The majority of the population of glial cells in the central nervous system consists of astrocytes, and impairment of astrocytes causes various disorders. It is useful to assess the multiple astrocytic properties in order to understand their complex roles in the pathophysiology. Although we can differentiate human astrocytes from induced pluripotent stem cells (iPSCs), it remains unknown how we can analyse and reveal the multiple properties of astrocytes in complexed human disease conditions. For this purpose, we tested astrocytic differentiation protocols from feeder-free iPSCs based on the previous method with some modifications. Then, we set up extra- and intracellular assessments of iPSC-derived astrocytes by testing cytokine release, calcium influx, autophagy induction and migration. The results led us to analytic methods with conditions in which iPSC-derived astrocytes behave as in vivo. Finally, we applied these methods for modelling an astrocyte-related disease, Alexander disease. An analytic system using iPSC-derived astrocytes could be used to recapture complexities in human astrocyte diseases.
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http://dx.doi.org/10.1111/jcmm.18214 | DOI Listing |
Vascular dementia (VaD) refers to a variety of dementias driven by cerebrovascular disease and is the second leading cause of dementia globally. VaD may be caused by ischemic strokes, intracerebral hemorrhage, and/or cerebral small vessel disease, commonly identified as white matter hyperintensities on MRI. The mechanisms underlying these white matter lesions in the periventricular brain are poorly understood.
View Article and Find Full Text PDFStem Cells
December 2024
Department of Psychiatry, University of Oxford, Headington, Oxford OX3 7JX, UK.
Human induced pluripotent stem cells (iPSCs) provide powerful cellular models of Alzheimer's disease (AD) and offer many advantages over non-human models, including the potential to reflect variation in individual-specific pathophysiology and clinical symptoms. Previous studies have demonstrated that iPSC-neurons from individuals with Alzheimer's disease (AD) reflect clinical markers, including β-amyloid (Aβ) levels and synaptic vulnerability. However, despite neuronal loss being a key hallmark of AD pathology, many risk genes are predominantly expressed in glia, highlighting them as potential therapeutic targets.
View Article and Find Full Text PDFJ Neurochem
January 2025
Department of Chemistry, Loughborough University, Loughborough, UK.
Altered energy metabolism in Alzheimer's disease (AD) is a major pathological hallmark implicated in the early stages of the disease process. Astrocytes play a central role in brain homeostasis and are implicated in multiple neurodegenerative diseases. Although numerous studies have investigated global changes in brain metabolism, redox status, gene expression and epigenetic markers in AD, the intricate interplay between different metabolic processes, particularly in astrocytes, remains poorly understood.
View Article and Find Full Text PDFBrain Behav Immun
November 2024
Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Department of Clinical Science, University of Bergen, Bergen, Norway.
Mounting evidence indicates the involvement of neuroinflammation in the development of schizophrenia (SCZ), but the potential role of astroglia in this phenomenon remains poorly understood. We assessed the molecular and functional consequences of inflammasome activation using induced pluripotent stem cell (iPSC)-derived astrocytes generated from SCZ patients and healthy controls (CTRL). Screening protein levels in astrocytes at baseline identified lower expression of the NLRP3-ASC complex in SCZ, but increased Caspase-1 activity upon specific NLRP3 stimulation compared to CTRL.
View Article and Find Full Text PDFStem Cells Dev
November 2024
Institute for Stem Cell Research and Regenerative Medicine, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
The quality of organoid models can be assessed by single-cell-RNA-sequencing (scRNA-seq) but often only bulk transcriptome data is available. Here we present a pipeline for the analysis of scRNA-seq data and subsequent "deconvolution," which is a method for estimating cell type fractions in bulk transcriptome data based on expression profiles and cell types found in scRNA-seq data derived from biopsies. We applied this pipeline on bulk iPSC-derived kidney and brain organoid transcriptome data to identify cell types employing two scRNA-seq kidney datasets and one brain dataset.
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