This work was supported by an MRC studentship to RAB, an MRC studentship to CSLB, and a WT project grant to JKD (WT089357MA). The work was also supported by a Welcome Trust Strategic award (097945/Z/11/Z). We thank Dr. E. Kremmer for the kind gift of the Dll1 antibody and Dr. O. Pourquie for the Lfng RNA probe.

All experiments were performed under project license number 6004219 in strict adherence to the Animals (Scientific Procedures) Act of 1986 and the UK Home Office Codes of Practice for the use of animals in scientific procedures.
1. PSM Explant Dissection
<ol>
	<li>Obtain the tail tissue from embryos produced by the timed mating of wild-type (CD1) mice13. Briefly, at embryonic day (E) 10.5, euthanize the pregnant donor mouse in a carbon dioxide chamber. Harvest the uterine horn and place it into 1x sterile phosphate-buffered saline (PBS) solution in accordance with Home Office License procedures or equivalent local rules. Transfer the uterine horn to a tissue culture dish containing fresh, sterile PBS. Perform all subsequent dissection steps in this solution.</li>
	<li>Under a stereomicroscope, cut the thick muscular membrane of the uterine horn using curved scissors and extract each embryo carefully using fine forceps. Take care to ensure that the tail tissue is not damaged in this process. Using curved scissors and fine forceps, dissect away the amniotic sac from each embryo, taking care not to damage the embryo.</li>
	<li>Use either a surgical needle or curved scissors to harvest the tail tissue of each embryo by cutting the embryo posterior to the rear limb buds.</li>
	<li>Balance the tail tissue ventral-side down using both forceps and a needle. Generate pairs of PSM explants from each embryonic tail by dissecting the tail tissue into two halves along the midline; perform a gentle rocking motion with a needle. Ensure that the neural tube, notochord, and PSM tissue are equally divided between the two explants.</li>
	<li>Pipette each contralateral PSM explant onto the underside of a 35-mm plastic culture dish lid in a small volume of pre-warmed (37 &#176;C) culture medium (DMEM-F12 + 0.1% L-glutamine substitute supplemented with 10% fetal calf serum, 10 nM human Bfgf, and 1% penicillin/streptomycin).</li>
	<li>Place the dish on the top of the lid and quickly invert it so that the PSM tissue is suspended from the lid in a &#34;hanging drop&#34; of medium. Culture the PSM explants in a humidified chamber at 37 &#176;C for 1 - 2 h.</li>
	<li>Transfer pairs of PSM explants to the individual wells of a 24-well tissue culture plate. Incubate in 4% paraformaldehyde in PBS for 1 h at room temperature (RT) or 4 &#176;C overnight (O/N). CAUTION: Paraformaldehyde is toxic, and appropriate safety measures must be taken when working with this solution. 
	NOTE: Perform all subsequent washing and incubation steps in a 24-well tissue culture plate.</li>
	<li>Wash the sample wells in PBS at RT on a rocking platform, using a fine plastic Pasteur pipette to exchange the PBS solution on the samples for fresh PBS 3 - 4 times. Process one PSM explant from each pair using immunohistochemistry (step 2) and the other using fluorescent in situ hybridization for a known clock gene (step 3).</li>
</ol>
2. Immunohistochemistry of PSM Explants
<ol>
	<li>Wash one PSM explant from each embryonic pair generated in step 1 in 2% Triton X-100 in PBS for 1 h at RT on a rocking platform, and then rinse samples briefly in PBS. Replace the PBS on the samples with blocking solution (2% bovine serum albumin (BSA) and 10% normal goat serum (NGS) in PBS + 0.1% Tween-20) and incubate O/N at 4 &#176;C on a rocking platform. 
	NOTE: All subsequent washes and incubation steps in this section must be performed at RT on a rocking platform, unless otherwise stated. Wash solutions can be easily changed using a fine-tipped plastic or glass Pasteur pipette.</li>
	<li>Dilute the desired primary antibody/antibodies in working buffer (0.1% BSA, 0.3% NGS, and 0.2% Triton X-100 in PBS). In this example, dilute the Dll1 and Notch1 antibodies 1:25 in working buffer. 
	NOTE: Optimization will be required to determine the appropriate dilution factor required in this step if alternative antibodies are used.</li>
	<li>Incubate explants in the antibody solution for 3 - 5 days at 4 &#176;C on a rocking platform. Be sure to include some samples with working buffer containing no primary antibody to act as secondary antibody controls.</li>
	<li>Recover the primary antibody solution in a 1.5-mL storage tube using a pipette and store it at 4 &#176;C. 
	NOTE: Recovered primary antibody can be used several times, depending on the antibody used.</li>
	<li>Perform 2 washes of the samples for 5 - 10 min each in PBS, followed by 3 washes for 10 min each in 2% Triton X-100 in PBS at RT on a rocking platform.</li>
	<li>Dilute fluorescently labeled secondary antibody/antibodies (epitope-matched to the primary antibody/antibodies used) in working buffer. Optionally, add 20 &#181;g/mL Hoechst 33342 to this solution to counterstain the nuclei. 
