The authors acknowledge Thomas Wu for processing NGS files. We thank James Ziai for the results discussions and manuscript review and Meredith Triplet and Rachel Taylor for internal manuscript revision.

All human tissues were acquired from commercial biobanks or accredited tissue banks under warranty that appropriate Institutional Review Board approval and informed consent were obtained.
NOTE: The protocol is performed using the Discovery Ultra and the GeoMx Digital Spatial Profiler. See the Table of Materials for details about reagents, equipment, and software used in this protocol.
1. Automated visualization protocol for spatial proteomics assays
<ol>
	<li>Program autostainer to apply fluorescent visualization antibodies
	<ol>
		<li>In the autostainer software, click on the home button and choose Protocols. Then, click on Create/Edit Protocols and select RUO DISCOVERY Universal as the procedure.</li>
		<li>Click on First Sequence &#62; Deparaffinization &#62; Depar v2. For Medium Temperature, choose 72 Deg C, then click on Pretreatment and select Cell Conditioning and CC1 Reservoir. For Very High Temperature, choose 100 Deg C, then click on CC1 8 Min and continue clicking until CC1 64 Min is selected.</li>
		<li>Click on Inhibitor &#62; DISCOVERY Inhibitor, and for Incubation Time, choose 8 Minutes. Then, click on Antibody &#62; High Temp Ab Incubation; for Low Temperature, choose 37 Deg C; for Antibody, choose the antibody number from the dispenser label (Antibody 6 in this example); for Plus Incubation Time, select 32 Minutes. 
		NOTE: This protocol has three different antibody numbers. The first antibody is FAP, the second is Pan-Cytokeratin, and the third is Antibody X.</li>
		<li>Click on Multimer HRP &#62; Multimer HRP Blocker, then for Antibody Blocking, choose Gt Ig Block, and for Incubation Time, choose 4 Minutes. Next, on Multimer HRP Reagent, select OMap anti-Rb HRP, and for Incubation Time, choose 16 Minutes. Then, click on Cy5, and for Long Incubation Time, choose 0 Hr 8 Min.</li>
		<li>Click on Dual Sequence and choose Antibody Denaturation, then select Antibody Denature CC2-1, and for Very High Temperature, choose 100 Deg C. Ensure that incubation time is 8 min. Next, click on DS Inhibitor and select Neutralize.</li>
		<li>Click on DS Antibody, and for Very Low Temperature, choose 37 Deg C. Then, in Antibody, choose the antibody number from the dispenser label (Antibody 3 in this example), and for Plus Incubation Time, select 32 Minutes.</li>
		<li>Click on DS Multimer HRP and choose DS Multimer HRP Blocker. Then, for Antibody Blocking, select Gt Ig Block, and for Incubation Time, choose 4 Minutes. Next, on Multimer HRP Reagent, select OMap anti-Ms HRP, and for Incubation Time, choose 16 Minutes. Then, click on DS Rhodamine 6G, and for Long Incubation Time, choose 0 Hr 8 Min.</li>
		<li>Click on Triple Stain and select TS Antibody Denaturation, then select Antibody Denature CC2-2, and for Very High Temperature, select 100 Deg C. Ensure that incubation time is 8 min. Next, click on TS Inhibitor and select TS Neutralize.</li>
		<li>Click on TS Antibody, and for Very Low Temperature, select 37 Deg C. Then, in Antibody, select the antibody number from the dispenser label (Antibody 7 in this example), and for Plus Incubation Time, select 32 Minutes.</li>
		<li>Click on TS Multimer HRP and choose TS Multimer HRP Blocker. Then, for Antibody Blocking, select Gt Ig Block, and for Incubation Time, choose 4 Minutes. Next, on Multimer HRP Reagent, select OMap anti-Rb HRP, and for Incubation Time, choose 16 Minutes. Then, click on TS FAM, and for Long Incubation Time, choose 0 Hr 8 Min.</li>
		<li>Add a title to the protocol. Select a protocol number, add a comment, and click on Active followed by Save.</li>
	</ol>
	</li>
	<li>Prepare slides, print labels, and start the autostainer run
	<ol>
		<li>Bake vendor procured FFPE (Formalin-fixed, paraffin-embedded) human tissue sections in an oven set to 70 &#176;C for 20-60 min. While slides are baking, print labels by clicking Create Label in the autostainer software. Next, click on Protocols, select the protocol number, and click on Close/Print. Add relevant information on the slide label and click on Print.</li>
		<li>Remove slides from the oven and let them cool down to room temperature (RT). Apply the previously printed protocol labels to the corresponding slides.</li>
		<li>Load refillable antibody dispensers with antibodies according to the antibody label number assigned in step 1.1. In this protocol, use Antibody 6 for FAP at 1 &#181;g/mL, use Antibody 3 for Pan-Cytokeratin at 0.1 &#181;g/mL, and use Antibody 7 for Antibody X at 2.5 &#181;g/mL. Dilute each antibody in the specified diluent and prime the refillable antibody dispensers.</li>
		<li>Gather blocking, detection, and amplification pre-filled reagent dispensers mentioned above and place them on a reagent tray. Load slides in the slide trays (up to 30 slides per run) and click on Running followed by Yes. Note that DAPI is not used for nuclear counterstaining.</li>
		<li>Confirm the start of the run by checking the run duration on the autostainer software. The protocol takes ~11 h for completion and can run overnight.</li>
		<li>The next day, ensure run completion by observing green flashing lights on the slide tray slots. Then, take the slides off the instrument and rinse the slides vigorously in 1x Reaction Buffer until the liquid coverslip solution is completely removed.</li>
	</ol>
	</li>
	<li>Spatial profiling platform protocol for proteomics assay
	<ol>
		<li>Follow the spatial proteomics protocol indicated in the note below. Include the following changes to the protocol to enable the 3-plex automated labeling procedure.</li>
		<li>Perform steps 1.2.1-1.2.6&#160;the day before starting the spatial proteomics protocol and move straight to the antigen retrieval step of the spatial proteomics protocol after performing step 1.2.6. Omit the visualization procedure outlined in the spatial profiling proteomics protocol.</li>
		<li>Replace SYTO 13 with SYTO 64 at 5000 nM to enable fluorophore integration. Dilute SYTO 64 in 1x TBS and incubate in a humidity chamber for 15 min.</li>
		<li>When setting up the scan parameters in the spatial profiling platform, select the filters and focusing channels according to the fluorophores used in the visualization panel to allow 3-plex integration. Use FITC for DISCOVERY FAM and set the exposure to 200 ms, use Cy3 for DISCOVERY Rhodamine 6G and set the exposure to 200 ms, use Texas Red for SYTO 64 and set the exposure to 50 ms (specify this as the focus channel), and finally, use Cy5 for DISCOVERY Cy5, set the exposure to 200 ms, and save changes. 
		NOTE: Detailed instructions of the spatial profiling proteomics protocol are found on the official website Support tab by selecting Documentation &#62; User Manuals. Here, look for the protein protocol for nCounter using Bond RX.</li>
	</ol>
	</li>
</ol>
2. ROI selection for spatial transcriptomics assays
<ol>
	<li>Bake FFPE cell pellet sections in an oven set to 70 &#176;C for 20-60 min. Follow the spatial profiling platform NGS protocol indicated in the note below and scan slides on the spatial profiling platform, selecting sizes of 50 &#181;m and 300 &#181;m. The following recommendation is highlighted for the spatial transcriptomics assay:
	<ol>
		<li>When preparing the library, if various sizes of ROIs were selected, pool together ROIs of similar sizes to make one library per size. This is to ensure that sufficient sequencing depths are achieved for all ROIs without bias from size. 
		NOTE: Detailed instructions of the spatial profiling platform are found on the official website Support tab by selecting Documentation &#62; User Manuals. Here, look for the RNA protocol for NGS applications using Bond RX.</li>
	</ol>
	</li>
</ol>

