11-Color Immunophenotyping Panel Using StarBright™ Dye-Conjugated Antibodies to Identify Major Bovine Blood Cell Populations

Technical Note:
11-Color Immunophenotyping Panel Using StarBright™ Dye-Conjugated Antibodies to Identify Major Bovine Blood Cell Populations

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Abstract

Studying the bovine immune system is vital for assessing cattle health and when conducting animal model studies. While flow cytometry is commonly used to determine immune status, its application in bovine studies is limited by panel size, as antibodies are only available conjugated to a restricted range of fluorophores. The introduction of antibodies against key bovine markers conjugated to StarBright Dyes addresses this challenge. This technical note describes how they can be used alongside traditional fluorophores in an 11-color immunophenotyping panel to identify bovine T-cell subsets, B cells, natural killer (NK) cells, and monocytes.

Introduction

Cows are a crucial source of meat and milk, making it essential to monitor their health effectively. Additionally, cows serve as valuable models for studying human diseases, such as bovine tuberculosis, which acts as an animal model for human tuberculosis. The examination of the immune system by immunophenotyping is one of the most effective ways of monitoring animal health and research. Flow cytometry is the primary application for immunophenotyping, however, there are some limitations. The amount of information from a single sample is restricted owing to a lack of bovine-specific antibodies conjugated to a wide range of fluorophores. The impact of this is suboptimal panels. While larger panels have been published, these are rare and currently there are only two published bovine optimized multicolor immune fluorescence panels (OMIPs), OMIP-085 (Roos et al., 2023a) and OMIP-089 (Roos et al., 2023b), where both panels utilize only eight colors and primarily focus on either T cells or B cells.

Bio-Rad’s extensive bovine antibody range, and crucially, the numerous fluorophores to which they are conjugated, including novel StarBright Dyes, are helping to address this issue. In this study, we present an 11-color validated panel for the identification of key bovine immune cell populations. Instead of focusing on a specific cell type, this one panel identifies a variety of cell types, including T cells, B cells, NK cells, and monocytes, as well as determining the activation status of B and T cells.

Materials and Methods

Panel Design

To achieve optimal results using flow cytometry, it is critical to adhere to best practices in panel design for generating high-quality data with highly resolved populations. Bio-Rad’s Multicolor Panel Builder and Fluorescence Spectraviewer tools were used to help with the panel design process.

• Antibodies detecting these markers were raised against bovine antigens or showed species cross-reactivity. For instance, a cross-reactive human leukocyte antigen (HLA) ABC antibody was used to detect bovine leukocyte antigen (BoLA), which is the name for the major histocompatibility complex (MHC) in cattle
• The samples were acquired on a five-laser (5-L) ZE5 Cell Analyzer (Bio-Rad Laboratories, Inc., 12014135) with UV option A. Bio-Rad’s Multicolor Panel Builder and Fluorescence Spectraviewer helped select suitable fluorophores based on their excitation and emission wavelengths
• Following best practice, the aim was to have minimal spillover and spread by distributing the fluorophores across the five lasers and detectors of the ZE5 Cell Analyzer. Supplementary Tables 1 and 2 show the spillover and spread
• Fluorophore pairs were identified that would have higher spread; Pacific Blue with DAPI, SBUV400, and SBV515. To mitigate the impact of spread, Pacific Blue was used for an antibody detecting T cells, SBUV400 for a marker on all nucleated cells, and SBV515 for a monocyte marker. Using these fluorophores for markers on different cell types, they would not be plotted against each other during analysis, therefore maintaining a high resolution of the cell populations
• All antibodies were titrated prior to use, ensuring excess antibody was not used, which would increase nonspecific binding and spread
• One day old heparinized bovine peripheral blood was used for staining; it was critical to include a live/dead dye, 4’6-diamidino-2-phenylindole (DAPI), to eliminate dead cells during analysis, which could otherwise result in false positives

The list of antibodies and viability dye used in the panel is shown in Table 1.

Table 1. Antibodies and viability dye used in the bovine multiplex panel.

* Cross-reactive antibodies.
A647, Alexa Fluor 647; A700, Alexa Fluor 700; Cy5, cyanine 5; DAPI, 4’6-diamidino-2-phenylindole; FITC, fluorescein isothiocyanate; HLA, human leukocyte antigen; PB, Pacific Blue; PE, phycoerythrin; SBB, StarBright Blue; SBUV, StarBright UltraViolet; SBV, StarBright Violet.

Staining Protocol and Data Collection

Bovine peripheral blood was treated with Red Cell Lysing Buffer (Bio-Rad, BUF04) to remove red blood cells and then frozen in fetal bovine serum (FBS) and 10% dimethyl sulfoxide (DMSO). Cells were rapidly defrosted at 37°C, centrifuged, and blocked in 10% bovine serum for 5 min at room temperature (RT), followed by incubation at RT for 1 hr with fluorescent dye–conjugated monoclonal antibodies, shown in Table 1. Following incubation, samples were washed three times in phosphate buffered saline (PBS) + 1% bovine serum albumin (BSA) (PBS/BSA) and resuspended in 200 µL of PBS/BSA. PUREBLUE DAPI (Bio-Rad, 1351303) was added 5 min prior to acquisition.

For compensation controls, cells were incubated with a single antibody or PUREBLUE DAPI. All antibodies were titrated prior to use and utilized at the optimal dilution.

Samples were acquired on a 5-L ZE5 Cell Analyzer with UV option A. 150,000 cells were acquired for the multiplex panel and 60,000 cells were acquired for single-stained controls.

Gating Strategy

Analysis was performed using FCS Express 7 Software (De Novo Software by Dotmatics). Dead cells were first excluded from downstream analysis by gating on cells that were DAPI-negative. Doublet discrimination was used to identify single cells, followed by gating on mononuclear cells distinguished by forward scatter area (FSC-A) and side scatter area (SSC-A) (Figure 1). Cell types and subpopulations were identified from the mononuclear cell gate using downstream gating strategies as shown in Figure 2.

Results

An immunophenotyping panel was created that identified the major T-cell, B-cell, NK-cell, and monocyte populations in a single panel. Within the T cell lineage, T helper (CD4+) and cytotoxic (CD8+) subsets were clearly distinguished (Figures 1 and 2). Naïve and activated B and T cells were also identified. The spillover and spreading matrices for the panel are shown in Appendix Tables 1 and 2.

Basic Gating

A basic gating strategy was used to remove dead cells and doublets. The lymphocyte and monocyte populations did not have clear separation using forward and side scatter, so a mononuclear gate including these cell populations was used (Figure 1).

Fig. 1. Basic gating strategy. The mononuclear cell population was identified after gating on live, single cells. A, area; DAPI, 4’6-diamidino-2-phenylindole; FSC, forward scatter; H, height; SSC, side scatter; W, width.

Mononuclear Gating

The initial downstream gating step was to identify HLA ABC-positive cells from the mononuclear gate as HLA ABC is present on the surface of almost all nucleated cells and will exclude any contaminating red blood cells.

An anti-CD14 antibody was used to distinguish the monocyte (CD14-positive) and lymphocyte (CD14-negative) populations; CD172a and CD163 markers were used to confirm monocyte identification. Within the CD14-negative HLA ABC-positive population, CD16 was used to identify NK cells, CD21 for B cells, and CD2 for T cells. The CD4-positive (T helper cells) and CD8-positive (cytotoxic T cells) subpopulations were detected within the CD2-positive T cells. Activated and naïve B cells, along with helper and cytotoxic T cells, were identified by CD62L expression (Figure 2).

Fig. 2. T-cell, B-cell, NK-cell, and monocyte populations. The mononuclear cell population was analyzed to identify monocytes, NK cells, CD4+ T helper cells, CD8+ cytotoxic T cells, and activated T cells. A647, Alexa Fluor 647; A700, Alexa Fluor 700; Cy5, cyanine 5; FITC, fluorescein isothiocyanate; HLA, human leukocyte antigen; NK, natural killer; PB, Pacific Blue; PE, phycoerythrin; SBB, StarBright Blue; SBUV, StarBright UltraViolet; SBV, StarBright Violet.

Conclusions

An 11-color immunophenotyping panel was successfully designed to identify key immune cell populations in cows. Major observations included:

• Simple gating strategy — to identify all major immune cell populations in bovine blood:
  • Mononuclear cells were identified as BoLA positive using a cross-reactive HLA ABC antibody
  • T helper cells (CD2+CD4+), naïve T helper cells (CD2+CD4+CD62L+), and activated/memory T helper cells (CD2+CD4+CD62L–)
  • Cytotoxic T cells (CD2+CD8+), naïve cytotoxic T cells (CD2+CD8+CD62L+), and activated/memory cytotoxic T cells (CD2+CD8+CD62L–)
  • B cells (CD2–CD21+), naïve B cells (CD2–CD21+CD62L+), and activated/memory B cells (CD2–CD21+CD62L–)
  • Monocytes (CD14+CD172a+CD163+)
  • NK cells (CD2–CD16+)
• Low spillover and spread values — panel design best practices were followed resulting in low compensation and spreading values. This led to high cell resolution and clear separation of positive and negative signals, allowing all major and subpopulations to be clearly identified
• No special staining buffer required — the panel was stained in a basic staining buffer, PBS + 1% BSA. This eliminated the cost of additional staining buffers, with the benefit of the panel fitting into existing staining workflows

This panel identified five different major cell types and determined the activation status of B and T cells in a single panel, which is more efficient than using separate panels for each cell type. The inclusion of new StarBright Dye–conjugated antibodies enabled the identification of more immune cell populations within a single experiment, without the need for secondary antibodies, simplifying and speeding up the staining process. Following best practice, it can also be used as a backbone panel by adding antibodies that detect other markers labeled using conjugation kits, such as the TrailBlazer™ StarBright Tag and Label Kits. Visit bio-rad-antibodies.com/cow-bovine-antibodies for more information on the expanding bovine flow antibody range.

Appendix

Supplementary Table 1. Spillover matrix. 

Values represent the amount of spillover for each fluorophore. The rows show the fluorophore, and the columns display the signal present in each detector. Colors progress from green to white to red as more spillover is present. Green indicates no or very low spillover, whereas red indicates more spillover is present between the two fluorophores. A647, Alexa Fluor 647; A700, Alexa Fluor 700; Cy-5, cyanine-5; DAPI, 4’6-diamidino-2-phenylindole; FITC, fluorescein isothiocyanate; PB, Pacific Blue; PE, phycoerythrin; SBB, StarBright Blue; SBUV, StarBright UltraViolet; SBV, StarBright Violet.

Supplementary Table 2. Spreading matrix. 

Values indicate the spillover spreading (SS) amount for each fluorophore into all detectors. The rows show the fluorophore-donated SS, and the columns display the detector-collected SS. Colors progress from green to white to red as more spreading is present. 0–3 indicates no or very low spreading. A647, Alexa Fluor 647; A700, Alexa Fluor 700; Cy-5, cyanine-5; DAPI, 4’6-diamidino-2-phenylindole; FITC, fluorescein isothiocyanate; PB, Pacific Blue; PE, phycoerythrin; SBB, StarBright Blue; SBUV, StarBright UltraViolet; SBV, StarBright Violet.

References

• Roos EO et al. (2023a) OMIP-085: Cattle B-cell phenotyping by an 8-color panel. Cytometry A. 103, 12–15.
• Roos EO et al. (2023b). OMIP-089: Cattle T-cell phenotyping by an 8-color panel. Cytometry A. 103, 279–282

Resources for Flow Cytometry

• Bovine Antibody Range
• StarBright Dyes
• Multicolour Panel Building
• Fluorescence Spectraviewer
• Panel Builder