The Chinese University of Hong Kong (CUHK) School of Life Sciences’ latest study has developed a big data analytical method to study senescent neurons in normal aging and demented brains. Professor Kim Hei-Man Chow and her research team developed a data-driven bioinformatics analysis workflow that facilitates the identification of distinct features and biomarkers of senescent neurons. The research team revealed that mature neurons that recommitting into a cell cycle-like senescent process have a significant impact on the onset and progression of Alzheimer’s disease and related dementia. Findings of this study have been published in the prestigious international journal PLOS Biology and were selected as a feature article in the journal. This work has also been recognized as one of the top picks by the editors of PLOS Biology for the year 2024.

The cell cycle in general refers to the continuous process of cell division, growth, and subsequent division. During this continual process, cells eventually become aged until they are replaced by newly generated cells. Unlike typical cells, fully differentiated neurons in the brain cease to divide and they themselves fail to regenerate once they become fully mature. According to previous studies published by the same research team, certain neuronal populations in the brain may re-commit into a cell cycle-driven senescent process under various stress conditions. However, due to the limitations in the traditional research methods, it has been challenging to predict the molecular changes happening accurately and objectively to these cells.

A new study approach that overcomes such hurdles

Due to the unpredictable distribution and relatively low density of these cell cycle re-engaging and senescing neurons emerging throughout the brain, traditional histology or bulk tissue transcriptomic techniques are unable to identify the molecular characteristics, development processes, as well as their potential impact on the neighboring cells. Furthermore, conventional experimental methods are unable to distinguish whether these aging neurons exhibit any disease specificity due to the diverse microenvironment of the brain. The research team has developed a customized bioinformatics analysis workflow based on the unique biological properties of neurons, which also enables integrated analyses of multiple brain single-nucleus transcriptomic data. The team testified this approach with multiple sets of transcriptomic data from the brains of individuals with Alzheimer's disease in different countries, along with clinical data of these patients. With the new study approach, they identified how the senescing neurons are characterized by specific markers of different stages of the cell cycle, senescent signatures, and functional changes. The team re-confirmed that neurons with reactivated cell cycle machineries do not complete the cell division process to regenerate new progeny neurons; rather they undergo accelerated aging and accumulate aberrantly to affect the overall brain function and homeostasis.

Accumulations of senescent neurons are related to more advanced stages of Alzheimer’s disease

According to Dr. Chow, “The total number of these cell cycle re-engaging and senescing neurons is positively correlated to more advanced stages of Alzheimer’s diseases, and their accumulation are related to more severe pathologies of the disease, with aberrant expression of known disease risk genes. Similarly, accumulations of these abnormal neurons were identified as well in the case of Parkinson's disease-Lewy Body dementia. This new study approach allows researchers to gain a deeper understanding on the intrinsic properties of these cells and their uniqueness in disease condition, which may assist the development of precision medicine.”

Using Alzheimer’s disease as the primary study model, the analyses revealed that cell cycle re-engaging and senescing neurons are functionally defective, as compared to healthy neighboring neurons. In the healthy aging brain, the numbers of these neurons undergoing early- and late-stage aging are regulated under normal brain tissue homeostasis. However, in Alzheimer’s disease, these cells accumulate instead. Moreover, they are characterized by a unique set of biomarkers that known to promote pro-inflammatory responses, metabolic dysfunction, and pathology-related molecular characteristics.

The research team also extended their analysis to investigate senescent neurons in a model of Parkinson's disease-Lewy Body dementia. Additionally, they conducted an extensive analysis using a mouse model of brain aging, which further validated the efficacy of their bioinformatics approach in characterizing the profiles of senescent neurons across different species. The demonstrated applicability of this analytical approach in diverse disease models and cross-species settings opens new opportunities and insights that complement traditional histological-based approaches in studying the roles of senescent neurons in brain aging and disease pathogenesis. The ability to uncover disease-specific molecular signatures and identify novel marker genes in senescent neurons may also pave the way for new directions in future diagnostics and the development of senotherapeutic strategies.

Other important contributors to the study include Dr. Wu Deng and Miss Jacquelyne Ka-Li Sun, Postdoctoral Associate and final year Ph.D student at the CUHK School of Life Sciences.

The complete article is available at: https://doi.org/10.1371/journal.pbio.3002559. Upon its acceptance, this paper was chosen as a featured highlight in the journal and a press release was shared via AAAS “EurekAlert!” at: https://www.eurekalert.org/news-releases/1041360. Furthermore, this work has been recognized as one of the top picks by the editors of PLOS Biology for the year 2024: https://doi.org/10.1371/journal.pbio.3002985.