Mass cytometry resolves multiple metal probes per cell with minimal signal overlap, maximizing the information obtained from a single sample. With over 100 detector channels and over 30 isotopic probes, mass cytometry provides researchers with an unparalleled ability to phenotypically and functionally profile cells from normal and diseased states.
The DVS line of reagents, instrumentation, and data analysis tools provide all the tools necessary for novel discovery applications using mass cytometry, including:
|How it works:|
|First, cells are stained in suspension with a panel of metal-conjugated probes directed against targets of interest. The cells are then introduced into an Inductively Coupled Plasma where they are individually atomized and ionized. The cloud of atomic ions for each single cell is extracted into the ion optics and time-of-flight regions of the mass cytometer where the ions are separated by mass. The masses corresponding to the metal-tagged probes are counted in discrete time-separated detector channels. Metal tags are chosen from rare elements whose natural concentration in a biological sample is below the detection limit. The intensity of the signal detected in each channel is directly proportional to the number of specific probe-derived ions striking the detector and thus the number of antibodies originally bound per cell. In a typical cell analysis experiment, four minutes of raw data collection is sufficient for analysis of 100,000 cells independent of the number of metal tags (one or all 32). The resulting Data is written in .txt or .fcs file formats compatible with many analysis programs, including DVS.Cytobank, which has a set of tools including histograms, bivariate plots, the SPADE clustering algorithm and heat maps that have been designed to facilitate analysis and visualization of large, high-dimensional CyTOF data sets. With this unique combination of reagents, instrumentation, and analysis tools, the CyTOF Mass cytometry platform unlocks the ability to comprehensively profile the signaling, cytokine, apoptotic and cell cycle-related responses in complex samples|
|Mass cytometry experiments routinely measure dozens of |
functional and phenotypic parameters in millions of single cells,
output as .txt and .fcs files. Analysis of such high dimensional
datasets is performed using a tool kit that includes not only
traditional histograms and bivariate plots, but also novel clustering
algorithms, neural networks, and dimensionality reduction methods.
The DVS.Cytobank cloud-based mass cytometry analysis platform has been designed to facilitate analysis and visualization
of large, high-dimensional CyTOF data sets, and provides:
· Premium analytics including the SPADE clustering algorithm and Dose Response heat maps.
· Standard analytics including histograms, bivariate plots and statistics
· Mass cytometry datasets, demos and tutorials
· Tools for organizing experimental data and managing collaborations
|Below are a few examples of high dimensional analysis tools used in mass cytometry experiments:|
|SPADE is an algorithm in DVS Cytobank that clusters cell populations into a minimum-spanning tree|
based on phenotypic similarity. It was designed specifically for CyTOF data (Qiu et al., 2011) in order
to visualize the large numbers of sub-populations which make up a complex tissue (such as whole
blood, bone marrow, or a tumor) on a single plot. SPADE has been used in to characterize kinase
signaling in bone marrow (Bendall et al., 2012), cell cycle in bone marrow and drug action in
peripheral blood (Bodenmiller et al., 2012).
|- Neural Networks|
|Unsupervised neural networks are a type of clustering algorithm used to model |
complex relationships and find patterns in data. Neural networks have been used
with CyTOF data to discover unexpected inter-cell type dependencies and to compare
|- Principle Component Analysis (PCA)|
|Principle component analysis (PCA) collapses multiple parameters into a limited number of|
information rich components, which capture the most salient information in a dataset.
It is a fundamental technique of systems biology and has been used to elucidate CyTOF
data describing the complex space of T-cell development (Newell et al., 2012).
|Understanding complex processes in heterogeneous tissues lies at the heart of the most important problems facing|
|Mass Cytometry enables comprehensive profiling of cellular:|
· Signaling state
· Cytokine/chemokine expression
· Health & Viability
|Routine mass cytometry experiments combine deep phenotyping with functional profiling in a single tube.|
Mass cytometry has been used in transformative high-dimensional studies in diverse disciplines including immunology,
cancer research, stem cell biology and drug profiling and some recent examples from the published literature include:
|Comprehensive analysis of the hematopoietic system|
|Bendall SC, Simonds EF, et. al. Science 332, 687.|
This study provides a system-wide view of immune signaling in healthy human hematopoiesis
including simultaneous characterization of all major cellular subsets in bone marrow
mononuclear cells with only 24 unique conditions taken from a single bone marrow sample.
|Deep phenotypic and functional profiling of CD8+ T cells|
|Newell EW, et. al. Immunity 36, 142.|
Analysis using 34 phenotypic and functional markers defined a continuum of CD8+
phenotypes connecting naïve and memory subsets and demonstrated that cytokine
usage in CD8+ T is combinatorial in nature, allowing for an unappreciated flexibility
in orchestrating a pathogen response.
|Drug profiling in human PBMCs|
|Bodenmiller B, Zunder ER, Finck R, et. al. Nat Biotech 30, 858.|
The effect of 27 inhibitors on 14 kinases in 14 subpopulations (63,504 IC50s) provided
comprehensive modeling of drug efficacy by cell type and signaling pathway,
enabling prediction of drug mechanism and potential effectiveness in disease models.