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Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD).

J Vis Exp

January 2025

Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Harvard Medical School;

A method to quantitate the stabilization of Mitochondria-Associated endoplasmic reticulum Membranes (MAMs) in a 3-dimensional (3D) neural model of Alzheimer's disease (AD) is presented here. To begin, fresh human neuro progenitor ReN cells expressing β-amyloid precursor protein (APP) containing familial Alzheimer's disease (FAD) or naïve ReN cells are grown in thin (1:100) Matrigel-coated tissue culture plates. After the cells reach confluency, these are electroporated with expression plasmids encoding red fluorescence protein (RFP)-conjugated mitochondria-binding sequence of AKAP1(34-63) (Mito-RFP) that detects mitochondria or constitutive MAM stabilizers MAM 1X or MAM 9X that stabilize tight (6 nm ± 1 nm gap width) or loose (24 nm ± 3 nm gap width) MAMs, respectively.

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iPSCs can serve as a renewable source of a consistent edited cell product, overcoming limitations of primary cells. While feeder-free generation of clinical grade iPSC-derived CD8 T cells has been achieved, differentiation of iPSC-derived CD4sp and regulatory T cells requires mouse stromal cells in an artificial thymic organoid. Here we report a serum- and feeder-free differentiation process suitable for large-scale production.

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Lithium-ion battery cathodes are manufactured by coating slurries, liquid suspensions that typically include carbon black (CB), active material, and polymer binder. These slurries have a yield stress and complex rheology due to CB's microstructural response to flow. While optimizing the formulation and processing of slurries is critical to manufacturing defect-free and high-performance cathodes, engineering the shear rheology of cathode slurries remains challenging.

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Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques presents a promising approach for rapid pathogen identification. Previous studies have demonstrated that purine degradation metabolites are the primary contributors to SERS spectra; however, generating these distinguishable spectra typically requires a long incubation time (>10 h) at room temperature. Moreover, the lack of attention to spectral variations between strains of the same bacterial species has limited the generalizability of ML models in real-world applications.

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