	NOTE: Optimization may be required to determine the appropriate dilution factor required in this step. In this example, a dilution factor of 1:400 was typically used.</li>
	<li>Centrifuge the secondary antibody solution for 10 min at 16 x g to prevent the formation of antibody aggregates. Add 250 - 500 &#181;L of the secondary antibody solution to each sample well, taking care not to use the last few microliters of the solution, which may contain antibody aggregates.</li>
	<li>Cover the sample plate with tin foil to minimize light exposure and incubate the samples in the secondary antibody solution for 3 - 5 days at 4 &#176;C in the dark.</li>
	<li>Prior to sample mounting, wash the samples twice for 10 min each in 0.1% Tween-20 in PBS (PBST) and once for 5 min in PBS at RT on a rocking platform (see step 4).</li>
</ol>
3. Fluorescent In Situ Hybridization (FISH) of PSM Explants
<ol>
	<li>If stored in an alternative vessel, transfer the remaining contralateral PSM explants to the individual wells of a 24-well tissue culture plate.</li>
	<li>Wash the samples for 10 min in 50% ethanol in PBST, and then perform 2 washes for 10 min each in 100% ethanol on a rocking platform at RT to dehydrate the tissue. 
	NOTE: All subsequent washes and incubation steps in this section must be performed at RT on a rocking platform, unless otherwise stated.</li>
	<li>Rehydrate the tissue by washing for 10 min in 50% ethanol in PBST, followed by washing twice for 5 min each in PBST. 
	NOTE: Steps 3.2 and 3.3 are necessary fixation steps required for this protocol and cannot be omitted.</li>
	<li>Incubate the samples with 10 &#181;g/mL proteinase K in 0.1% Tween-20 in PBS (PBST) for 5 min without agitation. Quickly remove the proteinase K and rinse the samples briefly with PBST before post-fixing the tissue for 30 min in 4% formaldehyde + 0.1% glutaraldehyde in PBST. CAUTION: Both formaldehyde and glutaraldehyde are toxic, and appropriate safety measures must be taken when working with these solutions. 
	NOTE: The following washing and incubation steps involving 50% and 100% hybridization mixes (steps 3.6 - 3.9) should be performed without agitation.</li>
	<li>After washing the samples twice for 10 min each in PBST, wash the samples once in 50% hybridization mix (suitable for intronic probes: 50% formamide, 5x saline-sodium citrate (SSC), 5 mM EDTA, 50 &#181;g/mL tRNA, 0.2% Tween-20, 0.1% SDS, and 100 &#181;g/mL heparin) in PBST prepared at RT. Incubate the samples in this solution for 10 min at 65 &#176;C without agitation.</li>
	<li>Rinse the samples twice with pre-warmed (65 &#176;C) hybridization mix before incubating the samples in hybridization mix for &#8805; 2 h (up to 48 h) at 65 &#176;C (longer incubation times improve the resulting signal-to-noise contrast). Remove the hybridization mix from the previous step, and replace it with 0.25 - 0.5 mL of pre-warmed (65 &#176;C) hybridization mix containing a digoxigenin (DIG)-labeled anti-sense RNA probe against a known segmentation clock component. 
	NOTE: For example, an intronic Lunatic fringe (Lfng(i)) probe was used at a concentration of 20 &#181;L/mL to detect nascent Lfng mRNA. The dilution used in this step is probe-dependent and will require optimization.</li>
	<li>Seal the plate using sticky tape to prevent evaporation and incubate the samples in the probe solution for two nights at 65 &#176;C.</li>
	<li>Using a fine-tipped plastic Pasteur pipette, recover the probe for reuse and store it at 20 &#176;C. Rinse the samples twice with pre-warmed (65 &#176;C) post-hybridization mix (50% formamide, 0.2% Tween-20, and 1x SSC) before washing the samples two more times for 20 min each at 65 &#176;C in pre-warmed post-hybridization mix.</li>
	<li>Wash the samples for 15 min at 65 &#176;C in pre-warmed 50% hybridization mix in 0.1% Tween-20 in Tris-buffered saline solution (TBST). Rinse the samples twice with TBST before washing for 30 min at RT in TBST on a rocking platform.</li>
	<li>Pre-incubate the explants in blocking solution (TBST + 2% blocking buffer reagent (BBR) + 20% heat-treated goat serum) for a minimum of 2 h. Replace this solution with fresh blocking solution containing a 1:200 dilution of horseradish peroxidase (HRP)-conjugated anti-digoxigenin antibody. Incubate the samples O/N at 4 &#176;C.</li>
	<li>After the antibody incubation, rinse the samples 3 times with TBST at RT and transfer them to individual wells of a new 24-well tissue culture plate. Wash the explants with TBST 3 times for 1 h each.</li>
	<li>At this point, transfer the samples into 0.5-mL storage tubes or the individual wells of a 48-well tissue culture plate to reduce the required volume of the Tyramide signal amplification (TSA) detection reagents in the following steps.</li>
	<li>Incubate samples in TSA amplification buffer (see the reagents list) at RT for 1 min without agitation using as small a volume as possible, ensuring that the samples are fully immersed in the solution.</li>
