RNA isolation from Peyer’s patch lymphocytes and mononuclear phagocytes to determine gene expression profiles using NanoString technology

Sampling and immune surveillance within gut-associated lymphoid tissues such as the intestinal Peyer’s patch (PP) occurs by an elegantly orchestrated effort that involves the epithelial barrier, B and T lymphocytes, and an extensive network of mononuclear phagocytes. Although we now understand more about the dynamics of antigen and microbial sampling within PPs, the gene expression changes that occur in individual cell subsets during sampling are not well characterized. This protocol describes the isolation of high-quality RNA from sorted PP, B and T-lymphocytes, and CD11c+ phagocytes for use with nCounter-NanoString technology. This method allows investigators to study gene expression changes within PPs in response to antigens, microbes, and oral vaccine delivery vehicles of interest that are sampled.


BACKGROUND
In addition to its absorptive functions, the small intestine is faced with a two-fold challenge; it must tolerate incoming antigens and microbes while serving as a site of immune surveillance [1]. This carefully orchestrated immune surveillance takes place within aggregate lymphoid structures called Peyer's patches (PPs) [1]. Selective transcytosis of luminal antigens, microbes, and oral vaccine delivery vehicles into PPs occurs through specialized enterocytes called microfold cells (M-cells) that are interspersed within the epithelial barrier called the follicle-associated epithelium (FAE) [2][3][4][5][6]. Below the FAE, in the sub-epithelial dome (SED), there is an extensive and dynamic network of dendritic cell (DCs) and macrophage subsets. A decade ago, Iwasaki and Kelsall identified three distinct (CD11c + ) DC subsets within PPs, including the lymphoid DCs (CD11b -CD8α + ) that reside in the interfollicular regions (IFR), the myeloid DCs (CD11b + CD8α) localized in the SED, and the "so-called" double negative (DN) DCs that lack both CD11band CD8αsurface expression and localize to the SED and IFR [7]. More recently, lysozyme-expressing DCs (Lyso DCs) have also been identified in the SED, as well as DCs that express the C-type lectin Langerin [7][8][9][10]. These DC subsets in the SED capture and present antigens to resident T-and B-lymphocytes, and are also known to influence specific T-helper (Th) responses [7,11,12]. Although we understand more about the mechanisms of how antigen and microbial sampling occur, the gene expression changes that take place within specific PP immune cells during sampling are not well characterized [10,13]. Recently, Bonnardel and colleagues developed a method to isolate and perform transcriptional analysis on PP mononuclear phagocyte subsets such as the CD11b + conventional DCs, the lysozyme-expressing monocyte-derived DC termed LysoDC, and the CD11c(hi) lysozyme-expressing macrophages [14][15][16]. Bonnardel's method demonstrates that there is much value in studying different PP cell subsets using transcriptomics [14,16].
To gain insight into the gene expression changes of specific PP cells at baseline and in response to vaccine delivery vehicles, we have developed a protocol that isolates high-quality RNA from specific PP cells such as B-and T-lymphocytes and CD11c + phagocytes. Total RNA isolated from sorted cells is then subjected to NanoString nCounter ® technology that uses a novel digital color-coded molecular barcode technology to measure gene expression based on the counts of the target RNA [17]. In these experiments, we specifically used the nCounter ® PanCancer immune profiling array panel to gain insight into the gene expression levels of over 770 genes within PPs by interrogating B-and T-lymphocytes and CD11c + phagocytes. To compare gene expression changes at baseline levels, we used Saccharomyces cerevisiae-derived β-glucan particles (GPs). Earlier studies using GPs demonstrated that these particles are taken up by both PP M-cells and CD11c + phagocytes and are a good positive control as they are known to stimulate an immune response [10,18,19]. It is important to note that PP cells are extremely delicate and the RNA is prone to rapid degradation; therefore, stabilization of RNA early in this procedure is critically important. This method allows for the investigator to reproducibly isolate stable RNA from sorted B-and T lymphocytes and CD11c + phagocytes to determine gene expression changes using any antigen, microbe or delivery vehicle of interest in a murine model.

