α-Synuclein inclusions, the lesions typical of Parkinson's disease, have been interpreted as protective deposits, toxic species or failed degradation products, each predicting a different composition and correlating that enrichment with neuron dysfunction and eventual degeneration. Distinguishing them requires measuring what an inclusion concentrates relative to the cell that built it. Here we developed Aggregate Deep Visual Proteomics, which uses ultrasensitive mass spectrometry to profile laser-microdissected inclusions against the remainder of the same cell or a matched host. In HEK293 cells, iPSC-derived dopaminergic neurons and post-mortem patient brain, each inclusion concentrated a selective subset of its cell's proteome, independent of abundance and dominated by membrane, in neurons largely presynaptic. Inclusions took up whole assemblies, with subunits moving together in 421 of 438 complexes, and the V-ATPase alone was enriched in all three systems. Degradation cargo stayed with the receptors that route it, consistent with sequestration and failed degradation acting together. Neuritic and somatic inclusions differed, the former rich in UBQLN2 and the latter in intermediate filaments. In the cell line, phosphorylated α-synuclein was tenfold higher in the inclusion than in the rest of the cell, with about one third of the rise already present before an inclusion was visible. Measured against its host, the composition of an inclusion becomes a readout of how it formed and a source of candidate markers and therapeutic targets.
α-Synuclein inclusions, the lesions typical of Parkinson's disease, have been interpreted as protective deposits, toxic species or failed degradation products, each predicting a different composition and correlating that enrichment with neuron dysfunction and eventual degeneration. Distinguishing them requires measuring what an inclusion concentrates relative to the cell that built it. Here we developed Aggregate Deep Visual Proteomics, which uses ultrasensitive mass spectrometry to profile laser-microdissected inclusions against the remainder of the same cell or a matched host. In HEK293 cells, iPSC-derived dopaminergic neurons and post-mortem patient brain, each inclusion concentrated a selective subset of its cell's proteome, independent of abundance and dominated by membrane, in neurons largely presynaptic. Inclusions took up whole assemblies, with subunits moving together in 421 of 438 complexes, and the V-ATPase alone was enriched in all three systems. Degradation cargo stayed with the receptors that route it, consistent with sequestration and failed degradation acting together. Neuritic and somatic inclusions differed, the former rich in UBQLN2 and the latter in intermediate filaments. In the cell line, phosphorylated α-synuclein was tenfold higher in the inclusion than in the rest of the cell, with about one third of the rise already present before an inclusion was visible. Measured against its host, the composition of an inclusion becomes a readout of how it formed and a source of candidate markers and therapeutic targets.
Forward genetic screening links genotype to phenotype by introducing random genetic perturbations and identifying phenotype-altering mutations. Although genome-scale screens are routine for simple phenotypes in cultured cells, extending them to complex image-based phenotypes remains challenging. Here, we present spatially resolved CRISPR screening (SPARCS), a microscopy-based platform for forward genetic screening on single-cell images. SPARCS physically isolates mutants in situ by automated laser microdissection, enabling image-based screening at unprecedented scale with multimodal hit phenotyping. We demonstrate SPARCS in genome-wide CRISPR knockout screens of autophagosome formation and activation of the immune sensor STING across 70 million cells. Via machine learning-based image analysis, SPARCS recovered most macroautophagy genes and identified GPHR as a pH-dependent regulator of STING. Mass spectrometry-based proteomics of isolated hit cells revealed endoplasmic reticulum (ER)/Golgi disruption and nominated additional STING regulators via in silico perturbation modeling. These results establish SPARCS as a scalable platform for genome-wide genetic screening of complex cellular phenotypes with a proteome-level readout.
