Summary

3维组织学音量和其研究小鼠乳腺腺体的应用改造

Published: July 26, 2014
doi:

Summary

We present an image registration approach for 3-dimensional (3D) histology volume reconstruction, which facilitates the study of the changes of an organ at the level of macrostructures made up of cells . Using this approach, we studied the 3D changes between wild-type and Igfbp7-null mammary glands.

Abstract

组织学容积重建有利于三维造型和器官在由细胞组成的宏观水平体积变化的研究。它也可以用于研究和验证在容积医学成像和治疗的新技术和算法。创建不同器官1,2,3的3D高解析度地图集是组织学容积重建的另一种应用。这提供了用于研究组织结构和各种细胞特征之间的空间关系的资源。我们提出了组织学容积重建,它使用一组光学blockface图像的图像配准方法。重构的组织学量表示已处理标本无殉后处理配准误差的可靠的形状。苏木精和曙红(H&E)染色的两种小鼠乳腺切片使用从海关提取边界点登记到相应的blockface图片在组织学和blockface图像试样的GES。登记的准确性目视评价。乳腺的宏观结构的取向也被肉眼评价在高的分辨率。

本研究界定的不同步骤这个图像配准的管道,从乳腺切除术,通过对三维组织学容积重建。虽然2D组织学图像显示对部分之间的结构差异,组织学三维体积提供了可视化的形状和乳腺的体积差异的能力。

Introduction

IGFBP7(胰岛素样生长因子结合蛋白7)是胰岛素样生长因子结合蛋白家族的一个成员,并且已经显示出结合的IGF-1受体4。下调的IGFBP7已知与不良预后乳腺癌5相关联,而IGFBP7在异种移植肿瘤模型中重新引入大大抑制生长6的肿瘤通过诱导细胞凋亡和细胞衰老7。为了研究IGFPB7的影响,一个IGFBP7空小鼠建立5(未发表资料)。虽然这些小鼠不发展的肿瘤,它们显示在卵巢,肌肉和肝脏的组织学变化,以及在乳腺发育模式(未发表数据)的缺陷。有缺陷的表型是首次显示为空的小鼠具有较小的窝产仔数和都无法维持多个大型窝(未发表资料)。

三维组织学卷有潜力提供有用了信息离子进行定量和比较分析病理结果在体积医学图像和评估。三维共聚焦,双光子显微镜可以在本地范围14提供的腺体高分辨率细胞形态的信息,但它具有的视图和深度的有限字段。组织学容积重建提供了更大的空间范围的更多信息。使用传统方法的组织切片,如收缩,膨胀,眼泪和褶皱的制备过程中的一些失真是始料不及的。这些扭曲使得很难寄存器串行组织学图像转换成3D堆叠来重构一个三维体积。作为连续部分与缺陷的数量增加了完整的部分之间的相似性被降低,从而使登记过程更加复杂。

不同的方法已经被提出来注册的组织切片,并建立一个连续的组织学沃lume明。一些技术依赖强度变化8,和其他人是根据第9的形状。对于一些样本的解剖结构,可作为地标10,11沿着与基于地标的登记方法12,13。但这些内部结构可能不会在整个体积和一些标本没有可靠的解剖结构可以识别被检测出。有些团体已经使用成对的注册方法,并连续登记的组织学图像一个到另一个使用轮廓或解剖结构16-18。注册序列组织切片彼此不使用参考图像可以传播准误差和改变组织学量的实际形状。成对登记方法依赖于对组织学切片和内部结构在整个图像的堆叠的形状的一致性;因此,它需要密集采样的标本,其可能并不总是可能的, 例如,对临床标本。

在这条管线,我们使用blockface图像作为一组用于组织学容积重建19参考图像。 Blockface图像被安装在切片机后采取的石蜡组织块,并且每个部分被切断之前。因而,损害个体的连续切片切不干扰的连续切片8,11,15登记。我们捕捉blockface图像与其他群体不同的方式。光学块面部图像是由一个远心透镜光学以消除或尽量减少桶和透视失真,这通常是使用在光学透镜经常发生时获得的。这是在其他公布的方式,使用常规镜头的执行blockface成像所提出的方法的优势之一。的图像被拍摄时的轻微倾斜的角度要使用该块的表面的反射的做卷烟之间的对比度增强UE和石蜡表面并消除深度的组织的阴影中,石蜡的表面之下。一种照相过滤器还用于偏振来自块表面与组织平衡对比度19的光。为了校正在旋转切片机块的位移,二至三孔加工成块的角上,这是很容易检测的blockface图像。这些孔的形心连同基于地标的刚性配准用于对齐blockface图像。

