该协议的总体目标是通过创建定制3D印刷脑持有者和切片机盒的精确对准与组织切片的磁共振成像(MRI)图像的卷。
Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.
In vivo MRI provides a noninvasive and sensitive measure of tissue integrity at the macroscopic level. Changes in MRI signal intensity seen in vivo are outcome measures in many ongoing clinical trials.1 While the intensity changes seen via MRI can identify areas of abnormality in the context of the whole brain, they are often not sufficiently specific to differentiate pathological processes. This is especially true of dynamic processes involving multiple pathologies. For example, in multiple sclerosis (MS) or its animal model, experimental autoimmune encephalomyelitis (EAE), inflammation, edema, myelin degradation, axonal destruction, gliosis, and neuronal death overlap. 2, 3 To obtain the necessary specificity regarding the underlying pathology, context must be taken into account, together with knowledge of the histology of the MRI-identified abnormal tissues.
However, even in well-controlled animal experiments, matching histology with in vivo MRI is fundamentally challenging for various reasons. First, the difference in dimensional scales between histology sections and MRI is of several orders of magnitude.4 Second, for proper comparison, the orientation of MRI slice plane must match the sectioning plane of the brain tissue when cut. Due to the shape of the brain, it is very difficult to make consistently straight and accurate cuts when the brain is sitting on a flat surface. Third, the large size of the brain relative to a potentially small area of interest (lesion, tumor, etc.) creates a “needle-in-a-haystack” scenario for the pathologist processing the tissue. Fourth, even when the target tissue is found, it is commonly processed in such a way as to render virtually impossible an association with the original MRI data. Finally, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, further complicating the comparison between histology sections and MRI images.
Previous attempts to overcome these challenges relied on the use of deformational algorithms to coregister the data and/or placement of fiducial markers within or around the tissue as a reference.5, 6, 7, 8 The former approach requires complex computational models that are particularly susceptible to complications due to data formatting, imaging artifacts, and changes caused by tissue processing.4 On the other hand, the latter approach introduces the possibility of contaminating or otherwise harming the tissue itself.9
The approach described here improves the transition between modalities through the use of postmortem MRI to bridge the gap between in vivo MRI and histology. Postmortem MRI provides three-dimensional (3D) images of the brain at higher resolution than can be achieved in vivo and furthermore provides the data needed for producing a morphologically accurate model of the brain surface. This digital model can then be used to create a 3D-printed custom holder for the brain. With careful positioning, the brain holder allows for precise, MRI-oriented brain sectioning, reducing the need for complex mathematical algorithms, and enables a focus on specific regions for targeted sampling.
Our laboratory recently introduced new methods for creating custom brain holders and slicers using postmortem MRI and 3D-printing technology for human10 and marmoset brains.4 The two methods allow for a more accurate correlation between MRI and histology in a research setting, and ultimately allow a deeper understanding of the specific pathology underlying MRI abnormalities. Carefully designed experiments, in which the brain is sampled repeatedly over time in vivo, can provide context for interpretation of the pathology, which in turn can add specificity to interpretation of the MRI. Here, we present a modified protocol in a unified framework that can be applied to any brain tissue, whether it derive from nonhuman primates, rodents, or humans. We provide detailed instructions, and a corresponding video, for the marmoset sectioning. Although the overall protocol applies to any type of brain, due to differences in MRI acquisition and tissue size, as well as the challenges encountered when dealing with specific brain types, there are some differences in the approach depending on the type of brain being processed. In this presentation, sections with “human” will denote differences in protocol specific to the human brain.
