Name: mapping the emergent spatial organization of mammalian cells using micropatterns and quantitative imaging
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Description: Watch this Scientific Journal Video about Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging at JoVE.com

This work was funded by a Sir Henry Wellcome post-doctoral fellowship (WT100133 to G.B.), a Wellcome Trust Senior Fellowship (WT103789AIA to S.L.), and a Wellcome Trust PhD studentship to (108906/Z/15/Z to D.W.). We are also grateful to Dr Manuel Thery for his advice on adapting the photopatterning technique.

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The authors have nothing to disclose

Here we describe a method for analyzing emergent patterning in cultures of cells. A simplified micropatterning approach is used for standardizing the shape and size of cell colonies, and we present image analysis tools and R scripts that enable the detection and quantification of patterns within these colonies.
The pipeline we propose is similar to some extent with a previously published method31 where the authors focus on the culture conditions, using commercially available micropatterns, to obtain reproducible germ layer formation in ESC colonies for the study of early gastrulation events in vitro. Our aim is more focused towards providing a generalizable pipeline for the discovery of pattern formation in vitro where the collective organization of the cells may only become apparent after statistical analysis. For this reason, we provide a robust image analysis workflow which enables the accurate identification and analysis of nuclear position in 3D space over multiple colonies (see also the &#8216;Advantage and limitations of the pattern detection method&#8217; section of this discussion). We also decided to develop a simple in-house micropatterning approach which offers a more flexible and cheaper alternative to commercially available solutions in the long term which we hope will be useful to the community.
Finally, we note that during the revision of this manuscript, a new package for the analysis of patterning in vitro similar to our R scripts has been released32. This new package accepts tables of cell features as input which can be obtained from high throughput imaging platforms. We believe that the table of nuclei features generated at step 7 of our protocol could in principle serve as an input to this new package although we have not tested this possibility ourselves.
Adaptability of the method to other cell types and colony geometries
We present this approach in the context of studying emergence of mesodermal transcription factors in cultures of pluripotent cells in the presence of serum. However, the method is readily adaptable to other cell types and to serum-free cultures although it may be necessary to optimize ECM/poloxamer 407 concentrations (see Table 1 for tested concentrations and Table 2 for a troubleshooting guide). The method can also be adapted to larger or smaller sizes of micropatterns and to a wide range of shapes according to the needs of the user. However, while establishing the method, it is important to be aware that not all shape/cell type combinations are optimal. For example, mESC express high levels of E-cadherin33, 34 enabling these cells to form collective structures spanning areas that are devoid of ECM. These cells do not strictly follow geometries with sharp angles or that include small holes in the pattern. Notice for example that on the ring of Figure 3B, the cells are in the process of colonizing the central area. In our hands a smaller central area did not force mESC to form ring-shaped colonies. It is therefore highly recommended to include a diversity of geometries while designing the photomask to be able to test and identify the optimal sizes and curvatures which will be suited for the cell type of choice.
Another important factor to take into consideration is the length of the experiment and the proliferation rate of the cells. For some rapidly proliferating cell types (including pluripotent cells) it can be difficult to maintain cells on micropatterns over many days (For mESC three days is a maximum). Also, seeding of cells on micropatterns does not always occur optimally for every colony, so it is advisable to seed an excess of colonies in order to have spares.
Advantage and limitations of the pattern detection method
One particular advantage of the method is the ability to detect &#8220;averaged&#8221; patterns by combining image analysis results from multiple replicate colonies (Figure 5). This can reveal patterning events that are not apparent from inspection of individual colonies. A disadvantage of this &#8216;averaging&#8217; approach is that it may miss certain types of repetitive patterns, for example small spots or narrow stripes. However, these types of pattern may instead be revealed with a combination of carefully chosen pattern sizes8. Also, the image analysis pipeline described here provides quantitative data at both single cell and colony resolution offering the possibility to investigate the level of inter-colony variability (Figure 5) or to perform neighbor analysis at multiple scales10.
Another important advantage of the averaging method is that it offers the opportunity to map the preferential location of many markers without being limited by available fluorophores of detection channels. Indeed, although we make use of only two markers of differentiation in the work presented here, the ability to standardize colonies and extract &#8220;averaged&#8221; patterns makes it possible to compare the distribution maps from different sets of colonies together in order to reveal the generalized spatial relationships of markers to each other.
Furthermore, although our focus here has been to study markers of differentiation, the analysis method can be extended to study other biological processes for which nuclear markers are available. For example micropatterning of a cell line containing a fluorescence ubiquitination cell cycle indicator35 (FUCCI) would make it possible to study how colony level geometry may influence cell cycle events in the group.
Future directions
The method is amenable to medium throughput image analysis, however, image acquisition is currently not fully automated and can become limiting for very large experiments. The regular arrangements of colonies should make it possible to create fully automatized acquisition routines similar to what has been developed for single cell averaging20. However, because the size of the field required to image a colony is large, possibly requiring mosaicking, and because colonies are three-dimensional, it is highly desirable to reduce both dataset size and acquisition time by imaging only relevant colonies. Therefore, future efforts may be dedicated to develop an &#8216;intelligent&#8217; microscope able to identify relevant colonies and adapt imaging coordinates to each sample. This will not only reduce time and effort but also prevent potential operator biases.
The analysis pipelines may also be made more efficient by reducing the number of steps the user needs to take. We have plans to build a pipeline-building mechanism and to integrate R directly into our software (see also issues pickcells-api#3 and pickcells-rjava#1 in the issue tracker of our code repositories [https://framagit.org/groups/pickcellslab/-/issues]). The full automation of the analysis procedure will reduce time and effort and limit potential user errors.
Finally, we note that our analysis method does not yet fully capture the dynamic nature of cellular patterning. Some limited dynamic information can be extracted by examining a time-series of snapshot images8,10,36. However, being able to record the history of the cell population is highly desirable if we wish to better understand how patterning emerges. One limitation is that accurate tracking of individual cells in a 3D dense cell population remains a very challenging task37. Our cell detection method uses the nuclear envelope and performs particularly well on dense and overlapping cell populations28. Live reporters of the nuclear envelope are readily available28,38 and one advantage of the micropatterning technique is that it may be used to prevent the cells from moving outside the field of view during long-term imaging. Overall, we are confident that automated tracking of cells will be achievable using a combination of recently established tools28,39,40 and that this should bring new insights into the fundamental principles of self-organization.

