Blair Steve, Williams Layne, Bishop Justin, Chagovetz Alexander
University of Utah, Salt Lake City, Utah, USA.
Methods Mol Biol. 2009;529:171-96. doi: 10.1007/978-1-59745-538-1_12.
In any microarray hybridization experiment, there are contributions at each probe spot due to the match and numerous mismatch target species (i.e., cross-hybridizations). One goal of temperature optimization is to minimize the contribution of mismatch species; however, achieving this goal may come at the expense of obtaining equilibrium reaction conditions. We employ two-component thermodynamic and kinetic models to study the trade-offs involved in temperature optimization. These models show that the maximum selectivity is achieved at equilibrium, but that the mismatch species controls the time to equilibrium via the competitive displacement mechanism. Also, selectivity is improved at lower temperatures. However, the time to equilibrium is also extended, so that greater selectivity cannot be achieved in practice. We also employ a two-color real-time microarray reader to experimentally demonstrate these effects by independently monitoring the match and mismatch species during multiplex hybridization. The only universal criterion that can be employed is to optimize temperature based upon attaining equilibrium reaction conditions. This temperature varies from one probe to another, but can be determined empirically using standard microarray experimentation methods.
在任何微阵列杂交实验中，由于匹配和众多错配靶标物种（即交叉杂交），每个探针点都会有相应的信号贡献。温度优化的一个目标是尽量减少错配物种的贡献；然而，实现这一目标可能会以牺牲获得平衡反应条件为代价。我们采用双组分热力学和动力学模型来研究温度优化过程中涉及的权衡。这些模型表明，在平衡状态下可实现最大选择性，但错配物种通过竞争取代机制控制达到平衡的时间。此外，在较低温度下选择性会提高。然而，达到平衡的时间也会延长，因此在实际中无法实现更高的选择性。我们还使用双色实时微阵列读取器，通过在多重杂交过程中独立监测匹配和错配物种，以实验方式证明这些效应。唯一可采用的通用标准是根据达到平衡反应条件来优化温度。这个温度因探针而异，但可以使用标准微阵列实验方法凭经验确定。
