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Published April 26, 2017 | Supplemental Material
Journal Article Open

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation

Abstract

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.

Additional Information

© 2017 Elsevier. Under an Elsevier user license. Received 17 August 2016, Revised 16 December 2016, Accepted 15 March 2017, Available online 5 April 2017. We thank Brandon Dekosky, Daechan Park, Euan Ashley, Eliezer Calo, the Stefan Heller laboratory, the Stanford FACS facility, Fluidigm's Tech Support and R&D groups, and members of the M.W.C. laboratory for technical assistance and critical commentary on the manuscript. We gratefully acknowledge funding from several sources, including a Distinguished Investigator award and a Discovery Center grant from the Paul Allen Family Foundation, as well as an NIH Pioneer Award (5DP1LM01150-05) to M.W.C.; a DOE Computational Science Graduate Fellowship (DE-FG02-97ER25308) and a Siebel Scholarship to D.N.M.; a Stanford Biomedical Big Data Science Postdoctoral Fellowship, NIH F32 Postdoctoral Fellowship (1F32GM119319-01), as well as the Burroughs Wellcome Fund's Postdoctoral Enrichment Program to D.V.V.; and a Systems Biology Center grant (P50 GM107615). S.C. Boutet was an employee at Fluidigm when the work described here was performed. Author Contributions: K.L., S.C.B., and M.W.C. conceived the study. K.L. performed experiments and image analysis. D.V.V. designed and conducted the bioinformatics and image analysis. M.M.D. performed experiments. T.K. contributed image analysis software. D.N.M., A.C., and D.P. consulted on the bioinformatics analysis. A.J. and T.M. supplied RNA FISH analysis scripts. S.C.B. consulted on Fluidigm C1 experiments. K.L., D.V.V., and M.W.C. wrote the paper with input from all authors. Data and Software Availability: The accession number for the scRNA-seq data reported in this paper is GEO: GSE94383. Custom scripts for the Fluidigm C1 are included in Data S1.

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Supplemental Material - 1-s2.0-S2405471217301321-mmc1.pdf

Supplemental Material - 1-s2.0-S2405471217301321-mmc2.csv

Supplemental Material - 1-s2.0-S2405471217301321-mmc3.csv

Supplemental Material - 1-s2.0-S2405471217301321-mmc4.zip

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