Trends in Immunology
ReviewImmunological Genome Project and systems immunology
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
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