Development of a High-Color Flow Cytometry Panel for Immunologic Analysis of Tissue Injury and Reconstruction in a Rat Model

Rats provide a promising avenue for studying the immune response to injury. Like mice, rats have enough genetic and immune similarities to humans to make them valuable animal models [Bryda, 2013; Wildner, 2019]. Additionally, they are large enough to make functional studies efficient and highly informative. Although rats have unique benefits to immunological research, their use has been limited because mice have been the preferred murine model in immunological studies [Wildner, 2019]. One barrier leading to the limited use of rats as animal models for immunological studies is a lack of prior work on validating and utilizing high-color flow cytometry panels, partially due to lack of diverse reagent availability that makes panel design more difficult. Flow cytometry is an essential tool for studying the immune system that allows researchers to characterize and identify different cell populations. In trauma, which involves many different immune cells, flow cytometry is essential for understanding how immune cells contribute to the regenerative process.

Previous use of flow cytometry in rat models of trauma and inflammation has proven useful for describing immune cell infiltration. Researchers have used flow cytometry to show both the adaptive and innate immune responses to volumetric muscle loss (VML) injury and biomaterial treatments to VML injury [Hurtgen et al., 2016, 2017; Haas et al., 2021]. Additionally, flow cytometry has been used to phenotype immune populations in different organs in rats [Trama et al., 2012; Rubio-Navarro et al., 2016]. Finally, a 9-color flow cytometry panel was developed to investigate LPS-induced pulmonary inflammation [Barnett-Vanes et al., 2016]. Although these studies offer much-needed insight into the function and activity of the rat immune system, they are technically limited to a few markers to identify general immune populations. Therefore, these studies cannot investigate intricate details about the immune system that may have profound implications on the results seen in various rat studies.

In immunological studies using mouse models, conversely, the increased availability of flow cytometry antibodies has allowed for more comprehensive explorations of the immune response to trauma and how this response can be modulated. Flow cytometry in mouse VML models has been essential in analyzing the immune responses to implantations of decellularized matrices from various tissue sources [Sadtler et al., 2017; Qiu et al., 2018]. It has also been used in work that has elucidated the differences in immune responses to synthetic and biological scaffolds [Sadtler et al., 2019]. Additionally, computational approaches, including dimensionality reduction of flow cytometry data, have become an important tool for immunological comparisons as well as the discovery and analysis of immune cell subsets in different samples [Hartmann et al., 2021; Hymel et al., 2021]. Dimensionality reduction is a technique that reduces the number or dimensions of variables in a data set, allowing for a more meaningful representation of the data [El Bouchefry et al., 2020].

Here, we designed and validated a 24-color flow cytometry panel to identify and compare different immune cell populations in the site of trauma (muscle tissue) as well as central immune organs such as the spleen, thymus, and mesenteric lymph nodes (MLNs) (Table 1). This panel allows for the identification of adaptive and innate immune cells. Moreover, distinct populations within other immune populations can be identified. For example, our use of antibodies for CD86 and CD163 identifies M1 and M2 macrophages, respectively. Additionally, the panel’s CD1d antibody identifies immune cells that can present lipid and other non-protein antigens. Meanwhile, the γδ T-cell receptor (TCR γδ) antibody recognizes a subset of CD3⁺ T cells that helps mount immune responses against non-protein antigens. This panel also uses chemokine receptors CCR1, CCR4, CCR5, and CCR8 to help characterize immune cell populations involved in T cell Type 1 and/or T cell Type 2 responses.

This panel provides an in-depth survey of the different immune populations involved in the normal function of lymphoid tissues and the VML immune response in rats. The use of markers that are not as widely studied in rat models allows this panel to discover new immune cell populations involved in general immune function and the immune response to tissue injury (e.g., VML). This paper shows the different immune populations that this panel can identify in VML, spleen, and MLN samples and demonstrates how dimensionality reduction can compare the diverse immune landscapes between samples. Here we show that this panel can serve as a powerful tool for scientists to examine the immune system of rats in a novel and innovative way.

