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Sep 15, 2011 (Vol. 31, No. 16)

Getting Up to Speed on Label-Free Methods

Data-Analysis Tool Aims to Skirt Challenges that Have Hampered Technique in Past

  • Overcoming These Limitations

    Using the Genedata Screener platform, researchers leverage the software’s support for the complete path from kinetics trace to normalized results and hits. With the capability to integrate with widely used SPR-based instruments such as the Octet® (ForteBIO), BIND® (SRU Biosystems) and EPIC (Corning), or impedance-based instruments such as ECIS™ (Applied Biophysics), CellKey™ (MDS Analytical Technologies) and xCELLigence™ (Roche Applied Science), users can adapt data analysis to meet specific screening requirements.

    The platform also enables:

    • Interactivity: Redefine aggregation rules at any time and immediately see the effect on hit lists, or review kinetics traces of hits. Result: Highly efficient screening, faster hit-to-lead process.
    • Scalability: From a single 96-well plate to a full library screen on 300,000 or more compounds, full-time resolution is supported while interactivity is maintained. This allows processing any dataset without dividing it into manageable pieces. Result: Increased productivity and consistent results.
    • Instrument independence: Setup and definition of aggregation rules are completely instrument-agnostic, allowing users to set up their own aggregation and QC guidelines, independent of the underlying technology platform. Result: Protection of earlier investments in screening instrumentation and ability to widely share data internally and externally.
  • Case Study

    Click Image To Enlarge +
    Figure 2. Hit List for Gi/Gq stimulators: Genedata Screener shows the strongest three hits listed here are identified by all three aggregation methods. However, within this result list there is also an ambiguous compound (highlighted, showing ambiguous signature).

    This example illustrates the analysis of a small compound library screen (10,000 compounds) for two types of GPCR stimulators.

    During assay development, the optimal aggregation rules were identified based on the plate quality scores. The data-analysis process consisted of two steps: 1) preprocessing the data from the instrument by the instrument’s software, creating two measurements per well, each thought to be specific to one receptor; and 2) importing this result set twice (one time for each receptor) into a screening data analysis and running the typical steps, normalization, and hit identification for each receptor.

    While the results of this experiment convinced the screening lab of label-free benefits, data analysis produced suboptimal results and required lengthy manual inspections of identified hits.

    Genedata Screener addresses both of these issues. It offers optimized aggregation rules, utilizing a larger section of the time traces and different methods (e.g., Slope and Area under the Curve). This allows more reliable discrimination between the two receptor types and identification of mixed or new phenotypes.

    In addition, Genedata Screener provides integrated data analysis on a single software platform to significantly reduce processing time. The entire library was analyzed for both receptors in a single session in less than ten minutes. Immediate accessibility of the time traces allowed ad hoc comparison of any numerical finding with the underlying original signal.

    This enabled the user to approve and combine results with knowledge on the expected phenotypic changes during the data analysis and allowed improved results as the risk to miss an effect is reduced. Figure 2 highlights the effective hit list creation and review.

    We increasingly see pharmaceutical companies, specialized contract research organizations, and academic screening centers leveraging the advantages of a single, integrated platform for label-free data analysis. A comprehensive and automated software solution such as Genedata Screener helps to reduce data-analysis cycles, improve screening lab productivity, and help drive optimal data analysis and trace identification.



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