Name: an unbiased approach of sampling tem sections in neuroscience
Uploaded: 2019-04-13T13:00:00
Duration: 10 min 56 s
Description: Watch this Scientific Journal Video about An Unbiased Approach of Sampling TEM Sections in Neuroscience at JoVE.com

Funded by the Austrian Science Fund, FWF, project number P 29370 B27

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The authors have nothing to disclose.

The workflow presented here allows the researcher to get data on ultrastructural features in an unbiased fashion. This is much less time consuming than volume investigations from serial sections. Several different applications are used to achieve this goal. At first, our custom-made RPS software (for details on availability, please contact the corresponding author) is used to introduce a random stage-shift to select the sampling area coordinates. This allows a systematic uniform random sampling of the ROI. Next, for the counting of specific structures, we adapted the disector method, where 2 consecutive sections with known distance are compared, in a novel way compared to previous studies13,14,15 as we used our custom-made RPS software for systematic uniform random sampling. This saves time as compared to 3D-reconstructing whole volumes from serial sections. The custom-made RPS software is specifically developed for one type of microscope which is a limiting factor for reproducing the workflow. An alternative from this specific software would be an application that allows scripting and is compatible with other microscope models.
We successfully used this approach for our comparative studies4,5. On ultra-thin sections of neuronal tissue, the area of interest was outlined and images were taken by systematic uniform random sampling within this area. It has to be noted that the area of interest, the polymorph layer of the dentate gyrus, is a rather small area to investigate which might be beneficial for our approach. Within a randomly placed disector, we evaluated the number of DCV and several ultrastructural features of synapses in the DGpl of adult mice housed in SE and EE as well as adult WT mice versus adult NPY knockout mice. Using our approach, the collected data showed changes in some of the investigated parameters. These findings confirmed the ones from other similar studies in juvenile animals2.
A drawback of the experimental usage of this workflow might be that this multi-application-approach is not ideal in terms of user-friendliness, as the users need to get comfortable with different user-interfaces (in our case, the user interface, TEM Serial Section, the RPS software, and SerialEM software). Learning to handle all the applications in an efficient way is time consuming and should be taken into account. However, investing the time in learning to use this workflow is still clearly favorable over the time which is needed to analyze whole volumes with serial section TEM. The method of using a disector placed by systematic uniform random sampling in the area of interest is sufficient to present reliable data1 without the need to investigate a high amount of sections/volume.
To maximize the outcome in our studies, it was vital to take good care during sample preparation, as the preservation of the tissue and the structures is not only crucial for evaluating structural features, but also for identifying the area of interest unambiguously. A pivotal factor, and perhaps another drawback of this method, is that a high quality of the ultra-thin sections pairs is required: there must be no holes or wrinkles that cover the area under investigation in either of the sections, and the section thickness must be maintained homogeneous. The researcher has to be well-trained in ultramicrotomy. Care has also to be taken when imaging the sections in the TEM, as the sections are sensitive to electron beam damage and can easily tear apart. Furthermore, it is important to choose the right number of sampling areas in the ROI. Depending on the experimental aim, the magnification of the electron micrographs has to be set carefully. For our experiments specifically, counting synapses in the central nervous system, 20 regions of interest on one section with the area of 30.25 &#181;m2&#160;are optimal. It is advised to train the personnel well in recognizing the features in question (in our case synapses, synaptic features and DCV) to get reliable results. In order to identify synapses, the synaptic vesicles must be identifiable and this requires a resolution of at least 10 nm. For this, a magnification of 5000X was optimal, but it has to be noted that the magnification depends on hardware parameters such as the type and position of the cameras and would need to be adapted for other microscope and/or camera types. It also has to be noted that the protocol uses applications specific for one TEM and that users with other models have to consider the differences in the setup.
We believe our workflow can be adapted for many other applications not only in neuroscience but in a wide field of biological science and also material science (when the high resolution of a TEM is needed) whenever the research question demands a systematic uniform random sampling and the amount of samples to be examined asks for a time efficient way of analysis. For example, we are currently interested in localizing iron stores in the human brain. For this, we have recently adapted our workflow, to enable elemental analysis on ultra-thin sections in randomly chosen areas. In order to minimize the number of applications that are needed for the workflow, we aimed to apply using the SerialEM software only, because it can be programmed to move the stage to pre-set points that can be selected in a random fashion. We created customized scripts to control the TEM, with the aim of automatizing the workflow entirely. This proved feasible except for the autofocusing in filtered imaging mode, which did not yield satisfying results. We thus used DM software for focusing and for obtaining the energy-filtered images.
In summary, we present software solutions that help in obtaining electron micrographs in an unbiased way.

