Trends in Immunology
Volume 34, Issue 12, December 2013, Pages 602-609
Journal home page for Trends in Immunology

Review
Immunological Genome Project and systems immunology

https://doi.org/10.1016/j.it.2013.03.004Get rights and content

Highlights

  • ImmGen is a public resource to study genes and their networks in the immune system.

  • ImmGen datasets are used to gain insights into cell lineage relatedness and origins.

  • A regulatory computational model identifies known and novel regulatory interactions.

  • Web-based ImmGen data browsers are portals to multivariate genomic data analysis.

Immunological studies of single proteins in a single cell type have been complemented in recent years by larger studies, enabled by emerging high-throughput technologies. This trend has recently been exemplified by the discovery of gene networks controlling regulatory and effector αβ T cell subset development and human hematopoiesis. The Immunological Genome Project (ImmGen) aims to decipher the gene networks underpinning mouse hematopoiesis. The first phase, completed in 2012, profiled the transcriptome of 249 immune cell types. We discuss the utilities of the datasets in high-resolution mapping of the hematopoietic system. The immune transcriptome compendium has revealed unsuspected cell lineage relations and the network reconstruction has identified novel regulatory factors of hematopoiesis.

Section snippets

A transcriptome compendium of mouse hematopoiesis

Classic immunology studies with a laser focus on a particular protein or biological process are becoming increasingly complemented by systems immunology studies that provide robust insights to understand fully the inner workings of the immune system. With technological advances, a systems immunology approach is feasible for individual laboratories, and not just for large consortia. However, the scope of individual enterprise still remains mostly restricted to a particular cell lineage 1, 2, 3,

Ontogenet – a novel method for reconstructing regulatory networks in tree-structured datasets

Ontogenet is a new method that combines linear regression with the tree structure of the dataset to predict a set of transcriptional regulators that would best account for the expression of each module [9]. A module is a set of genes that are coexpressed across the dataset. Regulators are selected from a predefined list of factors that regulate gene transcription (TFs and chromatin modifiers). Ontogenet is specifically devised to address some of the challenges – and leverage some of the unique

ImmGen regulatory model

The transcriptional response of the mouse hematopoietic system was separated into modules of coexpressed genes at two levels of resolution. At the lower resolution, 81 coarse-grained modules, including genes with broadly similar expression patterns, were defined. Each coarse-grained module was further separated into fine-grained modules, representing smaller groups of genes with more coherent and tighter expression patterns, resulting in 334 fine-grained modules. For example, the coarse-grained

Gene network architecture properties responsible for diversity of hematopoietic cell types and their function

One of the major goals of the ImmGen Phase 1 has been to define and sort operationally discrete cell subsets within a defined functional lineage as a systemic baseline measurement of transcriptome complexity. Compound perturbations of the system followed by iterative transcriptome samplings (Phase 2) will in principle yield the complete dynamic range of the system transcriptome and all dominant regulators. This task is to a large degree constrained by the availability of reagents (antibodies to

Insights from integrating ImmGen with other datasets

The power of ImmGen dataset is not only in the analysis of the data within it, as described above, but in the integration with external systemic datasets, which can amplify informational outputs and yield new paradigms. An example is the comparison of ImmGen with a similar, although much more limited, human dataset termed differentiation map or ‘D-MAP’, collected an expression compendium of 39 cell types (211 samples) from human immune and hematopoietic lineages [40]. Comparison of ImmGen and

Concluding remarks

The scope, uniform data collection and quantitation procedures, and centralized regulatory model construction of the ImmGen compendium have established the baseline measurement of variations in the hematopoietic transcriptomes that allow for many novel analyses. There remain some gaps in the survey (e.g., fetal hematopoietic system, nonlymphoid tissue-resident hematopoietic cell types). In many ways, the completion of Phase I is the starting point for unraveling the molecular circuits dictating

Acknowledgments

We thank the ImmGen laboratories, L. Lanier, S. Itzkovitz, A. Elbaz, and S. Tal for discussion, L. Gaffney for help with the figures, and eBioscience, Affymetrix, and Expression Analysis for support of the ImmGen Project. Supported by NIH CA100382, AI101301 to J.K., and AI072073 to ImmGen. The ImmGen Project Consortium consists of: Paul Monach, Susan A. Shinton, Richard R. Hardy, Radu Jianu, David Koller, Jim Collins, Roi Gazit, Brian S. Garrison, Derrick J. Rossi, Kavitha Narayan, Katelyn

References (48)

  • N. Novershtern

    Densely interconnected transcriptional circuits control cell states in human hematopoiesis

    Cell

    (2011)
  • M.M. Davis

    A prescription for human immunology

    Immunity

    (2008)
  • U. Klein

    Gene expression profile analysis of AIDS-related primary effusion lymphoma (PEL) suggests a plasmablastic derivation and identifies PEL-specific transcripts

    Blood

    (2003)
  • Y. Lee

    Induction and molecular signature of pathogenic TH17 cells

    Nat. Immunol.

    (2012)
  • I. Amit

    Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses

    Science

    (2009)
  • E. Glasmacher

    A genomic regulatory element that directs assembly and function of immune-specific AP-1–IRF complexes

    Science

    (2012)
  • C. Benoist

    Consortium biology in immunology: the perspective from the Immunological Genome Project

    Nat. Rev. Immunol.

    (2012)
  • A. Hijikata

    Construction of an open-access database that integrates cross-reference information from the transcriptome and proteome of immune cells

    Bioinformatics

    (2007)
  • T.S.P. Heng

    The Immunological Genome Project: networks of gene expression in immune cells

    Nat. Immunol.

    (2008)
  • V. Jojic

    Identification of transcriptional regulators in the mouse immune system

    Nat. Immunol.

    (2013)
  • N.A. Bezman

    Molecular definition of the identity and activation of natural killer cells

    Nat. Immunol.

    (2012)
  • E.L. Gautier

    Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages

    Nat. Immunol.

    (2012)
  • K. Narayan

    Intrathymic programming of effector fates in three molecularly distinct [gamma][delta] T cell subtypes

    Nat. Immunol.

    (2012)
  • N.R. Cohen

    Shared and distinct transcriptional programs underlie the hybrid nature of iNKT cells

    Nat. Immunol.

    (2012)
  • Cited by (0)

    View full text