Monthly Archives: January 2015


Conditional density-based analysis of T cell signaling in single-cell data

Smita Krishnaswamy1, Matthew H. Spitzer2, Michael Mingueneau3, Sean C. Bendall2, Oren Litvin1,Erica Stone4, Dana Pe’er1,*,†, Garry P. Nolan2,†

Author Affiliations
1Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA.
2Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
3Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA.
4Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
*Corresponding author. E-mail:
† These authors contributed equally to this work.

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4+ T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.


CellNet: Network Biology Applied to Stem Cell Engineering

Patrick Cahan, Hu Li, Samantha A. Morris, Edroaldo Lummertz da Rocha, George Q. Daley, James J. Collins5,

Refers To
Samantha A. Morris, Patrick Cahan, Hu Li, Anna M. Zhao, Adrianna K. San Roman, Ramesh A. Shivdasani, James J. Collins, George Q. Daley
Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet
Cell, Volume 158, Issue 4, 14 August 2014, Pages 889-902
PDF (4068 K) Supplementary content
Referred to by
Kee-Pyo Kim, Hans R. Schöler
CellNet—Where Your Cells Are Standing
Cell, Volume 158, Issue 4, 14 August 2014, Pages 699-701
PDF (596 K)
Samantha A. Morris, Patrick Cahan, Hu Li, Anna M. Zhao, Adrianna K. San Roman, Ramesh A. Shivdasani, James J. Collins, George Q. Daley
Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet
Cell, Volume 158, Issue 4, 14 August 2014, Pages 889-902
PDF (4068 K) Supplementary content

Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.


Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet


Conservation of trans-acting circuitry during mammalian regulatory evolution


Andrew B. Stergachis, Shane Neph, Richard Sandstrom, Eric Haugen, Alex P. Reynolds, Miaohua Zhang, Rachel Byron, Theresa Canfield, Sandra Stelhing-Sun, Kristen Lee, Robert E. Thurman, Shinny Vong, Daniel Bates, Fidencio Neri, Morgan Diegel, Erika Giste, Douglas Dunn, Jeff Vierstra, R. Scott Hansen, Audra K. Johnson, Peter J. Sabo, Matthew S. Wilken, Thomas A. Reh, Piper M. Treuting, Rajinder Kaul et al.

Nature 515, 365–370 (20 November 2014) doi:10.1038/nature13972
Received 21 February 2014 Accepted 15 October 2014 Published online 19 November 2014


The basic body plan and major physiological axes have been highly conserved during mammalian evolution, yet only a small fraction of the human genome sequence appears to be subject to evolutionary constraint. To quantify cis- versus trans-acting contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution. Here we show that mouse TF footprints conjointly encode a regulatory lexicon that is ~95% similar with that derived from human TF footprints. However, only ~20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Furthermore, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results indicate that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry, enabling and potentiating cis-regulatory plasticity.