Forward genetic screening links genotype to phenotype by introducing random genetic perturbations and identifying phenotype-altering mutations. Although genome-scale screens are routine for simple phenotypes in cultured cells, extending them to complex image-based phenotypes remains challenging. Here, we present spatially resolved CRISPR screening (SPARCS), a microscopy-based platform for forward genetic screening on single-cell images. SPARCS physically isolates mutants in situ by automated laser microdissection, enabling image-based screening at unprecedented scale with multimodal hit phenotyping. We demonstrate SPARCS in genome-wide CRISPR knockout screens of autophagosome formation and activation of the immune sensor STING across 70 million cells. Via machine learning-based image analysis, SPARCS recovered most macroautophagy genes and identified GPHR as a pH-dependent regulator of STING. Mass spectrometry-based proteomics of isolated hit cells revealed endoplasmic reticulum (ER)/Golgi disruption and nominated additional STING regulators via in silico perturbation modeling. These results establish SPARCS as a scalable platform for genome-wide genetic screening of complex cellular phenotypes with a proteome-level readout.
Understanding tissue function requires connecting spatial cellular context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We develop multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export, mxDVP achieves >6000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings reveal a spectrum of endocrine heterogeneity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.
Understanding tissue function requires connecting spatial cellular context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We develop multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export, mxDVP achieves >6000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings reveal a spectrum of endocrine heterogeneity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.
Background Precision oncology has largely relied on genomic and transcriptomic sequencing to guide therapy selection in patients with advanced cancer. However, these molecular layers do not fully capture the functional state of tumors, as proteins largely represent the active effectors of cellular processes and signaling pathways. Integrating proteomic and phosphoproteomic measurements with genomic data has therefore emerged as an important objective in translational oncology. In routine clinical workflows, tumor biopsies are frequently processed using DNA/RNA extraction kits such as the Qiagen AllPrep system, which generates a protein-rich flowthrough that is typically discarded. Methods Here we systematically evaluated whether the residual protein fraction generated during Qiagen AllPrep DNA/RNA extraction can serve as a reliable source for mass spectrometry–based proteomics in clinical tumor biopsies. Within the Copenhagen Prospective Personalized Oncology (CoPPO) precision oncology trial, residual protein fractions from metastatic cancer biopsies were recovered, precipitated, and processed using optimized workflows for liquid chromatography–mass spectrometry. We established procedures for protein recovery, digestion, and peptide cleanup, and evaluated multiple acquisition strategies including data-dependent acquisition (DDA) and data-independent acquisition (DIA). We further assessed sample stability during long-term storage, analytical reproducibility, scalability, and compatibility with phosphoproteomics. Results Optimized single-shot workflows consistently generated deep proteomes from AllPrep-derived biopsy material, quantifying more than 10,000 protein groups in DIA analyses. Protein integrity was preserved in samples stored at − 80 °C for up to five years. The resulting proteomes retained tissue-associated biological signatures and quantified protein products corresponding to approximately 71% of genes represented on the FoundationOne ® CDx panel. Phosphoproteomic analyses identified more than 10,000 phosphorylation sites associated with key oncogenic signaling pathways including AKT1, BRAF, and EGFR. Implementation of short liquid chromatography gradients enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality. Conclusions Residual protein fractions from Qiagen AllPrep DNA/RNA extraction provide a scalable resource for deep proteomic and phosphoproteomic profiling from the same biopsy used for genomic and transcriptomic analyses. This workflow enables integrated proteogenomic characterization from a single clinical sample and provides a framework for integrated proteogenomic studies in precision oncology.