	<li>Add TSA reagent (see the reagents list) to the sample amplification buffer at a dilution of 1:50. Quickly mix the solution until the TSA reagent is evenly distributed, cover the plate or tubes in tin foil, and incubate the samples for 60 - 90 min in the dark.</li>
	<li>Remove the TSA amplification solution and wash the samples in TBST 3 times for 5 min each. Transfer the explants back to a 24-well tissue culture plate to increase the wash volume and incubate the samples in 1% hydrogen peroxide in TBST for 1 h. Wash the samples with TBST 3 times for 5 min each, and then twice for 5 min each with PBST prior to sample mounting (see step 4).</li>
</ol>
4. Sample Preparation for Imaging
<ol>
	<li>Prepare one charged adhesion glass slide for each explant pair by adding 0.12-mm thick imaging spacers, which prevent the samples from being crushed by the addition of a coverslip. Remove the adhesive liner from one surface of a spacer and place it adhesive-side down onto a glass slide, pressing firmly to seal the spacer to the slide. 
	NOTE: For the remaining steps, endeavor to keep the samples in low light or in darkness to avoid photobleaching. Pipette explant pairs onto a prepared slide using a glass Pasteur pipette within the center of the spacer, ensuring that the dissected side of the explant faces the slide. Arrange contralateral pairs of explants side by side.</li>
	<li>Remove as much liquid as possible from the slide using a glass Pasteur pipette and wick off any residual moisture surrounding the samples using a piece of folded low-lint tissue paper.</li>
	<li>Allow the samples to adhere to the slide for 45 - 60 s, until the tissue begins to appear sticky and translucent. During this time, remove the remaining adhesive liner from the spacer using forceps. Do not allow the samples to dry out.</li>
	<li>Add a large drop of dual-function mountant and clearing solution (0.5% p-phenylenediamine and 20 mM Tris, pH 8.8, in 90% glycerol) to the samples within the center of the spacer. NOTE: This solution turns brown/black when permitted to oxidize.</li>
	<li>Carefully place a circular coverslip (no. 1.5) across the samples, ensuring that the mountant is evenly distributed and that all edges of the coverslip make contact with the spacer. Place the cover-slipped slide upside down onto some low-lint tissue paper.</li>
	<li>Press down firmly to ensure that the coverslip fully adheres to the spacer and that any excess mountant is removed. Repeat until no more mountant blots the paper.</li>
	<li>Clean and label the slide(s) appropriately, and store them in the dark until imaging, short-term at -20 &#176;C or long-term at -80 &#176;C. After removing the slides from storage, allow them to fully thaw before imaging.</li>
	<li>Image the mounted samples using a confocal microscope with tiled acquisition and a high magnification objective. Image the explant pairs using a 40X oil immersion objective at 4-&#181;m z-intervals using 488-nm, 568-nm and 647-nm laser lines to excite the green, red, and far-red fluorophores, respectively, employed for protein and mRNA detection in this study12. 
	NOTE: Tiled images were stitched post-acquisition to form a single image for analysis.</li>
</ol>
5. Post-acquisition Image Analysis
<ol>
	<li>Use image analysis software to define a region of interest within the PSM of each experimental sample.
	<ol>
		<li>To quantify expression levels, subtract background and threshold images to the level of a no-primary control sample prior to subsequent quantification. Define an origin, an axis, and a unit length for each sample.</li>
	</ol>
	</li>
	<li>Calculate the fluorescence intensity as a function of position along the normalized rostro-caudal axis for each of the M samples 12. After normalizing the intensity plots, place the intensity profiles side by side and obtain an intensity matrix f(i,j) that describes the intensity at the ith spatial position in the jth sample.</li>
</ol>
6. Temporal Ordering of Samples
<ol>
	<li>To infer temporal ordering of a known clock component, define its intensity matrix. Then, rearrange the columns of the intensity matrix so as to obtain a temporally periodic pattern. To do this, define the function 
	<img alt="Equation 1" src="/files/ftp_upload/55127/55127equation01.jpg" /> 
	where A(fj;k) represents the autocorrelation function of the jth column of f and AT is a target autocorrelation function, chosen to enforce the temporal periodicity of the pattern, given by 
	<img alt="Equation 2" src="/files/ftp_upload/55127/55127equation02.jpg" /></li>
	<li>Use the Metropolis-Hastings (or another minimization algorithm)12 to identify the order of the samples that minimize the function g. Thus, determine the order of the M samples that maximizes the temporal periodicity of a known clock component.</li>
	<li>Using the inferred temporal ordering of the M samples, construct an ordered kymograph for the expression pattern in the partnered channel12.</li>
</ol>

