Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions

Our next meeting will be at 3:00 on December 5th, in room 3160 of the Discovery building. Our Selected paper is Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions.
The abstract is as follows.

Massively parallel reporter assays (MPRAs) enable nucleotide-resolution dissection of transcriptional regulatory regions, such as enhancers, but only few regions at a time. Here we present a combined experimental and computational approach, Systematic high-resolution activation and repression profiling with reporter tiling using MPRA (Sharpr-MPRA), that allows high-resolution analysis of thousands of regions simultaneously. Sharpr-MPRA combines dense tiling of overlapping MPRA constructs with a probabilistic graphical model to recognize functional regulatory nucleotides, and to distinguish activating and repressive nucleotides, using their inferred contribution to reporter gene expression. We used Sharpr-MPRA to test 4.6 million nucleotides spanning 15,000 putative regulatory regions tiled at 5-nucleotide resolution in two human cell types. Our results recovered known cell-type-specific regulatory motifs and evolutionarily conserved nucleotides, and distinguished known activating and repressive motifs. Our results also showed that endogenous chromatin state and DNA accessibility are both predictive of regulatory function in reporter assays, identified retroviral elements with activating roles, and uncovered ‘attenuator’ motifs with repressive roles in active chromatin.


Computational and analytical challenges in single-cell transcriptomics

Oliver Stegle1, Sarah A. Teichmann1,2 and John C. Marioni1,2

1European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. 2Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. Correspondence to J.C.M.  e-mail: marioni@ebi.ac.uk doi:10.1038/nrg3833 Published online 28 January 2015



The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.


Proportionality: A Valid Alternative to Correlation for Relative Data

David Lovell
Queensland University of Technology, Brisbane, Australia
Vera Pawlowsky-Glahn
Dept. d’Informàtica, Matemàtica Aplicada i Estadística. U. de Girona, España
Juan José Egozcue
Dept. Applied Mathematics III, U. Politécnica de Catalunya, Barcelona, Spain
Samuel Marguerat
MRC Clinical Sciences Centre, Imperial College London, United Kingdom
Jürg Bähler
Research Department of Genetics, Evolution and Environment, University College London, United Kingdom


In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative—or compositional—data, differential expression needs careful interpretation, and correlation—a statistical workhorse for analyzing pairwise relationships—is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic ϕ which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.


The human transcriptome across tissues and individuals

Mele, Ferreira, & Reverter et al, 2015  

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

The human transcriptome across tissues and individuals


Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Ronglai Shen1,*, Adam B. Olshen2 and Marc Ladanyi3

Author Affiliations
1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY,
2Department of Epidemiology and Biostatistics and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA
3Department of Pathology and Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

*To whom correspondence should be addressed.

Received June 22, 2009.
Revision received August 25, 2009.
Accepted September 9, 2009.

Motivation: The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment.
Methods: We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance–covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation–Maximization algorithm.
Results: We demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types.
Availability: R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm
Contact: shenr@mskcc.org
Supplementary information: Supplementary data are available at Bioinformatics online.


Integrative analysis of 111 reference human epigenomes

Roadmap Epigenomics Consortium, Anshul Kundaje, et al.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.


Statistics requantitates the central dogma

Jingyi Jessica Li, Department of Statistics and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.
Mark D. Biggin, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA


Mammalian proteins are expressed at ∼103 to 108 molecules per cell (1). Differences between cell types, between normal and disease states, and between individuals are largely defined by changes in the abundance of proteins, which are in turn determined by rates of transcription, messenger RNA (mRNA) degradation, translation, and protein degradation. If the rates for one of these steps differ much more than the rates of the other three, that step would be dominant in defining the variation in protein expression. Over the past decade, system-wide studies have claimed that in animals, differences in translation rates predominate (25). On page 1112 of this issue, Jovanovic et al. (6), as well as recent studies by Battle et al. (7) and Li et al. (1), challenge this conclusion, suggesting that transcriptional control makes the larger contribution.

(full article) 

Impact of regulatory variation from RNA to protein

Alexis Battle1,2
Zia Khan3
Sidney H. Wang3
Amy Mitrano3
Michael J. Ford4
Jonathan K. Pritchard1,2,5
Yoav Gilad3

1Department of Genetics, Stanford University, Stanford, CA 94305, USA.
2Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
3Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
4MS Bioworks, LLC, 3950 Varsity Drive, Ann Arbor, MI 48108, USA.
5Department of Biology, Stanford University, Stanford, CA 94305, USA.


The phenotypic consequences of expression quantitative trait loci (eQTLs) are presumably due to their effects on protein expression levels. Yet the impact of genetic variation, including eQTLs, on protein levels remains poorly understood. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance (pQTLs). We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, eQTLs tend to have significantly reduced effect sizes on protein levels, which suggests that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on messenger RNA or ribosome levels, which suggests that they may arise from differences in posttranslational regulation.

(full article)


Defining an essential transcription factor program for naive pluripotency

S.-J. Dunn1,*, G. Martello2,*,†‡, B. Yordanov1,*, S. Emmott1, A. G. Smith2,3,†
1Computational Science Laboratory, Microsoft Research, Cambridge CB1 2FB, UK.
2Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 1QR, UK.
3Department of Biochemistry, University of Cambridge, Cambridge, UK.
Department of Molecular Medicine, University of Padua, 35131 Padua, Italy.
†Corresponding author. E-mail: graziano.martello@unipd.it (G.M.); austin.smith@cscr.cam.ac.uk (A.G.S.)
* These authors contributed equally to this work.


The gene regulatory circuitry through which pluripotent embryonic stem (ES) cells choose between self-renewal and differentiation appears vast and has yet to be distilled into an executive molecular program. We developed a data-constrained, computational approach to reduce complexity and to derive a set of functionally validated components and interaction combinations sufficient to explain observed ES cell behavior. This minimal set, the simplest version of which comprises only 16 interactions, 12 components, and three inputs, satisfies all prior specifications for self-renewal and furthermore predicts unknown and nonintuitive responses to compound genetic perturbations with an overall accuracy of 70%. We propose that propagation of ES cell identity is not determined by a vast interactome but rather can be explained by a relatively simple process of molecular computation.


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: dpeer@biology.columbia.edu
† 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