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Veronica Ibarra-Lopez, Sangeeta Jayakar, Yeqing Angela Yang, Zora Modrusan, and Sandra Rost&#160;are employees and stockholders of Genentech, a member of the Roche Group. Other companies that are part of Roche produce reagents and instruments used in this manuscript. Ciara Martin is a full-time employee of NanoString Technologies Inc, which produces reagents and instruments used in this manuscript.

To date, directly conjugated fluorescent antibodies in a manual protocol are most commonly used as visualization panels for spatial proteomics or spatial transcriptomics assays9,10. However, the use of directly conjugated fluorescent antibodies can be challenging for less abundant markers, limiting the selection of suitable antibodies. This protocol shows that labeling of visualization markers can be automated on an automated staining platform using TSA technologies to support spatial proteomics assays. This allows for more flexibility in the customization of visualization panels, which enables a more targeted ROI selection and acquisition of relevant biological data from specific areas of a tissue. TSA technology also allows the visualization of low abundance markers, and thereby increases the number of potential suitable antibodies. Additionally, automation of the visualization protocol ensures reproducibility and quality of the labeling procedure. 
Developing a 3-plex IF assay requires careful assessment of marker/fluorophore signal intensities on appropriate control tissues. It is recommended to titrate antibody concentrations to obtain the best signal-to-noise ratio, which will help to minimize bleedthrough into adjacent channels. Furthermore, epitope stability for each marker of interest should be tested to ensure that the epitope is not affected by repeated elution steps. Once the order of antibodies in the panel is confirmed, the assay needs to be fine-tuned using leave one out controls. Here, a specific marker is omitted in the panel, and the corresponding channel in the acquired image is reviewed for bleedthrough and cross-talk. 
It was shown here that TSA-based assays can be imaged on the spatial profiling platform, and that DAPI alternatives need to be used for nuclear staining. We tested SYTO dyes and determined that when deciding on which SYTO dye to use, the signal needs to be carefully assessed to avoid bleedthrough into adjacent channels. Another limitation of the use of SYTO dyes is that some SYTO dyes photobleach quicker, such as SYTO 64 and SYTO 83 (internal data), which could affect nuclear quantification. Therefore, light exposure of the stained slides should be avoided as much as possible. Other groups have shown the use of SYTO 1311,12, which is more photostable (internal data) and could be used in a panel as long as the markers of interest are assigned to fluorophores that are compatible with the nuclear dye of choice.
While automated TSA based fluorescent assays worked well for visualization in combination with spatial proteomics assays, it was determined here that combining a 3-plex automated visualization protocol with a spatial transcriptomics protocol resulted in loss of dynamic range, which could be due to the harsher protocol conditions of adding extra antigen retrieval/elution steps that might affect RNA integrity. Therefore, the 3-plex automated visualization protocol should only be adapted for proteomics assays, which is a limitation and requires alternative visualization approaches for RNA-based assays. As an alternative to same-slide visualization, serial sections stained with markers of interest could be overlaid to facilitate ROI selection13. In addition, the literature describes the use of in situ hybridization on adjacent slides to guide ROI selection14. However, these approaches could be limited by tissue availability, section-to-section variability, and cell-to-cell registrations.
We also tested the usability of different ROI sizes for spatial transcriptomics assays. Using small ROIs of 50 &#181;m enables the researcher to acquire data of specific regions allowing a more detailed readout of selected cells or areas of interest. Limitations of selecting small ROIs are that the number of cells present might not be abundant enough to get count values that can be distinguished from the background. One option is to select pure cell populations to capture less abundant targets. However, ROI selection for single cells is limited by high signal-to-noise ratio15. Furthermore, it is important to consider ROI sizes for library preparation if different sized ROIs are collected in a single experiment. If ROI sizes differ significantly, it may be beneficial to pool ROIs according to their sizes and construct separate libraries to prevent large ROIs from being over-represented in one library. This will also ensure that each ROI will get sufficient sequencing coverage according to its size.
Other factors to consider when performing Spatial Omics experiments are that the data could show variations due to slide-to-slide variability, and lower expression of certain targets can bring up background and differences in correlation values. Adequate controls and normalization approaches need to be considered when planning experiments and evaluating data. For spatial proteomics assays on nCounter, normalizing to housekeeping targets or to the negative control IgGs are the preferred methods according to internal experience and vendor recommendations where the correlation between the housekeeping targets or to the negative control IgGs needs to be evaluated16. However, depending on the tissues used, the best normalization needs to be defined for every study17,18. For example, literature states that for breast cancer Spatial Omics studies, GAPDH, which is commonly used for normalizing to housekeeping targets, has been less concordant than S6 and Histone19. Therefore, S6 and Histone have been the recommended housekeeping targets for normalization in breast cancer tissues19. 
For spatial transcriptomics assays, normalization strategies need to be defined depending on the study design (e.g., tissue types and ROI selection). Some strategies that are used in RNA-Seq, such as upperquartile or trimmed mean of M values (TMM) normalization, could be applied to normalize sequenced data19,20. 
Spatial transcriptomics assays provide information on thousands of targets and are becoming more commonly used to explore the whole transcriptome, where specific TME compartments are selected and analyzed in conjunction with single-nucleus RNA-seq20 or single-cell RNA sequencing21. We demonstrated that visualization protocols to guide ROI selection to assess the spatial distribution of targets could be automated for spatial proteomics assays but not for spatial transcriptomics assays. Additionally, we present that ROIs as small as 50 &#181;m can be selected to allow a more in-depth investigation of a particular tissue region. Future applications of this technology will improve understanding of the tumor microenvironment.