<ol><li>Oates, A. C., Morelli, L. G., Ares, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Patterning+embryos+with+oscillations%3A+structure%2C+function+and+dynamics+of+the+vertebrate+segmentation+clock%5BTitle%5D)+AND+%22Development%22%5BJournal%5D)">Patterning embryos with oscillations: structure, function and dynamics of the vertebrate segmentation clock.</a> Development. 139 (4), Cambridge, England. 625-639 (2012).</li>
<li>Krol, A. J., Roellig, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evolutionary+plasticity+of+segmentation+clock+networks%5BTitle%5D)+AND+%22Development%22%5BJournal%5D)">Evolutionary plasticity of segmentation clock networks.</a> Development. 138 (13), Cambridge, England. 2783-2792 (2011).</li>
<li>Dequéant, M. -L., Ahnert, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+Pattern+Detection+Methods+in+Microarray+Time+Series+of+the+Segmentation+Clock%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock.</a> PLoS ONE. 3 (8), 2856(2008).</li>
<li>Bailey, C., Dale, K. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aBailey%2C+C+%22Somitogenesis+in+Vertebrate+Development%22">Somitogenesis in Vertebrate Development</a>. , John Wiley & Sons, Ltd. Chichester, UK. 1-15 (2015).</li>
<li>Maroto, M., Bone, R. A., Somitogenesis Dale, J. K. <a target="_blank" href="http://dev.biologists.org/content/139/14/2453">Somitogenesis.</a> Development. 139 (14), Cambridge, England. 2453-2456 (2012).</li>
<li>Kageyama, R., Masamizu, Y., Niwa, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Oscillator+mechanism+of+notch+pathway+in+the+segmentation+clock%5BTitle%5D)+AND+%22Developmental+Dynamics%22%5BJournal%5D)">Oscillator mechanism of notch pathway in the segmentation clock.</a> Developmental Dynamics. 236 (6), 1403-1409 (2007).</li>
<li>Ferjentsik, Z., Hayashi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Notch+Is+a+Critical+Component+of+the+Mouse+Somitogenesis+Oscillator+and+Is+Essential+for+the+Formation+of+the+Somites%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">Notch Is a Critical Component of the Mouse Somitogenesis Oscillator and Is Essential for the Formation of the Somites.</a> PLoS Genetics. 5 (9), 1000662(2009).</li>
<li>Wiedermann, G., Bone, R. A., Silva, J. C., Bjorklund, M., Murray, P. J., Dale, J. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+balance+of+positive+and+negative+regulators+determines+the+pace+of+the+segmentation+clock%5BTitle%5D)+AND+%22eLife%22%5BJournal%5D)">A balance of positive and negative regulators determines the pace of the segmentation clock.</a> eLife. 4, 05842(2015).</li>
<li>Chapman, G., Sparrow, D. B., Kremmer, E., Dunwoodie, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Notch+inhibition+by+the+ligand+DELTA-LIKE+3+defines+the+mechanism+of+abnormal+vertebral+segmentation+in+spondylocostal+dysostosis%5BTitle%5D)+AND+%22Human+Molecular+Genetics%22%5BJournal%5D)">Notch inhibition by the ligand DELTA-LIKE 3 defines the mechanism of abnormal vertebral segmentation in spondylocostal dysostosis.</a> Human Molecular Genetics. 20 (5), 905-916 (2011).</li>
<li>Sparrow, D. B., Chapman, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Mechanism+for+Gene-Environment+Interaction+in+the+Etiology+of+Congenital+Scoliosis%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">A Mechanism for Gene-Environment Interaction in the Etiology of Congenital Scoliosis.</a> Cell. 149 (2), 295-306 (2012).</li>
<li>Okubo, Y., Sugawara, T., Abe-Koduka, N., Kanno, J., Kimura, A., Saga, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lfng+regulates+the+synchronized+oscillation+of+the+mouse+segmentation+clock+via+trans-repression+of+Notch+signalling%5BTitle%5D)+AND+%22Nature+communications%22%5BJournal%5D)">Lfng regulates the synchronized oscillation of the mouse segmentation clock via trans-repression of Notch signalling.</a> Nature communications. 3, 1141(2012).</li>
<li>Bone, R. A., Bailey, C. S. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Spatiotemporal+oscillations+of+Notch1%2C+Dll1+and+NICD+are+coordinated+across+the+mouse+PSM%5BTitle%5D)+AND+%22Development%22%5BJournal%5D)">Spatiotemporal oscillations of Notch1, Dll1 and NICD are coordinated across the mouse PSM.</a> Development. 141 (24), Cambridge, England. 4806-4816 (2014).</li>
<li>Shea, K., Geijsen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Dissection+of+6.5+dpc+mouse+embryos%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">Dissection of 6.5 dpc mouse embryos.</a> Journal of Visualized Experiments. (2), 160(2007).</li>
<li>Denkers, N., García-Villalba, P., Rodesch, C. K., Nielson, K. R., Mauch, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((FISHing+for+chick+genes%3A+Triple-label+whole-mount+fluorescence+in+situ+hybridization+detects+simultaneous+and+overlapping+gene+expression+in+avian+embryos%5BTitle%5D)+AND+%22Developmental+Dynamics%22%5BJournal%5D)">FISHing for chick genes: Triple-label whole-mount fluorescence in situ hybridization detects simultaneous and overlapping gene expression in avian embryos.</a> Developmental Dynamics. 229 (3), 651-657 (2004).</li>
<li>Stasiulewicz, M., Gray, S. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+conserved+role+for+Notch+signaling+in+priming+the+cellular+response+to+Shh+through+ciliary+localisation+of+the+key+Shh+transducer+Smo%5BTitle%5D)+AND+%22Development%22%5BJournal%5D)">A conserved role for Notch signaling in priming the cellular response to Shh through ciliary localisation of the key Shh transducer Smo.</a> Development. 142 (13), Cambridge, England. 2291-2303 (2015).</li>
</ol>

