Aging, mitochondrial dysfunction, and cerebral microhemorrhages: a preclinical evaluation of SS-31 (elamipretide) and development of a high-throughput machine learning-driven imaging pipeline for cerebromicrovascular protection therapeutic screening

Cerebral microhemorrhages (CMHs, also known as cerebral microbleeds) contribute to vascular cognitive impairment and dementia (VCID), with aging and hypertension being key risk factors. Mitochondrial oxidative stress is a hallmark of cerebrovascular aging, leading to endothelial dysfunction. This study tests the hypothesis that increased mitochondrial oxidative stress contributes to age-related CMH susceptibility and evaluates the mitochondrial-targeted antioxidative peptide SS-31 (elamipretide) as a potential protective agent in an aged, hypertensive mouse model. Concurrently, we developed a high-throughput, machine learning-driven imaging pipeline to enhance CMH quantification and facilitate the screening of anti-aging vasoprotective interventions. To detect CMHs, brain sections were labeled with diaminobenzidine (DAB) and digitized using a slide scanner-based imaging platform. We developed multiple quantification tools, including color space transformation for enhanced contrast separation and a supervised machine-learning approach utilizing a random forest algorithm to generate whole-brain 3D reconstructions and precisely localize CMHs. We optimized a semi-automated detection method integrating color space transformation and machine learning, benchmarking it against traditional manual counting and color deconvolution-based approaches. While SS-31 treatment did not significantly mitigate hypertension-induced CMH burden in aged mice, our high-throughput imaging pipeline provided a reliable, scalable, and unbiased approach to CMH detection, reducing processing time while improving accuracy. This methodological advancement paves the way for future preclinical studies evaluating therapeutic strategies for cerebrovascular protection in aging. Our findings underscore the need for multi-targeted interventions to mitigate CMH-related neurovascular impairments and prevent VCID.

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Publication Details

Journal
GeroScience
Published
2025-04-02
DOI
https://doi.org/10.1007/s11357-025-01634-5
Citations
7
Primary Topic
Intracerebral and Subarachnoid Hemorrhage Research
Type
article
Field-Weighted Citation Impact
9.72

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article

Aging, mitochondrial dysfunction, and cerebral microhemorrhages: a preclinical evaluation of SS-31 (elamipretide) and development of a high-throughput machine learning-driven imaging pipeline for cerebromicrovascular protection therapeutic screening

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GeroScience
Intracerebral and Subarachnoid Hemorrhage Research
9.72
article

Aging, mitochondrial dysfunction, and cerebral microhemorrhages: a preclinical evaluation of SS-31 (elamipretide) and development of a high-throughput machine learning-driven imaging pipeline for cerebromicrovascular protection therapeutic screening

Anna Ungvari, Siva Sai Chandragiri, Ádám Nyúl‐Tóth, Zoltán Ungvári, Anna Csiszár, Roland Patai, Stefano Tarantini, Zoltán Benyó, Boglárka Csík, Raghavendra Y. Nagaraja, Santny Shanmugarama, Rafał Gulej, Andriy Yabluchanskiy, Dorina Nagy, Shoba Ekambaram, Krish Patel, Kiana Vali Kordestan, Mark Nagykaldi
article en
7 citations

Abstract

Cerebral microhemorrhages (CMHs, also known as cerebral microbleeds) contribute to vascular cognitive impairment and dementia (VCID), with aging and hypertension being key risk factors. Mitochondrial oxidative stress is a hallmark of cerebrovascular aging, leading to endothelial dysfunction. This study tests the hypothesis that increased mitochondrial oxidative stress contributes to age-related CMH susceptibility and evaluates the mitochondrial-targeted antioxidative peptide SS-31 (elamipretide) as a potential protective agent in an aged, hypertensive mouse model. Concurrently, we developed a high-throughput, machine learning-driven imaging pipeline to enhance CMH quantification and facilitate the screening of anti-aging vasoprotective interventions. To detect CMHs, brain sections were labeled with diaminobenzidine (DAB) and digitized using a slide scanner-based imaging platform. We developed multiple quantification tools, including color space transformation for enhanced contrast separation and a supervised machine-learning approach utilizing a random forest algorithm to generate whole-brain 3D reconstructions and precisely localize CMHs. We optimized a semi-automated detection method integrating color space transformation and machine learning, benchmarking it against traditional manual counting and color deconvolution-based approaches. While SS-31 treatment did not significantly mitigate hypertension-induced CMH burden in aged mice, our high-throughput imaging pipeline provided a reliable, scalable, and unbiased approach to CMH detection, reducing processing time while improving accuracy. This methodological advancement paves the way for future preclinical studies evaluating therapeutic strategies for cerebrovascular protection in aging. Our findings underscore the need for multi-targeted interventions to mitigate CMH-related neurovascular impairments and prevent VCID.

GeroScienceVol. 47(3)
Semmelweis University (HU), University of Oklahoma Health Sciences Center (US)
University of Oklahoma, Semmelweis Egyetem, University of Oklahoma Health Sciences Center, National Institute on Aging
Life in Land
Openalex Percentile: Top 2%
Intracerebral and Subarachnoid Hemorrhage Research
9.72
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