In electron microscopy, it is often challenging to sample representative regions within the sections. We, as an observer, are often biased to look at specific regions drawn to our attention by conspicuous features of the sample, preventing a well distributed, unbiased sampling. Sampling bias can only be avoided if every part of the region of interest gets the same chance of ending up in an electron micrograph1. It is possible to avoid sampling bias without a software solution, for example, by pushing the trackball of the microscope manually without looking at the image, so as to select sampling regions wherever the stage stops. But strictly speaking, this is not a random procedure, because, consciously or subconsciously, the user can have an influence on the movement of the stage, and, moreover, this is not a sophisticated way of selecting sampling regions. Random sampling becomes especially important if pairs of sections are used to assess the number of structures in a certain volume, for example, for stereology1, which requires pairs of sections, a known distance apart. It would also be possible to look only at a single section and estimate the number of specific structures2, but with this approach investigators tend to overestimate the numerical density of larger structures, unless the structures are very small in comparison to the section thickness. Alternative approaches are to reconstruct volumes of tissue from serial sections and thus get the desired data3. But this is very time consuming and not a feasible approach for (bigger) comparative studies.
To overcome these problems, we have developed a workflow that allows the researcher to automatically select samples for obtaining electron micrographs at regular spacing within ultra-thin sections. The position of the electron micrographs is random, allowing unbiased sampling. The approach is suitable both for determining numerical densities of structures (for example, synapses within a certain neuropil volume4,5), and the dimensions of structural features (for example, the width of the synaptic cleft, or the diameter of the postsynaptic density4,5).
The workflow uses a custom-made random point sampling (RPS) software (written in Java script using Scripting software supplied with our microscope) that automatically calculates grid positions within a predefined region of interest in an ultra-thin section. The RPS software moves the stage of the electron microscope to these predefined points, so that an electron micrograph can be made at each point. First, the user defines a region of interest within the thin section. Next, the RPS software calculates grid positions within this region. The x/y coordinates of the first position are created randomly, and the remaining positions are placed at regular grid intervals in respect to the first position. Because every part of the region of interest has the same chance of being examined, this allows minimal data collection. This approach of sampling is also called systematic uniform random sampling (see references6,7 for more details).
For determining the numerical densities of structures, we work with pairs of sections that are a known distance apart. After obtaining an electron micrograph from the first section in one of the predetermined positions, TEM Serial Section software (part of the software package supplied with our electron microscope) moves to the corresponding point in the second section, in order to obtain an electron micrograph of the corresponding location. This is repeated for every location in the predetermined grid. In our approach, a disector is used to count the number of particles in each pair of electron micrographs8,9. A disector consists of a pair of counting frames, one for each section8,9. The numerical density of objects is determined by only counting objects visible on the first section (or reference section) but not on the second section (or lookup section). This allows to estimate the numerical densities of objects in a fast and efficient way8,9. Additionally on single sections, two-dimensional structural features can be measured.
We have applied this workflow successfully to assess differences in synapse numbers in the hippocampus of mice exposed to enriched environment (EE) housing conditions compared to standard environment (SE) housing conditions4,5, and also to evaluate the ultrastructural differences between wild type (WT) mice and neuropeptide Y (NPY) KO mice kept under SE and EE5. Our objective was to look specifically at structural features of neurons, such as the numerical synaptic density, the lengths of the active zone in cross-sections and of the postsynaptic density, the width of the synaptic cleft, and the number of synaptic vesicles, in order to assess changes in neuronal connectivity and activation between the different experimental conditions. Additionally, we were interested in the numerical density of dense-core vesicles (DCV) in neurons to determine the amount of stored neuropeptides in a certain brain area.
Based on the success of our approach for the studies described above, in our next step, we have adapted our workflow to select areas for unbiased elemental analyses within human brain samples. This was done to image iron, which is stored in ferritin molecules in both neurons and glial cells. For this, we compiled a script that allowed us to automate most of the operations for a random screening process of brain sections in a defined area.

