Biology subjects

Mirsadeghi, S.

Publications and source records attributed to Mirsadeghi, S..

4 recordsLinked to original sources

bioRxiv2024

APOE4 impacts cortical neurodevelopment and alters network formation in human brain organoids

Apolipoprotein E4 (APOE4) is the leading genetic risk factor for Alzheimers disease. While most studies examine the role of APOE4 in aging, imaging, and cognitive assessments reveal that APOE4 influences brain structure and function as early as infancy. Here, we examined human-relevant cellular phenotypes across neurodevelopment using induced pluripotent stem cell (iPSC) derived cortical and ganglionic eminence organoids (COs and GEOs). In COs, we showed that APOE4 decreased BRN2+ and SATB2+ cortical neurons, increased astrocytes and outer radial glia, and was associated with increased cell death and dysregulated GABA-related gene expression. In GEOs, APOE4 accelerated maturation of neural progenitors and neurons. Multi-electrode array recordings in assembloids revealed that APOE4 disrupted network formation and altered response to GABA, resulting in heightened excitability and synchronicity. Together, our data provides new insights into how APOE4 may influence cortical neurodevelopmental processes and network formation in the human brain.

neuroscience↗

bioRxiv2024

Dual effects of ARX poly-alanine mutations in human cortical and interneuron development

Infantile spasms, with an incidence of 1.6 to 4.5 per 10,000 live births, are a relentless and devastating childhood epilepsy marked by severe seizures but also leads to lifelong intellectual disability. Alarmingly, up to 5% of males with this condition carry a mutation in the Aristaless-related homeobox (ARX) gene. Our current lack of human-specific models for developmental epilepsy, coupled with discrepancies between animal studies and human data, underscores the gap in knowledge and urgent need for innovative human models, organoids being one of the best available. Here, we used human neural organoid models, cortical organoids (CO) and ganglionic eminences organoids (GEO) which mimic cortical and interneuron development respectively, to study the consequences of PAE mutations, one of the most prevalent mutation in ARX. ARXPAE produces a decrease expression of ARX in GEOs, and an enhancement in interneuron migration. That accelerated migration is cell autonomously driven, and it can be rescued by inhibiting CXCR4. We also found that PAE mutations result in an early increase in radial glia cells and intermediate progenitor cells, followed by a subsequent loss of cortical neurons at later timepoints. Moreover, ARX expression is upregulated in COs derived from patients at 30 DIV and is associated with alterations in the expression of CDKN1C. Furthermore, ARXPAE assembloids had hyperactivity which were evident at early stages of development. With effective treatments for infantile spasms and developmental epilepsies still elusive, delving into the role of ARXPAE mutations in human brain organoids represents a pivotal step toward uncovering groundbreaking therapeutic strategies.

neuroscience↗

bioRxiv2023

Transplanted Autologous Neural Stem Cells Show Promise in Restoring Motor Function in Monkey Spinal Cord Injury

Spinal cord injury (SCI) is a devastating condition that can result in permanent loss of motor and sensory function. In recent years, transplantation of neural stem cells (NSCs) has emerged as a promising therapeutic approach for SCI. However, adult NSCs residing in the central nervous system (CNS) show promise for cell-replacement therapy in neurodegenerative disorders. In this study, we aimed to investigate the efficacy of transplanting autologous NSCs isolated from the subventricular zone (SVZ-NSCs) into a spinal cord injury model in Rhesus monkeys. We induced SCI in eight Rhesus monkeys and then transplanted SVZ-NSCs into the injury site. Behavioral assessments and magnetic resonance imaging (MRI) were performed to evaluate the functional and structural recovery of the spinal cord. Histological analyses were also performed to assess the survival and differentiation of the transplanted cells. SVZ-NSCs were capable of expressing SOX2, Nestin, and GFAP in addition to self-renewing and spontaneous differentiation in vitro. The monkeys showed sensory and motor function recovery, spinal tract regeneration, and partial reconstruction of the spinal cord. Positive Reinforcement Training confirmed no adverse effects from the isolation procedure. Our findings suggest that NSCs transplantation could be a promising therapeutic approach for SCI in humans, and further studies are warranted to investigate its potential for clinical translation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/539673v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@1f9257forg.highwire.dtl.DTLVardef@cbd0aorg.highwire.dtl.DTLVardef@4ef324org.highwire.dtl.DTLVardef@13ce48b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

AimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.\n\nMethods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.\n\nConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of \"irregularity or abnormal waveforms\" in an in vitro model of cardiotoxicity experiments.

bioinformatics↗