In mammalian systems, patterning is an emergent property of the collective behavior of cells and so, patterns can form in vitro if appropriate cues are provided to the cells1,2,3,4,5,6. One way to reveal the intrinsic ability of the cells to self-organize in vitro is to force the cells to form groups/colonies of a defined shapes and sizes7,8,9,10. A technique which enables this is micropatterning11. Micropatterning makes it possible to precisely define the location where extracellular matrix (ECM) molecules are deposited on a surface. This, in turn dictates where the cells can adhere and therefore controls how cells spatially organize.
Micropatterning is a technique with numerous applications, for example, micropatterning enables standardization of initial conditions prior to differentiation12. Importantly, micropatterning makes it possible to easily control the size, shape and spacing of cell colonies and this property can be used to devise experiments aimed at interrogating the collective response of the cells to morphogen or to physical cues7,8,10,13,14,15,16,17.
Several micropatterning methods have been developed11. Photopatterning techniques are perhaps the easiest methods to establish18. These approaches also have the advantage of precision as they can be used to control the shape of single cells18,19,20. However, they also require expensive specialized equipment including a spin coater, a plasma chamber and a UVO (UV-Ozone) cleaner which are generally not readily available in standard biology laboratories. To facilitate adoption of the technique, we adapted the protocol to necessitate only the UVO lamp. We start from commercially available plastic slides which can be cut with scissors or with a hole punch to the desired format.
One important utility of micropatterns is the ability to standardize colonies in order to compare individual colonies across multiple replicates. This makes it possible to ask to what extent pattern formation within these colonies is reproducible, and to explore factors that influence the robustness of the patterning process. Importantly, quantification of &#8220;averaged&#8221; patterns across multiple standardized colonies can also reveal patterning processes that would not otherwise be apparent. The advantage of being able to quantify patterning on standardized colonies depends on being able to accurately measure protein expression, ideally at the single cell level. However, cells on micropatterns are often tightly packed, making them difficult to segment with high accuracy. Cells also often organize themselves in three rather than two dimensions, and it can be challenging to detect and preserve three-dimensional (3D) information during segmentation. Once cells have been successfully segmented, computational methods are needed for extracting patterning information from the resulting datasets.
We have developed segmentation and image analysis tools to help overcome these problems. This analysis method only uses free and open-source software and does not require knowledge of command line or programming to implement. To illustrate the method here, we use mouse embryonic stem (mES) cells which spontaneously express a marker of early differentiation brachyury (Tbra)21,22. While no apparent spatial arrangement is visually detectable, the method allows the creation of a map of the preferential positioning of T+ cells in colonies. We also show that Tbra patterning contrasts with the absence of a preferential localization of the cells expressing Id1, a direct readout of the bone morphogenetic protein (BMP) pathway23. We also discuss the current limitations of the method and how this technique may be adapted to other experimental systems.