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			<td height="40" style="height:40px;width:171px;">Pentobarbital</td>
			<td style="width:139px;">SigmaAldrich</td>
			<td style="width:135px;">P3761</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Formaldehyde</td>
			<td>Merck</td>
			<td>1040051000</td>
			<td>1kg</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Glutardialdehyde</td>
			<td>Science Services</td>
			<td>E 16210</td>
			<td>25%; 100ml; EM grade</td>
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			<td height="40" style="height:40px;">cacodylate buffer</td>
			<td>Merck</td>
			<td>C4945</td>
			<td style="width:167px;">250g; Dimethylarsinic acid sodium trihydrate</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Thionine acetate/Ceristain</td>
			<td>Merck</td>
			<td>861340</td>
			<td></td>
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			<td height="40" style="height:40px;">acetic acid</td>
			<td>Merck</td>
			<td>1000631000</td>
			<td>1 L</td>
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			<td height="40" style="height:40px;">Sodium hydroxide</td>
			<td>Merck</td>
			<td>1064951000</td>
			<td>1 kg, pellets</td>
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		<tr height="40">
			<td height="40" style="height:40px;">osmium tetraoxide</td>
			<td>Science Services</td>
			<td>E 19110</td>
			<td>10x1g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">TAAB embedding resin</td>
			<td>Science Services</td>
			<td>TAT001</td>
			<td>500g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:171px;">DMP-30</td>
			<td>Science Services</td>
			<td>TAD024</td>
			<td>100g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:171px;">DDSA</td>
			<td>Science Services</td>
			<td>TAD025</td>
			<td>500g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Uranyl acetate dihydrate</td>
			<td>Plano GmbH</td>
			<td>19481</td>
			<td>depleted, 25g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:171px;">Ultrastain 2</td>
			<td>Leica</td>
			<td>16707235</td>
			<td>Lead citrate</td>
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		<tr height="40">
			<td height="40" style="height:40px;">Toluidine blue solution</td>
			<td>Agar Scientific</td>
			<td>AGR1727</td>
			<td>10g</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Pioloform</td>
			<td>Plano GmbH</td>
			<td>R1275</td>
			<td>10g Powder</td>
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		<tr height="40">
			<td height="40" style="height:40px;">Proylenoxide</td>
			<td>SigmaAldrich</td>
			<td>82320-1L</td>
			<td>1L</td>
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		<tr height="62">
			<td height="62" style="height:62px;">DPX embedding medium</td>
			<td>Plano GmbH</td>
			<td>R1320</td>
			<td style="width:167px;">embedding medium for semi-thin sections on glass slide, 50 ml</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Vibratome, Leica VT 1000</td>
			<td>Leica Microsystems, Vienna, Austria</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:171px;">Leica Ultracut UCT, ultramicrotom</td>
			<td>Leica Microsystems, Wetzlar, Germany</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Tecnai G2 20</td>
			<td>FEI,Eindhoven, Netherlands</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:171px;">Megaview wide angle camera</td>
			<td>Olympus Soft Imaging Solution, M&#252;nster, Germany</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">US 1000 digital camera</td>
			<td>Gatan, Pleasanton, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">TEM Imaging Analysis Software</td>
			<td>FEI,Eindhoven, Netherlands</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">FEI Serial Section Software</td>
			<td>FEI,Eindhoven, Netherlands</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Fiji, ImageJ 1.52e</td>
			<td>National Institute of Health, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SPSS 20.0</td>
			<td>SPSS Inc., Chicago, IL, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SerialEM</td>
			<td>Regents of the University of Colorado</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">RPS (random point sampling) software 0.9a</td>
			<td>custom-made</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Disector v1.0.2 (ImageJ macro)</td>
			<td>custom-made</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EFTEMSerialEM (SerialEM script)</td>
			<td>custom-made</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

