Department of Genetics, Stanford University, Stanford, California, United States of America.
Department of Biochemistry & Biophysics, University of California, San Francisco, United States of America.
PLoS One. 2019 Mar 22;14(3):e0203725. doi: 10.1371/journal.pone.0203725. eCollection 2019.
Multiplexed bioassays, in which multiple analytes of interest are probed in parallel within a single small volume, have greatly accelerated the pace of biological discovery. Bead-based multiplexed bioassays have many technical advantages, including near solution-phase kinetics, small sample volume requirements, many within-assay replicates to reduce measurement error, and, for some bead materials, the ability to synthesize analytes directly on beads via solid-phase synthesis. To allow bead-based multiplexing, analytes can be synthesized on spectrally encoded beads with a 1:1 linkage between analyte identity and embedded codes. Bead-bound analyte libraries can then be pooled and incubated with a fluorescently-labeled macromolecule of interest, allowing downstream quantification of interactions between the macromolecule and all analytes simultaneously via imaging alone. Extracting quantitative binding data from these images poses several computational image processing challenges, requiring the ability to identify all beads in each image, quantify bound fluorescent material associated with each bead, and determine their embedded spectral code to reveal analyte identities. Here, we present a novel open-source Python software package (the mrbles analysis package) that provides the necessary tools to: (1) find encoded beads in a bright-field microscopy image; (2) quantify bound fluorescent material associated with bead perimeters; (3) identify embedded ratiometric spectral codes within beads; and (4) return data aggregated by embedded code and for each individual bead. We demonstrate the utility of this package by applying it towards analyzing data generated via multiplexed measurement of calcineurin protein binding to MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding) containing known and mutant binding peptide motifs. We anticipate that this flexible package should be applicable to a wide variety of assays, including simple bead or droplet finding analysis, quantification of binding to non-encoded beads, and analysis of multiplexed assays that use ratiometric, spectrally encoded beads.
多重分析物生物测定法，其中多个感兴趣的分析物在单个小体积内平行探测，大大加快了生物发现的步伐。基于珠的多重分析物生物测定法具有许多技术优势，包括近溶液相动力学、小样本量要求、在测定内进行多次重复以减少测量误差，并且对于某些珠材料，能够通过固相合成直接在珠上合成分析物。为了允许基于珠的多重化，可以将分析物合成到光谱编码珠上，其中分析物的身份与嵌入的代码之间存在 1:1 的关联。然后可以将珠结合的分析物文库混合并与荧光标记的大分子感兴趣物孵育，从而允许通过单独的成像来同时定量大分子与所有分析物之间的相互作用。从这些图像中提取定量结合数据提出了几个计算图像处理挑战，需要能够识别每个图像中的所有珠、量化与每个珠相关联的结合荧光材料，并确定它们的嵌入光谱代码以揭示分析物的身份。在这里，我们提出了一种新颖的开源 Python 软件包（mrbles 分析包），它提供了必要的工具来：（1）在明场显微镜图像中找到编码珠；（2）量化与珠边缘相关的结合荧光材料；（3）识别珠内的比率光谱代码；（4）返回按嵌入代码和每个单独珠聚集的数据。我们通过应用它来分析通过包含已知和突变结合肽基序的 MRBLE（具有比率条形码镧系元素编码的微球）的多重测量来测量钙调神经磷酸酶蛋白与 MRBLE 的结合产生的数据，证明了该软件包的实用性。我们预计这个灵活的软件包应该适用于各种测定法，包括简单的珠或液滴发现分析、非编码珠结合的定量分析以及使用比率、光谱编码珠的多重分析物测定法的分析。