Advances in multiplexing techniques continue to provide better characterization tools for targets present in tumors. The tumor microenvironment (TME) is a complex system of tumor cells, infiltrating immune cells, and stroma, where spatial information is critical to better understand and interpret mechanisms of interaction between biomarkers of interest1. With emerging techniques such as the GeoMx Digital Spatial Profiler (DSP) and 10x Visium, multiple targets can be detected and quantified simultaneously within their spatial context. The use of immunofluorescence protocols that facilitate tissue visualization can further improve the spatial profiling capabilities of these technologies.
The Spatial Omics technology we focused on for this method development consists of spatial proteomics and transcriptomics assays where oligonucleotides are attached to antibodies or RNA probes via a UV-sensitive photocleavable linker. Histological slides are labeled with these oligo-conjugated antibodies or probes and then imaged on the spatial profiling platform. Next, ROIs of different sizes and shapes are selected for illumination, and the photocleaved oligonucleotides are aspirated and collected in a 96-well plate. The photocleaved oligonucleotides are prepared to be quantified with either the Nanostring nCounter system or Next Generation Sequencing (NGS)2,3 (Figure 1)4,5.
Cell distributions vary within tissues, and the ability to characterize specific locations of cells using selected markers and different ROI sizes is of great importance to fully understand the tissue environment and identify specific features. In the Spatial Omics technology mentioned here, the standard visualization protocol uses directly conjugated antibodies and is a manual protocol. The standard markers to distinguish between tumor and stroma are panCytokeratin (panCK) and CD456,7, but additional markers are necessary to target specific cell populations of interest. Furthermore, the use of directly conjugated fluorescent antibodies lacks amplification, which limits antibody selection to abundant markers. Additionally, manual assays are subject to more variability than automated workflows8. Therefore, it is desirable to have a customizable, automated, and amplified visualization protocol for ROI selection.
Here, the study demonstrates that, for spatial proteomics assays, TSA technology can be used for visualization protocols on an automated platform resulting in a more targeted and standardized assay. In addition, TSA based assays enable the use of low-expressing markers, increasing the range of targets that can be selected for visualization. A 3-plex assay for panCK, FAP, and Antibody X was developed using an automated platform where panCK and FAP were used to differentiate between tumor and stroma, respectively. Antibody X is a stromal protein frequently encountered in tumors, but its biology and impact on anti-tumor immunity are not fully understood. Characterizing the immune contexture in areas rich in Antibody X can elucidate its role in anti-tumor immunity and therapeutic response, as well as its potential as a drug target.
While customized automated TSA visualization panels proved to be successful for spatial proteomics assays, the application of these assays for spatial transcriptomics assays could not be confirmed. This is most likely due to the reagents and the protocol used for the automated visualization protocols, which seem to compromise RNA integrity. Recognizing that an automated labeling protocol for visualization markers can be used for spatial proteomics assays but not for spatial transcriptomics assays provides important guidance on Spatial Omics technology assay designs.
Additionally, the study demonstrates that the spatial transcriptomics assay can be used to profile targets in regions as small as 50 &#181;m in diameter, or approximately 15 cells. Two different-sized ROIs were selected to test the ability of the assay to also detect transcripts in small ROIs. For each region of interest, oligos corresponding to 1,800 mRNA targets were collected and made into libraries according to the spatial profiling platform protocol. Libraries were individually indexed, subsequently pooled, and sequenced. This allowed the evaluation of both pooling efficiency and the capability of identifying specific cell populations in small ROIs.
This paper shows that for spatial proteomics assays, an automated protocol to guide ROI selection on specific markers of interest can be used to selectively target the interrogation of relevant tissue areas and characterize the spatial environment of the tissue. Furthermore, we demonstrate that smaller ROIs can be used for spatial transcriptomic assays to detect and characterize specific cell populations.

