Please wait a minute...
浙江大学学报(农业与生命科学版)  2022, Vol. 48 Issue (6): 797-806    DOI: 10.3785/j.issn.1008-9209.2022.07.181
研究论文     
利用微计算机断层扫描技术重建褐飞虱内部结构
舒润国1(),周行1,曹子雄2,贺康1,李飞1()
1.浙江大学农业与生物技术学院昆虫科学研究所, 浙江省作物病虫生物学重点实验室, 杭州 310058
2.Object Research Systems(ORS)公司, 加拿大 蒙特利尔 (魁北克) H3B 1A7
Reconstruction of internal structures of Nilaparvata lugens using micro-computer tomography technology (Micro CT)
Runguo SHU1(),Hang ZHOU1,Zixiong CAO2,Kang HE1,Fei LI1()
1.Zhejiang Provincial Key Laboratory of Biology of Crop Pathogens and Insects, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
2.Object Research Systems (ORS) Inc. , Montr al (Qu bec) H3B 1A7, Canada
 全文: PDF(5122 KB)   HTML
摘要:

褐飞虱(Nilaparvata lugens)是一种重要的水稻害虫。本研究利用微计算机断层扫描技术(micro-computer tomography technology, Micro CT)获得了褐飞虱成虫的断层扫描图,通过人工建模和深度学习相结合的方式对褐飞虱内部组织和器官进行三维建模,获得了褐飞虱中枢神经系统、肌肉组织、消化道和生殖系统的三维模型,保留了这些结构的原始形态,准确地还原了褐飞虱体内各组织和器官的空间排布,并利用Dragonfly软件对昆虫的内部组织进行了空间体积的测量分析。本研究建立并完善了昆虫组织和器官的三维建模技术,有助于更为精准地观察昆虫的内部组织结构,可用于昆虫形态和器官发育的表型观察,为昆虫发育和基因功能研究提供了新技术和新方法。

关键词: 微计算机断层扫描技术褐飞虱内部结构三维重构表型组学    
Abstract:

The brown planthopper (Nilaparvata lugens) is an important rice pest. In this study, the tomographic images of adult brown planthopper were obtained by using micro-computer tomography technology (Micro CT). Three-dimensional models of internal tissues and organs of the brown planthopper were established by combining manual modeling and deep learning. Three-dimensional models of the central nervous system, muscle tissue, alimentary canal and reproductive system of the brown planthopper were obtained. These models preserved the original morphologies of these structures and accurately restored the spatial arrangement of all tissues and organs within the brown planthopper. Measurements of the internal organization of insects were analyzed using Dragonfly software. This study established and refined the three-dimensional reconstruction technique of insect tissues and organs, which can contribute to a more precise view of the internal organization structure of insects and can be used for phenotypic observation of insect morphology and organ development. It provides a new technique for insect development investigation and gene function analysis in entomology.

Key words: micro-computer tomography technology (Micro CT)    brown planthopper    internal structure    three-dimensional reconstruction    phenomics
收稿日期: 2022-07-18 出版日期: 2022-12-27
CLC:  Q 964  
基金资助: 国家重点研发计划项目(2021YFD1400100);国家自然科学基金项目(31972354)
通讯作者: 李飞     E-mail: runguoshu@zju.edu.cn;lifei18@zju.edu.cn
作者简介: 舒润国(https://orcid.org/0000-0001-8810-2666),E-mail:runguoshu@zju.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
舒润国
周行
曹子雄
贺康
李飞

引用本文:

舒润国,周行,曹子雄,贺康,李飞. 利用微计算机断层扫描技术重建褐飞虱内部结构[J]. 浙江大学学报(农业与生命科学版), 2022, 48(6): 797-806.

