BACKGROUND: Detection of elements in individual cells by inductively coupled plasma (ICP) spectrometry has recently attracted significant interest in biological research, due to the unique ability of ICP spectrometry for trace element analysis. However, performing single-cell analysis using ICP optical emission spectrometry (ICP-OES) remains a challenge due to the small size and discrete nature of cells. This is while ICP-OES can serve as a cost-effective and label-free method for this purpose. Therefore, it is necessary to improve the current ICP-OES technique to facilitate the detection of elements in single cells, thereby unlocking novel applications.
RESULTS: A new conical ICP torch, which has been illustrated to offer better analytical performance than the conventional ones, was applied to achieve the detection of calcium in single micro-sized cells. A new heated chamber was designed and coupled with a high-efficiency nebulizer as the sample introduction system. For the detection of single SiO2 particles, the number of particle events obtained by the new sample introduction system was found to be up to 9 times higher than that of the conventional system without sacrificing the signal intensity. Subsequently, calcium in human breast cancer cells (MDA-MB-231), mice breast cancer cells (Py8119), and mice osteocytes (MLO-Y4) was successfully detected using the new ICP-OES system. The cell detection efficiency turned out to be around 2%-3% which is much higher than that the reported values in previous single-cell ICP-OES research. Finally, as a new application, the effect of Yoda1, a recently identified activator of Piezo1 calcium channel, on osteocytes was investigated. The calcium content in Yoda1-treated MLO-Y4 cells was seen increase by 36% compared to the control sample.
CONCLUSIONS: This research reveals the capability of ICP-OES in single-cell analysis for micro-sized cells which was made possible by the new conical ICP torch and the new sample introduction system. The ability to detect calcium in single mammalian cells enables the first ever application of this technique to assess the impact of the Yoda1 activator on the calcium level in osteocytes.

The emergence of new pathogens continues to fuel the need for advanced high-containment laboratories across the globe. Here we explore challenges and opportunities for integration of cytometry, a central technology for cell analysis, within high-containment laboratories. We review current applications in infectious disease, vaccine research, and biosafety. Considerations specific to cytometry within high-containment laboratories, such as biosafety requirements, and sample containment strategies are also addressed. We further tour the landscape of emerging technologies, including combination of cytometry with other omics, the application of automation, and artificial intelligence. Finally, we propose a framework to fast track the immersion of advanced technologies into the high-containment research setting to improve global preparedness for new emerging diseases.

In this work, we synthesized a green fluorescent dye derivative, 1,3,5,7-tetramethyl-BODIPY, with a heptyl substituent at the 8-position. The obtained highly hydrophobic compound was able to rapidly and irreversibly bind to eukaryotic cells. Incubation of cells with the dye over different periods of time or at different concentrations allowed us to control the degree of cell labeling and the level of fluorescence. This made it possible to modulate the fluorescence level of different eukaryotic cell cultures and then distinguish them by their level of fluorescence signal in the green channel in cytometric experiments. The labeled cells can be combined and further analyzed in the same test tube under identical conditions using the channels in which the dye does not fluoresce. This approach has been tested on a number of tumor cell cultures containing the HER2 receptor on their surface. The representation of the receptor in these cells was analyzed in one test tube in one run using a HER2-specific ligand based on the hybrid protein DARPin9_29-mCherry, which fluoresces in the red region of the spectrum.

In this work, we developed a simple method to fabricate a thickness-based continuous stiffness gradient for biological studies. It was made by glass slides, polydimethylsiloxane (PDMS) pre-polymer, spacer and clips only, without any sophisticated equipment. It is easy to fabricate in any general biological and pharmaceutical laboratories. The stiffness gradient was characterized in terms of apparent Young\'s modulus by atomic force microscopy (AFM) and the Young\'s modulus along the gradient was found to be 8.5-120kPa, which is within the physiological relevant range. HeLa-C3 cells were cultured on the gradient to study their morphological behavior according to the substrate stiffness. Furthermore, the drug efficiency of etoposide, an anti-cancer drug, was studied along the substrate stiffness gradient. It was found that HeLa-C3 cells cultured on the soft region of the gradient (8.5-11kPa) are more sensitive to etoposide. We believe the proposed device could promote cell investigations and drug screenings on a substrate with comparable stiffness to the native tissue.

