利用开源软件管道测量琼脂中固定活性污泥颗粒的形状和大小
Summary
活性污泥中颗粒的大小和形状是用不同方法测量的重要参数。不具有代表性的采样、次优图像和主观分析参数产生了不准确之处。为了最大限度地减少这些错误并便于测量, 我们提供了一个协议, 指定每个步骤, 包括开源软件管道。
Abstract
实验生物反应器, 如处理废水的生物反应器, 含有其大小和形状是重要参数的颗粒。例如, 活性污泥絮凝剂的大小和形状可以指示微尺度上的条件, 也直接影响污泥在澄清器中的沉降情况。
粒子大小和形状都是错误的 “简单” 测量。在采样、成像和分析粒子时, 可能会出现许多微妙的问题, 这些问题通常在非正式协议中没有得到解决。采样方法可能有偏差或没有提供足够的统计能力。样品本身可能保存不良或在固定过程中发生改变。图像的质量可能不够;重叠粒子、景深、放大倍率和各种噪声都会产生较差的结果。不明确的分析可能会引入偏差, 例如通过手动图像阈值和分割产生的偏差。
除了可重复性之外, 可负担性和吞吐量也是可取的。一种经济实惠、高吞吐量的方法可以实现更频繁的颗粒测量, 从而产生许多包含数千个粒子的图像。一种使用廉价试剂、通用解剖显微镜和自由可用的开源分析软件的方法允许可重复、可访问、可重复和部分自动化的实验结果。此外, 这种方法的产品可以格式良好, 定义明确, 易于理解的数据分析软件, 方便实验室内分析和实验室之间的数据共享。
我们提出了一个协议, 详细介绍了生产此类产品所需的步骤, 包括: 采样、样品制备和琼脂固定、数字图像采集、数字图像分析, 以及来自分析结果。我们还包括了一个开源数据分析管道来支持这一协议。
Introduction
该方法的目的是提供一种明确定义的、可重复的和部分自动化的方法, 用于确定生物反应器中颗粒的大小和形状分布, 特别是那些含有活性污泥絮凝剂和好氧颗粒的生物反应器中颗粒的大小和形状分布1,2. 这种方法背后的理由是提高我们现有的内部协议3、4 的可负担性、简单性、吞吐量和可重复性, 方便为他人测量颗粒, 并促进共享和数据的比较。
粒子测量分析有两大类–直接成像和使用光散射5等特性的推理方法。虽然推理方法可以自动化, 并且具有较大的吞吐量, 但设备成本很高。此外, 虽然推理方法可以准确地确定粒子6的等效大小, 但它们没有提供详细的形状信息7。
由于需要形状数据, 我们的方法基于直接成像。虽然存在一些高通量的成像方法, 但它们传统上需要昂贵的商业硬件或自定义构建的解决方案8,9。我们的方法是为了使用通用的、经济实惠的硬件和软件, 这些硬件和软件虽然吞吐量减少, 但产生的粒子图像远远超过许多分析10所需的最小值.
现有协议可能没有指定重要的采样和图像采集步骤。其他协议可以指定引入主观偏差的手动步骤 (如临时阈值11)。一个明确定义的方法, 指定采样, 固定和图像采集步骤结合免费提供的分析软件将加强实验室内的图像分析和实验室之间的比较。该协议的一个主要目标是提供一个工作流和工具, 该工作流和工具应能为同一样本带来来自不同实验室的可重现结果。
除了对图像分析过程进行规范化外, 此管道生成的数据还记录在一个定义明确、格式良好的文件12中, 适合流行的数据分析包13、14、缓动实验使用特定分析 (如自定义图形生成), 并促进实验室之间的数据共享。
该协议特别适用于需要颗粒形状数据、无法获得推理方法、不希望开发自己的图像分析管道、希望与他人轻松共享数据的研究人员
Protocol
Representative Results
Discussion
虽然图像分析系统相当强大, 并采取了 qc 步骤, 以确保不良图像被删除, 但适当注意采样、制版和图像采集中的具体问题可以提高数据的准确性和图像的比例。图像通过 qc。
采样浓度
假设已采取具有代表性的样品, 最重要的步骤是确保有足够的粒子存在于具有代表性的 9和高效的分析中, 同时不会使粒子重叠。
这相当于在?…
Divulgations
The authors have nothing to disclose.
