MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data.
Ontology highlight
ABSTRACT:
SUBMITTER: Krijgsman D
PROVIDER: S-EPMC8689354 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Bioluminescence microscopy has revealed that gene expression in individual cells can respond differently to the same stimulus. To understand this phenomenon, it is important to sequentially observe the series of events from cellular signal transduction to gene expression regulated by specific transcription factors derived from signaling cascades in individual cells. However, these processes have been separately analyzed with fluorescence and bioluminescence microscopy. Furthermore, in culture medium, the background fluorescence of luciferin-a substrate of luciferase in promoter assays of gene expression in cultured cells-confounds the simultaneous observation of fluorescence and bioluminescence. Therefore, we optimized conditions for optical filter sets based on spectral properties and the luciferin concentration based on cell permeability for fluorescence observation combined with bioluminescence microscopy. An excitation and emission filter set (492-506 nm and 524-578 nm) was suitable for green fluorescent protein and yellow fluorescent protein imaging of cells, and >100 ?M luciferin was acceptable in culture medium based on kinetic constants and the estimated intracellular concentration. Using these parameters, we present an example of sequential fluorescence and bioluminescence microscopic observation of signal transduction (translocation of protein kinase C alpha from the cytoplasm to the plasma membrane) coupled with activation of gene expression by nuclear factor of kappa light polypeptide B in individual cells and show that the gene expression response is not completely concordant with upstream signaling following stimulation with phorbol-12-myristate-13-acetate. Our technique is a powerful imaging tool for analysis of heterogeneous gene expression together with upstream signaling in live single cells.
| S-EPMC4745033 | biostudies-literature
Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
| S-EPMC6767556 | biostudies-literature
Project description:Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (A?) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (?17 ?m) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (?5 ?m), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.
| S-EPMC6885010 | biostudies-literature
Project description:The study of skeletal muscle continues to support the accurate diagnosis of mitochondrial disease and remains important in delineating molecular disease mechanisms. The heterogeneous expression of oxidative phosphorylation proteins and resulting respiratory deficiency are both characteristic findings in mitochondrial disease, hence the rigorous assessment of these at a single cell level is incredibly powerful. Currently, the number of proteins that can be assessed in individual fibres from a single section by immunohistochemistry is limited but imaging mass cytometry (IMC) enables the quantification of further, discrete proteins in individual cells. We have developed a novel workflow and bespoke analysis for applying IMC in skeletal muscle biopsies from patients with genetically-characterised mitochondrial disease, investigating the distribution of nine mitochondrial proteins in thousands of single muscle fibres. Using a semi-automated analysis pipeline, we demonstrate the accurate quantification of protein levels using IMC, providing an accurate measure of oxidative phosphorylation deficiency for complexes I-V at the single cell level. We demonstrate signatures of oxidative phosphorylation deficiency for common mtDNA variants and nuclear-encoded complex I variants and a compensatory upregulation of unaffected oxidative phosphorylation components. This technique can now be universally applied to evaluate a wide range of skeletal muscle disorders and protein targets.
| S-EPMC7501294 | biostudies-literature
Project description:By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.
| S-EPMC7058616 | biostudies-literature
Project description:We have developed an integrated spectroscopy system combining total internal reflection fluorescence microscopy imaging with confocal single-molecule fluorescence spectroscopy for two-dimensional interfaces. This spectroscopy approach is capable of both multiple molecules simultaneously sampling and in situ confocal fluorescence dynamics analyses of individual molecules of interest. We have demonstrated the calibration with fluorescent microspheres, and carried out single-molecule spectroscopy measurements. This integrated single-molecule spectroscopy is powerful in studies of single molecule dynamics at interfaces of biological and chemical systems.
| S-EPMC3277604 | biostudies-other
Project description:Electron microscopy (EM) has been employed for decades to analyze cell structure. To also analyze the positions and functions of specific proteins, one typically relies on immuno-EM or on a correlation with fluorescence microscopy, in the form of correlated light and electron microscopy (CLEM). Nevertheless, neither of these procedures is able to also address the isotopic composition of cells. To solve this, a correlation with secondary ion mass spectrometry (SIMS) would be necessary. SIMS has been correlated in the past to EM or to fluorescence microscopy in biological samples, but not to CLEM. We achieved this here, using a protocol based on transmission EM, conventional epifluorescence microscopy and nanoSIMS. The protocol is easily applied, and enables the use of all three technologies at high performance parameters. We suggest that CLEM-SIMS will provide substantial information that is currently beyond the scope of conventional correlative approaches.
| S-EPMC8109779 | biostudies-literature
Project description:BackgroundVisualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm2 area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art.ResultsMATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods.ConclusionMATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.
| S-EPMC8114487 | biostudies-literature
Project description:Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.
| S-EPMC7549788 | biostudies-literature
Project description:Surface-generated structured illumination microscopies interrogate the position of fluorescently labeled objects near surfaces with nanometer resolution along the z axis. However, these techniques are either experimentally cumbersome or applicable to a limited set of experimental systems. We present a new type of surface-generated structured illumination fluorescence microscopy, variable incidence angle fluorescence interference contrast microscopy (VIA-FLIC), in which the fluorescent sample is assembled above a reflective Si surface and the incidence angle of excitation light is varied by placing annular photomasks with different radii in the aperture diaphragm plane of the microscope. The variation in incidence angle alters the interference pattern of excitation light, and hence the intensity of detected fluorescence. Quantitative VIA-FLIC is tested by using a set of fluorophore-containing supported membranes separated from the Si surface by SiO2 layers of variable thicknesses. The resulting fluorescence intensity versus incidence angle curves depends on the separation from the Si surface and when fit with an appropriate model yield precise SiO2 thicknesses that are accurate with respect to the known SiO2 thicknesses. Since only a simple modification to a standard epifluorescence microscope is required, VIA-FLIC offers a versatile method to produce z-reconstructions with high resolution for a wide range of biological systems.
| S-EPMC1366776 | biostudies-literature