Sample to Insight
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Many biomedical researchers today have found that studying the gene expression of cells and tissues can help further their research goals, by providing them a rich systems-level window into biology.  IPA is a critical part of their workflows, because it imports the data that is generated (quantitative measurement of the perturbations in RNA levels in their system) and advances it from simply a set of genes with numbers into a detailed picture of what is occurring, or is likely to occur in those cells.  IPA is platform independent. It can be interrogated with data from any tool that generates expression values for known genes, transcripts, or proteins, for example from microarrays, next generation sequencing (e.g. RNA-Seq), or qPCR.   IPA can help with almost any transcriptomics-related question or application, for example:

  • Discovery experiments.  Rather than beginning with a particular hypothesis, discovery experiments are done to assess whether there are interesting genes, molecular processes, or biological pathways that have been perturbed in the experimental system, as a means to generate a hypothesis that can be validated or refuted in subsequent studies. For example, are there certain networks of gene products that are perturbed in a metastatic tumor vs. the primary tumor?   Are there likely intervention points for new or existing drugs?
  • Gene regulation. For example, which transcription factors or upstream regulators such as microRNAs or kinases may have been activated or inhibited to cause the expression pattern in the dataset?  Knowing the identity of upstream regulators can provide strong clues as to what the cells were experiencing in their environment.
  • Disease classification. Expression profiling of multiple genes provides better discrimination power for making accurate diagnoses.  Such disease signatures can be validated and used as biomarkers or diagnostics in the clinic.  Furthermore, the molecular composition of the signatures often provides insight into the nature of a particular disease and can lead to the development and testing of therapeutics.
  • Drug repositioning.  Drugs are generally developed against one or a very narrow spectrum of diseases and millions of dollars are invested to ensure they provide efficacious and safe exposure in humans.  Sometimes after marketing these drugs, new indications are discovered that benefit new patients as well as the drug’s manufacturer.  Expression profiling of approved drugs and comparison to profiles of diseased tissue can lead to discovery of new uses for these already approved entities.
  • Toxicogenomics.  Toxicogenomics combines traditional toxicology with transcriptomics (as well as with proteomics and metabolomics) to uncover molecular mechanisms involved in the expression of toxicity, and to derive molecular biomarkers that predict toxicity or the genetic susceptibility to it.
  • Drug target discovery. Genes that are shown to be activated in a pathological condition, whether directly upregulated in the condition or implied causally from an upstream regulator analysis may serve as promising targets for therapeutic development efforts.
  • Biomarker discovery.  Molecular biomarkers can be identified in transcriptomics experiments and can serve many purposes such as helping to monitor the course of disease or the efficacy of treatment, as a means to stratify patients who are likely to benefit from a certain therapy or likely to suffer adverse effects from it.