Biology subjects

Kim, A. A.

Publications and source records attributed to Kim, A. A..

3 recordsLinked to original sources

bioRxiv2024

Transcriptional profiles of murine oligodendrocyte precursor cells across the lifespan

Oligodendrocyte progenitor cells (OPCs) are highly dynamic, widely distributed glial cells of the central nervous system (CNS) that are responsible for generating myelinating oligodendrocytes during development. By also generating new oligodendrocytes in the adult CNS, OPCs allow formation of new myelin sheaths in response to environmental and behavioral changes and play a crucial role in regenerating myelin following demyelination (remyelination). However, the rates of OPC proliferation and differentiation decline dramatically with aging, which may impair homeostasis, remyelination, and adaptive myelination during learning. To determine how aging influences OPCs, we generated a novel transgenic mouse line that expresses membrane-anchored EGFP under the endogenous promoter/enhancer of Matrilin-4 (Matn4-mEGFP) and performed high-throughput single-cell RNA sequencing, providing enhanced resolution of transcriptional changes during key transitions from quiescence to proliferation and differentiation across the lifespan. Comparative analysis of OPCs isolated from mice aged 30 to 720 days, revealed that aging induces distinct inflammatory transcriptomic changes in OPCs in different states, including enhanced activation of HIF-1 and Wnt pathways. Inhibition of these pathways in acutely isolated OPCs from aged animals restored their ability to differentiate, suggesting that this enhanced signaling may contribute to the decreased regenerative potential of OPCs with aging. This Matn4-mEGFP mouse line and single-cell mRNA datasets of cortical OPCs across ages help to define the molecular changes guiding their behavior in various physiological and pathological contexts.

neuroscience↗

bioRxiv2024

Brain-wide mapping of oligodendrocyte organization and oligodendrogenesis across the murine lifespan

Insulating sheaths of myelin accelerate neuronal signaling in complex networks of the mammalian brain. In the CNS, myelin sheaths are exclusively produced by oligodendrocytes, which continue to be generated throughout life to change patterns of myelination. However, a brain-wide analysis of oligodendrocyte dynamics across the lifespan has not been performed. We developed a rapid, robust cellular mapping pipeline involving tissue clearing, lightsheet microscopy, atlas alignment, and automated segmentation to define the location of all oligodendrocytes in the mouse brain. This analysis demonstrated the remarkable consistency of oligodendrocyte patterns between hemispheres, individuals, and sexes, and established that oligodendrocyte maps estimate myelin coverage. We trained a vision transformer to identify newly generated oligodendrocytes from millions of mature cells, highlighting age- and region-specific differences in oligodendrogenesis, and revealing areas of enhanced oligodendrocyte resilience and regenerative capacity following demyelination, demonstrating the utility of this pipeline for uncovering brain-wide oligodendrocyte dynamics in health and disease.

neuroscience↗

bioRxiv2023

Machines learn ecological networks: automated discovery of ecological networks based on empirical data

Constructing ecological networks is known to be important and challenging in community ecology. In particular, to construct the holistic structure of ecological networks, identifying species interaction is essential but often costly and impalpable. Recent studies providing major challenges in assembling ecological networks have highlighted the need of new and more powerful approaches to reconstruct biological networks, including species interaction networks. In literature, there are no promising verifications in using machine leaning (ML) approaches to reconstruct ecological networks. In this work, we develop and employ a variety of ML methods, including penalized regression and graphical tools, to reconstruct ecological networks. For evaluation, we apply the methods to empirical time series data sets of 20 species abundances collected at Lake Constance in central Europe. We use resampled data to identify highly-ranked interactions among species and measure their consistency across 7 ML methods and 5,000 learning processes. We show that the best precision, recall, and F1 score were 0.48, 0.97, and 0.64, respectively, among all penalized regression methods under comparison. In summary, our study shows that machine learning methods offer promising data-driven and automated tools for reconstructing ecological networks and discovering underlying biological interactions among species.

ecology↗