Protocol

1。标本切除乳腺手术从野生型CDH1以及IGFBP7缺失小鼠3天发病后泌乳。 传播腺体到玻片上,以帮助恢复本地乳腺形态。 2,固定和组织处理修正了在中性缓冲4%PFA O / N的乳腺在4℃。 存储腺体在70%乙醇前,组织处理。 转让腺体小组织处理磁带。 使用自动组织脱水处理的组织脱水组织中增加70%乙醇的乙醇和二甲苯?…

Representative Results

传统的显微镜技术的缺陷是一个器官在微观层面的理解是在一个时间限制在一个现​​场的视图。即使是“完全披露”的幻灯片,它提供了整个滑动部分,不能提供三维信息。随着整个幻灯片,动态扫描技术,我们看到它的一个整体部分的能力有所提高,发展却推断结构需要组织学3D容积重建。 为了更好地表征IGFBP7空鼠标的不足,对腺体切除泌乳期后3天开始进行了三维重建?…

Discussion

在这项研究中,我们开发了一种图像配准工作流程,从串行2D组织学图像,它不要求在组织内的内部随机选择的地标或植入的基准标记,这可能会扭曲组织重建三维组织学量。通过所描述的方法,光学blockface图像本身被用作前切片的参考图像。我们用钻在石蜡块外部孔,以帮助对齐blockface图像和纠正在镜头前的石蜡块的2D横向运动。二维组织学图像是相一致的相应的二维图像blockface防止配准误差的…

Divulgations

The authors have nothing to disclose.

Acknowledgements

The authors would like to thank the Biomarker Imaging Research Laboratory (BIRL) at Sunnybrook Research Institute for their histology services. Support for this work was provided by the Terry Fox Foundation, the Canadian Breast Cancer Foundation‐the Prairie‐NWT as well as a CIHR grant, #MOP-97996.

Materials

16% PFA VWR International 15710 16% Paraformaldehyde solution
Small tissue processing cassettes VWR International CA95029-956
Leica ASP300 Automated Tissue processor Leica 14047643515
100% ethanol Fisher Scientific S25307B
Xylene VWR International  CA95057-822
Paraffin  Thermo Fisher 39501006 Paraplast Tissue Embedding Medium
Leica EG 1160 Embedding Centre Leica
Leica rotary microtome Leica
Milling machine Argo
Microscope slides VWR International  CA48312-015
H&E stain VWR International
Automatic stainer
Coverslips  VWR International  48404-452
MEDITE RCM 7000 Glass Coverslipper MEDITE
Leica SCN400 slide scanner Leica
MATLAB MathWorks Inc MATLAB 2007b Development software
MeVisLab MeVis Medical Solutions AG MeVisLab 2.1 3D visualization software

