Investigating the murine hepatic immune composition in diet-induced obesity using OMIP-104: Transferring an existing OMIP panel onto the CytoFLEX mosaic Spectral Detection Module

Introduction

Obesity-induced chronic inflammation, called meta-inflammation, results from an altered immune response in metabolic organs and contributes to the development of fatty liver disease and type 2 diabetes. To study these obesity-induced changes, a 30-color spectral flow cytometry panel was developed to study the immune composition in murine liver and white adipose tissue samples [1]. This panel aims to give a broad overview of the immunological landscape in murine metabolic tissues with a particular focus on metabolically relevant resident and recruited macrophage subsets. This panel differentiates Kupffer cells from recruited macrophages and identifies macrophage subsets in adipose tissue. It also includes markers for macrophage and dendritic cell activation states, enabling detailed analysis of immune cells involved in metabolic disorders.

This study aimed to assess the performance of OMIP-104 using the CytoFLEX mosaic Spectral Detection Module on the CytoFLEX LX Flow Cytometer. This evaluation was conducted without altering the published panel and it assesses the ease of transferring the panel to the CytoFLEX mosaic Spectral Detection Module, panel performance, and the ability to handle autofluorescence on a liver sample.

Materials

Instrument and Supplies

1. CytoFLEX LX flow cytometer (Beckman Coulter, Inc., USA, PN# AD51045) (Table 1)
2. CytoFLEX mosaic 88 Detection Module (Beckman Coulter, Inc., USA, PN# EP1006) (Table 1)
3. CytExpert for Spectral software (Beckman Coulter, Inc., USA).
4. Daily QC Fluorospheres (Beckman Coulter, Inc., USA, PN# 65719).
5. IR QC Daily QC Fluorospheres (Beckman Coulter, Inc., USA, PN# C06147).
6. Cytobank platform (Beckman Coulter, Inc., USA, PN# C47384)
7. For antibodies, please see Table 2.
8. For additional materials, please refer to the supplementary section cited in reference 1.

Table 1: Instruments and Detection Modules Configuration

Table 2: Antibody Details

• *Alexa Fluor (“AF”) are trademarks or registered trademarks of Thermo Fisher Scientific Inc.
• *Brilliant Ultraviolet (BUV) is a trademark or registered trademark of Becton, Dickinson and Company.
• *Brilliant Violet (“BV”) is a trademark or registered trademark of Becton, Dickinson and Company.
• *Spark Blue is a trademark or registered trademark of BioLegend.
• *APC-Fire and *Zombie are trademarks or registered trademarks of BioLegend.
• *LipidTox is a trademark or registered trademark of Invitrogen.

Note: Lyve-1 PE-Cy7 was not included in this test, as the data presented here was exclusively acquired on liver tissue samples on which Lyve-1 is not expressed. This marker is used specifically to gate on perivascular macrophages in adipose tissue and does not serve any further purpose in the liver, as demonstrated in figure 1 of reference 1.

Methods

The sample preparation and staining methods described in OMIP-104 were applied to the samples from a different cohort of mice used for this test. Briefly, the samples consist of CD45+ MACS-enriched cells isolated from the livers of male mice fed a western-type diet containing 60%-Kcal high fat diet supplemented with 1% cholesterol. For the test, leukocytes from six individual mice were pooled together.

For a detailed review of the methods used for sample preparation, antibody staining, and preparation of single-stained controls, please refer to the supplementary section cited in reference 1.

Data Acquisition

• Ensure the flow cytometer has been switched on, warmed up, and cleaned appropriately as per manufacturer and institutional instructions.
• Perform Quality Control, create an unmixing template, and an experiment template for a batch of experimental samples.
• Use Assay Settings for acquisition of experimental samples.

Results and Discussions

Spectrum Viewer

Figure 1: Spectrum viewer. Combination of fluorochromes generated using CytExpert for Spectral software.

Similarity Index

Figure 2: Similarity Index. Similarity index of selected fluorochromes used in this OMIP with Complexity score of 11.0, generated using CytExpert for Spectral software.