<table><tbody><tr><td>2-mercaptoethanol</td><td>Gibco</td><td>31350-010</td><td>handle in fume hood</td></tr><tr><td>1&amp;times; Master quartz anti-reflective chromium photomask</td><td>Toppan Photomask</td><td>Custom design</td><td></td></tr><tr><td>24-well plates</td><td>Corning</td><td>3526</td><td></td></tr><tr><td>4-well plates</td><td>Nunclon Delta</td><td>176740</td><td></td></tr><tr><td>5% Donkey Serum</td><td>Sigma</td><td>D9663</td><td></td></tr><tr><td>Anti Nuclear pore complex</td><td>Abcam</td><td>ab24609</td><td>Mouse monoclonal, use 1:1000</td></tr><tr><td>Anti-Id1</td><td>Biocheck</td><td>37-2</td><td>Rabbit polyclonal, use 1:200</td></tr><tr><td>Anti-Lamin B1</td><td>Abcam</td><td>ab16048</td><td>Rabbit polyclonal, use 1:1000</td></tr><tr><td>Anti-Tbra</td><td>R&amp;amp;D</td><td>AF2085</td><td>Goat ployclonal, use 1:400</td></tr><tr><td>Blocking Solution</td><td>NA</td><td>NA</td><td>0.1% TritonX-100&lt;br/&gt; 0.01% Pluronic F-127&lt;br/&gt; 5% Donkey Serum&lt;br/&gt; 0.003% Sodium Azide&lt;br/&gt; In PBS</td></tr><tr><td>CGR8 mESC</td><td>NA</td><td>NA</td><td>Reference: Mountford et al. PNAS, 1994</td></tr><tr><td>Fixation solution</td><td>NA</td><td>NA</td><td>4% PFA diluted in washing solution</td></tr><tr><td>Foetal bovine serum</td><td>Gibco</td><td>10270-106</td><td>Serum batches must be tested to ensure compatibility with ESC maintenance</td></tr><tr><td>Gelatin</td><td>Sigma</td><td>G1890</td><td></td></tr><tr><td>Glasgow Minimum Essential Medium</td><td>Sigma</td><td>G5154</td><td></td></tr><tr><td>Laboratory Film</td><td>VWR</td><td>291-1212</td><td></td></tr><tr><td>Layout Editor Software</td><td>LayoutEditor</td><td>NA</td><td>https://layouteditor.com/</td></tr><tr><td>Layout Editor Software</td><td>Klayout</td><td>NA</td><td>https://www.klayout.de/</td></tr><tr><td>L-glutamine</td><td>Gibco</td><td>25030-024</td><td></td></tr><tr><td>LIF</td><td>Millipore</td><td>ESG1107</td><td></td></tr><tr><td>MEM NEAA</td><td>Gibco</td><td>11140-035</td><td></td></tr><tr><td>mESC Culture medium</td><td>NA</td><td>NA</td><td>GMEM&lt;br/&gt; 10% FCS&lt;br/&gt; 100 U/ml LIF&lt;br/&gt; 100 nM 2-mercaptoethanol&lt;br/&gt; 1&amp;times; non-essential amino acids,&lt;br/&gt; 2 mM L- glutamine&lt;br/&gt; 1 mM sodium pyruvate</td></tr><tr><td>NH4Cl 50mM</td><td>Sigma</td><td>9718</td><td></td></tr><tr><td>Paraformaldehyde</td><td>Sigma</td><td>158127</td><td>CAUTION: Toxic, handle undiluted stocks in fume hood and wear protective equipment</td></tr><tr><td>PBS (immunostaining)</td><td>Sigma</td><td>P4417</td><td>Dilute in 200ml of ddH&lt;sub&gt;2&lt;/sub&gt;O</td></tr><tr><td>PBS (tissue culture)</td><td>Gibco</td><td>11140-035</td><td></td></tr><tr><td>Plastic slides</td><td>Ibidi</td><td>IB-10813</td><td></td></tr><tr><td>Poloxamer 407</td><td>Sigma</td><td>P2443</td><td></td></tr><tr><td>ProLong Gold Antifade Mountant</td><td>Molecular Probes</td><td>P36930</td><td></td></tr><tr><td>Sodium Azide</td><td>Sigma</td><td>8591</td><td>CAUTION: Toxic, handle in fume hood and wear protective equipment</td></tr><tr><td>Sodium pyruvate</td><td>Gibco</td><td>11360070</td><td></td></tr><tr><td>TritonX-100</td><td>Sigma</td><td>T8532</td><td></td></tr><tr><td>Trypsin</td><td>Gibco</td><td>25200056</td><td></td></tr><tr><td>UVO cleaner</td><td>Jetlight, USA</td><td>42-220</td><td>CAUTION: Follow manufacturer&amp;rsquo;s safety recommendations</td></tr><tr><td>Washing Solution</td><td>NA</td><td>NA</td><td>0.1% TritonX-100&lt;br/&gt; 0.01% Pluronic F-127&lt;br/&gt; In PBS</td></tr></tbody></table>

mapping the emergent spatial organization of mammalian cells using micropatterns and quantitative imaging

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro when stem cells are spatially confined onto micropatterns, thus setting up experimental models which offer unique opportunities to identify, in vitro, the fundamental principles of biological organisation.
Here we describe our own implementation of the methodology. We adapted a photo-patterning technique to reduce the need for specialized equipment to make it easier to establish the method in a standard cell biology laboratory. We also developed a free, open-source and easy to install image analysis framework in order to precisely measure the preferential positioning of sub-populations of cells within colonies of standard shapes and sizes. This method makes it possible to reveal the existence of patterning events even in seemingly disorganized populations of cells. The technique provides quantitative insights and can be used to decouple influences of the environment (e.g., physical cues or endogenous signaling), on a given patterning process.