an unbiased approach of sampling tem sections in neuroscience

Investigations of the ultrastructural features of neurons and their synapses are only possible with electron microscopy. Especially for comparative studies of the changes in densities and distributions of such features, an unbiased sampling protocol is vital for reliable results. Here, we present a workflow for the image acquisition of brain samples. The workflow allows systematic uniform random sampling within a defined brain region, and the images can be analyzed using a disector. This technique is much faster than extensive examination of serial sections but still presents a feasible approach to estimate the densities and distributions of ultrastructure features. Before embedding, stained vibratome sections were used as a reference to identify the brain region under investigation, which helped speed up the overall specimen preparation process. This approach was used for comparative studies investigating the effect of an enriched-housing environment on several ultrastructural parameters in the mouse brain. Based on the successful use of the workflow, we adapted it for the purpose of elemental analysis of brain samples. We optimized the protocol in terms of the time of user-interaction. Automating all the time-consuming steps by compiling a script for the open source software SerialEM helps the user to focus on the main work of acquiring the elemental maps. As in the original workflow, we paid attention to the unbiased sampling approach to guarantee reliable results.

Here, we describe a workflow to select the areas for TEM in an automatic and unbiased way. The user selects the area of interest within an ultra-thin section under the TEM, and uses several software solutions for the workflow including our RPS software that automatically calculates the coordinates of 20 sampling areas within the area of interest. Then, the TEM stage is moved to each sampling area for photography. This is possible both for analyzing single sections and for analyzing pairs of sections, a known distance apart, which allows selecting areas for imaging without the bias of the investigator.
This workflow proved valuable when documenting ultrastructural features in mouse brain sections4,5. In these studies, the polymorph cell layer of the dentate gyrus (DGpl) was examined as an area of interest. Our method proved suitable for examining the DG, because a coronal section of the dorsal DG (Bregma &#8722;1.3) fits into an ultra-thin section and its outlines are readily recognized at low magnification. If other brain regions were to be examined, it would be important to check their size and distinguishing features before planning the experiments.
Applying the workflow described here showed that EE housing increases the width of the synaptic cleft significantly in the DGpl4, in respect to standard housing conditions. Moreover, the number of dense core vesicles was significantly decreased in the EE-housed animals4, indicating the changes in the household of neuropeptides. When WT and NPY knockout mice were kept under EE conditions, the number of neurons/&#181;m&#179; in the DGpl increased while the density of DCV decreased in respect to SE-housing5. In contrast, NPY knockout resulted in a significant increase of synaptic vesicles in the reserve pool (undocked synaptic vesicles) independent of housing conditions5. The effect of EE on the width of the synaptic cleft was reversed in the NPY KO group resulting in a significant difference between EE-housed WT and NPY KO mice5. Taken together with behavioral studies of WT and NPY knockout animals held under both types of housing conditions, this indicates a crucial role of NPY for the neurobiological effects of EE5.
For visualizing the iron stored in the human brain, a script was compiled for SerialEM software to randomly choose the points of acquisition from within a whole ultrathin section, with the aim of obtaining an energy filtered electron micrograph at each of these points. After loading the section into the electron microscope, the script performed the stabilization of the section (i.e., the &#39;specimen-cooking&#39; routine) overnight. The script then randomly selected a number of points predefined by the user from those marked with a grating in Figure 3. By producing a test micrograph and checking its gray levels, the script checked if the chosen points were located on a visible part of the section, rejecting the points that landed on grid bars. Most of the workflow was handled by the script with minimal interaction of the user. However, it was not possible to automatically record energy filtered micrographs (with this setup), as there was too little light for the autofocus routine of SerialEM. Therefore, we switched to DM software as soon as the script had moved the stage to each point, adjusted the focus by hand, and made an EFTEM image of iron. An example for an EFTEM iron image from post-mortem human brain is shown in Figure 4.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58745/58745fig1.jpg" /> 
