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Assessment of engineered cells using CellNet and RNA-seq


CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.

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Figure 1: Inputs and outputs of CellNet.
Figure 2: Outline of the PROCEDURE.
Figure 3: Classification heatmap of the example query data.
Figure 4: C/T-specific GRN status of fibroblasts as they are reprogrammed to pluripotency.
Figure 5: Network influence score (NIS).
Figure 6: Precision recall curves for each murine RNA-seq C/T classifier.
Figure 7: Expression of C/T-specific genes.


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P.C. is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; grant no. K01DK096013). We thank E. Appleton for helpful comments on the protocol.

Author information

Authors and Affiliations



A.H.R. wrote code, performed analysis, and wrote the manuscript. R.M.S. wrote code and performed analysis. Y.T. analyzed data, debugged code, and edited the manuscript. J.K. debugged code and analyzed data. E.K.W.L. analyzed data and edited the manuscript. P.C. devised the method, wrote code, analyzed data, wrote the manuscript, and oversaw the project.

Corresponding author

Correspondence to Patrick Cahan.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison of GRN performance based on either total counts normalization or DESeq.

X-axis represents the Z-score for the predicted transcription factor- to target genes interactions. The Y-axis represents the area under the precision recall curve relative to randomly generated GRNs. AUPR was calculated as described previously8 using three sets of TF-to-target gene annotations as gold standards. The first gold standard is derived from lists of genes whose promoters are bound by transcription factors as determined by Chip-Seq data produced as part of the mouse ENCODE project28. The second gold standard is the Escape database, which is a compilation of genes whose promoters are bound by transcription factors in mouse embryonic stem cells defined by Chip-Chip or Chip-Seq data29. The third gold standard is derived from the determination of genes that are differentially expressed upon acute induction of one of 94 transcription factors ('Ko': named after the surname of the senior author of the associated study30).

Supplementary information

Supplementary Figures and Text

Supplementary Figure 1 and Supplementary Tables 1 and 2. (PDF 725 kb)

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Radley, A., Schwab, R., Tan, Y. et al. Assessment of engineered cells using CellNet and RNA-seq. Nat Protoc 12, 1089–1102 (2017).

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