Three skeletally mature (n = 3, 10–12 weeks of age) male Lewis rats (Charles River Laboratories, Wilmington, MA, USA) were used in this study. Animals were housed in standard large rodent cages and allowed food and water ad libitum. Laboratory animals were euthanized after 1 week via exsanguination by cardiac puncture while under isoflurane anesthesia. The VML procedure was performed as described by Goldman et al. [2021]. Briefly, under anesthesia (1–3% isoflurane) and in sterile conditions, experimental animals received an incision through the skin along the lateral aspect of the tibialis anterior (TA) muscle on both hind limbs. Skin and fascia were reflected from the anterior surface, and a metal spatula was positioned between the TA muscle and underlying extensor digitorum longus (EDL) muscle after blunt dissection. Finally, a full-thickness defect from the belly of the TA was removed using a 6-mm biopsy punch, and the wound was closed in layers with interrupted sutures for the fascia and stapling for the skin. All animals received preoperative Buprenorphine SR (1 mg/kg) via subcutaneous injection and were monitored for pain and general wellness for 3 days after the operation. Additionally, all animals were subcutaneously implanted with saline dispensing Alzet Osmotic pumps (Durect Corporation, Model #2ML4) in their backs. One week post operation, all animals were euthanized, and TA muscles from each animal were harvested and collected in 5 mL of RPMI media with L-glutamine (CORNING, catalog #10-040-CV).

Injured TA muscles, spleen, thymus, and mesenteric lymph nodes were harvested and placed in 5 mL of RPMI media with L-glutamine (CORNING). Then, the specimens were thoroughly minced with scissors and a 5 mL solution of 1 mg/mL of Liberase^TM (Thermolysin Medium) (Millipore Sigma, catalog #05401127001) and 0.4 mg/mL of DNase I (Millipore Sigma, catalog #10104159001) was added. The samples were digested at 37°C and 100 rpm in an incubator shaker.

After digestion, each tissue specimen was filtered through a 40 μm cell strainer (Falcon®, catalog #352340) and resuspended in 50 mL of RPMI media with L-glutamine. Afterward, tissues samples were centrifuged at 350 × g for 5 min. The supernatant was then discarded, and the samples were washed with 50 mL of 1× PBS. After discarding the supernatant, the samples were washed with 5 mL of 1x PBS and transferred to a 96-well V bottom plate. After 2 washes with 1× PBS, samples were resuspended in 100 μL of LIVE/DEAD® Fixable Aqua Dead Cell Stain Kit and incubated for 20 min at 4°C in the dark. Following incubation, the samples were washed twice in 1× PBS and resuspended in 5 μL of True-Stain Monocyte Blocker^TM (BioLegend®, catalog #426103), 7 μL of 1× PBS, and 10 μL of Brilliant Stain Buffer (BD Biosciences, catalog #566385) then incubated for 5 min in the dark at room temperature. Brilliant Stain Buffer was added in this step to increase the blocking volume so that samples could be properly resuspended. Afterward, samples were stained with the following antibody panel: CCR4 PerCP, CCR8 PE, CCR1 CF594, CCR5 APC, and XCR1 BV785. The samples were incubated at room temperature in the dark for 5 min and were stained with TCR γδ BUV615, and then incubated at room temperature in the dark for 10 min. These previous dyes were stained before adding the rest to increase their staining efficacy. The samples were finally stained with the following antibody panel: CD45 PE-Cy5, CD11b V450, CD45R BV711, CD3 VioGreen, CD4 BUV395, CD25 BV650, CD8 BUV496, CD49b APC-Vio 770, CD1d BV605, CD44 BB700, CD62L BV421, CD43 PE-Cy7, CD86 BV480, CD163 FITC, MHCII PerCP-eFluor 710, Thy1.1 BUV563. The samples were incubated for 30 min in the dark. Then, 50 μL of 1× PBS was added to each well, the plate was centrifuged at 350 × g for 5 min, and the supernatant was discarded. The samples were then washed twice more with 1× PBS for 3 washes. After, samples were resuspended in 100 μL of True-Nuclear^TM 1× Fix Concentrate and incubated at room temperature in the dark for 45 min. A volume of 50 µL of Perm Buffer was then added to each well, and the plate was centrifuged at 350 × g for 5 min. Samples were washed twice more with Perm Buffer before being resuspended in 90 μL of Perm Buffer and 10 μL of CD68 Alexa Fluor 700, then being incubated for 1 h in the dark at room temperature. Following incubation, 50 μL of Perm Buffer was added to each well, the plate was centrifuged at 350 × g for 5 min, and the supernatant was discarded. This wash was repeated 2 more times using 150 μL of Perm Buffer for a total of 3 washes. Finally, each well was resuspended in 200 μL of 1× PBS. Its contents were collected in a 5-mL polystyrene round-bottom tube with a cell-strainer cap (Falcon®, catalog #352235), being sure to deposit samples through the cell-strainer cap.