Background Precision oncology has largely relied on genomic and transcriptomic sequencing to guide therapy selection in patients with advanced cancer. However, these molecular layers do not fully capture the functional state of tumors, as proteins largely represent the active effectors of cellular processes and signaling pathways. Integrating proteomic and phosphoproteomic measurements with genomic data has therefore emerged as an important objective in translational oncology. In routine clinical workflows, tumor biopsies are frequently processed using DNA/RNA extraction kits such as the Qiagen AllPrep system, which generates a protein-rich flowthrough that is typically discarded. Methods Here we systematically evaluated whether the residual protein fraction generated during Qiagen AllPrep DNA/RNA extraction can serve as a reliable source for mass spectrometry–based proteomics in clinical tumor biopsies. Within the Copenhagen Prospective Personalized Oncology (CoPPO) precision oncology trial, residual protein fractions from metastatic cancer biopsies were recovered, precipitated, and processed using optimized workflows for liquid chromatography–mass spectrometry. We established procedures for protein recovery, digestion, and peptide cleanup, and evaluated multiple acquisition strategies including data-dependent acquisition (DDA) and data-independent acquisition (DIA). We further assessed sample stability during long-term storage, analytical reproducibility, scalability, and compatibility with phosphoproteomics. Results Optimized single-shot workflows consistently generated deep proteomes from AllPrep-derived biopsy material, quantifying more than 10,000 protein groups in DIA analyses. Protein integrity was preserved in samples stored at − 80 °C for up to five years. The resulting proteomes retained tissue-associated biological signatures and quantified protein products corresponding to approximately 71% of genes represented on the FoundationOne ® CDx panel. Phosphoproteomic analyses identified more than 10,000 phosphorylation sites associated with key oncogenic signaling pathways including AKT1, BRAF, and EGFR. Implementation of short liquid chromatography gradients enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality. Conclusions Residual protein fractions from Qiagen AllPrep DNA/RNA extraction provide a scalable resource for deep proteomic and phosphoproteomic profiling from the same biopsy used for genomic and transcriptomic analyses. This workflow enables integrated proteogenomic characterization from a single clinical sample and provides a framework for integrated proteogenomic studies in precision oncology.
NL
Nils-Göran Larsson
Professor, Medical Biochemistry, Karolinska Institutet
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Importantly, the effects were not limited to an energy deficit in heart muscle cells. Mitochondrial dysfunction was accompanied by immune cell recruitment and increasing fibrosis, showing that mitochondrial damage is sensed by the immune system and triggers a response that will further worsen heart function.
Cardiomyocyte-intrinsic somatic mtDNA mutations induce an OXPHOS-dependent immune response and promote progressive heart failure
Science Advances · Bubb et al. ·
RF
Dr. Rebecca Shelley Frabotta
Cell Biology · Duke University School of Medicine
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In this issue of the JCI, Kadri et al. used global phosphoproteomic profiling of primary human lung fibroblasts across a defined stiffness gradient to identify phosphorylation of NFATC4 at residues S213/S217 as a mechanosensitive switch that is both necessary and sufficient for the fibroblast-to-myofibroblast transition.
Mechanosensitive phosphorylation of NFATC4 at S213/S217 drives fibroblast-to-myofibroblast transition and fibrosis
Journal of Clinical Investigation · Kadri et al. ·
A large fraction of the uncharacterized proteome consists of microproteins. Viruses, rich in microproteins with potent activities, provide a unique system for exploring their functions and evolution. Here, we systematically annotate viral microproteins through a phenotype-first approach: construction of a >50,000-element small ORF library spanning the microprotein-coding potential of human-infecting viruses, followed by broad screens that enrich candidates for high-resolution profiling by Perturb-seq, mass spectrometry, AI-guided structure prediction, and mechanistic studies. This atlas identified >2,000 active microproteins that modulate host pathways through diverse mechanisms, including molecular mimicry, hijacking of key regulators, and altering subcellular localization. We further show the oncogenic potential of BNLF2b, an uncharacterized Epstein-Barr virus microprotein linked to nasopharyngeal carcinoma. Three principles emerge: viral microproteins are diverse and often multifunctional, show widespread convergence toward shared functions, and serve as reservoirs of evolutionary innovation. These findings elucidate how viral microproteins occupy a large functional landscape, establish them as versatile tools for host manipulation, and provide a framework for functional annotation of uncharacterized proteins, contributing toward predictive systems virology.
