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Biology subjects

Mantalaris, A.

Publications and source records attributed to Mantalaris, A..

2 recordsLinked to original sources

Intracellular biomacromolecule delivery by stimuli responsive protein vesicles loaded by hydrophobic ion pairing

Therapeutic biomacromolecules are highly specific, which results in controlled therapeutic effect and less toxicity than small molecules. However, proteins and nucleic acids are large and have significant surface hydrophilicity and charge, thus cannot diffuse into cells. These chemical features render them poorly encapsulated by nanoparticles. Protein vesicles are self-assembling nanoparticles made by warming elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper and a globular protein fused to a glutamate-rich leucine zipper. To impart stimuli-responsive disassembly and small size, ELP was modified to include histidine and tyrosine residues. Additionally, hydrophobic ion pairing (HIP) was used to load and release protein and siRNA cargos requiring endosomal escape. HIP vesicles enabled delivery of cytochrome c, a cytosolically active protein, and significant reduction in viability in traditional two-dimensional (2D) human cancer cell line culture and a biomimetic three-dimensional (3D) organoid model of acute myeloid leukemia. They also delivered siRNA to knockdown protein expression in a murine fibroblast cell line. By examining uptake of positive and negatively charged fluorescent protein cargos loaded by HIP, this work revealed the necessity of HIP for cargo release and how HIP influences protein vesicle self-assembly using microscopy, small angle x-ray scattering, and nanoparticle tracking analysis. HIP protein vesicles have the potential to broaden the use of intracellular proteins for various diseases and extend protein vesicles to deliver other biomacromolecules.

bioengineering↗

Subcellular spatially resolved gene neighborhood networks in single cells

Mesenchymal stem cell (MSC)-based therapies have offered promising treatments against several disorders. However, the clinical efficacy and consistency remain underdeveloped. Single-cell and bulk molecular analyses have provided considerable heterogeneity of MSCs due to origin, expansion, and microenvironment. Image-based cellular omics methods elucidate ultimate variability in stem cell colonies, otherwise masked by bulk omics approaches. Here, we present a spatially resolved Gene Neighborhood Network (spaGNN) method to produce transcriptional density maps and analyze neighboring RNA distributions in single human MSCs and chondrocytes cultured on 2D collagen-coated substrates. This proposed strategy provides cell classification based on subcellular spatial features and gene neighborhood networks. Machine learning-based clustering of resultant data yields subcellular density classes of 20-plex biomarkers containing diverse transcript and protein features. The spaGNN reveals tissue-source-specific MSC transcription and spatial distribution characteristics. Multiplexed spaGNN analysis allows for rapid examination of spatially resolved subcellular features and activities in a broad range of cells used in pre-clinical and clinical research.

systems biology↗