Since the red blood cell shape affects the oxygen transport, so a robust method to reconstruct the 3D shape of an RBC from different projections is presented. A robust one-piece polarizing holographic microscope setup is used to record inline holograms of normal and cancerous red blood cells (RBCs) with high stability. The inline holograms are corrected by flat fielding and windowed Fourier filtering methods to mitigate the zero-order and the defocused twin image due to the inline recording configuration to the least measure. The corrected inline holograms are then reconstructed by the angular spectrum method to extract the 2D wrapping phase-contrast images. The 2D wrapping phase-contrast images are then unwrapped using the graph cuts algorithm to extract the continuous 2D phase-contrast images. The continuous 2D phase-contrast images are reconstructed at different projections by the multiplicative technique to extract the 3D shape of the normal and the cancerous RBCs. Experimental results show that any deformation in the shape of the normal and the cancerous RBCs can be seen clearly at any rotational angle in 3D. This method, which is based on the degree of deformation from the best fitting, can be used as an alternative method of counting method for discrimination between normal and cancerous cells and hence diagnoses the disease easily.

Drug-resistant cells and anti-inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live-cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single-cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures.

Paper-based cultures are an emerging platform for preparing three-dimensional (3D) tissue- and tumor-like structures. The ability to stack individual sheets of cell-containing paper affords a modular means of assembling structures with defined cellular compositions and microenvironments. These layered stacks are easily separated at the end of an experiment, providing spatially resolved populations of live cells for further analysis. Here we describe a workflow in which cell viability, drug penetration, and drug metabolism are quantified in a spatially resolved manner. Specifically, we mapped the distribution of the drug irinotecan and its bioactive metabolite SN38 in a colorectal cancer cell-containing stacked structure with liquid chromatography-mass spectrometry (LC-MS). This paper provides the first example of a 3D culture platform that quantifies viability and drug metabolism in a spatially resolved manner. Our data show that cells at the bottom of the stack are more drug-resistant than layers in contact with the culture medium, similar to cells in the nutrient-poor center of a proliferating tumor being more drug-resistant than the rapidly dividing cells at its periphery. The powerful combination of quantitative viability and drug metabolism measurements will enable future studies to determine the exact mechanism(s) of drug resistance in different regions of a tumor.

The main objective for understanding fluorescence microscopy data is to investigate and evaluate the fluorescent signal intensity distributions as well as their spatial relationships across multiple channels. The quantitative analysis of 3D fluorescence microscopy data needs interactive tools for researchers to select and focus on relevant biological structures. We developed an interactive tool based on volume visualization techniques and GPU computing for streamlining rapid data analysis. Our main contribution is the implementation of common data quantification functions on streamed volumes, providing interactive analyses on large data without lengthy preprocessing. Data segmentation and quantification are coupled with brushing and executed at an interactive speed. A large volume is partitioned into data bricks, and only user-selected structures are analyzed to constrain the computational load. We designed a framework to assemble a sequence of GPU programs to handle brick borders and stitch analysis results. Our tool was developed in collaboration with domain experts and has been used to identify cell types. We demonstrate a workflow to analyze cells in vestibular epithelia of transgenic mice.

In this work, an integrated 3-dimensional microfluidic device was developed for simulation of the immune microenvironment of glioma niche through the co-culture of three kinds of related cells. Glioma cells, endothelial cells and macrophages were co-cultured together in the microfluidic device, spatially separated by the design of a coffer structure and the use of hydrogel. This platform enabled separate monitoring of the morphology change and migration of cells, as well as molecular interactions between different kinds of cells. Tumor cells were found to exhibit EMT like shape change to become thinner, and sensitive perception and taxis toward macrophages. The influence of tumor cells and the microenvironment, macrophages would be re-educated and the phenotype could be changed from M1 (tumor-suppressive) to M2 (tumor-supportive), which could be validated through cytokines analysis. This 3D microfluidic tumor model provides a powerful tool for studying the biological properties of glioma niche.

Label-free live cell imaging was performed using a custom-built high-speed confocal Raman microscopy system. For various cell types, cell-intrinsic Raman bands were monitored. The high-resolution temporal Raman images clearly delineated the intracellular distribution of biologically important molecules such as protein, lipid, and DNA. Furthermore, optical phase delay measured using quantitative phase microscopy shows similarity with the image reconstructed from the protein Raman peak. This reported work demonstrates that Raman imaging is a powerful label-free technique for studying various biomedical problems in vitro with minimal sample preparation and external perturbation to the cellular system.