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@molneurodegen.bsky.socialOct 8, 2026, 4:59 PM

AI-assisted discovery of an #ABCA1 /ABCG1 activator for restoring #mitophagy and mitigating #Alzheimers pathology

Zhipeng Ling, Jun-Ping Pan, Xianglu Xiao...Evandro Fei Fang, Guang Yang, Guocai Wang, Fei Xiao, Yubo Zhang

link.springer.com/article/10.1...

@charlyguardia.bsky.socialOct 7, 2026, 3:06 PM

👋 Check the new issue of the #PlacentalCellBio report by @biomednews.bsky.social
#Mitophagy #Placentation #Preeclampsia

biomed.news/bims-placeb/...

Subscribe to receive weekly issues of this report by going to biomed.news/bims-placeb!

@cells-mdpi.bsky.socialSep 30, 2026, 4:48 PM

#CallForReading

🔵 Nobiletin Ameliorates Aging of Chicken Ovarian Prehierarchical Follicles by Suppressing Oxidative Stress and Promoting Autophagy 🔵

👉 www.mdpi.com/2073-4409/13...

#PoultryScience #OvarianAging #Nobiletin #Mitophagy #Antioxidants

@labmed2025.bsky.socialSep 26, 2026, 2:21 PM

Mitophagy in blood cancers: an ally—or a vulnerability?

This LMD review explores its dual roles in tumor survival and cell death, plus its impact on immunity. Could context-specific modulation offer new therapeutic avenues?

doi.org/10.1016/j.lm...

#LabMedDiscovery #Mitophagy

@alicefrolov.bsky.socialSep 20, 2026, 5:00 PM

Mitochondrial membrane potential heterogeneity with age

I am sharing a data visualization exploring how mitochondrial membrane potential (Δψm) declines.

#MitochondrialBioenergetics #TMRM #MembranePotential #Mitophagy #SingleCellHeterogeneity #OxidativePhosphorylation #MtDNA
#ProtonMotiveForce

Mitochondrial membrane potential heterogeneity with age I am sharing a data visualization exploring how mitochondrial membrane potential (Δψm) declines and becomes more dispersed across the lifespan. The figure, titled "Mitochondrial membrane potential collapses and disperses with age," presents single-cell TMRM (tetramethylrhodamine methyl ester) intensity across four age groups in three tissues. I want to emphasize at the outset that the underlying dataset is simulated; it is constructed to illustrate a trend that is well grounded in the existing literature rather than to report novel empirical measurements. The chart employs a faceted raincloud design, with each tissue occupying its own facet. Within each facet, the four age cohorts are represented by a combination of a half-density (violin) contour, jittered single-cell points, and an overlaid boxplot. This composite format was chosen deliberately to convey both central tendency and the full distributional structure, which is critical here because the phenomenon of interest is not only a shift in the mean but an expansion of intercellular variance. Two features are salient. First, median TMRM intensity declines monotonically with advancing age across all three tissues, consistent with progressive dissipation of the proton-motive force and diminished electron transport chain coupling efficiency. Second, and more importantly, the distributions broaden with age, indicating increasing cell-to-cell heterogeneity in Δψm. This dispersion is compatible with mosaic accumulation of mtDNA mutations, clonal expansion of dysfunctional mitochondrial populations, and stochastic failure of quality-control mechanisms such as mitophagy. I would welcome discussion regarding appropriate normalization strategies for TMRM intensity, particularly the non-quench versus quench mode distinction, and how these affect interpretation of the observed variance structure.
@transpread.bsky.socialSep 17, 2026, 3:05 PM

A 2026 Targetome study by Xinzhi Wang’s team (China Pharmaceutical University) reports that celastrol suppresses CTSS-dependent autophagy impairment, promotes protective iNKT1 polarization, and reduces cholestasis and fibrosis.
#Mitophagy #Cathepsin S
Details: doi.org/10.48130/tar...

@mitoworld.bsky.socialSep 8, 2026, 10:07 PM

This week's #mitochondria features from @cvasilescu.bsky.social @biomednews.bsky.social :
💥 #Mitophagy in neuronal health & disease: mechanisms to #neurodegeneration
💥 Mitochondrial diversity in the brain
💥 Mitochondrial #bioenergetics & drug addiction

#Mitoscientists #mitochondrialdisease

@alicefrolov.bsky.socialSep 8, 2026, 6:21 PM

Figure title: "Mitochondrial membrane potential collapse and dispersion with age"

Subtitle: Per-cell TMRM fluorescence in fibroblasts across four age groups and two tissues

#MitochondrialBioenergetics #TMRM #MembranePotential #SingleCellFunction #mtDNAheteroplasmy #Mitophagy

Figure title: "Mitochondrial membrane potential collapse and dispersion with age" Subtitle: Per-cell TMRM fluorescence in fibroblasts across four age groups and two tissues I am sharing a figure exploring how mitochondrial membrane potential (ΔΨm) distributions shift with donor age at single-cell resolution. The visualization uses a faceted half-violin raincloud design: each facet pairs the smoothed density (the "cloud") of per-cell TMRM fluorescence with jittered raw observations (the "rain"), facilitating simultaneous inspection of central tendency, distributional shape, and the tails that population-averaged assays obscure. Facets are arranged across four age groups and two tissue sources (dermal and pulmonary fibroblasts). A methodological caveat before interpretation: the data are simulated. They are parameterized to reproduce trends reported in the literature rather than to present novel empirical measurements, and are intended to illustrate the analytical framework and expected effect structure. Two features are salient. First, a progressive downward shift in median TMRM signal with age, consistent with age-associated ΔΨm depolarization attributable to declining electron transport chain flux and proton-motive force. Second, and arguably more informative, a marked increase in interquartile spread and bimodality in the older cohorts. This dispersion reflects mitochondrial heterogeneity — the coexistence of polarized and depolarized subpopulations within isogenic cultures — that likely arises from clonal expansion of mtDNA deletions, stochastic mitophagy insufficiency, and asymmetric organelle partitioning across mitotic divisions. I would emphasize that variance itself is a phenotype here. Reporting only mean ΔΨm collapses precisely the signal of interest. The raincloud geometry makes this explicit and, I would argue, should be standard for single-cell functional readouts.