<table><tbody><tr><td>10x Tris buffered saline (TBS)</td><td>Cell Signaling Technologies</td><td>12498S</td><td>Diluted to 1x TBS in DEPC treated water</td></tr><tr><td>Antibody X (not disclosed)</td><td></td><td></td><td>antibody blinded due to confidentiality</td></tr><tr><td>DEPC-treated water</td><td>ThermoFisher</td><td>AM9922</td><td>Another can be used</td></tr><tr><td>DISCOVERY Cell Conditioning ( CC1)</td><td>Ventana</td><td>950-500</td><td></td></tr><tr><td>DISCOVERY Cy5 Kit</td><td>Ventana</td><td>760-238</td><td>Referred as Cy5</td></tr><tr><td>DISCOVERY FAM Kit</td><td>Ventana</td><td>760-243</td><td>Referred as FAM</td></tr><tr><td>DISCOVERY Goat Ig Block</td><td>Ventana</td><td>760-6008</td><td>Referred as Gt Ig Block</td></tr><tr><td>DISCOVERY OmniMap anti-Ms HRP</td><td>Ventana</td><td>760-4310</td><td>Referred as OMap anti-Ms HRP</td></tr><tr><td>DISCOVERY OmniMap anti-Rb HRP</td><td>Ventana</td><td>760-4311</td><td>Referred as OMap anti-Rb HRP</td></tr><tr><td>DISCOVERY Rhodamine 6G Kit</td><td>Ventana</td><td>760-244</td><td>Referred as Rhodamine 6G</td></tr><tr><td>DISCOVERY ULTRA Automated Slide Preparation System</td><td>Ventana</td><td>05 987 750 001 / N750-DISU-FS</td><td>Referred as autostainer on the manuscript</td></tr><tr><td>FAP [EPR20021] Antibody</td><td>Abcam</td><td>Ab207178</td><td></td></tr><tr><td>GeoMx Digital Spatial Profiler</td><td>NanoString</td><td>GMX-DSP-1Y</td><td>Referred as spatial profiling platform on the manuscript</td></tr><tr><td>Humidity chamber</td><td>Simport</td><td>M920-2</td><td>Another can be used</td></tr><tr><td>Pan-Cytokeratin [AE1/AE3] Antibody</td><td>Abcam</td><td>Ab27988</td><td></td></tr><tr><td>ProLong Gold Antifade Mountant</td><td>ThermoFisher</td><td>P36934</td><td></td></tr><tr><td>Python</td><td>Python</td><td></td><td>Statistical analysis</td></tr><tr><td>Reaction Buffer (10x)</td><td>Ventana</td><td>950-300</td><td></td></tr><tr><td>Statistical analysis software</td><td>GraphPad</td><td>Prism 7</td><td>Statistical analysis</td></tr><tr><td>SYTO 64</td><td>ThermoFisher</td><td>S11346</td><td></td></tr><tr><td>ULTRA Cell Conditioning (ULTRA CC2)</td><td>Ventana</td><td>950-223</td><td></td></tr><tr><td>Ventana Antibody Diluent with Casein</td><td>Ventana</td><td>760-219</td><td>Referred as specified diluent on the manuscript</td></tr><tr><td>Ventana Primary antibody dispenser</td><td>Ventana</td><td></td><td>Catalog number depends on dispenser number</td></tr></tbody></table>

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.

