A Compendium of Signals and Responses Triggered by Prodeath and Prosurvival Cytokines

Abstract

Cell-signaling networks consist of proteins with a variety of functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities and biochemical activities of these signaling proteins, the ways in which they are combined into networks to process and transduce signals are poorly understood. Network-level understanding of signaling requires data on a wide variety of biochemical processes such as posttranslational modification, assembly of macromolecular complexes, enzymatic activity, and localization. No single method can gather such heterogeneous data in high throughput, and most studies of signal transduction therefore rely on series of small, discrete experiments. Inspired by the power of systematic datasets in genomics, we set out to build a systematic signaling dataset that would enable the construction of predictive models of cell-signaling networks. Here we describe the compilation and fusion of ϳ10,000 signal and response measurements acquired from HT-29 cells treated with tumor necrosis factor-␣, a proapoptotic cytokine, in combination with epidermal growth factor or insulin, two prosurvival growth factors. Nineteen protein signals were measured over a 24-h period using kinase activity assays, quantitative immunoblotting, and antibody microarrays. Four different measurements of apoptotic response were also collected by flow cytometry for each time course. Partial least squares regression models that relate signaling data to apoptotic response data reveal which aspects of compendium construction and analysis were important for the reproducibility, internal consistency, and accuracy of the fused set of signaling measurements. We conclude that it is possible to build self-consistent compendia of cellsignaling data that can be mined computationally to yield important insights into the control of mammalian cell responses. Molecular & Cellular Proteomics 4:1569 -1590, 2005.

Isoelectric focusing technology quantifies protein signaling in 25 cells

“…3d and Table 1). Similar results to these, but generated by using a conventional Western blot approach have been reported (31). The Western blots used in that study allowed determination of relative phosphorylation of ERK, but not percent phosphorylation, or the relative amounts of ERK 1 vs. ERK 2 phosphorylation, or of monophosphorylated vs. diphosphorylated species.…”

Transcriptional regulation via the NF-κB signaling module

“…Third, high throughput measurements of coordinated signaling pathways, secondary and tertiary signaling events will be required. It is increasingly apparent that gene expression programs are specified through complex interactions of signaling networks involving feedback and autocrine cascades (Gaudet et al, 2005;Janes et al, 2006). Although NF-kB appears to be a central regulator in many physiological processes, accessory signaling pathways may prove critical in determining cell-, context-and stimulus-specific gene regulation.…”

Three-Dimensional In Vitro Tri-Culture Platform to Investigate Effects of Crosstalk Between Mesenchymal Stem Cells, Osteoblasts, and Adipocytes

“…All Box-Cox-transformed data were mean-centered and scaled to unit variance prior to analysis as a means of normalization to allow all variables to be considered equally scaled in the principal components (PCs) or latent variables. 9 For these analyses, the data set (total of 504 data points) was organized into an N · K matrix X that denotes the measured gene expression levels with time as well as cell type and culture type. Principal component analysis (PCA) was first performed to discern possible clusters of observations and their qualitative similarities among the global data set in an unbiased fashion.…”

“…5,6 Toward this end, use of biomaterials may provide a way to recreate these 3D environments, while allowing the study of complex cellular interactions. This includes the application of methods for high-throughput, multivariate analyses of highcontent data (e.g., from gene microarrays, suspension arrays, time-of-flight-mass spectrometry, and microscopy images) [7][8][9][10][11] that yield system-level information of complex cellular processes at or close to a single-cell level. However, innovative strategies that more closely mimic in vivo microenvironments need to be further coupled with the sophisticated methods outlined above.…”