Animals
Swiss Webster mice (8-12 weeks old) were obtained from Taconic Farms (Hudson, NY). Animals were housed under conventional, specific pathogen-free conditions and were treated in compliance with the Wadsworth Center's Institutional Animal Care and Use Committee (IACUC) guidelines. We recommend usage of age-matched mice for consistency to reduce outlier variability.

Ethics statement
Experiments described in this study that involve mice were reviewed and approved by the Wadsworth Center's Institutional Animal Care and Use Committee (IACUC) under protocol # 15-450. The Wadsworth Center complies with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and was issued assurance number A3183-01. Moreover, the Wadsworth Center is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Obtaining this voluntary accreditation status reflects that Wadsworth Center's Animal Care and Use Program meets all of the standards required by law, and goes beyond the standards as it strives to achieve excellence in animal care and use. In these experiments, we used 1 × 10 8 highly purified, Saccharomyces cerevisiae derived β-glucan particles (GPs) as a positive control. As a second positive control, 1 × 10 8 purified β-glucan particles with exposed mannans (GMPs) were used in 200 µl of 1× PBS. Delivery of particles was achieved using a 22 G × 1.5 in blunt-end feeding needle. Both GPs and GMPs are known to stimulate the immune response [19,21]. NOTE: This step is critical, as addition of the SUPERase·In RNase inhibitor immediately stabilizes RNA when PPs are harvested. Without the SUPERase·In RNase inhibitor, RNA recovery will be poor. Keeping everything on ice is extremely important.

Recipes
CAUTION: DO NOT USE RNAlater RNA Stabilizer Reagent (Qiagen cat. #76104) specifically for PPs, as this reagent will significantly reduce the viability of PP cells to 40%. The RNA quality was also found to be very poor. We found that RNAlater made PPs float in solution during the harvesting step, suggesting that these tissues were damaged. PPs naturally sink to the bottom of the tube in solution.

2.1.
Euthanize mice by CO 2 asphyxiation, according to institutional guidelines.

2.2.
Cleanse the mouse abdomen with 70% ethanol prior to necropsy. Perform a standard necropsy that involves a 1 cm incision using straight surgical grade scissors along the midline beginning about 1.5 cm from the base of the rib cage. Expose the peritoneal cavity and identify the cecum. Snip the terminal small intestine at the ileal-cecal junction, gently remove the small intestine in its entirety, and snip the stomach-ileal junction to release intact intestine.

2.3.
Identify individual PPs located on the anti-mesenteric side of the intestine. Mice typically contain 5 to 10 PPs spaced more or less evenly from the duodenum (proximal) to ileum (distal).

2.4.
Using curved surgical scissors, gently excise individual PPs and place them immediately in cold HBSS that contains 50 µl of SUPERase·In RNase inhibitor (tube 1). The scissors should be placed curve-side up just above the PP and then gently applied to the tissue. Add RNAase Zap to surgical instruments and then rinse with ethanol between every mouse.

NOTE:
Two to three mice yield plenty of cells to be able to isolate RNA from all three PP cell types. To avoid cell clumping, isolate PPs from less than 5 mice per group.

2.5.
Decant HBSS in tube 1 and transfer PPs into tube 2 that contains 2 ml of HBSS with 30 µl of SU-PERase·In RNase inhibitor.

POL Scientific
protocol Staining cells for cell sorting NOTE: Before moving on to this step, wipe petri dish, pestle and cell strainer with RNase Zap decontamination solution.

3.
To generate a cell suspension, decant tube 2 into a 70 μm nylon mesh cell strainer that is resting on a petri dish. This setup should be on ice. Grind PPs with pestle or with the back part of a syringe plunger. The 2 ml should yield approximately 1200 µl after grinding the PPs. Transfer the cell suspension from the petri dish into a microfuge tube [21]. If you use RNAlater RNA Stabilizer Reagent instead of SUPERase·In RNase inhibitor, PPs will feel like rubber at this step and will not yield high quality RNA.

3.1.
Save 10-15 µl of cells to count them in step 3.5.
CRITICAL STEP: Prepare flow buffer that contains SUPERase· In RNase inhibitor, as this buffer will be used for all subsequent steps to stain and wash cells for cell sorting. It is recommended to prepare 20 ml of flow buffer at a 1:250 dilution.