A large fraction of the uncharacterized proteome consists of microproteins. Viruses, rich in microproteins with potent activities, provide a unique system for exploring their functions and evolution. Here, we systematically annotate viral microproteins through a phenotype-first approach: construction of a >50,000-element small ORF library spanning the microprotein-coding potential of human-infecting viruses, followed by broad screens that enrich candidates for high-resolution profiling by Perturb-seq, mass spectrometry, AI-guided structure prediction, and mechanistic studies. This atlas identified >2,000 active microproteins that modulate host pathways through diverse mechanisms, including molecular mimicry, hijacking of key regulators, and altering subcellular localization. We further show the oncogenic potential of BNLF2b, an uncharacterized Epstein-Barr virus microprotein linked to nasopharyngeal carcinoma. Three principles emerge: viral microproteins are diverse and often multifunctional, show widespread convergence toward shared functions, and serve as reservoirs of evolutionary innovation. These findings elucidate how viral microproteins occupy a large functional landscape, establish them as versatile tools for host manipulation, and provide a framework for functional annotation of uncharacterized proteins, contributing toward predictive systems virology.
Educational evaluation reveals enhanced student motivation and perceived learning in biomedical engineering, indicating the value of serious games for complex trauma curricula.
Educational evaluation reveals enhanced student motivation and perceived learning in biomedical engineering, indicating the value of serious games for complex trauma curricula.
Genome-wide association study reveals genetic links between childhood dental caries and adult cardiometabolic disease, highlighting shared biological pathways in oral and systemic health.
Genome-wide association study reveals genetic links between childhood dental caries and adult cardiometabolic disease, highlighting shared biological pathways in oral and systemic health.
The transcription factor MAFF is identified as a key regulatory link between inflammation and lipid metabolism, downregulating LDLR expression during inflammation.
The transcription factor MAFF is identified as a key regulatory link between inflammation and lipid metabolism, downregulating LDLR expression during inflammation.
Fungal skin infections represent a significant global health burden, affecting approximately one billion people annually. Despite their prevalence and major global health impact, the molecular mechanisms underlying pathogenicity remain largely uncharacterized. Here we present the genomes of 66 fungal strains, representing highly prevalent and clinically relevant species associated with human skin infections. Using comparative genomics, we investigate whether the taxonomic groups in this collection of skin fungi differ in their genome architecture, metabolic capacity, secreted enzyme repertoires, adhesin content, and predicted virulence determinants. These analyses reveal substantial variation in genome size and gene contents, indicating genome compaction occurred as the fungi transitioned from free-living to host-associated lifestyles. We report two non-hybrid strains of Trichosporon ovoides, the causative agent of white piedra. Our analyses reveal substantial differences in metabolic adaptations across skin-associated fungi, corresponding to body-site and nutrient niches. Significant differences are also observed in the distribution of virulence factors and adhesins, which are imperative for biofilm formation and antifungal resistance. We discuss metabolic adaptation and virulence mechanisms revealed by our data in the context of clinical presentations, highlighting shared and lineage-specific signatures of adaptations. Together, these insights advance our understanding of skin-associated fungi and their mechanisms of infection.
Fungal skin infections represent a significant global health burden, affecting approximately one billion people annually. Despite their prevalence and major global health impact, the molecular mechanisms underlying pathogenicity remain largely uncharacterized. Here we present the genomes of 66 fungal strains, representing highly prevalent and clinically relevant species associated with human skin infections. Using comparative genomics, we investigate whether the taxonomic groups in this collection of skin fungi differ in their genome architecture, metabolic capacity, secreted enzyme repertoires, adhesin content, and predicted virulence determinants. These analyses reveal substantial variation in genome size and gene contents, indicating genome compaction occurred as the fungi transitioned from free-living to host-associated lifestyles. We report two non-hybrid strains of Trichosporon ovoides, the causative agent of white piedra. Our analyses reveal substantial differences in metabolic adaptations across skin-associated fungi, corresponding to body-site and nutrient niches. Significant differences are also observed in the distribution of virulence factors and adhesins, which are imperative for biofilm formation and antifungal resistance. We discuss metabolic adaptation and virulence mechanisms revealed by our data in the context of clinical presentations, highlighting shared and lineage-specific signatures of adaptations. Together, these insights advance our understanding of skin-associated fungi and their mechanisms of infection.