Runguo SHU,Hang ZHOU,Zixiong CAO,Kang HE,Fei LI. Reconstruction of internal structures of Nilaparvata lugens using micro-computer tomography technology (Micro CT). Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48(6): 797-806.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2022.07.181        https://www.zjujournals.com/agr/CN/Y2022/V48/I6/797

项目 Item参数 Specification

X射线源

X-ray source

40~100 kV,10 W;点尺寸<5 μm,4 W

X射线探测器

X-ray detector

1 600万像素sCMOS探测器

(4 096像素×4 096像素)

样品尺寸

Object size

最大直径75 mm,最大高度70 mm

仪器尺寸

Instrument dimension

宽×深×高为1 160 mm×520 mm×

330 mm(150 kg)

电源

Power supply

100~240 V 交流电,50~60 Hz,最大

电流3 A

表1  SkyScan 1272 CMOS技术参数
图1  Micro CT内、外部结构A. 上样装置;B. SkyScan 1272高分辨率三维X射线显微镜;C. 成像路径示意图。
图2  原始Micro CT图像的三维建模流程
图3  样本姿态矫正前(A1~A3 )和姿态矫正后(B1~B3 )的Micro CT图像X-Y、X-Z、Y-Z代表Micro CT数据的3个平面。
图4  有效信号放大前(A)和有效信号放大后(B)的灰度密度直方图
图5  训练数据的相关位置信息

体素

Voxel

样本1

Sample 1

样本2

Sample 2

样本3

Sample 3

样本4

Sample 4

样本5

Sample 5

总计

Total

总体素

Total voxels

51 06244 10046 23040 42044 100225 912

标记体素

Marked voxels

51 062(100.00%)44 100(100.00%)46 230(100.00%)40 420(100.00%)44 100(100.00%)225 912(100.00%)

目标区域

Target region

4 651(9.11%)3 362(7.62%)3 586(7.76%)1 049(2.60%)527(1.20%)13 175(5.83%)

背景区域

Background region

46 411(90.89%)40 738(92.38%)42 644(92.24%)39 371(97.40%)43 573(98.80%)212 737(94.17%)
表2  训练数据相关的体素信息
Patch大小 Patch size64
Patch移动步长 Patch stride ratio0.25
损失函数 Loss function

OrsDiceLoss

(1-DiceLoss)

优化算法 Optimization algorithmAdadelta

训练集Patch数

Patch count of training set

32

验证集Patch数

Patch count of validation set

7

数据增强设置

Data augmentation setting

20

训练集Patch数(增强)

Patch count of training set (augmentation)

6 321

验证集Patch数(增强)

Patch count of validation set (augmentation)

7
表3  深度学习模型训练参数
图6  深度学习模型对应的损失函数曲线(A)和监控图(B)
图7  手工修复与立体模型优化阶段的肌肉模型输出结果A.深度学习模型分割的肌肉ROI(绿色);B.经过手工修复的ROI(紫色);C.平滑后的ROI(橙色);D.最终的肌肉网格模型(黄褐色)。
图8  褐飞虱内部结构三维模型

组织和器官

Tissue and organ

体积

Volume/mm3

中枢神经系统 Central nervous system0.02
肌肉组织 Muscle tissue0.10
消化道 Alimentary canal0.01
生殖系统(雄性) Reproductive system (male)0.03
生殖系统(雌性) Reproductive system (female)0.04
表4  褐飞虱三维模型结构的体积

基于Micro CT的褐飞虱三维重构系统

3D reconstruction system of BPH based on Micro CT

基于冷冻电镜的褐飞虱三维重构系统

3D reconstruction system of BPH based on Cryo-EM

项目

Item

费用

Cost/(104 CNY)

项目

Item

费用

Cost/(104 CNY)