The photo-patterning method described here makes it possible to precisely organize cultured cells into colonies of defined shapes and sizes. The success of this procedure should be clearly apparent immediately after the cell seeding procedure (step 3.7) as adhering cells will cluster according to the photomask design as shown in Figure 2a. At 1 h after cell seeding, individual patterns may not be fully confluent (only a few cells per pattern), however, as the cells proliferate over time, patterns will become fully colonized with only very few cells outside the adhesive surfaces (Figure 2b). The exact appearance of the culture will be cell line dependent. For example, mESC form domed-shape colonies10. A chip where patterning is not clear 1 to 2 h after cell seeding indicates failure of the procedure (Figure 2c,d).
Large and thick colonies can sometimes be challenging to stain homogeneously. We suggest to fix and permeabilize the cells in one step (section 4) as this can improve antibody penetration29. This is the reason why the chosen fixative solution contains a detergent. Figure 3 shows the fluorescence signal which is expected after immunostaining. Notice that bright Id1 positive cells are found within dense regions (bright NPC regions) of the colonies (Figure 3A). Hints such as this are useful to assess the quality of the antibody staining procedure. Notice also that the micropatterns created with the present technique are autofluorescent. This signal (Figure 3A,B left most images) is useful during the analysis stage to spatially register colonies with one another and create the results shown in Figure 5. The autofluorescence signal is generally the brightest when the sample is excited with a 405 nm laser and this channel should be left without staining for this purpose. Figure 3 also shows how the cells are precisely constrained on patterns of different shapes.
The analysis of imaging data is performed in PickCells, a free and open-source software developed in our lab (Blin et al., in preparation). This software includes the image analysis modules to read and sort confocal images (Figure 4-1), to segment (Figure 4-2,4-4) and curate segmented objects (Figure 4-3,4-5), to compute object features such as coordinates or average intensity (Figure 4-6) and to export the data (Figure 4-7,4-8). Importantly, we developed a robust nuclear segmentation method called Nessys28 which is particularly suited for dense and heterogeneous populations of cells such as cells grown on micropatterns (Figure 3). Figure 4-2 shows a representative output of the Nessys module where each individual cell is accurately given a unique color identity. Only minimal editing should be necessary, however editing is feasible should the user decide so (Figure 4-3). Finally PickCells provides a number of visualization modules to visualize the data. An example is given in Figure 4-6: a ring-shaped colony is rendered in 3D where nuclei are color-coded according to their position along the z-axis. Once the analysis is validated in PickCells, data can be exported to create the spatial maps in R using the scripts available at (https://framagit.org/pickcellslab/hexmapr) as shown in Figure 530.
We have shown recently that spatial confinement of mESC on small (30,000 &#181;m2) disc or ellipse micropatterns guides the patterning of a subpopulation of cells expressing the mesodermal marker Tbra10. Thus, to illustrate our method here, we ask if patterning of Tbra may be influenced by BMP signaling in larger colonies (90,000 &#181;m2). Figure 5A shows that when mESC are grown on large disc micropatterns, Tbra+ cells are preferentially restricted to the periphery of the pattern (Tbra+ density map), where the local cell density is the lowest (see the blue map on the left of Figure 5A). This patterning of Tbra is confirmed by the map of mean Tbra intensity.
These data demonstrate that the method can reveal sub-visual information. Indeed, from Figure 3, visual inspection of one colony is not sufficient to identify any form of spatial organization in Tbra expression. This is notably explained by the important colony to colony variability which is quantified and shown in the right-most panel of Figure 5A.
The technique also shows that no detectable patterning exists for Id1 (a target of BMP signaling) which may indicate that T patterning is not driven by BMP signaling in this context.
Micropatterning makes it possible to force colonies to adopt almost any desired geometry. This is particularly useful to interrogate how the system responds to various geometries. For example, we may reason that if a morphogen gradient builds up at the center of the colony, creating a hole in the colony would disrupt this gradient. Interestingly we still observe patterning on a ring micropattern albeit in a less robust manner (Figure 5B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59634/59634fig1.jpg" /> 
Figure 1: Overview of the method. The diagram showing the main steps of the method. For each step, the estimated amount of time is indicated under the name of the task and a schematic illustrates the purpose of the procedure. A reference to a relevant figure is also provided when available. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59634/59634fig1large.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/59634/59634fig2.jpg" /> 
Figure 2: Culture appearance 1 h and 48 h after seeding the cells on micropatterns. Brightfield images of mESC seeded onto micropatterns. (a) Expected cell organization 1 h after seeding, patterns should be clearly identifiable. (b) Expected result after 48 h of cultures. mESC have proliferated and are still strictly confined to the pattern shapes. (c&#8211;d) Possible non-optimal outcomes, either very few cells adhere to the plastic except at the periphery of the slide (c) or cells adhere in between the patterns (d). See Table 2 for a troubleshooting guide. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59634/59634fig2large.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/59634/59634fig3.jpg" /> 
Figure 3: Representative confocal images of immunostained colonies grown on micropatterns. 
Representative mESC colonies after immunofluorescence for (A) Id1 and Nuclear Pore Complex or (B) Tbra and LaminB1. For each staining, a colony grown on a disc micropattern and a colony grown on a ring micropattern is shown. Individual channels are provided as gray scale images. Notice the clear auto-fluorescence signal of the micropattern (405 nm laser excitation). Scale bar represents 50 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59634/59634fig3large.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/59634/59634fig4.jpg" /> 