The authors have nothing to disclose.

Critical Steps within the Protocol
The present protocol describes a sensitive method to perform the quantitative analysis of low-level protein expression and oscillatory dynamics in E10.5 mouse PSM explants. A robust protocol for both immunohistochemistry and fluorescent in situ hybridization (FISH) is followed by high-resolution whole-mount confocal imaging, and then by image analysis and temporal segmentation of kymographs to generate a spatiotemporal map of protein expression across the PSM. A high signal-to-noise ratio in protein and mRNA detection is essential to ensure the success of this technique. Care must be taken to thoroughly exchange all solutions effectively during the wash steps and to maintain the temperature of the 65 &#176;C washes in the relevant stages of step 3. It is most advantageous to take the time to source efficacious antibodies and RNA probes against the targets of interest and to test these reagents thoroughly on whole-mount samples prior to starting this protocol.
Modifications and Troubleshooting
The main issues that may be encountered when performing this protocol arise from poor signal detection strength and quality. This is largely dependent upon the efficacy of the antibodies or RNA probes used for the immunohistochemistry or FISH steps in the protocol, respectively. A number of different steps may require optimization before adequate signal detection is achieved. One common cause for poor signal detection is improper fixation; it is imperative that either fresh PFA or PFA stored at 4 &#176;C for no longer than one week is used to fix the samples. The length of fixation may also require optimization, depending on the antibody or RNA probe used. For antibodies, it is advised to follow the manufacturer&#39;s instructions where possible, while for RNA probes, we advise the consultation of the published literature.
In this study, we used an RNA probe that specifically detects the pre-mRNA of the clock gene Lfng. Due to its relative lack of abundance, detection of Lfng pre-mRNA requires a long period of incubation with the probe in hybridization mix containing 5x saline-sodium citrate (SSC) for good signal detection. The same conditions may apply to other probes that detect weakly expressed mRNAs, but in our experience, the detection of more stable mRNA targets may require a shorter probe hybridization step and lower SSC concentrations in the hybridization mix (e.g., 1.3x SSC). For both immunohistochemistry and FISH, the protocol must first be optimized on whole embryos, and the optimal concentration of antibody or probe must be determined empirically.
Limitations of the Technique
As mentioned above, the success of this technique is highly dependent upon the quality of the protein and mRNA detection. We have outlined several suggestions as to how protein and mRNA detection can be improved, but in the absence of high-quality fluorescent signal detection, there is no way the experiment can proceed. The number of protein targets that can be analyzed in each tissue sample is limited by the spectral resolution of the confocal microscope and by the epitopes of the antibodies used. In this study, we were able to use up to three epitopes for protein detection alongside a DNA stain on each sample12. This protocol only permits the detection of one mRNA target, although current alternative methods could be employed to increase this to up to three targets14.
Significance of the Technique with Respect to Existing/Alternative Methods
The method described here provides a sensitive technique to detect low-level protein fluctuations in whole-mount PSM explants. The quantification of these dynamics is possible by performing FISH for a known clock gene in corresponding contralateral explants. A library of kymographs is generated that can be organized over one segmentation clock cycle, highlighting the spatiotemporal expression dynamics of a target of interest within this time frame. A key difference in this technique over others is the use of computational automation to temporally order large data sets, which permits the spatiotemporal expression dynamics of novel clock components to be analyzed in an unbiased manner. For example, this technique provided insight into how Dll1 and Notch1 proteins and their oscillations are co-regulated across the entire PSM. Alternative methods in this context have also relied upon immunostaining, but they did not detect the small fluctuations in Dll1 and Notch1 protein levels in the caudal PSM that were evident using this method. Instead, they reported a steady gradient of expression that is strongest in the rostral region9,10,11. This could be due to the fact that this protocol has a lengthier primary antibody incubation period (3 - 5 days, as opposed to overnight), which may be required to detect lower levels of protein. As the levels of Dll1 and Notch1 expression are relatively high in the rostral PSM, this may have influenced the authors to image the samples at a lower exposure setting than would be necessary to detect the caudal protein expression. One further potential discrepancy arises from the use of unfixed tissue in the study by Chapman et al., in which the transitory expression of Dll1 and Notch1 in the caudal PSM may have been less well-preserved9.
Future Applications or Directions after Mastering the Technique
Once this protocol has been mastered, high-throughput expression analysis can be performed for any protein of interest in the PSM. PSM explants generated from several mouse litters can be processed at once to generate the high sample number necessary for analysis. Although we have only used wild-type embryos in these studies, it is possible to perform this analysis using genetically modified embryos in order to assess the importance of one or more factors on protein expression dynamics. Beyond the PSM, this protocol can be adapted to other systems that are composed of two contralateral halves and can be used to sensitively detect low-level protein expression and oscillatory dynamics. One example for which this protocol could be adapted is the study of dynamic protein expression in the mouse neural tube, since contralateral halves could be generated and cultured, and Notch activity has been shown to be both present and important for patterning15. We encourage other groups to adapt this protocol to other systems and to provide feedback for future improvement.

Somites are the first segments formed in the elongating body axis in developing vertebrate species and are the precursors of the spine, ribs, and dermis tissue, as well as of muscle and endothelial cells. During somitogenesis, epithelial somites form from the unsegmented presomitic mesoderm (PSM) (reviewed in Reference 1). This process is regulated by the &#34;segmentation clock,&#34; which consists of a network of oscillatory genes and proteins, mostly belonging to the Notch signaling pathway. The segmentation clock consists of various negative feedback loops, which enable the pulsatile production of Notch activity within a single cell2 (reviewed in References 3 - 6). While the intracellular method of oscillation is well characterized, it is still largely unknown how these oscillations are coordinated across the PSM tissue. It has been recently shown, through both experimental and theoretical studies, that these oscillations are essential to the process of somitogenesis and that the Notch pathway plays a crucial role in the process of both segmentation and oscillatory gene expression7,8. However, it has been widely reported that Notch receptor 1 (Notch1) and Delta-like ligand (Dll)-1 have static gradients in the PSM9,10,11.
We hypothesized that Notch-dependent oscillations of the PSM segmentation clock depend upon the periodic activation of the main Notch pathway receptor and ligand, Notch1 and Dll1, respectively, across the mouse PSM. The conclusions of previous studies that reported a static rostral-caudal gradient of these proteins were due, we predict, to a lack of sensitivity in immunostaining techniques. They were therefore unable to detect low-level fluctuations of Dll1 and Notch1 in the caudal PSM.
We have devised a method to more closely examine these factors, combining experimental data with mathematical modeling to predict a mechanism by which the oscillations of the proteins of clock components are coordinated across the PSM12.
The overall goal of this method is to detect and quantify low-level, dynamic protein expression in the PSM and to map the expression profiles of proteins of interest according to the expression of the known clock gene, Lunatic fringe (Lfng). Since one cycle of the segmentation clock in the mouse embryo takes 2 h to complete, various samples are required to build a complete spatiotemporal profile of Dll1 and Notch1 protein expression during one Lfng oscillation in the PSM. We have thus developed this protocol to allow for the high-throughput detection of low-level protein expression in whole-mount, contralateral PSM explants. However, this technique can also be useful for studies that aim to characterize low-level protein dynamics within any embryonic tissue that can be split into contralateral halves.