Micro CT成像系统 Micro CT imaging system300冷冻电镜成像系统 Cryo-EM imaging system950
Dragonfly建模软件 Dragonfly modeling software25Amira建模软件 Amira modeling software70
图形工作站 Graphics workstation2图形工作站 Graphics workstation3
总计 Total327总计 Total1 023
最高成像精度 Maximum imaging accuracy/μm4.0最高成像精度 Maximum imaging accuracy/nm50
表5  使用不同三维重构系统的成本对比
1 CNUDDE V, BOONE M N. High-resolution X-ray computed tomography in geosciences: a review of the current technology & applications[J]. Earth-Science Reviews, 2013, 123(1): 1-17. DOI:10.1016/j.earscirev.2013.04.003
doi: 10.1016/j.earscirev.2013.04.003
2 MORTON E J, WEBB S, BATEMAN J E, et al. Three-dimensional X-ray microtomography for medical and biological applications[J]. Physics in Medicine and Biology, 1990, 35(7): 805-820. DOI:10.1088/0031-9155/35/7/001
doi: 10.1088/0031-9155/35/7/001
3 BOUDEWIJNS R, THIBAUT H J, KAPTEIN S J F. STAT2 signaling restricts viral dissemination but drives severe pneumonia in SARS-CoV-2 infected hamsters[J]. Nature Communications, 2020, 11: 5838. DOI:10.1038/s41467-020-19684-y
doi: 10.1038/s41467-020-19684-y
4 WU D, WU D, FENG H, et al. A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits[J]. Plant Communications, 2021, 2(2): 100165. DOI:10.1016/j.xplc.2021.100165
doi: 10.1016/j.xplc.2021.100165
5 SCHOBORG T A, SMITH S L, SMITH L N, et al. Micro-computed tomography as a platform for exploring Drosophila development[J]. Development, 2019, 146(23): dev176685. DOI:10.1242/dev.176685
doi: 10.1242/dev.176685
6 WIPFLER B, POHL H, YAVORSKAYA M I, et al. A review of methods for analysing insect structures—the role of morphology in the age of phylogenomics[J]. Current Opinion in Insect Science, 2016, 18: 60-68. DOI:10.1016/j.cois.2016.09.004
doi: 10.1016/j.cois.2016.09.004
7 BETZ O, WEGST U, WEIDE D, et al. Imaging applications of synchrotron X-ray phase-contrast microtomography in biological morphology and biomaterials science.Ⅰ. General aspects of the technique and its advantages in the analysis of millimetre-sized arthropod structure[J]. Journal of Microscopy, 2007, 227(1): 51-71. DOI:10.1111/j.1365-2818.2007.01785.x
doi: 10.1111/j.1365-2818.2007.01785.x
8 SMITH D B, BERNHARDT G, RAINE N E, et al. Exploring miniature insect brains using micro-CT scanning techniques[J]. Scientific Reports, 2016, 6(1): 21768. DOI:10.1038/srep21768
doi: 10.1038/srep21768
9 SOMBKE A, LIPKE E, MICHALIK P. Potential and limitations of X-ray micro-computed tomography in arthropod neuroanatomy: a methodological and comparative survey[J]. The Journal of Comparative Neurology, 2015, 523(8): 1281-1295. DOI:10.1002/cne.23741
doi: 10.1002/cne.23741
10 GRIMALDI D A, PEÑALVER E, BARRÓN E, et al. Direct evidence for eudicot pollen-feeding in a Cretaceous stinging wasp (Angiospermae; Hymenoptera, Aculeata) preserved in Burmese amber[J]. Communications Biology, 2019, 2: 408. DOI:10.1038/s42003-019-0652-7