Computational protein engineering provides a powerful approach to address longstanding clinical challenges. Severe insulin resistance syndromes caused by mutations in the insulin receptor (IR) are life-threatening disorders for which effective long-term therapies remain lacking. Here, we define the in vivo activity and therapeutic potential of RF-409, a de novo-designed IR agonist that activates the receptor through a mechanism distinct from insulin. RF-409 exhibits markedly prolonged circulation compared to insulin and produces sustained improvements in glucose homeostasis without detectable adverse effects on body composition or liver function. In a patient-derived IR D707A mouse model of severe insulin resistance, RF-409-but not insulin-activates the mutant receptor, restoring glucose regulation and ameliorating hyperglycemia, hyperinsulinemia, lipoatrophy, and pancreatic atrophy. Mechanistically, RF-409 engages the IR through a noncanonical binding geometry while stabilizing an active conformation resembling that induced by insulin. Phosphoproteomic profiling shows that RF-409 elicits broadly insulin-like signaling with distinct temporal features in receptor-proximal regulation. Together, these findings establish a framework for reactivating dysfunctional receptors and suggest broader applications beyond rare receptoropathies, including diabetes and liver disease.
Computational protein engineering provides a powerful approach to address longstanding clinical challenges. Severe insulin resistance syndromes caused by mutations in the insulin receptor (IR) are life-threatening disorders for which effective long-term therapies remain lacking. Here, we define the in vivo activity and therapeutic potential of RF-409, a de novo-designed IR agonist that activates the receptor through a mechanism distinct from insulin. RF-409 exhibits markedly prolonged circulation compared to insulin and produces sustained improvements in glucose homeostasis without detectable adverse effects on body composition or liver function. In a patient-derived IR D707A mouse model of severe insulin resistance, RF-409-but not insulin-activates the mutant receptor, restoring glucose regulation and ameliorating hyperglycemia, hyperinsulinemia, lipoatrophy, and pancreatic atrophy. Mechanistically, RF-409 engages the IR through a noncanonical binding geometry while stabilizing an active conformation resembling that induced by insulin. Phosphoproteomic profiling shows that RF-409 elicits broadly insulin-like signaling with distinct temporal features in receptor-proximal regulation. Together, these findings establish a framework for reactivating dysfunctional receptors and suggest broader applications beyond rare receptoropathies, including diabetes and liver disease.
Cellular responses to amino acid fluctuations often hinge on ubiquitin-mediated control of metabolic enzymes, yet the underlying E3 ligase pathways remain poorly defined. Using quantitative proteomics and active cullin-RING ligase (CRL) profiling, we identify LRRC58 as a cysteine-responsive substrate receptor whose stability increases sharply under cysteine starvation. Proteomics reveals an inverse relationship between LRRC58 and the metabolic enzyme cysteine dioxygenase 1 (CDO1), suggesting a cysteine-linked regulatory axis. Biochemical reconstitution and cryo-EM structures show that LRRC58 forms an active CUL2- or CUL5-based CRL that selectively positions CDO1 for ubiquitylation at Lys8. Disease mutant versions of CDO1 mapping to the LRRC58 interface and impaired for the endogenous ubiquitylation pathway were degraded through orthogonal targeting by a VHL-based degrader. Together, our proteomics-guided discovery pipeline, cellular stability studies, and structural analyses uncover a metabolically-tuned LRRC58-CDO1 pathway that links cysteine availability to selective proteasomal turnover, reveals principles of metabolite-regulated CRL activity, and showcases mechanisms distinguishing endogenous and targeted protein degradation.
