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用于血清抗体库自动半定量分析的蛋白质阵列处理软件。

Protein array processing software for automated semiquantitative analysis of serum antibody repertoires.

作者信息

Yadav Ajeet Singh, Ooi Chin Hong, An Hongjie, Nguyen Nam-Trung, Kijanka Gregor S

机构信息

Queensland Micro-Nanotechnology Centre, Griffith University, Nathan, QLD 4111, Australia.

出版信息

Biomicrofluidics. 2023 Sep 15;17(5):054101. doi: 10.1063/5.0169421. eCollection 2023 Sep.

Abstract

Effective immunotherapies activate natural antitumor immune responses in patients undergoing treatment. The ability to monitor immune activation in response to immunotherapy is critical in measuring treatment efficacy over time and across patient cohorts. Protein arrays are systematically arranged, large collections of annotated proteins on planar surfaces, which can be used for the characterization of disease-specific and treatment-induced antibody repertoires in individuals undergoing immunotherapy. However, the absence of appropriate image analysis and data processing software presents a substantial hurdle, limiting the uptake of this approach in immunotherapy research. We developed a first, automated semiquantitative open-source software package for the analysis of widely used protein macroarrays. The software allows accurate single array and inter-array comparative studies through the tackling of intra-array inconsistencies arising from experimental disparities. The innovative and automated image analysis process includes adaptive positioning, background identification and subtraction, removal of null signals, robust statistical analysis, and protein pair validation. The normalized values allow a convenient semiquantitative data analysis of different samples or timepoints. Enabling accurate characterization of sample series to identify disease-specific immune profiles or their relative changes in response to treatment may serve as a diagnostic or predictive tool of disease.

摘要

有效的免疫疗法可激活接受治疗患者的天然抗肿瘤免疫反应。监测免疫疗法引发的免疫激活的能力对于衡量不同时间及不同患者群体的治疗效果至关重要。蛋白质阵列是在平面表面系统排列的大量注释蛋白质集合,可用于表征接受免疫疗法的个体中疾病特异性和治疗诱导的抗体库。然而,缺乏合适的图像分析和数据处理软件构成了重大障碍,限制了这种方法在免疫疗法研究中的应用。我们开发了首个用于分析广泛使用的蛋白质宏阵列的自动化半定量开源软件包。该软件通过解决因实验差异导致的阵列内不一致问题,实现准确的单阵列和阵列间比较研究。创新的自动化图像分析过程包括自适应定位、背景识别与扣除、去除无效信号、稳健的统计分析以及蛋白质对验证。归一化值便于对不同样本或时间点进行半定量数据分析。能够准确表征样本系列以识别疾病特异性免疫谱或其对治疗的相对变化,可作为疾病的诊断或预测工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7404/10505068/967591afab70/BIOMGB-000017-054101_1-g001.jpg

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