这里列出的协议使MRI和组织切片之间准确的比较。的协议,提出在可应用于人类或小动物,例如狨猴或啮齿类动物的大脑的统一格式。具体到大(人)和小(非人灵长类和啮齿类动物)的大脑突出显示,并在所附的视频和附图的差异,我们证明在狨猴中的应用。虽然该方法是简单的,该方法需要许多步骤以及使用多种类型的软件。此外,一些问题可能会影响这种方法的准确度是很重要的就更不用说了。
体内 MRI的图像质量是一个重要因素。为了最大限度地减少在MRI和数字化的组织学图像之间的图像分辨率的视差,应该使用尽可能小的磁共振成像的体素尺寸。这个概念也适用于死后的MRI的图像质量。虽然增加在死后的MRI采集时间允许更高的图像分辨率,该制剂可以引入的图像伪影,例如有关气泡焦信号丢失。这些文物可以掩盖该组织区域以及影响其轮廓。此外,在尸检的MRI的组织的尺寸很可能是由在固定过程和持续时间的影响。而在体内 体外 MRI匹配可以通过采集期间利用在切片几何设置解剖标志紧密近似非线性登记将仍然是必要的,相匹配这两个MRI图像达到更高的精确度。
大脑保持器和限幅器的设计也是一个关键步骤。在创建脑的数字模型,平滑算法应用于该相对于固定脑模型稍微放大。这使大脑容易插入到其支架和限幅器,并降低在支架锐利边缘及#39; S轮廓。但是,如果该模型是过大(例如,超过5%),大脑可能死后MRI和/或切片中移动。另外重要的一点是要适应脑模型的设计,使得小脑正确放置在3D打印对象的内部。当小脑已尸检脑提取期间被损坏这可以是特别具有挑战性的。
当打印脑切片机和保持器,三维打印机的类型,也必须仔细地选择。一些多喷墨打印机使用烘箱移除支撑材料需要后处理。而这些打印机可以产生是水密和相对比桌面熔融沉积成型(FDM)打印机更耐用的物体,在加热过程以除去载体可以稍微翘曲的框,产生刀片的间隙是不完全垂直于大脑轮廓。
大脑切片的过程是另一个关键的一步。之前切割日Ë全脑入板,以确保大脑紧紧地坐在大脑切片机里面是非常重要的:应该有,当施加到大脑轻微的压力没有运动。这将有可能使叶片通过脑切在由研究者设定的精确位置。连续的,平衡的压力应在切割时被应用到刀片夹。取决于叶片的锐度,而组织的刚性,有轻微的横向切割运动可以是用于保持平坦切断面是有利的。
石蜡嵌入处理也可以是MRI和组织学之间的未对准的一个来源。如果该组织板坯不平坦靠在盒在嵌入处理期间坐在,会有切片机的切割平面和板坯的表面的地方之间的倾斜。这将需要切割不可用部分,以找到其中所有的组织暴露的平面。以校正倾斜单程通过改变观察面的角度对高各向同性分辨率死后MRI检查。然而,这几乎是不可能的上通常具有各向异性的分辨率(通常厚冠状切片)收购体内MRI执行。
最后,组织能,以及在幻灯片(折叠,龟裂,皱纹)的工作经历在福尔马林固定期限和石蜡包埋(收缩)有些变形。一些这些变形可以通过将在水浴4-5微米的部分转印到幻灯片之前进行校正。其它变形可通过对死后MRI图像进行组织学数字化图像的可变形图像配准来部分地解决。然而,仔细和熟练的实践尽量减少变形是最有效的方法来匹配MRI卷组织切片。
总之,这里介绍的方法使INVestigators准确评估MRI表现的基本病理。更一般地,它是用于识别和/或验证对调查研究针对特定的病理过程,如炎症或髓鞘新颖的MRI标记物有希望的方法。
The authors have nothing to disclose.
The Intramural Research Program of NINDS supported this study. We thank the NIH Functional Magnetic Resonance Imaging Facility. We thank Jennifer Lefeuvre and Cecil Chern-Chyi Yen for assistance with postmortem MRI acquisition. We thank John Ostuni and the Section on Instrumentation Core Facility for assistance with 3D printing. Figure 1 of this work used snapshots from MeshLab, a tool developed with the support of the 3D-CoForm project.
7T/30cm USR AVIII Bruker MRI | Bruker Biospin | ||
38 mm Bruker Biospin volume coil | Bruker Biospin | ||
Fomblin | Solvay Solexis | ||
50 ml Falcon Centrifuge Tubes, Polypropylene, Sterile | Corning | 21008-951 | |
Fisherbrand Gauze Sponges | Fisher Scientrific | 13-761-52 | |
Parafilm M All-Purpose Laboratory Film | Bemis | ||
Leica RM2235 rotary microtome | Leica | ||
Leica Disposable Blades, low profile (819) | Leica | ||
Cresyl Violet Acetate, 0.1% Aqueous | Electron Microscopy Sciences | 26089-01 | |
Luxol Fast Blue, 0.1% in 95% Alcohol | Electron Microscopy Sciences | 26056-15 | |
ETOH | |||
Ultimaker 2 Extended | Ultimaker | ||
.75 kg Official Ultimaker Branded PLA Filament, 2.85 mm, Silver Metallic | Ultimaker | ||
Axio Observer.Z1 | Zeiss | ||
Zen 2 (Blue Edition) | Zeiss | ||
Netfabb Professional 5.0.1 | Netfabb | http://www.netfabb.com/professional.php | |
Meshmixer 10.9.332 | Autodesk | http://www.meshmixer.com/download.html | |
Mipav 7.2 | NIH CIT | http://mipav.cit.nih.edu | |
Cura | Ultimaker | https://ultimaker.com/en/products/cura-software |