FLINO: a new method for immunofluorescence bioimage normalization.
FLINO: a new method for immunofluorescence bioimage normalization.
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DOI：
10.1093/bioinformatics/btab686
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发表时间：
2022-01-03
期刊：
影响因子：
--
通讯作者：
Ginty F
中科院分区：
文献类型：
--
作者：
Graf J;Cho S;McDonough E;Corwin A;Sood A;Lindner A;Salvucci M;Stachtea X;Van Schaeybroeck S;Dunne PD;Laurent-Puig P;Longley D;Prehn JHM;Ginty F
单细胞及其在组织中的空间组织的多重免疫荧光生物成像对未来精确诊断和治疗的发展具有很大的希望。目前的多路复用管道通常涉及在多个组织载玻片上进行多轮免疫荧光染色。这引入了可以隐藏潜在生物信号的实验批效应。重要的是要有强大的算法，可以纠正批处理效应，同时不引入偏差到数据中。数据归一化方法的性能在不同的分析管道中可能会有所不同。为了评估差异，至关重要的是要有一个代表分析的真实数据集。提出了一种新的免疫荧光图像归一化方法，并对替代方法和工作流程进行了评估。使用核染料DAPI对同一组织进行多轮免疫荧光染色，以表示虚拟切片和基本事实。DAPI被抑制在给定的组织载玻片上，产生相同底层结构的多个图像，但经历多个代表性的组织处理步骤。该基础真实数据集用于评估和比较多种归一化方法，包括中位数，分位数，平滑分位数，中位数比率归一化和m值的修剪平均值。将这些方法应用于无偏网格对象和分段细胞对象工作流中，对24个多路生物标志物进行处理。发现对数空间中网格对象的上四分位数归一化与通过中间分位数直接归一化分割的单元对象获得几乎相同的性能。然后将开发的基于网格的技术应用于滑动控制进行评估。每张幻灯片使用五个或更少的控制会给数据带来偏差。十个或更多的幻灯片上控制能够健壮地纠正批处理效果。本文的基础数据以及用于执行图像规范化方法和工作流评估的FLINO r -脚本可以从https://github.com/GE-Bio/FLINO下载。补充数据可在生物信息学网站获得。
Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO. Supplementary data are available at Bioinformatics online.
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影响因子：
3.7
作者：
Espín-Pérez A;Portier C;Chadeau-Hyam M;van Veldhoven K;Kleinjans JCS;de Kok TMCM
通讯作者：
de Kok TMCM
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
48
作者：
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
2.1
作者：
Hicks, Stephanie C.;Okrah, Kwame;Bravo, Hector Corrada
通讯作者：
Bravo, Hector Corrada
影响因子：
48
作者：
Mortazavi, Ali;Williams, Brian A.;Wold, Barbara
通讯作者：
Wold, Barbara