Acknowledgements
这项工作得到了国家科学基金会 cbet 1336544 的赠款的支持。
fiji、r 和 python 徽标与以下商标政策一起使用:
python: https://www.python.org/psf/trademarks/
r: https://www.r-project.org/Logo/ , 根据 cc-by-sa 4.0 许可证列于: https://creativecommons.org/Licenses/by-sa/4.0/
斐济: https://imagej.net/Licensing
Materials
| 10% Bleach solution | Chlorox | 31009 | For workspace disinfection. |
| 15 mL centrifuge tube with cap | Corning | 430790 | Per sample. |
| 50 mL Erlenmeyer flask | Corning | 4980-50 | Other vessels are suitable so long as they can contain > 40 mL of sample and allow mixing |
| 500 mL Kimax Bottle | Kimble-Chase | 14395-50 | Or otherwise sufficient for agar handling |
| Agar | BD | 214010 | Solid, to prepare 7.5% gel. 7 mL per sample. |
| Data analysis software | N/A | N/A | R or Python are suggested |
| Deionized water | N/A | N/A | Sufficient to prepare stain and agar. If unavailable, tap should be fine. |
| Desktop computer | N/A | N/A | Image analysis is not CPU intensive, any 'ordinary' desktop computer circa 2017 should be sufficient. |
| External hard drive | Seagate | STEB5000100 | Not fully required, but extremely useful given the number an size of images. 2 or more TB of storage suggested. |
| FIJI | NIH | version 1.51d | Version is ImageJ core. Plugins are updated as of writing. Available at: https://imagej.net/Fiji/Downloads |
| GIT | Open Source | version 2.19.1 or later | Available at: https://git-scm.com/ |
| Image capture software | ToupView | version 3.7.5177 | Any compatible with camera, may come with camera. Should allow saving TIFF images with spatial calibration data. |
| Mechanical (X/Y) Stage | OMAX | A512 | Not fully required, but greatly aids image acquisition. |
| Methylene blue | Fisher | M291-100 | Solid, to prepare 1% w/v solution. 5 uL solution per sample. |
| Microscope camera | OMAX | A35140U | Any digitial camera compatible with microscope. Resolution providing at least 5 um per pixel at 10x magnification and a dynamic range of at least 8 bits per pixel per color channel is suggested. |
| Optical Stage Micrometer | OMAX | A36CALM1 | Or otherwise sufficient for spatial calibration. |
| Petri dish, 100 mm | Fisher | FB0875712 | 1 per sample. |
| PPE | N/A | N/A | Standard lab coat, gloves, and eyewear. |
| Sparmoria macro | NCSU | version 0.2.1 | Available at github repository : https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis |
| Stereo/dissecting microscope | Nikon | SMZ-2T | Should provide 10 to 20x magnficiation and allow digital photos either with a buit-in camera or profide a mounting point for a CCD. |
References
- Show, K. Y., Lee, D. J., Tay, J. H. Aerobic granulation: Advances and challenges. Applied Biochemistry and Biotechnology. 167 (6), 1622-1640 (2012).
- Adav, S. S., Lee, D. -. J., Show, K. -. Y., Tay, J. -. H. Aerobic granular sludge: Recent advances. Biotechnology Advances. 26 (5), 411-423 (2008).
- Initial Investigations of Aerobic Granulation in an Annular Gap Bioreactor. North Carolina State University Available from: https://repository.lib.ncsu.edu/handle/1840.16/2162 (2004)
- . Effect of Hydrodynamics on Aerobic Granulation Available from: https://repository.lib.ncsu.edu/handle/1840.16/8761 (2012)
- Tay, J. H., Liu, Q. S., Liu, Y. Microscopic observation of aerobic granulation in sequential aerobic sludge blanket reactor. Journal of Applied Microbiology. 91 (1), 168-175 (2001).
- Kelly, R. N., et al. Graphical Comparison of Image Analysis and Laser Diffraction Particle Size Analysis Data Obtained From the Measurements of Nonspherical Particle Systems. AAPS Pharm SciTech. 7 (3), E1-E14 (2006).
- Walisko, R., et al. The Taming of the Shrew -Controlling the Morphology of Filamentous Eukaryotic and Prokaryotic Microorganisms. Advances in Biochemical Engineering/Biotechnology. 149, 1-27 (2015).
- Campbell, R. A. A., Eifert, R. W., Turner, G. C. Openstage: A Low-Cost Motorized Microscope Stage with Sub-Micron Positioning Accuracy. PLoS ONE. 9 (2), e88977 (2014).
- Dias, P. A., et al. Image processing for identification and quantification of filamentous bacteria in in situ acquired images. BioMedical Engineering OnLine. 15 (1), 64 (2016).
- Liao, J., Lou, I., de los Reyes, F. L. Relationship of Species-Specific Filament Levels to Filamentous Bulking in Activated Sludge. Applied and Environmental Microbiology. 70 (4), 2420-2428 (2004).
- Cromey, D. W. Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images. Science and Engineering Ethics. 16 (4), 639-667 (2010).
- Wickham, H. Tidy Data. Journal of Statistical Software. 59 (10), (2014).
- van Rossum, G. . Python tutorial. , (1995).
- Schindelin, J., Arganda-Carreras, I., Frise, E. FIJI: an open-source platform for biological-image analysis. Nature Methods. 9 (7), 676-682 (2012).
- . SParMorIA: Sludge Particle Morphological ImageAnalysis Available from: https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis (2018)
- Ferreira, T., Rasband, W. . ImageJ User Guide: IJ 1.42 r. , (2012).
- Sezgin, M., Sankur, B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging. 13 (1), 146-166 (2004).
- Kröner, S., Doménech Carbó, M. T. Determination of minimum pixel resolution for shape analysis: Proposal of a new data validation method for computerized images. Powder Technology. 245, 297-313 (2013).
- McKinney, W. Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. , 51-56 (2010).
- Waskom, M., et al. . seaborn: v0.5.0 (November 2014). , (2014).
- . dplyr: A Grammar of Data Manipulation Available from: https://cran.r-project.org/web/packages/dplyr/index.html (2016)
- Riley, R. S., Ben-Ezra, J. M., Massey, D., Slyter, R. L., Romagnoli, G. Digital Photography: A Primer for Pathologists. Journal of Clinical Laboratory Analysis. 18 (2), 91-128 (2004).
- Singh, S., Bray, M. A., Jones, T. R., Carpenter, A. E. Pipeline for illumination correction of images for high-throughput microscopy. Journal of Microscopy. 256 (3), 231-236 (2014).
- Eastwood, B. S., Childs, E. C. Image Alignment for Multiple Camera High Dynamic Range Microscopy. Proceedings. IEEE Workshop on Applications of Computer Vision. , 225-232 (2012).
- High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis. IEEE Journal of Selected Topics in Signal Processing (Special Issue on: Digital Image Processing Techniques for Oncology) Available from: https://www.lfb.rwth-aachen.de/en/ (2009)
- Jain, V., et al. Supervised Learning of Image Restoration with Convolutional Networks. 2007 IEEE 11th International Conference on Computer Vision. , 1-8 (2007).