Figure 4: Flow chart of the image analysis procedure. A list of 3D confocal images is imported into PickCells for analysis (1). This example shows an experiment with two distinct shapes (discs and rings as in Figure 2). The image naming convention is shown on the left and PickCells interface on the right. Then, the Nessys module is used to automatically segment nuclei (2). In the screenshot, each individual nucleus is given a unique color indicating accurate segmentation. The autofluorescence of the pattern is also segmented, this time, using the &#8216;basic segmentation&#8217; module (4). Background appears in blue and white signal will be defined as the pattern shape. Segmented shapes are then visually inspected to ensure accurate segmentation, and edited if required using the segmentation editor module (3&#8211;5). The screenshots show the outline of the detected shapes. The pink and yellow shapes have been edited. Finally, objects features are computed and exported to file to be later processed into R (6&#8211;7). A screenshot of a colony rendered as a 3D view is provided (6). For steps 2 to 6 the icons found in the PickCells interface at the time of writing this article are given next to the step index. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59634/59634fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59634/59634fig5.jpg" /> 
Figure 5: Representative results for two different transcription factors and micropattern shapes 
Binned spatial map for mESC grown for 48 h on (A) disc shaped micropatterns or (B) ring shaped micropatterns. For each micropattern shape, the map of cell density, irrespective of cell phenotype, is shown on the left with a blue color scale. Then for each marker (Tbra on the top row and Id1 on the bottom row), three distinct maps are provided, from left to right: cell density map of marker expressing cells only (threshold based analysis), the map of the average marker intensity (log2) and the map of the standard deviation of the marker intensity. Intensities are given as arbitrary fluorescence units. For each map, the micropattern shape is given as a white outline. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59634/59634fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>ECM type</td>
			<td>Gelatin</td>
			<td>Fibronectin</td>
			<td>Basement Membrane Matrix</td>
		</tr>
		<tr>
			<td rowspan="2">Concentration</td>
			<td>ECM concentration</td>
			<td>1 mg/mL</td>
			<td>20 &#956;g/mL</td>
			<td>200 &#956;g/mL</td>
		</tr>
		<tr>
			<td>Poloxamer 407 concentration</td>
			<td>500 &#956;g/mL</td>
			<td>400 &#956;g/mL</td>
			<td>1 mg/mL</td>
		</tr>
		<tr>
			<td rowspan="3">Tested with</td>
			<td>mESC</td>
			<td>YES</td>
			<td>YES</td>
			<td>YES</td>
		</tr>
		<tr>
			<td>mEpiSC</td>
			<td>NO</td>
			<td>YES</td>
			<td>YES</td>
		</tr>
		<tr>
			<td>Serum free medium</td>
			<td>NO</td>
			<td>YES</td>
			<td>YES</td>
		</tr>
	</tbody>
</table>
Table 1: Tested concentrations of poloxamer 407 and ECM. This table provides an overview of the concentrations of ECM and poloxamer 407 that we tested in our lab. For each ECM/poloxamer 407 combination, the cell type for which patterning was successfully achieved is shown as well as whether the culture contained serum or not. mESC = mouse embryonic stem cell, mEpiSC = mouse epiblast stem cell.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Procedure</td>
			<td>Observation</td>
			<td>Possible Issue</td>
			<td>Solution</td>
		</tr>
		<tr>
			<td rowspan="8">Micropatterning</td>
			<td rowspan="3">Low cell attachment</td>
			<td>inappropriate ECM/poloxamer 307 concentration ratio</td>
			<td>Increase the ECM/poloxamer 307 concentration ratio</td>
		</tr>
		<tr>
			<td>Cell attachment time too short</td>
			<td>Increase incubation time to give enough time to the cells to properly adhere to patterns (step 3.4). To optimise this step, checking the cells under a microscope can help detect a change in cell morphology indicating that the cells have started to adhere.</td>
		</tr>
		<tr>
			<td>Washes too intense (step 3.6)</td>
			<td>When replacing medium, avoid pipetting medium directly onto the chips. Instead, gently pipet the medium on the walls of the well instead</td>
		</tr>
		<tr>
			<td rowspan="3">Cells adhere between the patterns</td>
			<td>inappropriate ECM/poloxamer 307 concentration ratio</td>
			<td>Decrease the ECM/poloxamer 407 concentration ratio</td>
		</tr>
		<tr>
			<td>Cell attachment time too long</td>
			<td>Decrease incubation time (step 3.4).</td>
		</tr>
		<tr>
			<td>Washes ineffective (step 3.6)</td>
			<td>Shaking the plate vigorously is usually sufficient to detach the cells in excess. For cell types which tend to adhere strongly to the chip, pipetting directly onto the chip may improve the outcome. Increasing the number of washes may also help, notably to ensure that no cell remain floating in the medium after this step.</td>
		</tr>
		<tr>
			<td rowspan="2">Cells do not strictly follow the pattern shape</td>
			<td>&#39;Incompatible&#8217; cell type and pattern geometry</td>
			<td>Plan/design multiple geometries/sizes to be added onto the photomask to be able to test and identify the optimal pattern size for a given pattern shape and cell type. Please, see the &#8216;Limitations&#8217; section in the discussion</td>
		</tr>
		<tr>
			<td>Non-optimal photo-patterning, this can be diagnosed by observing the sharpness of the autofluorescence signal. Pattern boundaries should look sharp as in Fig. 2. If the borders of the patterns appear blurred then the photo-patterning step needs to be improved.</td>
			<td>Blurred pattern edges indicate that the plastic slide was not close enough to the surface of the mask during the illumination step. Ensure that the pieces holding the slides to the photomask are even and that a constant and sufficient pressure is applied to the assembly during the illumination procedure.</td>
		</tr>
		<tr>
			<td rowspan="4">Staining</td>
			<td rowspan="2">Non-homogeneous staining</td>
			<td>antibody incubation time too short</td>
			<td>Increase antibody incubation time (up to 24h at room temperature)</td>
		</tr>
		<tr>
			<td>colonies flattened during the mounting procedure</td>
			<td>Mount micropattern slides in a chamber such as chamlide or cytoo-chambers to perform both immunostaining and imaging without the need for mounting the cells. This will better preserve the colonies 3D-structure.</td>
		</tr>
		<tr>
			<td>Detaching colonies during the staining procedure</td>
			<td>Dewetting of the chip</td>
			<td>Leave enough medium or use 2 pipettes, one to remove the medium and the other to add the fresh solution</td>
		</tr>
		<tr>
			<td>Colonies appear sheared under the microscope</td>
			<td>colonies were sheared while mounting the chip on the microscopy slide</td>
			<td>Be very gentle when mounting the chips. Alternatively, mount micropatterns slides in a chamber such as chamlide or cytoochambers to perform both immunostaining and imaging without the need for mounting the cells. This also preserves the colony ultrastructure.</td>
		</tr>
	</tbody>
</table>
Table 2: Troubleshooting guide. This table provides an overview of possible sub-optimal outcomes. The potential sources of the problems are also listed together with recommended solutions.

Watch this Scientific Journal Video about Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging at JoVE.com

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

The method presented here uses micropatterning together with quantitative imaging to reveal spatial organization within mammalian cultures. The technique is easy to establish in a standard cell biology laboratory and offers a tractable system to study patterning in vitro.

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro ...

mapping-emergent-spatial-organization-mammalian-cells-using

Nanyang Technological University

Research

JoVE Journal

Developmental Biology

6.5K Views.  University of Edinburgh. Deze methode maakt het mogelijk om te manipuleren en te kwantificeren hoe de cellen zich collectief organiseren. Dit is belangrijk omdat we patroon kunnen modelleren in in vitro en disentangle cues strak gekoppeld in vivo. De techniek vereist minimale specialistische apparatuur, waardoor het kan worden vastgesteld in een standaard biologie lab, en het is kwantitatief, waardoor de ontdekking van sub-visuele patronen gebeurtenissen.Deze methode kan gemakkelijk worden toegepast op elk cel...

Video: Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated micro-plates. Under these conditions, cells spread and divide in all directions resulting in an inherent variability in cell shape, morphology and behavior. The high cell-to-cell variance of the overall population impedes the success of HCA, especially for drug development. The ability of micropatterns to normalize the shape and internal polarity of every individual cell provides a tremendous opportunity for solving this critical bottleneck 1-2.
To facilitate access and use of the micropatterning technology, CYTOO has developed a range of ready to use micropatterns, available in coverslip and microwell formats. In this video article, we provide detailed protocols of all the procedures from cell seeding on CYTOOchip micropatterns, drug treatment, fixation and staining to automated acquisition, automated image processing and final data analysis. With this example, we illustrate how micropatterns can facilitate cell-based assays. Alterations of the cell cytoskeleton are difficult to quantify in cells cultured on homogenous substrates, but culturing cells on micropatterns results in a reproducible organization of the actin meshwork due to systematic positioning of the cell adhesion contacts in every cell. Such normalization of the intracellular architecture allows quantification of even small effects on the actin cytoskeleton as demonstrated in these set of protocols using blebbistatin, an inhibitor of the actin-myosin interaction.

Adhesive micropatterns that normalize cellular architecture can be used to increase sensitivity in the detection of drug effects, improve reproducibility and simplify automated image acquisition and analysis. Such technology will benefit drug/siRNA screening assays, performed on conventional cell culture supports and consequently suffering from excessive cell-to-cell variability.

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated ...

Bioengineering

Stencil Micropatterning of Human Pluripotent Stem Cells for Probing Spatial Organization of Differentiation Fates

Human pluripotent stem cells (hPSCs), including embryonic stem cells and induced pluripotent stem cells, have the intrinsic ability to differentiate into all three germ layers. This makes them an attractive cell source for regenerative medicine and experimental modeling of normal and diseased organogenesis. However, the differentiation of hPSCs in vitro is heterogeneous and spatially disordered. Cell micropatterning technologies potentially offer the means to spatially control stem cell microenvironments and organize the resultant differentiation fates. Micropatterning hPSCs needs to take into account the stringent requirements for hPSC survival and maintenance. Here, we describe stencil micropatterning as a method that is highly compatible with hPSCs. hPSC micropatterns are specified by the geometries of the cell stencil through-holes, which physically confine the locations where hPSCs can access and attach to the underlying extracellular matrix-coated substrate. Due to this mode of operation, there is greater flexibility to use substrates that can adequately support hPSCs as compared to other cell micropatterning methods. We also highlight critical steps for the successful generation of hPSC micropatterns. As an example, we demonstrate that stencil micropatterning of hPSCs can be used to modulate spatial polarization of cell-cell and cell-matrix adhesions, which in turn determines mesoendoderm differentiation patterns. This simple and robust method to micropattern hPSCs widens the prospects of establishing experimental models to investigate tissue organization and patterning during early embryonic development.

Human pluripotent stem cells (hPSCs) have the intrinsic ability to differentiate and self-organize into distinct tissue patterns; although this requires the presentation of spatial environmental gradients. We present stencil micropatterning as a simple and robust method to generate biochemical and mechanical gradients for controlling hPSC differentiation patterns.

Human pluripotent stem cells (hPSCs), including embryonic stem cells and induced pluripotent stem cells, have the intrinsic ability to differentiate into ...

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

In the primary visual cortex of non-rodent mammals, neurons are clustered according to their preference for stimulus features such as orientation1-4, direction5-7, ocular dominance8,9 and binocular disparity9. Orientation selectivity is the most widely studied feature and a continuous map with a quasi-periodic layout for preferred orientation is present across the entire primary visual cortex10,11. Integrating the synaptic, cellular and network contributions that lead to stimulus selective responses in these functional maps requires the hybridization of imaging techniques that span sub-micron to millimeter spatial scales. With conventional intrinsic signal optical imaging, the overall layout of functional maps across the entire surface of the visual cortex can be determined12. The development of in vivo two-photon microscopy using calcium sensitive dyes enables one to determine the synaptic input arriving at individual dendritic spines13 or record activity simultaneously from hundreds of individual neuronal cell bodies6,14. Consequently, combining intrinsic signal imaging with the sub-micron spatial resolution of two-photon microscopy offers the possibility of determining exactly which dendritic segments and cells contribute to the micro-domain of any functional map in the neocortex. Here we demonstrate a high-yield method for rapidly obtaining a cortical orientation map and targeting a specific micro-domain in this functional map for labeling neurons with fluorescent dyes in a non-rodent mammal. With the same microscope used for two-photon imaging, we first generate an orientation map using intrinsic signal optical imaging. Then we show how to target a micro-domain of interest using a micropipette loaded with dye to either label a population of neuronal cell bodies or label a single neuron such that dendrites, spines and axons are visible in vivo. Our refinements over previous methods facilitate an examination of neuronal structure-function relationships with sub-cellular resolution in the framework of neocortical functional architectures.

A method is described for labeling neurons with fluorescent dyes in predetermined functional micro-domains of the neocortex. First, intrinsic signal optical imaging is used to obtain a functional map. Then two-photon microscopy is used to label and image neurons within a micro-domain of the map.

In the primary visual cortex of non-rodent mammals, neurons are clustered according to their preference for stimulus features such as orientation1-4, ...

Neuroscience

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.

In this article we describe the full experimental procedure to reconstruct, with high resolution, the fine brain anatomy of fluorescently labeled mouse brains. The described protocol includes sample preparation and clearing, specimen mounting for imaging, data post-processing and multi-scale visualization.

Understanding the architecture of mammalian  brain at single-cell resolution is one of the key issues of neuroscience.  However, mapping neuronal soma and ...

Micropatterning Transmission Electron Microscopy Grids to Direct Cell Positioning within Whole-Cell Cryo-Electron Tomography Workflows

Whole-cell cryo-electron tomography (cryo-ET) is a powerful technology that is used to produce nanometer-level resolution structures of macromolecules present in the cellular context and preserved in a near-native frozen-hydrated state. However, there are challenges associated with culturing and/or adhering cells onto TEM grids in a manner that is suitable for tomography while retaining the cells in their physiological state. Here, a detailed step-by-step protocol is presented on the use of micropatterning to direct and promote eukaryotic cell growth on TEM grids. During micropatterning, cell growth is directed by depositing extra-cellular matrix (ECM) proteins within specified patterns and positions on the foil of the TEM grid while the other areas remain coated with an anti-fouling layer. Flexibility in the choice of surface coating and pattern design makes micropatterning broadly applicable for a wide range of cell types. Micropatterning is useful for studies of structures within individual cells as well as more complex experimental systems such as host-pathogen interactions or differentiated multi-cellular communities. Micropatterning may also be integrated into many downstream whole-cell cryo-ET workflows, including correlative light and electron microscopy (cryo-CLEM) and focused-ion beam milling (cryo-FIB).

The goal of this protocol is to direct cell adhesion and growth to targeted areas of grids for cryo-electron microscopy. This is achieved by applying an anti-fouling layer that is ablated in user-specified patterns followed by deposition of extra-cellular matrix proteins in the patterned areas prior to cell seeding.

Whole-cell cryo-electron tomography (cryo-ET) is a powerful technology that is used to produce nanometer-level resolution structures of macromolecules ...

Biochemistry

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Micropatterning is an established technique in the cell biology community used to study connections between the morphology and function of cellular compartments while circumventing complications arising from natural cell-to-cell variations. To standardize cell shape, cells are either confined in 3D molds or controlled for adhesive geometry through adhesive islands. However, traditional micropatterning techniques based on photolithography and deep UV etching heavily depend on clean rooms or specialized equipment. Here we present an infrared laser assisted micropatterning technique (microphotopatterning) modified from Doyle et al. that can be conveniently set up with commercially available imaging systems. In this protocol, we use a Nikon A1R MP+ imaging system to generate micropatterns with micron precision through an infrared (IR) laser that ablates preset regions on poly-vinyl alcohol coated coverslips. We employ a custom script to enable automated pattern fabrication with high efficiency and accuracy in systems not equipped with a hardware autofocus. We show that this IR laser assisted micropatterning (microphotopatterning) protocol results in defined patterns to which cells attach exclusively and take on the desired shape. Furthermore, data from a large number of cells can be averaged due to the standardization of cell shape. Patterns generated with this protocol, combined with high resolution imaging and quantitative analysis, can be used for relatively high throughput screens to identify molecular players mediating the link between form and function.

The protocol presented here enables automated fabrication of micropatterns that standardizes cell shape to study cytoskeletal structures within mammalian cells. This user-friendly technique can be set up with commercially available imaging systems and does not require specialized equipment inaccessible to standard cell biology laboratories.

Micropatterning is an established technique in the cell biology community used to study connections between the morphology and function of cellular ...

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in vertebrates. Thus, it is critical to uncover the molecular mechanisms of how motor axons navigate to, arborize, and innervate their peripheral muscle targets during development and degeneration. Although transgenic Hb9::GFP mouse lines have long served to visualize motor axon trajectories during embryonic development, detailed descriptions of the full spectrum of axon terminal arborization remain incomplete due to the pattern complexity and limitations of current optical microscopy. Here, we describe an improved protocol that combines light sheet fluorescence microscopy (LSFM) and robust image analysis to qualitatively and quantitatively visualize developing motor axons. This system can be easily adopted to cross genetic mutants or MN disease models with Hb9::GFP lines, revealing novel molecular mechanisms that lead to defects in motor axon navigation and arborization.

Here, we describe a protocol for visualizing motor neuron projection and axon arborization in transgenic Hb9::GFP mouse embryos. After immunostaining for motor neurons, we used light sheet fluorescence microscopy to image embryos for subsequent quantitative analysis. This protocol is applicable to other neuron navigation processes in the central nervous system.

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in ...

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical properties, synaptic connections, and in vivo functions, but their basic functional and anatomical organization from cellular to network scale is poorly understood. Here we describe a method for the three-dimensional imaging of fluorescently-labeled neurons across large areas of the brain for the investigation of the cortical cellular organization. Specific types of neurons are labeled by the injection of fluorescent retrograde neuronal tracers or expression of fluorescent proteins in transgenic mice. Block brain samples, e.g., a hemisphere, are prepared after fixation, made transparent with tissue clearing methods, and subjected to fluorescent immunolabeling of the specific cell types. Large areas are scanned using confocal or two-photon microscopes equipped with large working distance objectives and motorized stages. This method can resolve the periodic organization of the cell type-specific microcolumn functional modules in the mouse neocortex. The procedure can be useful for the study of three-dimensional cellular architecture in the diverse brain areas and other complex tissues.

Here we describe a procedure for tissue clearing, fluorescent labeling, and large-scale imaging of mouse brain tissue which, thereby, enables visualization of the three-dimensional organization of cell types in the neocortex.

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical ...

Precise Brain Mapping to Perform Repetitive In Vivo Imaging of Neuro-Immune Dynamics in Mice

The central nervous system (CNS) is regulated by a complex interplay of neuronal, glial, stromal, and vascular cells that facilitate its proper function. Although studying these cells in isolation in vitro or together ex vivo provides useful physiological information; salient features of neural cell physiology will be missed in such contexts. Therefore, there is a need for studying neural cells in their native in vivo environment. The protocol detailed here describes repetitive in vivo two-photon imaging of neural cells in the rodent cortex as a tool to visualize and study specific cells over extended periods of time from hours to months. We describe in detail the use of the grossly stable brain vasculature as a coarse map or fluorescently labeled dendrites as a fine map of select brain regions of interest. Using these maps as a visual key, we show how neural cells can be precisely relocated for subsequent repetitive in vivo imaging. Using examples of in vivo imaging of fluorescently-labeled microglia, neurons, and NG2+ cells, this protocol demonstrates the ability of this technique to allow repetitive visualization of cellular dynamics in the same brain location over extended time periods, that can further aid in understanding the structural and functional responses of these cells in normal physiology or following pathological insults. Where necessary, this approach can be coupled to functional imaging of neural cells, e.g., with calcium imaging. This approach is especially a powerful technique to visualize the physical interaction between different cell types of the CNS in vivo when genetic mouse models or specific dyes with distinct fluorescent tags to label the cells of interest are available.

This protocol describes a chronic cranial window implantation technique that can be used for longitudinal imaging of neuro-glio-vascular structures, interactions, and function in both healthy and diseased conditions. It serves as a complementary alternative to the transcranial imaging approach that, while often preferred, possesses some critical limitations.

The central nervous system (CNS) is regulated by a complex interplay of neuronal, glial, stromal, and vascular cells that facilitate its proper function. ...

Immunology and Infection

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei.
Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases.
We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

This protocol describes methods for conducting magnetic resonance imaging, clearing, and immunolabeling of intact mouse brains using iDISCO+, followed by a detailed description of imaging using light-sheet microscopy, and downstream analyses using NuMorph.

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of ...

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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