Automated visualization protocol to guide ROI selection 
In this paper, we present the use of an automated, custom TSA-based IF protocol to visualize the tissue and select specific ROIs. Visualization panel development using melanoma and human normal skin as control tissues consisted of epitope stability testing, fine-tuning of marker intensities, and bleedthrough assessment through leave one out controls. To test if epitope stability of the antibodies is affected by repetitive elution steps, FAP and Antibody X signals were qualitatively assessed after labeling in each position of the 3-plex. Antibody X signal remained consistent in all positions, while FAP signal intensity decreased after one elution (Figure 2). Previous internal studies showed that the panCK epitope is stable throughout a 3-plex assay. Therefore, FAP was chosen to be in the 1st position in the 3-plex, panCK was placed in the 2nd, and Antibody X in the 3rd position. Since FAP signal was weaker overall compared to Antibody X and panCK, FAP was paired with the brighter fluorophore, Cy5.
The automated 3-plex was optimized with leave one out controls, where each marker was omitted from the 3-plex protocol once to confirm that there was no bleedthrough into neighboring channels (Figure 3).
The fluorophores used on the automated staining platform were tested on the spatial profiling platform to confirm that the filters are compatible with the TSA fluorophores in the 3-plex visualization panel. Since the 3-plex protocol uses FITC, Cy3, and Cy5, and the DAPI channel is used for UV cleavage on the spatial profiling platform, a nuclear counterstain in the Texas Red channel had to be used. Two different nuclear dyes, SYTO 59 and SYTO 64, were tested, and the dyes showed a distinct nuclear signal in colon tissues. However, SYTO 59 also displayed some bleedthrough signal in the Cy5 channel while the Cy5 channel was clean when using SYTO 64 (Figure 4). Therefore, SYTO 64 was the preferred choice for nuclear detection.
Further testing of SYTO 64 showed that this dye was not photostable and photobleached quickly during imaging. Once the signal is bleached, the imaging system focuses on the background, which could be interpreted as a non-specific signal (Figure 5). Increasing the concentration of the nuclear dye to increase signal intensity helped minimize this issue.
Tissues labeled with the panCK/CD45 manual visualization protocol, which uses directly conjugated antibodies, were compared to tissues labeled with the panCK/FAP/Antibody X automated custom visualization protocol. Both protocols share panCK as a common marker, and the signal was comparable between the two protocols (Figure 6).
To ensure that a modified visualization protocol has no impact on the upstream spatial proteomics assay, we compared the count values obtained after Spatial Profiling Platform collection and Counting Platform processing when using the panCK/CD45 manual visualization protocol. This uses directly conjugated antibodies or the 3-plex automated custom visualization protocol, which uses TSA. Four colorectal cancer (CRC) tissues were labeled with the panCK/CD45 manual visualization protocol or the 3-plex automated custom visualization protocol in combination with the spatial proteomics assay. Similar ROIs were chosen for each protocol, and count data of the corresponding ROIs were compared using housekeeping normalization for S6 and Histone. Similar trends were observed between these ROIs as shown in the log2 heatmap (Figure 7A), with a Spearman R value of 0.88 for all markers included in the spatial proteomics assay (Figure 7B). Of the 31 antibodies used in the assays, 23 antibodies had a Spearman R value higher than or equal to 0.5.
As spatial proteomics and transcriptomic protocols differ, we evaluated if the automated visualization protocol could also be applied to spatial transcriptomics assays. The automated visualization protocol requires the IHC assay to be performed before RNA detection, whereas the original manual protocol detects RNA first before applying the visualization protocol. The 3-plex automated visualization protocol was tested side by side with the CK/CD45 manual visualization protocol in combination with the spatial transcriptomics assay. Sequencing data for the 3-plex automated visualization protocol Quartile 3 count (Q3), normalized on the spatial profiling software, presented lower values when compared to the CK/CD45 manual visualization protocol (Figure 8A), which is also reflected by the low Spearman R values of 0.15 (Figure 8B). Also, a loss of dynamic range was observed when using the 3-plex visualization automated protocol (Figure 8C).
This loss of counts with the automated visualization protocol indicates that this protocol compromises RNA integrity, and that detecting the RNA prior to performing the visualization assay is necessary to conserve RNA quality. Since this automated visualization panel requires the protein part to be performed prior to RNA detection, this protocol is not suitable for RNA assays.
Size of ROI selection in spatial transcriptomics assays 
To better understand the impact of ROI size on RNA data output, RNA counts of different sized ROIs were compared. The spatial transcriptomics assay was performed on SUDHL1 and JURKAT cell lines to minimize differences inherent to tissues. Circular ROIs of 50 &#181;m and 300 &#181;m diameter were selected and compared to each other using Q3 normalization on the spatial profiling software. RNA counts for selected targets on different cell lines were comparable between the two ROI sizes after normalization (Figure 9A,B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62651/62651fig01.jpg" /> 
Figure 1: Digital Spatial Profiling workflow. (A) Slides are labeled with antibodies (spatial proteomics assay) or probes (spatial transcriptomics assay) as well as visualization markers. (B) Slides are placed on the spatial profiling platform to image and select ROIs. (C) Oligos are cleaved by UV light and collected in a 96-well plate. This process is repeated for every ROI selected. (D) The samples are processed, and data is generated using the counting platform or a sequencer. This figure has been modified from Nanostring technologies website4,5. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig01large.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/62651/62651fig02.jpg" /> 
Figure 2: Epitope stability. (A-C) Antibody X and (D-F) FAP tested on human normal skin and melanoma, respectively. Each marker in the (A,D) 1st, (B,E) 2nd, and (C,F) 3rd position of a 3-plex. Antibody X is stable in all positions, while (D) FAP shows decreasing signaling intensity after (E-F) one and two elutions, respectively. Antibody X in green, FAP in red, DAPI in grey. Scale bars are at 200 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig02large.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/62651/62651fig03.jpg" /> 
Figure 3: Leave one out controls for the 3-plex automated custom visualization protocol on human pancreas tissues. For all images, Antibody X is depicted in green, panCK in cyan, and FAP in red. The first row shows the (A) 3-plex and the single channels for (B) Antibody X in FITC, (C) panCK in Cy3, and (D) FAP in Cy5. The second row shows the leave one out control 3-plex for (E) Antibody X and (F-H) the single channels for each marker. The third row shows the leave one out control 3-plex for (I) panCK and (J-L) the single channels for each marker. The fourth row shows the leave one out control 3-plex for (M) FAP and (N-P) the single channels for each marker. No bleedthrough was observed in any of the leave one out controls as indicated by lack of signal in the corresponding channels when tested (F) without FAP, (K) without panCK, and (P) without Antibody X. Scale bars are at 500 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig03large.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/62651/62651fig04.jpg" /> 
Figure 4: SYTO dye testing. Texas Red channel displays the nuclear signal in Colon for (A) SYTO 59 with bleedthrough into (B) Cy5 channel (red). Texas Red channel shows the nuclear signal in Colon for (D) SYTO 64 without bleedthrough into (E) Cy5 channel. Texas Red and Cy5 channels in Colon displayed together to show (C) bleedthrough of SYTO 59 and (F) no bleedthrough for SYTO 64. Scale bars are at 50 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig04large.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/62651/62651fig05.jpg" /> 
Figure 5: SYTO 64 photostability. Colon samples labeled with SYTO 64 (A) before and (B) after photobleaching. Image B shows the non-specific signal that becomes more visible after nuclear signal loss. Scale bars are at 50 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62651/62651fig06.jpg" /> 
Figure 6: Comparison of panCK labeling. panCK signal on colorectal cancer (CRC) samples with (A) panCK/CD45 manual visualization protocol and (B) panCK/FAP/Antibody X automated visualization protocol. No differences in panCK signal were observed between the two protocols. Scale bars are at 200 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/62651/62651fig07.jpg" /> 
Figure 7: Comparison of count values for the spatial proteomics assay using two different visualization protocols on similar ROIs of four CRC tissues. (A) Log 2&#160;Heatmap of all the markers included in the spatial proteomics assay comparing the manual panCK/CD45 protocol (black) to the automated 3-plex protocol (grey). (B) Log 2 Spearman correlation for all the markers presents an R value of 0.88. Statistical analysis was performed using GraphPad Prism 7 Software or with Python programming language. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/62651/62651fig08.jpg" /> 
Figure 8: Comparison of count values for the spatial transcriptomics assay using two different visualization protocols. (A) Log 2&#160;Heatmap of selected targets included in the spatial transcriptomics assay comparing the manual panCK/CD45 protocol (black) to the 3-plex automated protocol (grey). (B) Log 2 Spearman correlation for all the markers presents an R value of 0.15. (C) The mean (blue) and standard deviation where the dynamic range is lost in the 3-plex automated protocol. Statistical analysis was performed using GraphPad Prism 7 Software or with Python programming language. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/62651/62651fig09.jpg" /> 
Figure 9: Comparison of RNA count values for the spatial transcriptomics assay on cell pellets. Comparable results were observed for 50 &#181;m (black) and 300 &#181;m (grey) diameter ROIs for selected targets on (A) SUDHL1 and (B) JURKAT cell lines. No significant difference was observed using t-test. Mean and standard deviation shown for n = 3 ROIs per size. Statistical analysis was performed using GraphPad Prism 7 Software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62651/62651fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection at JoVE.com

