Microenvironment Perturbagen (MEP) LINCS

The MEP-LINCS center is one of six Data and Signature Generating Centers of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program.
  
The goal of MEP-LINCS is to develop computational strategy and generate data to elucidate how microenvironment signals affect cell intrinsic intracellular transcriptional- and protein-defined molecular networks to generate experimentally observable cellular phenotypes.

Data generated from the MEP-LINCS project is freely available to download on Synapse.

We will infer regulatory relationships by combining measurements of microenvironment perturbagen-induced changes in multiple cellular phenotypes, RNA expression, and regulatory protein expression in a core set of cell lines with measurements of responses of the same lines to chemical and genomic perturbagens made across the LINCS Data and Signature Generating Centers.
  
Our data will complement existing perturbagen response LINCS data by providing information on microenvironment perturbagen-induced changes and by providing quantitative image based measurements of seven cellular response phenotypes plus associated changes in gene transcription and regulatory protein expression. Integrative analysis of these data will enable us to address four key questions:

  1. How are microenvironment peturbagen-induced cellular phenotypes regulated by specific molecular networks?
  2. Do subsets of microenvironment-induced perturbations elicit common molecular network changes and phenotypic responses?
  3. Do specific molecular network signatures influence multiple cellular phenotypes?
  4. Are the microenvironment perturbagen-induced network changes similar to the genetic or chemically induced network changes identified in other LINCS projects?

Assay overview

Introduction

The biological behaviors of normal and diseased cells and their responses to therapeutic agents are strongly influenced by the regulatory signals they receive from the microenvironment (ME) in which they reside.  These signals come from direct interactions with insoluble extracellular matrix and cellular proteins as well as soluble proteins, peptides, or glycoproteins.

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Identify signaling networks that control cellular phenotypes

The behavior of cells receiving these signals ultimately is determined by the interaction of multiple signals received with the regulatory networks intrinsic to the target cell.  However, the mechanisms by which combinatorial ME signals influence intrinsic regulatory networks and the cellular phenotypes they control are not well established.  We posit that these can be inferred from measurements of cellular and molecular responses to growth on a wide range of soluble and insoluble substrates.

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Microenvironment Microarray (MEMA)

MEP-LINCS will use the novel microenvironment microarray (MEMA) platform to assess the impacts of nearly 3000 distinct pairwise combinations of microenvironment perturbagens (MEPs) on specific biological and molecular endpoints. We use high content fluorescence imaging to assess biological response endpoints of cells grown on MEMAs, including proliferation, differentiation status,  and cell cycle.

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Workflow

Associations between quantitative response phenotype features extracted from multi-color cell images, as well as RNA and protein profiles, will be established using both statistical and pathway mapping algorithms. This complement of signatures will provide an integrated understanding of the cellular phenotypes and regulatory networks affected by microenvironment perturbagens. Once identified, we will use both supervised and unsupervised analysis approaches to identify regulatory networks common to subsets of the microenvironment or phenotypic molecular signatures. Our expectation is that some subnetworks will be relatively universal for mediating response to microenvironment perturbations, while others will be specific to subsets of them.

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Data Description and Download

Data and Metadata generated from the MEP-LINCS project is freely available to download from Synapse.org.

Contact information

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References

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  3. Hu, J. et al. Non-parametric quantification of protein lysate arrays. Bioinformatics 23, 1986-94, 2007.
  4. Vaisberg EA, Lenzi D, Hansen RL, Keon BH, Finer JT. An Infrastructure for High-Throughput Microscopy: Instrumentation, Informatics, and Integration. Methods Enzymol. 414, 484–512, 2006.
  5. Zhang, L. et al. Serial dilution curve: a new method for analysis of reverse phase protein array data. Bioinformatics 25, 650-4, 2009.