression levels or kinase activity. Validation in larger cohorts will be necessary.

Background Prediction of post-stroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. Methods Data from 13 studies that included 45 min of middle cerebral artery occlusion on 148 mice were pooled. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were co-registered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. Results We detected both a subacute and residual motor-functional deficit after stroke in mice. Different functional severity grades and recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomical regions within the ischemic lesion had particular impact on the prediction of long-term outcome. Prediction accuracy depended on the degree of functional impairment. Conclusions For the first time, we identified and characterized predictors of post-stroke outcome in a large cohort of mice and found strong concordance with clinical data. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.

Extracellular vesicles are easily accessible in various biofluids and allow the assessment of disease-related changes of the proteome. This has made them a promising target for biomarker studies, especially in the field of neurodegeneration where access to diseased tissue is very limited. Genetic variants in the LRRK2 gene have been linked to both familial and sporadic forms of Parkinson’s disease. With LRRK2 inhibitors entering clinical trials, there is an unmet need for biomarkers that reflect LRRK2-specific pathology and target engagement. In this study, we used induced pluripotent stem cells derived from a patient with Parkinson’s disease carrying the LRRK2 G2019S mutation and an isogenic gene corrected control to generate human dopaminergic neurons. We isolated extracellular vesicles and neuronal cell lysates and characterized their proteomic signature using data-independent acquisition proteomics. We performed differential expression analysis and identified 595 significantly differentially regulated proteins in extracellular vesicles and 3205 in cell lysates. Next, we performed gene ontology enrichment analyses on the dysregulated proteins and found close association to biological processes relevant in neurodegeneration and Parkinson’s disease. Finally, we focused on proteins that were dysregulated in both the extracellular and cellular proteomes and provide a list of ten promising biomarker candidates that are functionally relevant in neurodegeneration and linked to LRRK2 associated pathology. Among those was the sonic hedgehog signaling molecule, a protein that has tightly been linked to LRRK2-related disruption of cilia function. In conclusion, we characterized the cellular and extracellular proteome of dopaminergic neurons carrying the LRRK2 G2019S mutation and propose an experimentally based list of promising biomarker candidates for future studies.