Single cell profiling of potentiated phospho-protein networks in cancer cells.

By Jonathan M. Irish, R. Hovland, Peter O. Krutzik, O. Perez, Ø. Bruserud, B. Gjertsen, Garry P. Nolan. Cell, 2004

711 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b3fb7092f8662cfab317292ed7a728afdb45fb4c

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Citations in the corpus

Single-cell RNA-seq: advances and future challenges (2014, neutral): “As this requires perme-abilization and fixation of cells (8,50), it may hamper subsequent transcriptomic analysis.”
Cytobank: providing an analytics platform for community cytometry data analysis and collaboration. (2014, methodology): “A method that has worked well is calculating the median response value for each marker and converting it into a fold change by comparing it to a reference sample (Irish et al. 2004).”
Web-Based Analysis and Publication of Flow Cytometry Experiments (2010, background): “New single-cell proteomics ﬂow cytometry techniques, such as phospho-speciﬁc ﬂow cytometry (Krutzik and Nolan, 2003), clinical signaling proﬁles (Irish et al., 2004), and ﬂuorescent cell barcoding (Krutzik and Nolan, 2006), have especially highlighted the need for a new analysis paradigm that…”
Understanding eukaryotic linear motifs and their role in cell signaling and regulation. (2008, methodology): “used FACS and a set of labeled antibodies raised against phosphorylated motifs using a library of synthetic peptides (132).”
From signatures to models: understanding cancer using microarrays (2005, methodology): “Finally, new biological assays, such as in situ gene-expression signatures using laser capture microdis-section 60,61 or fluorescence microscopy data, can provide more refined observations about gene activation in individual cancer cells 54,55 .”
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia (2013, methodology): “In the future, we expect viSNE to be instrumental in the analysis of mass cytometry data integrating the surface marker panel with a panel of functional markers that probe signaling, cell cycle and metabolism, under many experimental perturbations (such as cytokines and drugs) 3 .”
Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis (2015, neutral): “functional responses to environmental cues, reflecting the broader cellular network beyond what can be inferred from the unperturbed state (Irish et al., 2004).”