Alpha-synuclein-associated changes in PINK1-PRKN-mediated mitophagy are disease context dependent.

Alpha-synuclein (αsyn) aggregates are pathological features of several neurodegenerative conditions including Parkinson disease (PD), dementia with Lewy bodies, and multiple system atrophy (MSA). Accumulating evidence suggests that mitochondrial dysfunction and impairments of the autophagic-lysosomal system can contribute to the deposition of αsyn, which in turn may interfere with health and function of these organelles in a potentially vicious cycle. Here we investigated a potential convergence of αsyn with the PINK1-PRKN-mediated mitochondrial autophagy pathway in cell models, αsyn transgenic mice, and human autopsy brain. PINK1 and PRKN identify and selectively label damaged mitochondria with phosphorylated ubiquitin (pS65-Ub) to mark them for degradation (mitophagy). We found that disease-causing multiplications of αsyn resulted in accumulation of the ubiquitin ligase PRKN in cells. This effect could be normalized by starvation-induced autophagy activation and by CRISPR/Cas9-mediated αsyn knockout. Upon acute mitochondrial damage, the increased levels of PRKN protein contributed to an enhanced pS65-Ub response. We further confirmed increased pS65-Ub-immunopositive signals in mouse brain with αsyn overexpression and in postmortem human disease brain. Of note, increased pS65-Ub was associated with neuronal Lewy body-type αsyn pathology, but not glial cytoplasmic inclusions of αsyn as seen in MSA. While our results add another layer of complexity to the crosstalk between αsyn and the PINK1-PRKN pathway, distinct mechanisms may underlie in cells and brain tissue despite similar outcomes. Notwithstanding, our finding suggests that pS65-Ub may be useful as a biomarker to discriminate different synucleinopathies and may serve as a potential therapeutic target for Lewy body disease.

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.