Gating Strategy

Figure 3A shows a representative manual gating strategy used in this panel to delineate the major leukocyte subsets on a liver sample with relevant fluorescence minus one (FMO) control displayed in figure 3B. For an extensive overview of immune subsets, and their respective characterizing markers, please refer to Table 3.

Figure 3: Gating strategy. A) A time gate was set to validate the stability of acquisition (not shown) after which debris, doublets and non-viable cells were first excluded, and total tissue leukocytes were next identified as CD45+CD31-. Subsequently, all the major immune cell populations, and their relevant subsets, were identified as displayed. B) Fluorescence minus one control. FMOs measured for activation markers LipidTOX Deep Red, CD63–AlexaFluor 700, CD9–APC-Fire750, CD36–BV570, CCR2–PE-Cy5 and TREM2–PE. Samples were pre-gated on total Mph as shown in (A). FMOs are displayed in orange, stained samples are displayed in blue. Plots were generated using the Cytobank data analysis platform.

Note: In the original OMIP-104 gating strategy, the process involved an initial exclusion of B cells and neutrophils, followed by gating on CD64 and F4/80 to identify macrophages. From the CD64 and F4/80 double negatives, NK cells were then identified by gating on CD11b by NK1.1. However, the approach was revised here, and the gating strategy was adjusted. In the revised approach, after the initial exclusion of B cells and neutrophils, NK cells are first excluded by gating on CD11b by NK1.1. Subsequently, macrophages are identified from the NK1.1 negatives by gating on CD64 and F4/80. This adjustment in the gating strategy does not affect the final data. However, it is noted here to maintain transparency and provide an exact replication of the OMIP-104 gating strategy, should it be required for comparative studies or consistency in methodology.

Table 3: List of Immune Cell Populations in Liver Tissue

Autofluorescence (AF) Subtraction and Unmixing

Most of the single-stained controls for spectral unmixing were generated using cells to match the fluorescent spectra of the fully stained panel. However, for some markers, cells did not provide a solid spectrum. For example, beads were used for Siglec-F – BV480 because this marker is expressed only on eosinophils, which have a unique autofluorescence spectrum and are difficult to separate using FSC/SSC alone. Therefore, the BV480 spectrum was easier to determine using beads. Four unique autofluorescent populations were identified from a pool of unstained samples (Figure 4).

Our observations indicate that subtracting the spectra of four unique autofluorescent signatures during the unmixing process reduces unmixing errors and thereby improves resolution compared to using a single autofluorescent signature (Figure 5 A-B). The data presented in Figure 5 highlights the importance of extracting multiple AF signatures for improved unmixing accuracy.

Figure 4: Fluorescent spectra of unique autofluorescent populations identified from unstained controls. Spectra were generated using CytExpert for Spectral software.

Figure 5: Autofluorescence subtraction. A) Unmixing using a single autofluorescent signature. B) Unmixing using four unique autofluorescent signatures. Data demonstrates improved resolution with multiple AF signatures. Spectra and plots were generated using CytExpert for Spectral software.

Discussion and Conclusion

OMIP-104 serves as a robust tool for analyzing immune cell subsets in mouse metabolic organs and can be easily adapted for studying various tissues or specific leukocyte subsets. The panel transitions seamlessly to the CytoFLEX mosaic Spectral Detection Module, where we have successfully identified all key leukocyte populations and their subsets, as demonstrated in Figure 3. Since this panel was not initially designed for the CytoFLEX mosaic Spectral Detection Module, titration and optimization of experimental settings could further enhance its performance. For example, no fluorochromes designed for the 808 nm near-infrared laser were included in the current panel. These fluorochromes offer an easy solution to expand the panel or replace fluorochromes that result in spread errors. This establishes a reliable foundation for users to confidently apply antibody panels from other instruments onto the CytoFLEX mosaic Spectral Detection Module.

Abbreviations

References

1. Lambooij JM, Tak T, Zaldumbide A, Guigas B. OMIP-104: A 30-color spectral flow cytometry panel for comprehensive analysis of immune cell composition and macrophage subsets in mouse metabolic organs. Cytometry. 2024;105(7):493–500.