<table><tbody><tr><td>DMEM-F12</td><td>Gibco (ThermoFisher Scientific)</td><td>11320033</td><td></td></tr><tr><td>GlutaMAX&amp;trade;-1 (100x)</td><td>Gibco (ThermoFisher Scientific)</td><td>35050</td><td></td></tr><tr><td>Fetal Bovine Serum, qualified, E.U.-approved, South America origin</td><td>Gibco (ThermoFisher Scientific)</td><td>10270106</td><td></td></tr><tr><td>Recombinant Human FGF-basic (154 a.a.)</td><td>Peprotech</td><td>100-18B</td><td></td></tr><tr><td>Penicillin/Streptomycin</td><td>Gibco (ThermoFisher Scientific)</td><td>15140122</td><td></td></tr><tr><td>anti-mouse monoclonal Notch1 antibody^</td><td>BD Pharmingen</td><td>552466</td><td></td></tr><tr><td>anti-rat polyclonal Dll1 antibody^*</td><td>N/A</td><td>N/A</td><td></td></tr><tr><td>&lt;em&gt;Lfng&lt;/em&gt; intronic anti-sense RNA probe^*</td><td>N/A</td><td>N/A</td><td></td></tr><tr><td>16% paraformaldehyde</td><td>Pierce (ThermoFischer Scientific)</td><td>PI28908</td><td></td></tr><tr><td>Proteinase K, recombinant, PCR grade&amp;nbsp;</td><td>Roche (Sigma-Aldrich)</td><td>31158</td><td></td></tr><tr><td>Phosphate buffered saline (PBS), pH 7.4</td><td>Made in house</td><td>N/A</td><td></td></tr><tr><td>Triton-X 100</td><td>Sigma-Aldrich</td><td>T8787</td><td></td></tr><tr><td>Bovine serum albumin (BSA)</td><td>Sigma-Aldrich</td><td>5470</td><td></td></tr><tr><td>Normal goat serum (NGS) (heat-treated)</td><td>Gibco (ThermoFisher Scientific)</td><td>16210072&amp;nbsp;</td><td></td></tr><tr><td>Hoechst 33342</td><td>ThermoFischer Scientific</td><td>H3570</td><td></td></tr><tr><td>Tween-20</td><td>Sigma-Aldrich</td><td>P9416</td><td></td></tr><tr><td>Ethanol</td><td>Sigma-Aldrich</td><td>46139</td><td></td></tr><tr><td>Glutaraldehyde</td><td>Sigma-Aldrich</td><td>340855</td><td></td></tr><tr><td>Formamide</td><td>Sigma-Aldrich</td><td>F9037</td><td></td></tr><tr><td>Saline-sodium citrate (SSC)</td><td>Sigma-Aldrich</td><td>93017</td><td></td></tr><tr><td>EDTA</td><td>Sigma-Aldrich</td><td>798681&amp;nbsp;</td><td></td></tr><tr><td>tRNA</td><td>Roche (Sigma-Aldrich)</td><td>101095</td><td></td></tr><tr><td>Heparin</td><td>Sigma-Aldrich</td><td>H3149&amp;nbsp;</td><td></td></tr><tr><td>Tris-buffered saline (TBS)</td><td>Made in house</td><td>N/A</td><td></td></tr><tr><td>Blocking Buffer Reagent&amp;nbsp;</td><td>Roche (Sigma-Aldrich)</td><td>11096176001</td><td></td></tr><tr><td>anti-DIG horseradish peroxidase (HRP) conjugated antibody</td><td>Roche (Sigma-Aldrich)</td><td>11207733910</td><td></td></tr><tr><td>Tyramide signal amplification (TSA) kit</td><td>Perkin Elmer</td><td>NEL744001KT</td><td></td></tr><tr><td colspan="4">*The Dll1 antibody and RNA probe used in this study are not commercially available. Please see acknowledgements for sources.</td></tr><tr><td colspan="4">^Antibodies/RNA probes should be sourced which are applicable to the research interests of the reader.</td></tr></tbody></table>

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.

This protocol permits the visualization of the spatiotemporal profile of a protein of interest alongside clock gene transcription in the mouse PSM12. For example, Dll1 (Figure 1A-C) and Notch1 (Figure 1D-F) protein expression are shown to oscillate out of synchrony with the nascent transcription of the Notch-regulated segmentation clock gene Lfng. Quantification of Dll1, Notch1, and Lfng(i) signal intensity in relation to the antero-posterior (AP) axis of the PSM (Figure 1G) reveals clear oscillatory expression dynamics for these targets (Figure 1H-J). The spatiotemporal profile of Dll1 and Notch1 protein expression throughout the clock cycle are clearly visualized and quantified using this protocol through the post-acquisition image analysis of high-resolution fixed tissue image data.
<img alt="Figure 1" src="/files/ftp_upload/55127/55127fig1.jpg" /> 
Figure 1: Spatio-temporal Visualization and Quantification of Dll1 and Notch1 Protein Expression Dynamics. (A-F) Pairs of explants from six E10.5 embryos (A-F) showing the spatial distribution of Dll1 protein (A-C) or Notch1 protein (D-F) in one half alongside the detection of Lfng pre-mRNA (Lfng(i)) in the corresponding contralateral half of each pair. Panels are arranged according to Phase 1 (A and D), Phase 2 (B and E), and Phase 3 (C and F) of the segmentation clock cycle, as determined by the spatial profile of Lfng(i) expression. The extent of the expression domains for Dll1 (green), Notch1 (red), and Lfng(i) (gray) along the antero-posterior axis of the PSM have been demarcated by color-coded bars. The dotted lines demarcate the positions of the most recently formed somite(s), the outer edges of the PSM, and the adjacent neural tissue (C and E). Scale bars (bottom left of each panel, A-F) represent 100 &#181;m. (G) An example intensity plot depicting the axial variation in signal intensity across the PSM. The data is plotted from two explant pairs showing Lfng pre-mRNA (black hashed line) in one explant compared to Notch1 protein (red) in the contralateral explant (Embryo 1), as well as Lfng pre-mRNA (black solid line) in another explant compared to Dll1 protein (green) in the contralateral explant (Embryo 2). Measured signal intensity (y-axis) is plotted against axial position (x-axis; anterior PSM [A] to the right and posterior PSM [P] to the left). (H) A kymograph showing the spatial distribution of Dll1, Notch1, and Lfng(i) across numerous PSMs. Each row of the kymograph represents the signal intensity of an individual PSM explant. Rows are arranged in temporal sequence according to the spatiotemporal distribution of Lfng pre-mRNA (I) The spatiotemporal distribution of Dll1, Notch1, and Lfng(i) through multiple clock oscillations is simulated by the periodic extension of the data shown in (H), highlighting the oscillatory nature of Dll1 and Notch1 expression dynamics. (J) Pulsatile Notch1 protein expression in the caudal PSM is highlighted by magnification of the region demarcated in the virtual kymograph shown in (I). Modified from Reference 12. <a href="http://cloudflare.jove.com/files/ftp_upload/55127/55127fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55127/55127fig2.jpg" /> 
Figure 2: Quantification of the Spatio-temporal Dynamics of Dll1 and Notch1 Protein Expression. (A) An example intensity plot depicts axial variation in signal intensity across the PSM. Data plotted from two explant pairs showing Lfng pre-mRNA (black hashed line) in one explant compared to Notch1 protein (red) in the contralateral explant half, as well as Lfng pre-mRNA (black solid line) in a half explant from a second tail compared to Dll1 protein (green) in the contralateral explant half of the second tail. Measured intensities (y-axis) are plotted against axial position (x-axis; rostral [A] to the right and caudal [P] to the left). (B-H) Kymographs show the spatial distribution of Notch1, Dll1, NICD, and Lfng(i) across numerous PSMs. (B and C) NICD (B) and Dll1 (C) expression in PSM sections; (D and E) Lfng(i) (D) and Dll1 (E) in contralateral explant halves; (F and G) Lfng(i) (F) and Notch1 (G) in contralateral explant halves. From Reference 12. <a href="http://cloudflare.jove.com/files/ftp_upload/55127/55127fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps at JoVE.com