One drop of UltraComp eBeads^TM Compensation Beads (Thermo Fisher Scientific, catalog #01-2222-42) was added to 24 wells in a 96-well V bottom plate (Greiner Bio-one, catalog #651180). Each well was resuspended in 150 μL of 1× PBS, and the plate was centrifuged at 350 × g for 5 min. After the supernatant was decanted, each well - except for the well to be used for the CD68 reference control -received one flow cytometry antibody dye and 1× PBS. The beads were stained using the following antibody panel: LIVE/DEAD^TM Fixable Blue Dead Cell Stain Kit, CD45 PE-Cy5, CD11b V450, CD45R BV711, CD3 VioGreen, TCRγδ BUV615, CD4 BUV395, CCR4 PerCP, CCR8 PE, CCR1 CF594, CCR5 APC, CD25 BV650, CD8 BUV496, CD49b APC-Vio 770, CD1d BV605, CD44 BB700, CD62L BV421, CD43 PE-Cy7, XCR1 BV785, CD86 BV480, CD163 FITC, MHC II PerCP-eFluor 710, Thy1.1 BUV563 (Table 1).

After antibody dye addition, the plate was incubated at 4°C in the dark for 30 min. Following incubation, the plate was centrifuged at 350 × g for 5 min, and the supernatant was decanted. Beads in each well were then resuspended in 150 μL of True-Nuclear^TM 1× Fix Concentrate from the True-Nuclear^TM Transcription Factor Buffer Set (BioLegend®, catalog #424401) then incubated at room temperature in the dark for 20 min. The plate was centrifuged at 350 × g for 5 min, and after discarding the supernatant, 150 μL of the True-Nuclear^TM 1× Perm Buffer (Perm Buffer) from the True-Nuclear^TM Transcription Factor Buffer Set was added to each well. The plate was centrifuged at 350 × g for 5 min, and the supernatant was discarded. This wash was repeated twice for a total of 3 washes. After, Perm Buffer and CD68 Alexa Fluor 700 antibody were added to the well for the CD68 reference control. Perm Buffer was added to the rest of the wells and was incubated at 4°C in the dark for 30 min. Following incubation, 80 μL of Perm Buffer was added to each well, the plate was centrifuged at 350 × g for 5 min, and the supernatant was discarded. This wash was repeated 2 more times using 150 μL of Perm Buffer for a total of 3 washes. Finally, each well was resuspended in 200 μL of 1× PBS, and its contents were collected in a flow cytometry tube. Samples were collected using a 5 Laser Cytek® Aurora Spectral Flow Cytometer.

Samples were collected using a 5 Laser Cytek® Aurora Spectral Flow Cytometer. Flow cytometry data files were analyzed in the Cytek® SPECTROFLO® SOFTWARE and the FlowJo Flow Cytometry Analysis Software (BD Biosciences). Spectral unmixing was performed by SPECTROFLO software using single-color controls and 3 identified autofluorescence populations. Data was subsequently graphed and analyzed using GraphPad Prism. All data displayed are mean ± standard deviation.

