Biology subjects

Esfandiari, L.

Publications and source records attributed to Esfandiari, L..

4 recordsLinked to original sources

bioRxiv2025

Matrix stiffness modulated release of spheroid-derived extracellular vesicles and discovery of Piezo1 cargo

Augmented extracellular matrix (ECM) stiffness is a mechanical hallmark of cancer. Mechanotransduction studies have extensively probed the mechanisms by which ECM stiffness regulates intracellular communication. However, the influence of stiffness on intercellular communication aiding tumor progression in three-dimensional microenvironments remains unknown. Small extracellular vesicles (EVs) are communicators of altered biophysical cues to distant sites through EV-ECM interactions and EV-mediated recipient cell-ECM interactions. Here we demonstrate stiffness-mediated modulation of small EVs secretion and cargo from three-dimensional oral squamous cell carcinoma spheroids. Using a spheroid culture platform with varying matrix stiffness properties, we show that small EVs carry parental biomolecular cargo, including mechanosensitive Piezo1 ion channel and adhesion molecule CD44. We comprehensively validate the presence of both markers in our EV populations using proteomic and genetic analysis. Transcriptomic analysis of microRNA and long non-coding RNA cargo of small EVs released from soft and stiff ECM spheroids revealed enrichment of tumorigenic and metastatic profiles in EVs from stiff ECM cultures compared to that of soft ones. Gene set enrichment analysis of a comparative dataset obtained by overlaying spheroid mRNA and EV miRNA profiles identified key oncogenic pathways involved in cell-EV crosstalk in the spheroid model.

cancer biology↗

bioRxiv2024

Enhanced Piezoelectric Performance of PVDF-TrFE Nanofibers through Annealing for Tissue Engineering Applications

This study investigates bioelectric stimulations role in tissue regeneration by enhancing the piezoelectric properties of tissue-engineered grafts using annealed poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) scaffolds. Annealing at temperatures of 80{degrees}C, 100{degrees}C, 120{degrees}C, and 140{degrees}C was assessed for its impact on material properties and physiological utility. Analytical techniques such as Differential Scanning Calorimetry (DSC), Fourier-Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD) revealed increased crystallinity with higher annealing temperatures, peaking in {beta}-phase content and crystallinity at 140{degrees}C. Scanning Electron Microscopy (SEM) showed that 140{degrees}C annealed scaffolds had enhanced lamellar structures, increased porosity, and maximum piezoelectric response. Mechanical tests indicated that 140{degrees}C annealing improved elastic modulus, tensile strength, and substrate stiffness, aligning these properties with physiological soft tissues. In vitro assessments in Schwann cells demonstrated favorable responses, with increased cell proliferation, contraction, and extracellular matrix attachment. Additionally, genes linked to extracellular matrix production, vascularization, and calcium signaling were upregulated. The foreign body response in C57BL/6 mice, evaluated through Hematoxylin and Eosin (H&E) and Picrosirius Red staining, showed no differences between scaffold groups, supporting the potential for future functional evaluation of the annealed group in tissue repair.

bioengineering↗

bioRxiv2024

Promoting Human Intestinal Organoid Formation and Stimulation Using Piezoelectric Nanofiber Matrices

Human organoid model systems have changed the landscape of developmental biology and basic science. They serve as a great tool for human specific interrogation. In order to advance our organoid technology, we aimed to test the compatibility of a piezoelectric material with organoid generation, because it will create a new platform with the potential for sensing and actuating organoids in physiologically relevant ways. We differentiated human pluripotent stem cells into spheroids following the traditional human intestinal organoid (HIO) protocol atop a piezoelectric nanofiber scaffold. We observed that exposure to the biocompatible piezoelectric nanofibers promoted spheroid morphology three days sooner than with the conventional methodology. At day 28 of culture, HIOs grown on the scaffold appeared similar. Both groups were readily transplantable and developed well-organized laminated structures. Graft sizes between groups were similar. Upon characterizing the tissue further, we found no detrimental effects of the piezoelectric nanofibers on intestinal patterning or maturation. Furthermore, to test the practical feasibility of the material, HIOs were also matured on the nanofiber scaffolds and treated with ultrasound, which lead to increased cellular proliferation which is critical for organoid development and tissue maintenance. This study establishes a proof of concept for integrating piezoelectric materials as a customizable platform for on-demand electrical stimulation of cells using remote ultrasonic waveforms in regenerative medicine.

bioengineering↗

bioRxiv2024

Machine Learning Approaches in Label-Free Small Extracellular Vesicles Analysis with Surface-Enhanced Raman Scattering (SERS) for Cancer Diagnostics

Early diagnosis remains of pivotal importance in reducing patient morbidity and mortality in cancer. To this end, liquid biopsy is emerging as a tool to perform broad cancer screenings. Small extracellular vesicles (sEVs), also called exosomes, found in bodily fluids can serve as important cancer biomarkers in these screenings. Our group has recently developed a label-free electrokinetic microchip to purify sEVs from blood. Herein, we demonstrate the feasibility to integrate this approach with surface-enhanced Raman scattering (SERS) analysis. SERS can be used to characterized extracted sEVs through their vibrational fingerprint that changes depending on the origin of sEVs. While these changes are not easily identified in spectra, they can be modeled with machine learning (ML) approaches. Common ML approaches in the field of spectral analysis use dimensionality reduction method that often function as a black box. To avoid this pitfall, we used Shapley additive explanations (SHAP) is a type of explainable AI (XAI) that bridges ML models and human comprehension by calculating the specific contribution of individual features to a models predictions, directly correlating model/decisions with the original data. Using these approaches we demonstrated a proof-of-concept model predictive of cancer from isolated sEVs, integrating the electrokinetic device and SERS. This work explores the use of explainable AI to perform diagnostic analysis on complex SERS data of clinical samples, while reporting interpretable biochemical information. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/581099v2_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1cbcb8aorg.highwire.dtl.DTLVardef@9ffc00org.highwire.dtl.DTLVardef@15999a9org.highwire.dtl.DTLVardef@1775726_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