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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 ...

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Research

JoVE Journal

Developmental Biology

5.9K Views.  University of Copenhagen. 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.

Video: Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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 ...

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these proteins contain an intrinsically disordered domain (IDD) fused with the DNA-binding domain (DBD) of another protein and orchestrate widespread transcriptional changes to promote malignancy. These fusions are often the sole recurring genomic aberration in the cancers they cause, making them attractive therapeutic targets. However, targeting oncogenic transcription factors requires a better understanding of the mechanistic role that low-complexity, IDDs play in their function. The N-terminal domain of EWSR1 is an IDD involved in a variety of oncogenic fusion transcription factors, including EWS/FLI, EWS/ATF, and EWS/WT1. Here, we use RNA-sequencing to investigate the structural features of the EWS domain important for transcriptional function of EWS/FLI in Ewing sarcoma. First shRNA-mediated depletion of the endogenous fusion from Ewing sarcoma cells paired with ectopic expression of a variety of EWS-mutant constructs is performed. Then RNA-sequencing is used to analyze the transcriptomes of cells expressing these constructs to characterize the functional deficits associated with mutations in the EWS domain. By integrating the transcriptomic analyses with previously published information about EWS/FLI DNA binding motifs, and genomic localization, as well as functional assays for transforming ability, we were able to identify structural features of EWS/FLI important for oncogenesis and define a novel set of EWS/FLI target genes critical for Ewing sarcoma. This paper demonstrates the use of RNA-sequencing as a method to map the structure-function relationship of the intrinsically disordered domain of oncogenic transcription factors.

Intrinsically disordered domains are important for oncogenic fusion transcription factor function. To therapeutically target these proteins, a more detailed understanding of the regulatory mechanisms employed by these domains is required. Here, we use transcriptomics to map important structural features of the intrinsically disordered EWS domain in Ewing sarcoma.

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these ...

Cancer Research

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Biology

Spatial High-resolution Analysis of Gene Expression Levels in Tendons

In recent years, many protocols have been developed for high-resolution transcriptomics in many different medical and biology fields. However, matrix-rich tissues, and specifically, tendons were left behind due to their low cell number, low RNA amount per cell, and high matrix content, which made them complicated to analyze. One of the recent and most important single-cell tools is the spatial analysis of gene expression levels in tendons. These RNA spatial tools have specifically high importance in tendons to locate specific cells of new and unknown populations, validate single-cell RNA-seq results, and add histological context to the single-cell RNA-seq data. These new methods will enable the analysis of RNA in cells with exceptional sensitivity and the detection of single-molecule RNA targets at the single-cell level, which will help to molecularly characterize tendons and promote tendon research. 
In this method paper, we will focus on the available methods to analyze spatial gene expression levels on histological sections by using novel in situ hybridization assays to detect target RNA within intact cells at single-cell levels. First, we will focus on how to prepare the tendon tissue for the different available assays and how to amplify target-specific signals without background noise but with high sensitivity and high specificity. Then, the paper will describe specific permeabilization methods, the different probe designs, and the signal amplification strategies currently available. These unique methods of analyzing transcription levels of different genes in single-cell resolution will enable the identification and characterization of the tendon tissue cells in young and aged populations of various animal models and human tendon tissues. This method will also help analyze gene expression levels in other matrix-rich tissues such as bones, cartilage, and ligaments.

This article describes how to perform an optimized in situ protocol for tendons. This method discusses tissue preparation, section permeabilization, probe design, and signal amplification methods.

In recent years, many protocols have been developed for high-resolution transcriptomics in many different medical and biology fields. However, matrix-rich ...

