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      viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

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          Abstract

          New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.

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          Most cited references17

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          Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

          Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.
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            Causal protein-signaling networks derived from multiparameter single-cell data.

            Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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              Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.

              A novel instrument for real time analysis of individual biological cells or other microparticles is described. The instrument is based on inductively coupled plasma time-of-flight mass spectrometry and comprises a three-aperture plasma-vacuum interface, a dc quadrupole turning optics for decoupling ions from neutral components, an rf quadrupole ion guide discriminating against low-mass dominant plasma ions, a point-to-parallel focusing dc quadrupole doublet, an orthogonal acceleration reflectron analyzer, a discrete dynode fast ion detector, and an 8-bit 1 GHz digitizer. A high spectrum generation frequency of 76.8 kHz provides capability for collecting multiple spectra from each particle-induced transient ion cloud, typically of 200-300 micros duration. It is shown that the transients can be resolved and characterized individually at a peak frequency of 1100 particles per second. Design considerations and optimization data are presented. The figures of merit of the instrument are measured under standard inductively coupled plasma (ICP) operating conditions ( 900 for m/z = 159, the sensitivity with a standard sample introduction system of >1.4 x 10(8) ion counts per second per mg L(-1) of Tb and an abundance sensitivity of (6 x 10(-4))-(1.4 x 10(-3)) (trailing and leading masses, respectively) are shown. The mass range (m/z = 125-215) and abundance sensitivity are sufficient for elemental immunoassay with up to 60 distinct available elemental tags. When 500) can be used, which provides >2.4 x 10(8) cps per mg L(-1) of Tb, at (1.5 x 10(-3))-(5.0 x 10(-3)) abundance sensitivity. The real-time simultaneous detection of multiple isotopes from individual 1.8 microm polystyrene beads labeled with lanthanides is shown. A real time single cell 20 antigen expression assay of model cell lines and leukemia patient samples immuno-labeled with lanthanide-tagged antibodies is presented.
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                Author and article information

                Journal
                Nature Biotechnology
                Nat Biotechnol
                Springer Science and Business Media LLC
                1087-0156
                1546-1696
                June 2013
                May 19 2013
                June 2013
                : 31
                : 6
                : 545-552
                Article
                10.1038/nbt.2594
                4076922
                23685480
                f1c31392-06d5-470f-9b07-8187e0733e71
                © 2013

                http://www.springer.com/tdm

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