AFM methods for studying the morphology and micromechanical properties of the membrane of human buccal epithelium cell

Deep Learning-driven Automatic Nuclei Segmentation of Label-free Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging

Chromatin-sensitive Partial Wave Spectroscopic (csPWS) microscopy offers a non-invasive glimpse into the mass density distribution of cellular structures at the nanoscale, leveraging the spectroscopic information. Such capability allows us to analyze the chromatin structure and organization and the global transcriptional state of the cell nuclei for the study of its role in carcinogenesis. Accurate segmentation of the nuclei in csPWS microscopy images is an essential step in isolating them for further analysis. However, manual segmentation is error-prone, biased, time-consuming, and laborious, resulting in disrupted nuclear boundaries with partial or over-segmentation. Here, we present an innovative deep-learning-driven approach to automate the accurate nuclei segmentation of label-free live cell csPWS microscopy imaging data. Our approach, csPWS-seg, harnesses the Convolutional Neural Networks-based U-Net model with an attention mechanism to automate the accurate cell nuclei segmentation of csPWS microscopy images. We leveraged the structural, physical, and biological differences between the cytoplasm, nucleus, and nuclear periphery to construct three distinct csPWS feature images for nucleus segmentation. Using these images of HCT116 cells, csPWS-seg achieved superior performance with a median Intersection over Union (IoU) of 0.80 and a Dice Similarity Coefficient (DSC) score of 0.88. The csPWS-seg overcame the segmentation performance over the baseline U-Net model and another attention-based model, SE-U-Net, marking a significant improvement in segmentation accuracy. Further, we analyzed the performance of our proposed model with four loss functions: binary cross-entropy loss, focal loss, dice loss, and Jaccard loss. The csPWS-seg with focal loss provided the best results compared to other loss functions. The automatic and accurate nuclei segmentation offered by the csPWS-seg not only automates, accelerates, and streamlines csPWS data analysis but also enhances the reliability of subsequent chromatin analysis research, paving the way for more accurate diagnostics, treatment, and understanding of cellular mechanisms for carcinogenesis.

Stereoscopic Imaging of Single Molecules at Plasma Membrane of Single Cell Using Photoreduction-Assisted Electrochemistry

Stereoscopic imaging of single molecules at the plasma membrane of single cell requires spatial resolutions in 3 dimensions (x-y-z) at 10-nm level, which is rarely achieved using most optical super-resolution microscopies. Here, electrochemical stereoscopic microscopy with a detection limit down to a single molecule is achieved using a photoreduction-assisted cycle inside a 20-nm gel electrolyte nanoball at the tip of a nanopipette. On the basis of the electrochemical oxidation of Ru(bpy)3 2+ into Ru(bpy)3 3+ followed by the reduction of Ru(bpy)3 3+ into Ru(bpy)3 2+ by photogenerated isopropanol radicals, a charge of 1.5 fC is obtained from the cycling electron transfers involving one Ru(bpy)3 2+/3+ molecule. By using the nanopipette to scan the cellular membrane modified with Ru(bpy)3 2+-tagged antibody, the morphology of the cell membrane and the distribution of carcinoembryonic antigen (CEA) on the membrane are electrochemically visualized with a spatial resolution of 14 nm. The resultant stereoscopic image reveals more CEA on membrane protrusions, providing direct evidence to support easy access of membrane CEA to intravenous antibodies. The breakthrough in single-molecule electrochemistry at the cellular level leads to the establishment of high-resolution 3-dimensional single-cell electrochemical microscopy, offering an alternative strategy to remedy the imperfection of stereoscopic visualization in optical microscopes.

Human buccal epithelial cells as a model system for molecular analysis of DNA, RNA and protein

We report use of human buccal epithelial cells as an easy model system for isolation and molecular analysis of genomic DNA, RNA, and protein to study any gene of interest by Polymerase Chain Reaction (PCR), RNA expression by Reverse Transcription-PCR (RT-PCR), protein-profiling, and expression by western blot as well as DNA-methylation by Msp I/Hpa II-restriction digestion. We used simple methods to isolate genomic DNA, RNA and protein from human buccal cells collected by oral swab and cultured them in-vitro. The microscopic observation of haematoxylin and eosin (EA-50) stained cells, genomic PCR of house-keeping genes (GAPDH and β-actin), RT-PCR of GAPDH and β-actin mRNAs and whole cell protein-profiling by Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) were carried out. Expression of β-actin protein in supernatant and pellet fractions of the cells was determined by western blot analysis. MTT-assay was carried out to assess the cell viability and cell growth. Green Fluorescence Protein (GFP)-DNA was expressed in these cells by transient transfection. DNA-methylation in the genome was analyzed by Msp I/ Hpa II restriction digestion and agarose gel electrophoresis. Thus these methods can be used for molecular analysis of DNA, RNA and protein from the human buccal epithelial cells for studying and monitoring health, disease, population genetics/genomics and epidemiology under different conditions.