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatial transcriptomic techniques are a sophisticated tool in biomedical research to visualize spatially registered gene expression patterns. Imaging and analysis of multiple samples with spatial imaging platforms can be costly. Performing these tests over multiple experimental conditions, as seen in developmental studies, further increases costs. To reduce costs, this study sought to optimize the techniques and strategies of spatial transcriptomic specimen arrangement for developmental studies. Here, the study utilized zebrafish, which are a well-established developmental vertebrate model that is transparent during development, have ~70% genetic homology to humans, and a highly annotated genome ideal for transcriptomic analysis. Because of their small size, developing zebrafish also allows for compact placement of serial sections across several biological replicates. Herein, we report optimized fixation, cryosectioning, and reliable alignment of multiple fish samples within the imaging area of a multiplex in situ hybridization spatial imaging platform. With this method, zebrafish as young as 15 days post fertilization (dpf) from at least 4 different molds and up to 174 sections can be successfully cryosectioned, collected within the imaging area of 22 mm 10.5 mm (for an in situ spatial transcriptomic slide), and processed simultaneously. Based on section quality, sample alignment, and sample size per slide, this method in zebrafish optimizes output and per sample cost of spatial transcriptomic techniques.

Here, we present a method for aligning and cryosectioning multiple Zebrafish (Danio rerio) larvae samples and collecting them on a single slide for spatial transcriptomic analysis.

Spatial transcriptomic techniques are a sophisticated tool in biomedical research to visualize spatially registered gene expression patterns. Imaging and ...

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the single cell level uncovers novel regulatory mechanisms obscured in measurements performed at the population level. Two methods based on microscopy and flow cytometry are presented to demonstrate how such data can be acquired. The expression of a fluorescent reporter induced upon activation of the high osmolarity glycerol MAPK pathway in yeast is used as an example. The specific advantages of each method are highlighted. Flow cytometry measures a large number of cells (10,000) and provides a direct measure of the dynamics of protein expression independent of the slow maturation kinetics of the fluorescent protein. Imaging of living cells by microscopy is by contrast limited to the measurement of the matured form of the reporter in fewer cells. However, the data sets generated by this technique can be extremely rich thanks to the combinations of multiple reporters and to the spatial and temporal information obtained from individual cells. The combination of these two measurement methods can deliver new insights on the regulation of protein expression by signaling pathways.

Two complementary methods based on flow cytometry and microscopy are presented which enable the quantification, at the single cell level, of the dynamics of gene expression induced by the activation of a MAPK pathway in yeast.

The quantification of gene expression at the  single cell level uncovers novel regulatory mechanisms obscured in measurements  performed at the population ...

Imaging Spatial Reorganization of a MAPK Signaling Pathway Using the Tobacco Transient Expression System

Visualization of dynamic signaling events in live cells has been a challenge. We expanded an established transient expression system, the biomolecular fluorescent complementation (BiFC) assay in tobacco epidermal cells, from testing protein-protein interaction to monitoring spatial distribution of signal transduction in plant cells. In this protocol, we used the BiFC assay to show that the interaction and the signaling between the Arabidopsis MAPKKK YODA and MAPK6 occur at the plasma membrane. When the scaffold protein BASL was co-expressed, the YODA-MAPK interaction redistributed and spatially co-polarized with CFP-BASL. This modified tobacco expression system allows for quick examination of signaling localization and dynamic changes (less than 4 days) and can accommodate multiple of fluorescent protein colors (at least three). We also presented detailed methods to quantify protein distribution (asymmetric spatial localization, or &#34;polarization&#34;) in tobacco cells. This advanced tobacco expression system has a potential to be used widely for quick testing of dynamic signaling events in live plant cells.

At the subcellular level, signaling events are dynamically modulated by developmental and environmental cues. Here we describe a protocol that employs the tobacco transient expression system to monitor dynamic protein-protein interaction and to disclose spatial organization of signal transduction in plant cells.

Visualization of dynamic signaling events in live cells has been a challenge. We expanded an established transient expression system, the biomolecular ...

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying cell composition, discrimination of functional states, and molecular pathways underlying observed tissue functions and animal behaviors. However, the isolation of intact, healthy single cells from adult mammalian tissues for subsequent downstream single cell molecular analysis can be challenging. This protocol describes the general processes and quality control checks necessary to obtain high-quality adult single cell preparations from the nervous system or skin that enabled subsequent unbiased single cell RNA sequencing and analysis. Guidelines for downstream bioinformatic analysis are also provided.

This protocol describes the general processes and quality control checks necessary for preparing healthy adult mammalian single cells for droplet-based, high throughput single cell RNA-Seq preparations. Sequencing parameters, read alignment, and downstream single-cell bioinformatic analysis are also provided.

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying ...

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify transcriptional regulatory networks that govern developmental processes it is necessary to measure the spatial and temporal gene expression profiles of these individual cell types. Therefore, insight into the spatio-temporal control of gene expression is essential to gain understanding of how biological and developmental processes are regulated. Here, we describe a laser-capture microdissection (LCM) method to isolate small number of cells from three barley embryo organs over a time-course during germination followed by transcript profiling. The method consists of tissue fixation, tissue processing, paraffin embedding, sectioning, LCM and RNA extraction followed by real-time PCR or RNA-seq. This method has enabled us to obtain spatial and temporal profiles of seed organ transcriptomes from varying numbers of cells (tens to hundreds), providing much greater tissue-specificity than typical bulk-tissue analyses. From these data we were able to define and compare transcriptional regulatory networks as well as predict candidate regulatory transcription factors for individual tissues. The method should be applicable to other plant tissues with minimal optimization.

Presented here is a protocol for laser-capture microdissection (LCM) of plant tissues. LCM is a microscopic technique for isolating areas of tissue in a contamination-free manner. The procedure includes tissue fixation, paraffin embedding, sectioning, LCM and RNA extraction. RNA is used in the downstream tissue-specific, temporally resolved analysis of transcriptomes.

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Summary

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