doi: 10.1038/s42003-019-0652-7
11 MINTER N J, FRANKS N R, BROWN K A R. Morphogenesis of an extended phenotype: four-dimensional ant nest architecture[J]. Journal of the Royal Society Interface, 2012, 9(68): 586-595. DOI:10.1098/rsif.2011.0377
doi: 10.1098/rsif.2011.0377
12 娄永根,程家安.植物的诱导抗虫性[J].昆虫学报,1997,40(3):320-331. DOI:10.3321/j.issn:0454-6296.1997.03.018
LOU Y G, CHENG J A. Induced insect resistance in plants[J]. Acta Entomologica Sinica, 1997, 40(3): 320-331. (in Chinese with English abstract)
doi: 10.3321/j.issn:0454-6296.1997.03.018
13 陈若篪,程遐年.褐飞虱起飞行为与自身生物学节律、环境因素同步关系的初步研究[J].南京农业大学学报,1980(2):42-49. DOI:10.7685/j.issn.1000-2030.1980.02.007
CHEN R C, CHENG X N. The take-off behavior of brown planthopper (Nilaparvata lugens) and its synchronous relations to the biological rhythm and environmental factors[J]. Journal of Nanjing Agricultural University, 1980(2): 42-49. (in Chinese with English abstract)
doi: 10.7685/j.issn.1000-2030.1980.02.007
14 2022年全国农作物重大病虫害发生趋势预报[J].中国植保导刊,2022,42(4):107-108.
Forecast of occurrence trend of major crop diseases and pests in 2022[J]. China Plant Protection, 2022, 42(4): 107-108. (in Chinese)
15 黄水金,黄荣华.我国褐飞虱的若干研究进展[J].江西农业学报,2001(4):43-50. DOI:10.3969/j.issn.1001-8581.2001.04.009
HUANG S J, HUANG R H. Some research progress of Nilaparvata lugens in China[J]. Acta Agriculturae Jiangxi, 2001(4): 43-50. (in Chinese with English abstract)
doi: 10.3969/j.issn.1001-8581.2001.04.009
16 DONG S, WANG P, ABBAS K. A survey on deep learning and its applications[J]. Computer Science Review, 2021, 40: 100379. DOI:10.1016/j.cosrev.2021.100379
doi: 10.1016/j.cosrev.2021.100379
17 弗朗索瓦•肖莱.Python深度学习[M].张亮,译.北京:人民邮电出版社,2018:10-11.
CHOLLET F. Deep Learning with Python[M]. ZHANG L, trans. Beijing: Post and Telecom Press, 2018: 10-11. (in Chinese)
[1] 王斯亮,罗序梅,张传溪. 褐飞虱神经肽及其受体基因的功能筛查[J]. 浙江大学学报(农业与生命科学版), 2022, 48(6): 766-775.
[2] 王青松,柳永,王新,孙宏,姚晓红,吴逸飞,汤江武,葛向阳. 基于质构量化分析的净水菌胶囊制备及其性能研究[J]. 浙江大学学报(农业与生命科学版), 2015, 41(6): 712-722.
[3] 郭燕, 巨青松, 姚洪渭*, 蒋明星, 叶恭银, 程家安. 环境因子变化对褐飞虱非特异性酯酶活性的影响[J]. 浙江大学学报(农业与生命科学版), 2013, 39(6): 591-599.
[4] 黄玉吉1,2, 陈斌1*, 张传溪2*. 褐飞虱体内Himetobi P病毒的检测及组织定位[J]. 浙江大学学报(农业与生命科学版), 2013, 39(5): 473-590.
[5] 戈林泉, 周国鑫, 王祺, 祝树德, 娄永根. 水稻β-石竹烯合成酶基因OsCAS的克隆鉴定、原核表达及其遗传转化[J]. 浙江大学学报(农业与生命科学版), 2009, 35(4): 365-371.
[6] 王霞 杜孟浩 周国鑫等. 水杨酸与过氧化氢信号途径在褐飞虱诱导的水稻挥发物释放中的作用[J]. 浙江大学学报(农业与生命科学版), 2007, 33(1): 15-23.
[7] 杜孟浩  严兴成  娄永根  程家安. 褐飞虱唾液中诱导水稻释放挥发物的活性组分研究[J]. 浙江大学学报(农业与生命科学版), 2005, 31(3): 237-244.
[8] 吕仲贤  俞晓平  HEONG Kong-luen  胡萃. 氮营养对褐飞虱在IR64稻株上取食和产卵行为的影响[J]. 浙江大学学报(农业与生命科学版), 2005, 31(1): 62-70.
[9] 马波  娄永根  程家安. 几种生物因子对褐飞虱诱导的水稻挥发物活性的影响[J]. 浙江大学学报(农业与生命科学版), 2004, 30(6): 589-595.
[10] 赵伟春  程家安  陈正贤. 褐飞虱抗原检测最佳ELISA条件的建立[J]. 浙江大学学报(农业与生命科学版), 2002, 28(6): 629-634.