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

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 ...

spatial-profiling-protein-rna-expression-tissue-an-approach-to-fine

Nanyang Technological University

Research

JoVE Journal

Cancer Research

4.8K Views.  Genentech. Dit protocol helpt bij het begeleiden van de selectie van regio's die van belang zijn voor ruimtelijke omics-technologieën, waardoor een meer gerichte karakterisering van weefsels of celpopulaties mogelijk is. Een geautomatiseerd protocol voor ROI-selectie in proteomics-assays is robuuster en reproduceerbaarder dan handmatige protocollen, en het gebruik van 50 micrometer ROIs voor transcriptomics-assays maakt profilering van specifieke celpopulaties mogelijk. Begin met het programmeren van d...

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

Tissue-specific miRNA Expression Profiling in Mouse Heart Sections Using In Situ Hybridization

micro-RNAs (miRNAs) are single-stranded RNA transcripts that bind to messenger RNAs (mRNAs) and inhibit their translation or promote their degradation. To date, miRNAs have been implicated in a large number of biological and disease processes, which has signified the need for the reliable detection methods of miRNA transcripts. Here, we describe a detailed protocol for digoxigenin-labeled (DIG) Locked Nucleic Acid (LNA) probe-based miRNA detection, combined with protein immunostaining on mouse heart sections. First, we performed an in situ hybridization technique using the probe to identify miRNA-182 expression in heart sections from control and cardiac hypertrophy mice. Next, we performed immunostaining for cardiac Troponin T (cTnT) protein, on the same sections, to co-localize miRNA-182 with the cardiomyocyte cells. Using this protocol, we were able to detect miRNA-182 through an alkaline phosphatase based colorimetric assay, and cTnT through fluorescent staining. This protocol can be used to detect the expression of any miRNA of interest through DIG-labeled LNA probes, and relevant protein expression on mouse heart tissue sections.

Tissue-specific miRNA Expression Profiling in Mouse Heart Sections Using In Situ Hybridization

micro-RNAs (miRNAs) are short and highly homologous RNA sequences, serving as post-transcriptional regulators of messenger RNAs (mRNAs). Current miRNA detection methods vary in sensitivity and specificity. We describe a protocol that combines in situ hybridization and immunostaining for concurrent detection of miRNA and protein molecules on mouse heart tissue sections.

micro-RNAs (miRNAs) are single-stranded RNA transcripts that bind to messenger RNAs (mRNAs) and inhibit their translation or promote their degradation. To ...

Genetics

Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling

As technological platforms, approaches such as next-generation sequencing, microarray, and qRT-PCR have great promise for expanding our understanding of the breadth of molecular regulation. Newer approaches such as high-resolution RNA sequencing (RNA-Seq)1 provides new and expansive information about tissue- or state-specific expression such as relative transcript levels, alternative splicing, and micro RNAs2-4. Prospects for employing the RNA-Seq method in comparative whole transcriptome profiling5 within discrete tissues or between phenotypically distinct groups of individuals affords new avenues for elucidating molecular mechanisms involved in both normal and abnormal physiological states. Recently, whole transcriptome profiling has been performed on human brain tissue, identifying gene expression differences associated with disease progression6. However, the use of next-generation sequencing has yet to be more widely integrated into mammalian studies.
Gene expression studies in mouse models have reported distinct profiles within various brain nuclei using laser capture microscopy (LCM) for sample excision7,8. While LCM affords sample collection with single-cell and discrete brain region precision, the relatively low total RNA yields from the LCM approach can be prohibitive to RNA-Seq and other profiling approaches in mouse brain tissues and may require sub-optimal sample amplification steps. Here, a protocol is presented for microdissection and total RNA extraction from discrete mouse brain regions. Set-diameter tissue corers are used to isolate 13 tissues from 750-&mu;m serial coronal sections of an individual mouse brain. Tissue micropunch samples are immediately frozen and archived. Total RNA is obtained from the samples using magnetic bead-enabled total RNA isolation technology. Resulting RNA samples have adequate yield and quality for use in downstream expression profiling. This microdissection strategy provides a viable option to existing sample collection strategies for obtaining total RNA from discrete brain regions, opening possibilities for new gene expression discoveries.

Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling  |

RNA expression profiling of discrete mouse brain regions requires a precise and repeatable tissue collection strategy. A protocol that uses both coronal brain sectioning and tissue corer-assisted microdissection is described here. The yield and quality of total RNA obtained from the resulting samples confirms the utility of the outlined method.

As technological platforms, approaches such as next-generation sequencing, microarray, and qRT-PCR have great promise for expanding our understanding of ...

Neuroscience

Laser Capture Microdissection of Mouse Embryonic Cartilage and Bone for Gene Expression Analysis

Laser capture microdissection (LCM) is a powerful tool to isolate specific cell types or regions of interest from heterogeneous tissues. The cellular and molecular complexity of skeletal elements increases with development. Tissue heterogeneity, such as at the interface of cartilaginous and osseous elements with each other or with surrounding tissues, is one obstacle to the study of developing cartilage and bone. Our protocol provides a rapid method of tissue processing and isolation of cartilage and bone that yields high quality RNA for gene expression analysis. Fresh frozen tissues of mouse embryos are sectioned and brief cresyl violet staining is used to visualize cartilage and bone with colors distinct from surrounding tissues. Slides are then rapidly dehydrated, and cartilage and bone are isolated subsequently by LCM. The minimization of exposure to aqueous solutions during this process maintains RNA integrity. Mouse Meckel&rsquo;s cartilage and mandibular bone at E16.5 were successfully collected and gene expression analysis showed differential expression of marker genes for osteoblasts, osteocytes, osteoclasts, and chondrocytes. High quality RNA was also isolated from a range of tissues and embryonic ages. This protocol details sample preparation for LCM including cryoembedding, sectioning, staining and dehydrating fresh frozen tissues, and precise isolation of cartilage and bone by LCM resulting in high quality RNA for transcriptomic analysis.

This protocol describes laser capture microdissection for the isolation of cartilage and bone from fresh frozen sections of the mouse embryo. Cartilage and bone can be rapidly visualized by cresyl violet staining and collected precisely to yield high quality RNA for transcriptomic analysis.

Laser capture microdissection (LCM) is a powerful tool to isolate specific cell types or regions of interest from heterogeneous tissues. The cellular and ...

Developmental Biology

Laser Microdissection Applied to Gene Expression Profiling of Subset of Cells from the Drosophila Wing Disc

Heterogeneous nature of tissues has proven to be a limiting factor in the amount of information that can be generated from biological samples, compromising downstream analyses. Considering the complex and dynamic cellular associations existing within many tissues, in order to recapitulate the in vivo interactions thorough molecular analysis one must be able to analyze specific cell populations within their native context. Laser-mediated microdissection can achieve this goal, allowing unambiguous identification and successful harvest of cells of interest under direct microscopic visualization while maintaining molecular integrity. We have applied this technology to analyse gene expression within defined areas of the developing Drosophila wing disc, which represents an advantageous model system to study growth control, cell differentiation and organogenesis. Larval imaginal discs are precociously subdivided into anterior and posterior, dorsal and ventral compartments by lineage restriction boundaries. Making use of the inducible GAL4-UAS binary expression system, each of these compartments can be specifically labelled in transgenic flies expressing an UAS-GFP transgene under the control of the appropriate GAL4-driver construct. In the transgenic discs, gene expression profiling of discrete subsets of cells can precisely be determined after laser-mediated microdissection, using the fluorescent GFP signal to guide laser cut. 
Among the variety of downstream applications, we focused on RNA transcript profiling after localised RNA interference (RNAi). With the advent of RNAi technology, GFP labelling can be coupled with localised knockdown of a given gene, allowing to determinate the transcriptional response of a discrete cell population to the specific gene silencing. To validate this approach, we dissected equivalent areas of the disc from the posterior (labelled by GFP expression), and the anterior (unlabelled) compartment upon regional silencing in the P compartment of an otherwise ubiquitously expressed gene. RNA was extracted from microdissected silenced and unsilenced areas and comparative gene expression profiling determined by quantitative real-time RT-PCR. We show that this method can effectively be applied for accurate transcriptomics of subsets of cells within the Drosophila imaginal discs. Indeed, while massive disc preparation as source of RNA generally assumes cell homogeneity, it is well known that transcriptional expression can vary greatly within these structures in consequence of positional information. Using localized fluorescent GFP signal to guide laser cut, more accurate transcriptional analyses can be performed and profitably applied to disparate applications, including transcript profiling of distinct cell lineages within their native context.

Laser Microdissection Applied to Gene Expression Profiling of Subset of Cells from the Drosophila Wing Disc

Laser microdissection was applied to analyse gene expression profiling in specific compartments of Drosophila wing disc subjected to localised RNAi in vivo. RNA extracted from equivalent areas of silenced and unsilenced compartments was analysed by quantitative RT-PCR to determine comparative gene expression profiling within the context of native tissue microecology.