Figure 1.&#160;Illustration of the workflow.&#160;(A) The main steps of the sample preparation are shown (in direction of the arrow): vibratome sections (i), embedding(ii), cutting semi-thin and ultra-thin sections (iii), and catching pairs of ultra-thin sections on copper grids (iv). (B) Usage of both the microscope supplier&#39;s software and custom-made RPS software to create sampling area coordinates that determine the recording sites. The corner points of the sections are stored in the microscopy software and the ROI is outlined (identified with the help of the semi-thin sections) (i); a grid of sampling areas is generated over the ROI, a random shift (red arrow) is introduced in x and y. The shift is smaller than the distance between the sampling areas, (black arrows), so that instead of the black squares, which have no shift, the blue squares, which are shifted randomly, are used to determine the recording sites (ii); only sampling areas within the borders of the ROI (drawn as black and blue squares) are used for recording. Micrographs are made both in the reference section (black squares) and in corresponding locations in the lookup section (blue squares) (iii). (C) A micrograph is made at each sampling area at a sufficient magnification; a montage consisting of several merged images is made if needed (in this case, 2 x 2 images were merged together) (i); a counting frame is generated and shown as an overlay in the image, structures within the frame and on the &#39;acceptance lines&#39; (dashed frame lines) will be counted, but not those on the &#39;forbidden lines&#39; (solid frame lines) (ii). <a href="https://www.jove.com/files/ftp_upload/58745/58745fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58745/58745fig2.jpg" /> 
Figure 2. Measuring ultrastructural parameters on a pair of ultra-thin sections with an unbiased counting frame. (A) Overview of the ImageJ windows used for counting and analyzing cellular structures. Left-hand side shows the electron micrograph with the counting frame as an overlay. On the right-hand side: the ImageJ user interface (blue rectangle) with the drop-down menu (top) and the Toolbar (below); the ObjectJ object editor (green rectangle); the ObjectJ Tools window (orange rectangle) where the tools for measuring can be selected and the ObjectJ results window (black rectangle) where all the measurements are documented. (B) Detail from A showing the marked synapses on the electron micrograph (synapses on both sections - arrows; synapse only on the lookup section- arrowheads). Those are marked using the Multipoint-Tool (red circle) from the Toolbar in ImageJ. (C) The names and measuring parameters of the synaptic features are defined using the ObjectJ Tools. The Marker-Tool and the feature of interest are selected and the outline drawn in the image by left-mouse-clicks along the structure. <a href="https://www.jove.com/files/ftp_upload/58745/58745fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58745/58745fig3.jpg" /> 
Figure 3.&#160;Selecting sampling areas for elemental mapping within a ROI for energy filtered transmission electron microscopy. SerialEM is used to select a region of interest and to suggest a high number of sampling areas at a regular spacing (pink crosses in the images A and B). A script then randomly selects sampling areas (from these predefined coordinates) for acquisition. Sampling areas with poor illumination are rejected to avoid grid bars and the script stops as soon as 20 well illuminated sampling areas have been selected within the ROI. (A) Overview of the whole area of interest, outlined by a polygon, with a high number of possible sampling areas marked with X. (B) Detail of A showing the sampling areas before random selection. <a href="https://www.jove.com/files/ftp_upload/58745/58745fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58745/58745fig4.jpg" /> 
Figure 4.&#160;Example of elemental map (EFTEM) acquisition. (A) Bright field TEM micrograph of area around acquisition point. A randomly selected area was used to obtain an elemental map and is marked by a black rectangle. (B) EFTEM pre-edge window image made in the randomly selected area. (C) EFTEM post-edge window image made at the same area. (D) Elemental map of iron of the same area (M-edge). <a href="https://www.jove.com/files/ftp_upload/58745/58745fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about An Unbiased Approach of Sampling TEM Sections in Neuroscience at JoVE.com

An Unbiased Approach of Sampling TEM Sections in Neuroscience

We introduce a novel workflow for electron microscopy investigations of brain tissue. The method allows the user to examine neuronal features in an unbiased fashion. For elemental analysis, we also present a script that automatizes most of the workflow for randomized sampling.

Investigations of the ultrastructural features of neurons and their synapses are only possible with electron microscopy. Especially for comparative ...

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