Cellular responses to amino acid fluctuations often hinge on ubiquitin-mediated control of metabolic enzymes, yet the underlying E3 ligase pathways remain poorly defined. Using quantitative proteomics and active cullin-RING ligase (CRL) profiling, we identify LRRC58 as a cysteine-responsive substrate receptor whose stability increases sharply under cysteine starvation. Proteomics reveals an inverse relationship between LRRC58 and the metabolic enzyme cysteine dioxygenase 1 (CDO1), suggesting a cysteine-linked regulatory axis. Biochemical reconstitution and cryo-EM structures show that LRRC58 forms an active CUL2- or CUL5-based CRL that selectively positions CDO1 for ubiquitylation at Lys8. Disease mutant versions of CDO1 mapping to the LRRC58 interface and impaired for the endogenous ubiquitylation pathway were degraded through orthogonal targeting by a VHL-based degrader. Together, our proteomics-guided discovery pipeline, cellular stability studies, and structural analyses uncover a metabolically-tuned LRRC58-CDO1 pathway that links cysteine availability to selective proteasomal turnover, reveals principles of metabolite-regulated CRL activity, and showcases mechanisms distinguishing endogenous and targeted protein degradation.
A multimodal AI laboratory agent was developed to capture and communicate proteomics experimental practices by linking written protocols with hands-on laboratory work through integrated analysis of video, speech, and text.
A multimodal AI laboratory agent was developed to capture and communicate proteomics experimental practices by linking written protocols with hands-on laboratory work through integrated analysis of video, speech, and text.
Pathogenic mutations in Leucine-rich repeat kinase 2 ( LRRK2 ) are the predominant genetic cause of Parkinson’s disease (PD) and often increase kinase activity, making LRRK2 inhibitors promising treatment options. Although LRRK2 kinase inhibitors are advancing clinically, non-invasive readouts of LRRK2-linked pathway modulation remain limited. Profiling urinary proteomes from 1,215 individuals across three cohorts and integrating whole-genome sequencing from >500 participants to map genotype–proteome associations, we identified 177 urinary proteins associated with pathogenic LRRK2, enriched for lysosomal/glycosphingolipid, immune, and membrane-trafficking pathways. Machine learning narrowed the features to a cohort-agnostic 30-protein panel that classified G2019S carriers with a mean ROC AUC of 0.91 across independent tests. To evaluate translation, we performed multi-organ and urinary proteomics in rat gain- and loss-of-function models (BAC- LRRK2 G2019S and Lrrk2 KO ) and after Lrrk2 inhibition (MLi-2 and PF-475), revealing tissue-specific responses—strongest in kidney—and cross-species overlap, including 24 brain proteins detectable in human urine. Rat-derived perturbations predicted LRRK2 mutation status in patients (AUC 0.75) and reversed with Lrrk2 inhibition, supporting their pharmacodynamic utility. Together, our findings establish urine as a scalable, non-invasive matrix that captures systemic and brain-relevant consequences of LRRK2 dysfunction and nominate candidate pharmacodynamic markers set to support LRRK2-directed trials.
Pathogenic mutations in Leucine-rich repeat kinase 2 ( LRRK2 ) are the predominant genetic cause of Parkinson’s disease (PD) and often increase kinase activity, making LRRK2 inhibitors promising treatment options. Although LRRK2 kinase inhibitors are advancing clinically, non-invasive readouts of LRRK2-linked pathway modulation remain limited. Profiling urinary proteomes from 1,215 individuals across three cohorts and integrating whole-genome sequencing from >500 participants to map genotype–proteome associations, we identified 177 urinary proteins associated with pathogenic LRRK2, enriched for lysosomal/glycosphingolipid, immune, and membrane-trafficking pathways. Machine learning narrowed the features to a cohort-agnostic 30-protein panel that classified G2019S carriers with a mean ROC AUC of 0.91 across independent tests. To evaluate translation, we performed multi-organ and urinary proteomics in rat gain- and loss-of-function models (BAC- LRRK2 G2019S and Lrrk2 KO ) and after Lrrk2 inhibition (MLi-2 and PF-475), revealing tissue-specific responses—strongest in kidney—and cross-species overlap, including 24 brain proteins detectable in human urine. Rat-derived perturbations predicted LRRK2 mutation status in patients (AUC 0.75) and reversed with Lrrk2 inhibition, supporting their pharmacodynamic utility. Together, our findings establish urine as a scalable, non-invasive matrix that captures systemic and brain-relevant consequences of LRRK2 dysfunction and nominate candidate pharmacodynamic markers set to support LRRK2-directed trials.