3.2.
Using a 96-round bottom plate, add 50 µl of cells per well for single color controls. For B-cells, T-cells and DCs that will be sorted, fill ten wells per cell type, with 100 µl of cells per well. Spreading cells into multiple wells prevents cell clumping in subsequent steps.

3.3.
Spin the plate for 2 min at 2100 rpm (887× g), to pellet the cells and decant supernatant by inverting the plate.

3.6.
After the 15 min incubation, spin the plate for 2 min at 2100 rpm (887× g), and decant supernatant by inverting the plate.

3.7.
Resuspend the cells in 100 µl of fluorophore-conjugated antibodies added directly to cell suspensions (1:200 for B and T-cells and 1:40 for CD11c + phagocytes) and incubate on ice for 30 min with continuous rocking.

3.8.
Spin the plate for 2 min at 2100 rpm (887× g), and decant supernatant by inverting the plate.

3.10.
Spin the plate for 2 min at 2100 rpm (887× g), and remove the supernatant by inverting the plate.

3.11.
Resuspend the cells from the plate into 3 ml of flow buffer; some of cells will clump. Pass the 3 ml cell suspension through a filtered cap flow tube that contains 1ml of flow buffer resulting in a total of 4 ml of cell suspension for cell sorting. The RNeasy Mini Kit calls for cell wall lysis by using a syringe, which leads to RNA degradation. We therefore developed an alternative method for cell lysis using the Spin-X filter column (step 4.1).

9.14.
Remove cartridge and seal with adhesive tape to prevent evaporation. Tape is provided in kit.

9.15.
Take cartridge and place in digital analyzer for RNA copy counts.
Digital analyzer 9.16. Upload the Reporter Library file in the provided flash drive.

9.17.
Create a CDF file with sample name and description and upload into the digital analyzer.

9.18.
Insert cartridge with the seal (step 9.14) into the digital analyzer.

9.19.
Initiate counts by pressing start.

9.20.
When the digital analyzer is complete download your data for analysis.
9.21. Follow instructions on nSolver or self-analyze.

STATISTICAL ANALYSIS
NanoString analysis was performed in duplicate per experimental condition with the nCounter Analysis System using the PanCancer Im-mune profiling kit. The results were analyzed using the raw count with DEseq2 [22,23]. A transcript was considered differentially expressed when up or downregulated at least 2-fold and the P-adjusted value ≤ 0.05.