References

  1. Sunkin, S. M., et al. Brain Atlas: An integrated spatiotemporal port for exploring the central nervous system. Nucleic Acids Research. 41, 996-1008 (2012).
  2. Shen, E. H., Overly, C. C., Jones, A. R. The Allen Human Brain Atlas: Comprehensive gene expression mapping of the human brain. Trends in Neurosciences. 35 (12), 711-714 (2012).
  3. Trifunović, D., Karali, M., Camposampiero, D., Ponzin, D., Banfi, S., Marigo, V. A high-resolution RNA expression atlas of retinitis pigmentosa genes in human and mouse retinas. Invest. Ophthalmol. Vis. Sci. 49 (6), 2330-2336 (2008).
  4. Evdokimova, V., et al. IGFBP7 binds to the IGF-1 receptor and blocks its activation by insulin-like growth factors. Science Signaling. 5 (255), 92 (2012).
  5. Burger, A., Leyland-Jones, B., Banerjee, K., Spyropoulos, D., Seth, A. Essential roles for IGFBP-3 and IGFBP-rP1 in breast cancer. European J. Cancer. 41 (11), 1515-1527 (2005).
  6. Amemiya, Y., et al. Insulin like growth factor binding protein-7 reduces growth of human breast cancer cells and xenografted tumors. Breast Cancer Res Treat. 126 (2), 373-384 (2011).
  7. Benatar, T., et al. IGFBP7 reduces breast tumor growth by induction of senescence and apoptosis pathways. Breast Cancer Res Treat. 133 (2), 563-573 (2012).
  8. Bardinet, E., et al. Co-registration of histological, optical and MR data of the human brain. Medical Image Computing and Computer-Assisted Intervention-Part I. , 548-555 (2002).
  9. Jacobs, M. A., Windham, J. P., Soltanian-Zadeh, H., Peck, D. J., Knight, R. A. Registration and warping of magnetic resonance images to histological sections. Medical Physics. 26 (8), 1568-1578 (1999).
  10. Zhan, Y., Ou, Y., Feldman, M., Tomaszeweski, J., Davatzikos, C., Shen, D. Registering histologic and MR images of prostate for image-based cancer detection. Academic radiology. 14 (11), 1367-1381 (2007).
  11. Dauguet, J., et al. Three-dimensional reconstruction of stained histological slices and 3D non-linear registration with in vivo MRI for whole baboon brain. Journal of Neuroscience Methods. 164 (1), 191-204 (2007).
  12. Lazebnik, R. S., Lancaster, T. L., Breen, M. S., Lewin, J. S., Wilson, D. L. Volume registration using needle paths and point landmarks for evaluation of interventional MRI treatments. IEEE Transactions on Medical Imaging. 22 (5), 653-660 (2003).
  13. Breen, M. S., Lazebnik, R. S., Wilson, D. L. Three-dimensional registration of magnetic resonance image data to histological sections with model-based evaluation. Annals of Biomedical Engineering. 33 (8), 1100-1112 (2005).
  14. Mori, H., Borowsky, A. D., Bhat, R., Ghajar, C. M., Seiki, M., Bissell, M. J. . The American Journal of Pathology. 180 (6), 2249-2256 (2012).
  15. Gibb, M., Gilbert, D., Heiner, M., et al. Resolving the three-dimensional histology of the heart. Computational Methods in Systems Biology. , 2-16 (2012).
  16. Wu, M. L., et al. Three-dimensional virtual microscopy of colorectal biopsies. Archives of Pathology & Laboratory Medicine. 129 (4), 507-510 (2005).
  17. Arganda-Carreras, I., et al. 3D Reconstruction of histological sections: Application to mammary gland tissue. Microscopy Research and Technique. 73 (11), 1019-1029 (2010).
  18. Song, Y., Treanor, D., Bulpitt, A. J., Magee, D. R. 3D reconstruction of multiple stained histology images. Journal of Pathology Informatics. 4 (2), 7 (2013).
  19. Shojaii, R., Karavardanyan, T., Yaffe, M., Martel, A. L. Validation of histology image registration. SPIE Medical Imaging. 7962, 79621E, doi:10.1117/12.878762. 7962 (7962E), (2011).
  20. Ridler, T. W., Calvard, S. Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man, and Cybernetics. 8 (8), 630-632 (1978).
  21. Freeman, H. Computer processing of line-drawing images. ACM Computing Surveys (CSUR. 6 (1), 57-97 (1974).
  22. Giardina, C. Accuracy of curve approximation by harmonically related vectors with elliptical loci). Computer Graphics and Image Processing. 6 (3), 277-285 (1977).
  23. Shojaii, R., Martel, A. L. A novel edge point selection method for registration of histology images. Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy. (OPTIMHisE) Workshop, MICCAI. , (2009).
  24. Besl, P., McKay, N. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (2), 239-256 (1992).
  25. Chatterjee, S., et al. Loss of Igfbp7 causes precocious involution in lactating mouse mammary gland. PLoS ONE. 9 (2), e87858 (2013).
  26. Manjunath, B. S., Chellappa, R. Unsupervised texture segmentation using Markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 13 (5), 478-482 (1991).
  27. Krishnamachari, S., Chellappa, R. Multiresolution Gauss-Markov random field models for texture segmentation. IEEE Transactions on Image Processing: a publication of the IEEE Signal Processing Society. 6 (2), 251-267 (1997).

Play Video

Citer Cet Article
Shojaii, R., Bacopulos, S., Yang, W., Karavardanyan, T., Spyropoulos, D., Raouf, A., Martel, A., Seth, A. Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands. J. Vis. Exp. (89), e51325, doi:10.3791/51325 (2014).

View Video