Heterogeneous nature of tissues has proven to be a limiting factor in the amount of information that can be generated from biological samples, ...

Biology

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, diagnosis, prognosis and treatment. In recent years, microRNAs (miRNAs) have emerged as promising alternate biomarkers for prostate cancer diagnosis and prognosis. However, the development of miRNAs as effective biomarkers for prostate cancer heavily relies on their accurate detection in clinical tissues. miRNA analyses in prostate cancer clinical specimens is often challenging owing to tumor heterogeneity, sampling errors, stromal contamination etc. The goal of this article is to describe a simplified workflow for miRNA analyses in archived FFPE or fresh frozen prostate cancer clinical specimens using a combination of quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH). Within this workflow, we optimize the existing methodologies for miRNA extraction from FFPE and frozen prostate tissues and expression analyses by Taqman-probe based miRNA RT-PCR. In addition, we describe an optimized method for ISH analyses formiRNA detection in prostate tissues using locked nucleic acid (LNA)- based probes. Our optimized miRNA ISH protocol can be applied to prostate cancer tissue slides or prostate cancer tissue microarrays (TMA).

Here we describe a simplified protocol for microRNA (miRNA) expression analyses in archived Formalin-Fixed, Paraffin-Embedded (FFPE) or fresh frozen prostate cancer (PCa) clinical tissues employing quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH).

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, ...

Medicine

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types. Healthy prostate is comprised of fibromuscular stroma surrounding discrete epithelial-lined secretory lumens and a very small population of immune and neuroendocrine cells. In contrast, areas of prostate cancer have increased dysplastic luminal epithelium with greatly reduced or absent stromal population. Given the profound difference between stromal and epithelial cell types, it is imperative to separate the cell types for any type of downstream molecular analysis. Despite this knowledge, the bulk of gene expression studies compare benign prostate to cancer without micro-dissection, leading to stromal bias in the benign samples. Laser-capture micro-dissection (LCM) is an effective method to physically separate different cell types from a specimen section. The goal of this protocol is to show that RNA can be successfully isolated from LCM-collected human prostatic epithelium and used for downstream gene expression studies such as RT-qPCR and RNAseq.

The goal of this protocol is to use laser-capture micro-dissection as an effective method to isolate pure populations of cell types from heterogeneous prostate tissues for downstream RNA analysis.

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types.  Healthy prostate is comprised of ...

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 ...

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Endothelial cells (ECs) are crucial for vascular and whole-body function through their dynamic response to environmental cues. Elucidating the transcriptome and epigenome of ECs is paramount to understanding their roles in development, health, and disease, but is limited in the availability of isolated primary cells. Recent technologies have enabled the high-throughput profiling of EC transcriptome and epigenome, leading to the identification of previously unknown EC cell subpopulations and developmental trajectories. While EC cultures are a useful tool in the exploration of EC function and dysfunction, the culture conditions and multiple passages can introduce external variables that alter the properties of native EC, including morphology, epigenetic state, and gene expression program. To overcome this limitation, the present paper demonstrates a method of isolating human primary ECs from donor mesenteric arteries aiming to capture their native state. ECs in the intimal layer are dissociated mechanically and biochemically with the use of particular enzymes. The resultant cells can be directly used for bulk RNA or single-cell RNA-sequencing or plated for culture. In addition, a workflow is described for the preparation of human arterial tissue for spatial transcriptomics, specifically for a commercially available platform, although this method is also suitable for other spatial transcriptome profiling techniques. This methodology can be applied to different vessels collected from a variety of donors in health or disease states to gain insights into EC transcriptional and epigenetic regulation, a pivotal aspect of endothelial cell biology.

The protocol describes the isolation, culture, and profiling of endothelial cells from human mesenteric artery. Additionally, a method is provided to prepare human artery for spatial transcriptomics. Proteomics, transcriptomics, and functional assays can be performed on isolated cells. This protocol can be repurposed for any medium- or large-size artery.

Endothelial cells (ECs) are crucial for vascular and whole-body function through their dynamic response to environmental cues. Elucidating the ...

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Gene expression analysis of human kidney tissue is an important tool to understand homeostasis and disease pathophysiology. Increasing the resolution and depth of this technology and extending it to the level of cells within the tissue is needed. Although the use of single nuclear and single cell RNA sequencing has become widespread, the expression signatures of cells obtained from tissue dissociation do not maintain spatial context. Laser microdissection (LMD) based on specific fluorescent markers would allow the isolation of specific structures and cell groups of interest with known localization, thereby enabling the acquisition of spatially-anchored transcriptomic signatures in kidney tissue. We have optimized an LMD methodology, guided by a rapid fluorescence-based stain, to isolate five distinct compartments within the human kidney and conduct subsequent RNA sequencing from valuable human kidney tissue specimens. We also present quality control parameters to enable the assessment of adequacy of the collected specimens. The workflow outlined in this manuscript shows the feasibility of this approach to isolate sub-segmental transcriptomic signatures with high confidence. The methodological approach presented here may also be applied to other tissue types with substitution of relevant antibody markers.

We describe a protocol for laser microdissection of sub-segments of the human kidney, including the glomerulus, proximal tubule, thick ascending limb, collecting duct and interstitium. The RNA is then isolated from the obtained compartments and RNA sequencing is carried out to determine changes in the transcriptomic signature within each sub-segment.

Gene expression analysis of human kidney tissue is an important tool to understand homeostasis and disease pathophysiology. Increasing the resolution and ...

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.

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

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