We were mainly limited by the lack of available fluorophores for rat immune cells when developing this panel. We initially constructed a gating tree to identify the immune populations we were interested in staining for and the markers needed to identify these populations (Fig. 1). This gating tree would also be useful to guide the post-staining analysis of our samples in FlowJo. We then performed an internet search for a list of available fluorophores for each marker. Here, our goal was to cover immunological cell populations broadly while enabling us to do more in-depth phenotyping for subset identification, activation, and polarization. Differentiating immune cell populations began with gating by CD45 expression (Fig. 1a), followed by gating for macrophages, monocytes, lymphocytes, and granulocytes (Fig. 1b). Within these populations, we could gate for macrophage polarization down an M1/M2 pathway (Fig. 1c), identify innate-like γδ T cells by the expression of the ɣδ T cell receptor (Fig. 1d), and granulocyte activation via the expression of CD62L and a high side-scatter (Fig. 1e). Within the cells that were non-myeloid and designated as TCRɣδ-, we could further identify T cells (CD3+) and non-T cell lymphocytes (CD3-; Fig. 1f). T cells could be characterized as CD8+ cytotoxic T lymphocytes and CD4+ helper T cells (Fig. 1g). Furthermore, they could be characterized as activated versus memory or naïve T cells by their expression of CD62L and CD44 (Fig. 1h). Within the population that was CD3-, we identified B cells (B220/CD45R+) and NK Cells (CD49b+; Fig. 1i). When evaluating T cell populations, they could be phenotyped by polarization through the expression of chemokine receptors and CD25 (Fig. 1j).

After obtaining this list, we assigned specific fluorophores to each marker following some guiding principles. First, we assigned brighter fluorophores to lower expressed markers and dimmer fluorophores to highly expressed antigens. Second, although technical specifications of the spectral cytometer are rated up to 98% spectral similarity, we minimized the similarities between our fluorophores with a maximum spectral similarity of 82% (this was between fluorophors V450 and BV421). Finally, we assigned fluorophores to the markers with the lowest amount of commercially available fluorophores, then moved to fluorophores with more commercially available fluorophores, careful to follow the previous 2 guiding principles as much as possible. We used the Cytek Full Spectrum Viewer to construct the panel’s similarity matrix and calculate its complexity index (Fig. 2). The similarity matrix shows how similar a fluorophore’s spectrum is to the other fluorophores in the panel using a similarity index between 0 and 1, where 0 means the fluorophores’ spectra are entirely different, and 1 means the fluorophores’ spectra are the same. The complexity index measures the interference between a combination of fluorophores and predicts the impact this interference will have on the ability to distinguish all the fluorophores from each other when they are combined into a full stain [Park et al., 2020]. The lower the complexity index, the more distinguishable the fluorophores will be from each other. Well-designed panels with 10 or fewer fluorophores have a complexity index between 2 and 3, while well-designed panels with 35–40 fluorophores have a complexity index between 40 and 50. As our panel had 24 fluorophores, the complexity index had to be between 3 and 40 to be considered well-designed. When we looked at our fluorophores in the Cytek Full Spectrum Viewer, none of our similarity indices in our similarity matrix were above 0.98, and our complexity index was 9.24 (Fig. 2).

Another critical factor to consider in flow cytometry is autofluorescence (AF). Cells naturally have AF, and when they are stimulated by a biological event such as inflammation, their AF increases with intensity. When staining cells with fluorescent markers, AF can negatively impact the resolution of fluorescent signals and, at times, present as a false positive signature on another fluor. Using a spectral cytometer, we characterized and extracted AF from different tissue samples by plotting the samples on a detector plot of the 7th violet channel (V7) versus the 7th ultraviolet channel (UV7). This method was done because of the emission spectrum for AF peaks in the V7 and UV7 channels. Different cell populations were distinguished based on AF signatures on this plot. Each of these distinct cell populations was exported and used as a single-color control when performing spectral unmixing to remove the AF background.

AF varied among the different tissues analyzed, with these distinct AF signatures displayed in Figures 3 and 4. Even within lymphoid organs, such as the lymph node, spleen, and thymus, there was variation in AF spectral profile, with the spleen having the greatest intensity of AF compared to the other organs (Fig. 3). Notably, in the context of the muscle injury model where there was a larger spread and diversity of AF spectra (Fig. 4a), the heterogeneous nature of these spectra could be isolated into 3 different immune cell populations (Fig. 4b) that correlated with the scatter profile of lymphocytes (Fig. 4c, d), granulocytes (Fig. 4e, f), and macrophages (Fig. 4g, h). All these populations had AF maxima in the ultraviolet and violet emission channels. The macrophage-like population shows the highest overall AF spectra that spread into the red and green laser emission channels and the more common UV and blue lasers. When evaluating fluorophore selection, it is important to consider AF as a variable in spectral panel design. Fluorophores with similar emission maxima as the AF of a given sample or cell population should be avoided or designed so that they appear on cells without high AF, this is especially true in the context of violet and UV-emitting dyes where AF tends to peak.

After staining tissue samples, immune populations were identified according to a gating tree (Fig. 1). Following spectral unmixing, cell populations were readily identified through manual gating (Fig. 5). We used fluorescence minus one (FMO) controls to gate these different immune populations accurately. In the traumatic muscle defect, 45.5% of the live cells (Live/Dead Blue negative) were immune cells. Most of the immune cells in the injury site were CD11b+ myeloid cells (91.67% ± 0.97). Within the live CD45+ cell population, we identified CD68+ macrophages (38.17% ± 3.59), SSC^hi granulocytes (2.58% ± 0.15), CD49b+ NK cells (1.21 ± 0.30), CD45R+ B cells (1.01% ± 0.01), CD3+ T cells (2.06% ± 0.34), and ɣδ T cells (0.20% ± 0.04; Fig. 6a). As with prior research on mouse trauma models, there was an abundant population of macrophages, significantly greater than all other cell types present (p < 0.0001; Fig. 6b). Within CD3+ cells, 6.13% were CD4+ helper T cells (6.13 ± 0.66) and 11.43% were CD8+ cytotoxic T lymphocytes (11.43 ± 0.57; Fig. 6c). Most T cells were CD44+ CD62L-activated T cells (72.6% ± 4.46; Fig. 6d). Furthermore, when evaluating T cell polarization, we found that the majority of T cells expressed CD25 (p < 0.0001), suggesting a regulatory phenotype, followed by CCR8 or CCR4+ Th2-like T cells and CCR1 or CCR5+ Th1-like T cells (Th2 vs. Th1 p-adj = 0.0008; Fig. 6e). Additionally, macrophages displayed a prominent M2-like phenotype with a larger proportion of CD163+ M2 macrophages when compared to CD86+ M1 macrophages (Fig. 6f).

In addition to manual gating, we performed computational analyses using hierarchical clustering algorithms tSNE (t-distributed stochastic neighbor embedding) and UMAP (uniform manifold approximation and projection). Three replicates of muscle injury samples were concatenated into one file before down-sampling to 100,000 events and then performing either t-SNE (Fig. 7a, b) or UMAP (Fig. 7c, d). Several different immune cell populations can be visualized through these projections and compared against manual gating (Fig. 7b, d, e).

In this study, we developed a 24-color flow cytometry panel to immunophenotype the immune response to a traumatic injury at the muscle injury site and survey the immune populations in lymphoid organs. This panel can be utilized for both broad-based immunophenotyping and a detailed immune activation and polarization analysis. As with other models of trauma, we found a large prevalence of macrophages in the injury site. Additionally, we found a greater proportion of Treg-like and Th2-like T cells when evaluating adaptive immune cells than Th1-like T cells. In the context of muscle regeneration, both Th2 T cells and Treg’s have been implicated in the healing and regenerative processes. Specifically, interleukin-4 (IL-4) reliant upon Th2 T cells has been correlated with muscle regeneration, and in the absence of Th2 T cells, there was an imbalance in fibroadipogenic lineage commitment [Sadtler et al., 2016]. Th2-associated immune cell populations such as eosinophils have also been implicated in both muscle and liver regeneration [Goh et al., 2013; Heredia et al., 2013]. Regulatory T cells have been implicated in muscle repair and utilized in therapeutic modalities in pre-clinical studies [Schiaffino et al., 2017; Cho et al., 2019]. These data provide a platform for future immunological evaluation of trauma and regeneration in a rat model.

There are several potential considerations and limitations of this study that should be taken into account. Immune cells are heterogeneous, and their presentation can differ depending on tissue context. Here, we included markers for macrophages, such as CD68 and CD11b; however, some macrophages do not express CD68, such as tissue-resident macrophages [Davies et al., 2013]. In addition, the granulocyte gating mainly relies on a side-scatter profile and does not include multiple protein markers that would be preferred for positive-signal identification. Further identification of cells, such as Tregs, through additional markers would allow for a stronger positive identification of these cell types as in the case of CD25. This marker, though used to identify Tregs, can also be expressed on different cell types. The addition of other analyses such as qRT-PCR, RNA sequencing, and soluble protein measurements would be important to form a complete profile of the immune response to trauma.

Another consideration not addressed in this work is the role of sex as a biological variable. Male and female rats have differences in their immune system [Klein et al., 2016]. As only male rats were used, it is possible that the immune populations we identified would look different in female rats, and any studies involving therapeutic efficacy of biomaterials or pharmaceuticals should include this variable. Furthermore, such studies should also involve an evaluation over a timecourse. As this work has focused on the platform development for a flow cytometry panel that can identify specific immune populations, a 1-week time point was chosen when identifying immune populations in our VML model as immune cell infiltration peaks about 1 week after the onset of an injury [MacLeod et al., 2016]. One final consideration for our work is that this panel is focused on the muscle setting. Although this panel was designed to investigate the immune response to VML, the immune populations identified in this panel are also found in other disease contexts. For example, in cancer, M1 macrophage polarization (identified by CD68 and CD86) and M2 macrophage polarization (identified by CD68 and CD163) play an essential role in the anti- and pro-tumor function of the immune system [Liu et al., 2021]. Additionally, ɣδ T cell (identified by CD3 and TCR ɣδ) have been implicated in cancer, infection, and autoimmune disease [Lawand et al., 2017]. The importance of the immune populations this panel can identify in other disease contexts means that our panel can be applied to study the immune system in other clinical models.

The authors would like to thank Connor Dolan and Andrew Clark from the DoD-VA Extremity Trauma & Amputation Center of Excellence (EACE) team at the Uniformed Services University of the Health Sciences for performing the surgeries on rats for this study and Dr. Parinaz Fathi for technical assistance. The authors would also like to thank Jessica Gucwa, from Cytek Biosciences, for scholarly discussions.

This study was conducted in compliance with the Animal Welfare Act and following the Guide for the Care and Use of Laboratory Animals. All procedures on animals were approved by the Institutional Animal Care and Use Committee at the Uniformed Services University of the Health Sciences (USUHS) and conducted in the American Association for Accreditation of Laboratory Animal Care (AAALAC)-accredited facilities of the Department of Laboratory Animal Research at USUHS, protocol #SUR-20-024.

The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of Uniformed Services University of the Health Sciences (USUHS), the Department of Defense (DoD), the Department of Health and Human Services (HHS), the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.

This study was funded by the National Institutes of Health Intramural Research Program of the National Institute for Biomedical Imaging and Bioengineering. Support was provided by the US Army Medical Research and Development Command (Award #W81XWH-21-2-0002), the DoD-VA Extremity Trauma, and Amputation Center of Excellence (Award# HU00012020038).

K.M.A. developed the flow cytometry panel and wrote and edited the manuscript. K.M.A., T.B.N., A.L.A., S.D., M.K., and J.S. processed tissue samples from rats and performed the experiments. R.L. aided with the flow cytometry data analysis and figure design. C.L.D. provided the rats used for this study. S.M.G. and C.L.D. edited the manuscript and provided scholarly discussion. K.S. provided instruction for developing the panel, analyzed data, wrote and edited the manuscript.

All data generated or analyzed during this study are included in this article. Any inquiries can be directed to the corresponding author.