Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm

Zunder, Eli R.; Finck, Rachel; Behbehani, Gregory K.; Amir, El-ad David; Krishnaswamy, Smita; Gonzalez, Veronica D.; Lorang, Cynthia G.; Bjornson, Zach B.; Spitzer, Matthew H.; Bodenmiller, Bernd; Fantl, Wendy J.; Pe’er, Dana; Nolan, Garry P.

doi:10.1038/nprot.2015.020

Cited by 512 publications

(573 citation statements)

References 13 publications

(16 reference statements)

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“…After a 7-d culture, cells were restimulated and stained for various surface and intercellular proteins as specified in SI Appendix, Table S2. Cell barcoding and preparation for mass cytometry were conducted as previously described (72,73). Samples were measured using mass cytometry (CyTOF Helios mass cytometer; Fluidigm).…”

Section: Methodsmentioning

confidence: 99%

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Eizenberg-Magar

Rimer

Zaretsky

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4 + T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.C ell differentiation is controlled by complex gene regulatory networks that determine the expression of a large number of genes in response to external stimuli. CD4 + T cells, central regulators of immune responses, can differentiate into a number of phenotypes (or cell states), each defined by the expression of a panel of genes, such as lineage-specifying transcription factors (TFs) and cytokines (1). Differentiation outcome depends on a number of factors, including strength of antigen stimulation (2, 3), interactions with antigen presenting cells, and the signaling environment defined by secreted cytokines (1). By applying specific cytokine signals, it is possible to direct antigen-activated T cells toward differentiation into a number of specific phenotypes. Although the molecular interactions and regulatory networks that govern the differentiation process are being revealed at increasing resolution (4, 5), understanding the logic of operation of these complex networks and developing predictive mathematical models for cellular responses remain a challenge.Mathematical models that describe complex cellular processes are difficult to construct because of a lack of complete information about the underlying networks of molecular interactions and in particular, missing quantitative data regarding the parameters of biochemical interactions within these networks. Alternatively, a "black box" approach can be used, constructing a model of the system by analyzing its response to different input conditions (Fig. 1A). This technique enables study of complex systems in a simplified manner, because its implementation merely requires measurable inputs and outputs, without quantifying the response of each component inside the box. Approaches based on this concept are highly useful for describing engineered systems and were also applied for studying som...

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Section: Methodsmentioning

confidence: 99%

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Eizenberg-Magar

Rimer

Zaretsky

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Samples were incubated with Cell-ID Intercalator-Ir (Fluidigm) to detect debris and doublets, and run on a CyTOF2/Helios instrument (Fluidigm). Samples were debarcoded using the Single Cell Debarcoder, a stand-alone MATLAB application (44). Data were analyzed using Cytobank and R. A run control from the same normal donor was used in each experiment to normalize the phosphoprotein data as follows:…”

Section: Methodsmentioning

confidence: 99%

Identification of enhanced IFN-γ signaling in polyarticular juvenile idiopathic arthritis with mass cytometry

Throm¹,

Moncrieffe²,

Orandi³

et al. 2018

JCI Insight

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“…Data were then normalized [3], and bead events were removed. Cell doublet removal and de-barcoding of cells into their corresponding wells was done using a doublet-free filtering scheme and single-cell deconvolution algorithm [4]. Subsequently, data was processed using Cytobank (http://www.cytobank.org/).…”

Section: Mass Cytometry Analysismentioning

confidence: 99%

Learning Edge Rewiring in EMT From Single Cell Data

Krishnaswamy

Zivanovic

Sharma

et al. 2017

Preprint

Self Cite

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Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data.We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGFβ. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3D-DREMI) to predict and validate the effect of drug perturbations on EMT.

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Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm

Cited by 512 publications

References 13 publications

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Identification of enhanced IFN-γ signaling in polyarticular juvenile idiopathic arthritis with mass cytometry

Learning Edge Rewiring in EMT From Single Cell Data

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Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm

Cited by 512 publications

References 13 publications

Diverse continuum of CD4+T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4+T-cell states is determined by hierarchical additive integration of cytokine signals

Identification of enhanced IFN-γ signaling in polyarticular juvenile idiopathic arthritis with mass cytometry

Learning Edge Rewiring in EMT From Single Cell Data

Contact Info

Product

Resources

About

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals