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.

<ol>
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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&#215; 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&#38;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 
			0.01% Pluronic F-127 
			5% Donkey Serum 
			0.003% Sodium Azide 
			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 
			10% FCS 
			100 U/ml LIF 
			100 nM 2-mercaptoethanol 
			1&#215; non-essential amino acids, 
			2 mM L- glutamine 
			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 ddH2O</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&#8217;s safety recommendations</td>
		</tr>
		<tr>
			<td>Washing Solution</td>
			<td>NA</td>
			<td>NA</td>
			<td>0.1% TritonX-100 
			0.01% Pluronic F-127 
			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/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/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/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/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/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

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JoVE Journal

Developmental Biology

6.4K Views.  University of Edinburgh. 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.

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

Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro

We have developed a protocol improving current Embryoid Body (EB) culture which allows the study of self-organization, symmetry breaking, axial elongation and cell fate specification using aggregates of mouse embryonic stem cells (mESCs) in suspension culture. Small numbers of mESCs are aggregated in basal medium for 48 hr in non-tissue-culture-treated, U-bottomed 96-well plates, after which they are competent to respond to experimental signals. Following treatment, these aggregates begin to show signs of polarized gene expression and gradually alter their morphology from a spherical mass of cells to an elongated, well organized structure in the absence of external asymmetry cues. These structures are not only able to display markers of the three germ layers, but actively display gastrulation-like movements, evidenced by a directional dislodgement of individual cells from the aggregate, which crucially occurs at one region of the elongated structure. This protocol provides a detailed method for the reproducible formation of these aggregates, their stimulation with signals such as Wnt/&#946;-Catenin activation and BMP inhibition and their analysis by single time-point or time-lapse fluorescent microscopy. In addition, we describe modifications to current whole-mount mouse embryo staining procedures for immunocytochemical analysis of specific markers within fixed aggregates. The changes in morphology, gene expression and length of the aggregates can be quantitatively measured, providing information on how signals can alter axial fates. It is envisaged that this system can be applied both to the study of early developmental events such as axial development and organization, and more broadly, the processes of self-organization and cellular decision-making. It may also provide a suitable niche for the generation of cell types present in the embryo that are unobtainable from conventional adherent culture such as spinal cord and motor neurones.

Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro

We have developed a protocol to generate aggregates of mouse embryonic stem cells that display self-organization, symmetry breaking and elongation paralleling axial development. This technique allows the study of axial developmental processes and the generation of cell types that are otherwise difficult to perform in monolayer culture.

We have developed a protocol improving current Embryoid Body (EB) culture which allows the study of self-organization, symmetry breaking, axial elongation ...

Tracking Cells in GFP-transgenic Zebrafish Using the Photoconvertible PSmOrange System

The rapid development of transparent zebrafish embryos (Danio rerio) in combination with fluorescent labelings of cells and tissues allows visualizing developmental processes as they happen in the living animal. Cells of interest can be labeled by using a tissue specific promoter to drive the expression of a fluorescent protein (FP) for the generation of transgenic lines. Using fluorescent photoconvertible proteins for this purpose additionally allows to precisely follow defined structures within the expression domain. Illuminating the protein in the region of interest, changes its emission spectrum and highlights a particular cell or cell cluster leaving other transgenic cells in their original color. A major limitation is the lack of known promoters for a large number of tissues in the zebrafish. Conversely, gene- and enhancer trap screens have generated enormous transgenic resources discretely labeling literally all embryonic structures mostly with GFP or to a lesser extend red or yellow FPs. An approach to follow defined structures in such transgenic backgrounds would be to additionally introduce a ubiquitous photoconvertible protein, which could be converted in the cell(s) of interest. However, the photoconvertible proteins available involve a green and/or less frequently a red emission state1 and can therefore often not be used to track cells in the FP-background of existing transgenic lines. To circumvent this problem, we have established the PSmOrange system for the zebrafish2,3. Simple microinjection of synthetic mRNA encoding a nuclear form of this protein labels all cell nuclei with orange/red fluorescence. Upon targeted photoconversion of the protein, it switches its emission spectrum to far red. The quantum efficiency and stability of the protein makes PSmOrange a superb cell-tracking tool for zebrafish and possibly other teleost species.

We established the photoconvertible PSmOrange system as a powerful, straight-forward and cost inexpensive tool for in vivo cell tracking in GFP transgenic backgrounds. This protocol describes its application in the zebrafish model system.

The rapid development of transparent zebrafish embryos (Danio rerio) in combination with fluorescent labelings of cells and tissues allows visualizing ...

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 ...

The Production of Pluripotent Stem Cells from Mouse Amniotic Fluid Cells Using a Transposon System

Induced pluripotent stem (iPS) cells are generated from mouse and human somatic cells by forced expression of defined transcription factors using different methods. Here, we produced iPS cells from mouse amniotic fluid cells, using a non-viral-based transposon system. All obtained iPS cell lines exhibited characteristics of pluripotent cells, including the ability to differentiate toward derivatives of all three germ layers in vitro and in vivo. This strategy opens up the possibility of using cells from diseased fetuses to develop new therapies for birth defects.

In this study, we generate induced pluripotent stem cells from mouse amniotic fluid cells, using a non-viral-based transposon system.

Induced pluripotent stem (iPS) cells are generated from mouse and human somatic cells by forced expression of defined transcription factors using ...

Spatial and Temporal Analysis of Active ERK in the C. elegans Germline

The evolutionarily conserved&#160;extracellular signal transducing RTK-RAS-ERK pathway is an important kinase-signaling cascade that controls multiple cellular and developmental processes principally via activation of ERK, the terminal kinase of the pathway. Tight regulation of ERK activity is essential for normal development and homeostasis; overly active ERK results in excessive cellular proliferation, while underactive ERK causes cell death. C. elegans is a powerful model system that has helped characterize the function and regulation of RTK-RAS-ERK signaling pathway during development. In particular, the RTK-RAS-ERK pathway is essential for C. elegans germline development, which is the focus of this method. Using antibodies specific to the active, diphosphorylated form of ERK (dpERK), the stereotypical localization pattern can be visualized within the germline. Because this pattern is both spatially and temporally controlled, the ability to reproducibly assay dpERK is useful to identify regulators of the pathway that affect dpERK signal duration and amplitude and thus germline development. Here we demonstrate how to successfully dissect, stain, and image dpERK within the C. elegans gonad. This method can be adapted for spatial localization of any signaling or structural protein in the C. elegans gonad, provided an antibody compatible with immunofluorescence is available.

Spatial and Temporal Analysis of Active ERK in the C. elegans Germline

We present an immunofluorescence&#160;imaging-based method for spatial and temporal localization of active ERK in the dissected C. elegans gonad. The protocol described here can be adapted for visualization of any signaling or structural protein in the C. elegans gonad, provided a suitable antibody reagent is available.

The evolutionarily conserved&#160;extracellular signal transducing RTK-RAS-ERK pathway is an important kinase-signaling cascade that controls multiple ...

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

During somitogenesis, pairs of epithelial somites form in a progressive manner, budding off from the anterior end of the pre-somitic mesoderm (PSM) with a strict species-specific periodicity. The periodicity of the process is regulated by a molecular oscillator, known as the "segmentation clock," acting in the PSM cells. This clock drives the oscillatory patterns of gene expression across the PSM in a posterior-anterior direction. These so-called clock genes are key components of three signaling pathways: Wnt, Notch, and fibroblast growth factor (FGF). In addition, Notch signaling is essential for synchronizing intracellular oscillations in neighboring cells. We recently gained insight into how this may be mechanistically regulated. Upon ligand activation, the Notch receptor is cleaved, releasing the intracellular domain (NICD), which moves to the nucleus and regulates gene expression. NICD is highly labile, and its phosphorylation-dependent turnover acts to restrict Notch signaling. The profile of NICD production (and degradation) in the PSM is known to be oscillatory and to resemble that of a clock gene. We recently reported that both the Notch receptor and the Delta ligand, which mediate intercellular coupling, themselves exhibit dynamic expression at both the mRNA and protein levels. In this article, we describe the sensitive detection methods and detailed image analysis tools that we used, in combination with the computational modeling that we designed, to extract and overlay expression data from distinct points in the expression cycle. This allowed us to construct a spatio-temporal picture of the dynamic expression profile for the receptor, the ligand, and the Notch target clock genes throughout an oscillation cycle. Here, we describe the protocols used to generate and culture the PSM explants, as well as the procedure to stain for the mRNA or protein. We also explain how the confocal images were subsequently analyzed and temporally ordered computationally to generate ordered sequences of clock expression snapshots, hereafter defined as "kymographs," for the visualization of the spatiotemporal expression of Delta-like1 (Dll1) and Notch1 throughout the PSM.

The segmentation clock drives oscillatory gene expression across the pre-somitic mesoderm (PSM). Dynamic Notch activity is key to this process. We use imaging and computational analyses to extract temporal dynamics from spatial expression data to demonstrate that Delta ligand and Notch receptor expression oscillate in the vertebrate PSM.

During somitogenesis, pairs of epithelial somites form in a progressive manner, budding off from the anterior end of the pre-somitic mesoderm (PSM) with a ...

Structure-function Studies in Mouse Embryonic Stem Cells Using Recombinase-mediated Cassette Exchange

Gene engineering in mouse embryos or embryonic stem cells (mESCs) allows for the study of the function of a given protein. Proteins are the workhorses of the cell and often consist of multiple functional domains, which can be influenced by posttranslational modifications. The depletion of the entire protein in conditional or constitutive knock-out (KO) mice does not take into account this functional diversity and regulation. An mESC line and a derived mouse model, in which a docking site for FLPe recombination-mediated cassette exchange (RMCE) was inserted within the ROSA26 (R26) locus, was previously reported. Here, we report on a structure-function approach that allows for molecular dissection of the different functionalities of a multidomain protein. To this end, RMCE-compatible mice must be crossed with KO mice and then RMCE-compatible KO mESCs must be isolated. Next, a panel of putative rescue constructs can be introduced into the R26 locus via RMCE targeting. The candidate rescue cDNAs can be easily inserted between RMCE sites of the targeting vector using recombination cloning. Next, KO mESCs are transfected with the targeting vector in combination with an FLPe recombinase expression plasmid. RMCE reactivates the promoter-less neomycin-resistance gene in the ROSA26 docking sites and allows for the selection of the correct targeting event. In this way, high targeting efficiencies close to 100% are obtained, allowing for insertion of multiple putative rescue constructs in a semi-high throughput manner. Finally, a multitude of R26-driven rescue constructs can be tested for their ability to rescue the phenotype that was observed in parental KO mESCs. We present a proof-of-principle structure-function study in p120 catenin (p120ctn) KO mESCs using endoderm differentiation in embryoid bodies (EBs) as the phenotypic readout. This approach enables the identification of important domains, putative downstream pathways, and disease-relevant point mutations that underlie KO phenotypes for a given protein.

Proteins often contain multiple domains that can exert different cellular functions. Gene knock-outs (KO) do not consider this functional diversity. Here, we report a recombination-mediated cassette exchange (RMCE)-based structure-function approach in KO embryonic stem cells that allows for the molecular dissection of various functional domains or variants of a protein.

Gene engineering in mouse embryos or embryonic stem cells (mESCs) allows for the study of the function of a given protein. Proteins are the workhorses of ...

Horizontal Whole Mount: A Novel Processing and Imaging Protocol for Thick, Three-dimensional Tissue Cross-sections of Skin

Processing a tissue of interest to generate a microscopic image that supports a scientific argument can be challenging. The acquisition of high-quality microscopic images is not entirely dependent upon the quality of the microscope, but also upon the methods of tissue processing, which often involve multiple critical actions or steps. Furthermore, mesenchymal cell types in the skin and other tissues represent a new challenge for tissue preparation and imaging. Here, we present a complete process, from tissue harvest to microscopy. Our technique, called &#34;horizontal whole mount,&#34; is one that novices can quickly become proficient in and that allows for antigen preservation and detection in 60-300 &#181;m-thick sections cut with a cryostat. Sections of this thickness provide enhanced visualization of tissue microarchitecture in a three-dimensional environment. In addition, the protocol preserves mesenchymal cells in a manner that enhances image quality when compared to standard cryostat or paraffin sections, thereby increasing the efficacy and reliability of immunostaining. We believe that this protocol will benefit all laboratories that visualize skin, and possibly other tissues and organs.

This work presents a novel processing and imaging protocol for thick, three-dimensional tissue cross-section analysis that enables the full exploitation of confocal imaging modalities. This protocol preserves antigenicity and represents a robust system to analyze skin histology and potentially other tissue types.

Processing a tissue of interest to generate a microscopic image that supports a scientific argument can be challenging. The acquisition of high-quality ...

Computational Analysis of the Caenorhabditis elegans Germline to Study the Distribution of Nuclei, Proteins, and the Cytoskeleton

The Caenorhabditis elegans (C. elegans) germline is used to study several biologically important processes including stem cell development, apoptosis, and chromosome dynamics. While the germline is an excellent model, the analysis is often two dimensional due to the time and labor required for three-dimensional analysis. Major readouts in such studies are the number/position of nuclei and protein distribution within the germline. Here, we present a method to perform automated analysis of the germline using confocal microscopy and computational approaches to determine the number and position of nuclei in each region of the germline. Our method also analyzes germline protein distribution that enables the three-dimensional examination of protein expression in different genetic backgrounds. Further, our study shows variations in cytoskeletal architecture in distinct regions of the germline that may accommodate specific spatial developmental requirements. Finally, our method enables automated counting of the sperm in the spermatheca of each germline. Taken together, our method enables rapid and reproducible phenotypic analysis of the C. elegans germline.

Computational Analysis of the Caenorhabditis elegans Germline to Study the Distribution of Nuclei, Proteins, and the Cytoskeleton

We present an automated method for three-dimensional reconstruction of the Caenorhabditis elegans germline. Our method determines the number and position of each nucleus within the germline and analyses germline protein distribution and cytoskeletal structure.

The Caenorhabditis elegans (C. elegans) germline is used to study several biologically important processes including stem cell development, apoptosis, and ...

In Vitro Generation of Somite Derivatives from Human Induced Pluripotent Stem Cells

In response to signals such as WNTs, bone morphogenetic proteins (BMPs), and sonic hedgehog (SHH) secreted from surrounding tissues, somites (SMs) give rise to multiple cell types, including the myotome (MYO), sclerotome (SCL), dermatome (D), and syndetome (SYN), which in turn develop into skeletal muscle, axial skeleton, dorsal dermis, and axial tendon/ligament, respectively. Therefore, the generation of SMs and their derivatives from human induced pluripotent stem cells (iPSCs) is critical to obtain pluripotent stem cells (PSCs) for application in regenerative medicine and for disease research in the field of orthopedic surgery. Although the induction protocols for MYO and SCL from PSCs have been previously reported by several researchers, no study has yet demonstrated the induction of SYN and D from iPSCs. Therefore, efficient induction of fully competent SMs remains a major challenge. Here, we recapitulate human SM patterning with human iPSCs in vitro by mimicking the signaling environment during chick/mouse SM development, and report on methods of systematic induction of SM derivatives (MYO, SCL, D, and SYN) from human iPSCs under chemically defined conditions through the presomitic mesoderm (PSM) and SM states. Knowledge regarding chick/mouse&#160;SM development was successfully applied to the induction of SMs with human iPSCs. This method could be a novel tool for studying human somitogenesis and patterning without the use of embryos and for cell-based therapy and disease modeling.

We present here a protocol for the differentiation of human induced pluripotent stem cells into each somite derivative (myotome, sclerotome, dermatome, and syndetome) in chemically defined conditions, which has applications in future disease modeling and cell-based therapies in orthopedic surgery.

In response to signals such as WNTs, bone morphogenetic proteins (BMPs), and sonic hedgehog (SHH) secreted from surrounding tissues, somites (SMs) give ...