Dataset Information

Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.

ABSTRACT: Background: Mass cytometry (Cytometry by Time of Flight, CyTOF) allows single-cell characterization on the basis of specific metal-based cell markers. In addition, other metals in the mass range such as silver can be detected per cell. Bacteria are known to be sensible to silver and a protocol was developed to measure both the number of affected cells per population and the quantities of silver per cell. Methods: For mass cytometry ruthenium red was used as a marker for all cells of a population while parallel application of cisplatin discriminated live from dead cells. Silver quantities per cell and frequencies of silver containing cells in a population were measured by mass cytometry. In addition, live/dead subpopulations were analyzed by flow cytometry and distinguished by cell sorting based on ruthenium red and propidium iodide double staining. Verification of the cells' silver load was performed on the bulk level by using ICP-MS in combination with cell sorting. The protocol was developed by conveying both, fast and non-growing Pseudomonas putida cells as test organisms. Results: A workflow for labeling bacteria in order to be analyzed by mass cytometry was developed. Three different parameters were tested: ruthenium red provided counts for all bacterial cells in a population while consecutively applied cisplatin marked the frequency of dead cells. Apparent population heterogeneity was detected by different frequencies of silver containing cells. Silver quantities per cell were also well measurable. Generally, AgNP-10 treatment caused higher frequencies of dead cells, higher frequencies of silver containing cells and higher per-cell silver quantities. Due to an assumed chemical equilibrium of free and bound silver ions live and dead cells were associated with silver in equal quantities and this preferably during exponential growth. With ICP-MS up to 1.5 fg silver per bacterial cell were detected. Conclusion: An effective mass cytometry protocol was developed for the detection and quantification of silver in single bacterial cells of different physiological states. The silver quantities were generally heterogeneously distributed among cells in a population, the degree of which was dependent on micro-environmental conditions and on silver applied either in ion or nanoparticle-aggregated form.

SUBMITTER: Guo Y

PROVIDER: S-EPMC5511850 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.

Guo Yuting Y Baumgart Sabine S Stärk Hans-Joachim HJ Harms Hauke H Müller Susann S

Frontiers in microbiology 20170717

<b>Background:</b> Mass cytometry (Cytometry by Time of Flight, CyTOF) allows single-cell characterization on the basis of specific metal-based cell markers. In addition, other metals in the mass range such as silver can be detected per cell. Bacteria are known to be sensible to silver and a protocol was developed to measure both the number of affected cells per population and the quantities of silver per cell. <b>Methods:</b> For mass cytometry ruthenium red was used as a marker for all cells o ...[more]

PMID: 28769897

Similar Datasets

Project description:IntroductionDiabetic ocular complications include sight-threatening consequences and decreased corneal sensitivity, characterized by decreased tear production, corneal sensitivity and delayed corneal epithelial wound healing. The pathogenesis of diabetic corneal disorders remains largely unknown. Growing evidence implies the participation of immune cells in the development of diabetic corneal diseases. Nonetheless, the immunological changes that result in diabetic corneal problems are largely unknown.MethodsMass cytometry by time of flight (CyTOF) was used to investigate immune cell cluster alterations associated with diabetic corneal disorders. CyTOF test was performed on corneal cells at a single level from 21-week-old diabetic (db/db) and non-diabetic (db/m) mice. A panel of 41 immune-related markers monitored different immune cell types in diabetic corneas. To investigate the proportion of each immune cell subpopulation, an unsupervised clustering method was employed, and T-distributed stochastic neighbor embedding was used to visualize the distinctions between different immune cell subsets.ResultsThrough CyTOF test, we identified 10 immune cell subsets in the corneal tissues. In a novel way, we discovered significant immune alterations in diabetic corneas, including pronounced alterations in T cells and myeloid cell subgroups in diabetic corneas linked to potential biomarkers, including CD103, CCR2, SiglecF, Ly6G, and CD172a. Comprehensive immunological profiling indicated remarkable changes in the immune microenvironment in diabetic corneas, characterized by a notable decrease in CD103+CD8+ tissue-resident memory T (TRM) cells and Tregs, as well as a dramatic increase of γδT cells and subsets of CD11b+Ly6G+ myeloid-derived suppressor cells (MDSCs).ConclusionCyTOF analysis revealed significant alterations in the immune microenvironment during the development of diabetic corneal complications. This study mapped the immune microenvironment landscape of type 2 diabetic corneas, providing a fundamental understanding of immune-driven diabetic corneal disorders.

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.

Dataset Information

Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.

Publications

Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets