Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.

中文翻译：

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精确测量小型遗传网络中的动力学和再现性。

基因表达的量化已成为理解遗传网络的核心工具。在许多系统中，唯一可行的测量蛋白质水平的方法是免疫荧光法，它因准确性有限而臭名昭著。以早期果蝇胚胎为例，我们表明，仔细识别和控制实验误差可以实现高度准确的基因表达测量。我们在不同的宿主物种中生成抗体，允许在单个胚胎中同时染色四个果蝇缺口基因。对数百个表达谱的仔细错误分析表明，观察到的胚胎间波动中只有不到 20% 源于实验错误。这些测量不仅可以提取非常准确的平均基因表达谱，还可以提取它们自然发生的生物起源波动和相应的互相关。我们使用此分析以~1 分钟的精度提取间隙基因谱动态。这些新的测量和分析技术的结合揭示了由于将位置精度从母体梯度传递到配对规则基因的集体网络动力学，配置文件的可重复性增加了两倍。