ANTICIPATED RESULTS
PPs are aggregate lymphoid structures that can be easily seen with the naked eye and are located on the anti-mesenteric side of the small intestine (Fig. 1A). Each PP contain multiple follicles that are individually equipped with an epithelial barrier, an extensive network of mononuclear phagocytes, and T-and B-lymphocytes that play an important role in sampling luminal antigens, microbes, and oral vaccine delivery vehicles (Fig. 1B) [10]. To assess the transcriptomics of individual cell types in response to β-glucan delivery vehicles, PPs were harvested and RNA was isolated. It is important to note that isolation of PPs cells can be challenging because PP cells are extremely delicate and difficult to maintain as viability decreases by roughly 15% every 30 min [21]. Additionally, in the absence of RNA inhibitors, the RNA quality from PPs is extremely poor with less than 10% recovery. To overcome these challenges, we treated PP single cell suspensions with two different commercially available RNase inhibitors, RNAlater and SUPERase·In RNase inhibitor. We found that treatment with RNAlater caused PPs to float in solution, yielding a hard and tacky texture that was not ideal for making single cell suspensions. Cell viability using the RNAlater reagent was determined to be 46% (Fig. S2). We noted that PPs treated with SUPERase·In RNase inhibitor remained in the bottom of the tube, as expected, and single cell suspensions yielded 98% viability (Fig. S2). Most importantly, we found that viability of PP single cell suspensions remained above 90% after 2-4 h on ice with SUPERase·In RNase inhibitor, a significant improvement from our previous protocol, which was done without inhibitors [24]. After determining which RNase inhibitor was optimal in prolonging cell viability and providing RNA stability, we used cell sorting to separate PP B220 + B-cells, CD3 + T-cells and CD11c + phagocytes ( Fig.  2A). Using this method, we sorted a single PP suspension of cells into three separate tubes that contained CD45R/B220 + (CD3 -/CD11c -) B-cells, CD3 + (CD45R/B220 -/CD11c) T-cells and CD11c + (CD45R/B220 -/CD3 -) phagocytes. During sorting, we carefully selected the gates denoted in green ( Fig. 2A-2E) such that there was no cross-contamination of each of the cell types of interest. Reanalysis of the sorted cells revealed that we accomplished 99% purity for B220 + B-cells (Fig. 2G), 99% purity for CD3 + T-cells (Fig. 2J), and 73% purity for CD11c + phagocytes (Fig.  2M). Although we could not achieve a higher percentage of purity for CD11c + phagocytes due to their complicated shape, we determined that there was no contamination by B220 + or CD3 + cells (Fig. 2N). We also found that using a combination of both magnetic bead cell isolation and cell sorting did not improve cell purity but, rather, it decreased RNA yield to 10%-15%, due to the increased experimental time and cell losses in each step (data not shown). Using both bioanalyzer nano and pico chips, we determined that RNA isolated using RNA-later was highly degraded (Fig. S3A, lanes 2-4). SUPERase·In RNase inhibitor alone yielded multiple bands. However, many of these bands were not clearly defined due to DNA contamination (Fig. S3A, lanes 6-8). RNA purified by SUPERase·In RNase inhibitor was of higher quality, although the preparation was contaminated with DNA. We therefore subjected the RNA preparation to DNase treatment, which resulted in DNA-free RNA. The bioanalyzer picochip results yielded reproducible bands that represent 28S (4.7 kb) and 18S (1.9 kb) rRNA (Fig. S3B, lanes  2-4). We also compared two RNA extraction methods by sorting cells directly unto Trizol reagent or Spin-X columns that contained a 0.22 µm membrane. Spin-X columns were subjected to dry ice and RNeasy reagents to extract RNA from cells. We determined that the Spin-X column RNA extraction method consistently yielded DNA-free RNA of high quality in the bioanalyzer data ( Fig. S3B and data not shown). We also determined the limits of detection of the RNA isolated per cell type and per single mouse. We found that PP RNA isolation for B-and T -lymphocytes can be achieved at an individual mouse level as both POL Scientific protocol are abundant within PPs. For CD11c + phagocytes, RNA amounts fall below the limits of detection; therefore, more than one mouse must be used for this cell type (data not shown).

Figure 2. Cell sorting and reanalysis of isolated PP B-cells, T-cells and CD11c + phagocytes.
Single cell suspension of PPs were stained for the cell surface markers: B220 for B-cells, CD3 for T-cells and CD11c for CD11c + phagocytes. FACS sorting panels: A. Forward (FSC) and side scatter (SSC) plot represents the entire single cell suspension. Cells were gated on (B) B220 -/CD3cells, which represented 31% of the population. C. CD11c + phagocytes which represented 8% of the population. D. CD11ccells (representing 85% of the population). E. B220 + CD3cells (representing 48% of the population) and B220 -CD3 + cells (representing 16% of the population). Reanalysis panels: F-H. The purity for each cell type. For B220 + B-cells, a 99% purity was obtained. I-K. For CD3 + T-cells, a 99% purity was obtained. Neither B or T-cells were contaminated with CD11c + phagocytes. L-N. For CD11c + phagocytes, 73% purity was obtained; these also were not contaminated by B-or T-cells. To assess the gene expression profiles of each cell type, we used the nCounter ® mouse Pan Cancer Immune profiling kit that profiles over 770 murine immune system-related genes. To obtain both reproducible and biologically relevant results from PP RNA, it was critical to start with high quality RNA. To compare the differential expression of genes per cell type in comparison to the untreated control, we gavaged mice with β-glucan particles (GPs) derived from Saccharomyces cerevisiae cell walls. GPs are highly purified, spherical hollow shells that mainly consist of β-1, 3-D-glucans. Because GPs are recognized by Dectin-1 and complement 3 receptors, they are considered useful antigen presenting cell-targeted vaccine adjuvant/delivery vehicles. The inner hollow cavity of GPs can be loaded to deliver antigens to macrophages and dendritic cells [20]. In addition to GPs, we also used GMPs, which are considered a lower purity β-glucan particle that contain residual mannan content in the particle cell wall. GMPs have also been shown to have immunostimulatory properties [19]. Confocal microscopy of PPs from mice gavage with GPs and GMPs show that both types of particles are sampled by PPs CD11c + phagocytes ( Fig. 3A and 3B). Since GPs and GMPs can serve as both antigen-presenting cell-targeted delivery systems and as adjuvants, we were particularly interested in differential gene expression of CD11c + phagocytes [20].
To determine highly significant differential gene expression, we analyzed the RNA raw counts with DEseq2. Differential expression of transcripts was stringently defined, requiring transcripts to be up or downregulated at least 2-fold and the P-adjusted value to be ≤ 0.05. Mice were gavaged with GPs and GMPs, and PPs were harvested after 24 h. When comparing the differential expression within each cell type at baseline and with stimulation by GPs and GMPs, the results reveal that stimulation with GPs did not yield any significant differential gene expression in B-cells and T-cells. Only one gene, interleukin 1 receptor type 1, (IL-1r1), showed differential expression in CD11c + phagocytes. In contrast, GMPs stimulated gene expression in CD11c + phagocytes but not in B-and T-cells. The four genes that were most significantly upregulated in CD11c + phagocytes by GMPs were interleukin-22 (IL-22), (21-fold), deleted in malignant brain tumors 1 (Dmbt1) 11fold, IL-1r1 (6-fold), and Fibronectin-1 (Fn1) (6-fold). IL-22, is a key cytokine that has been demonstrated to induce antimicrobial activity, stimulating tissue-damage protection and tissue repair and remodeling [25]. The Dmbt1 gene encodes a glycoprotein with multiple scavenger receptor domains that may be involved in the activation of the complement mannose lectin pathway, and influences IL-22 [26]. IL-1r1 is the receptor for IL-1, a proinflammatory cytokine that influences IL-22 [27]. Fibronectin-1 (Fn1) is involved in cell adhesion and motility [28] ( Table 1). In addition, principal component analysis of the data show that gene expression profiles are clustered based on cell types regardless of treatment, indicating that there are larger changes in gene expression between cell types (Fig. 4). To determine whether we could find additional cell specific transcripts, we performed DEseq2 analysis between control B-cells, T-cells, and CD11c + phagocytes. Our stringent cut-off criteria were the same as described earlier. The results reveal that there were a total of 396 differentially expressed genes. Among those transcripts, 29 genes were only upregulated in B-cells, 103 genes were only upregulated in T-cells, and 97 genes were only upregulated in CD11c + phagocytes (Fig. 5). We also found that 63 differentially expressed genes were only downregulated in B-cells, 70 genes that were only downregulated in T-cells, and 34 genes that were only downregulated in DCs (Fig. S4). The data used in our representative heatmaps demonstrates the reproducibility of this method across all cell types under experimental conditions involving stimulation with GPs and GMPs.  The reported method here provides a reproducible approach to isolating high-quality RNA from PP, B-cells, T-cells and CD11c + phagocytes which allowed the application of NanoString technology to determine changes in the transcriptional profiles of these cells. To achieve optimal results with this protocol, it is important to use an RNase inhibitor that is suited for PPs immediately after harvesting tissues. Careful cell sorting is also an important part of this method, as cells must be properly gated to ensure the highest cell purity during cell sorting. Lastly, the stringent data analysis that we describe will identify differentially expressed genes with high confidence. As an example of the utility of this method, we were able identify changes in genetic expression of GPs and GMPs β-glucan delivery vehicles that can target PP CD11c + phagocytes. A major advantage of the presented method is that the investigator can perform intestinal immune profiling per cell type with any antigen or microbe of interest that is introduced into the intestinal mucosa.

TROUBLESHOOTING
Possible problems and their troubleshooting solutions are listed in Table 2.