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Jun 2020

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Isolation of Microglia and Analysis of Protein Expression by Flow Cytometry: Avoiding the Pitfall of Microglia Background Autofluorescence
小胶质细胞的分离及蛋白表达的流式细胞仪分析:避免小胶质细胞背景荧光的陷阱   

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Abstract

Microglia are a unique type of tissue-resident innate immune cell found within the brain, spinal cord, and retina. In the healthy nervous system, their main functions are to defend the tissue against infectious microbes, support neuronal networks through synapse remodeling, and clear extracellular debris and dying cells through phagocytosis. Many existing microglia isolation protocols require the use of enzymatic tissue digestion or magnetic bead-based isolation steps, which increase both the time and cost of these procedures and introduce variability to the experiment. Here, we report a protocol to generate single-cell suspensions from freshly harvested murine brains or spinal cords, which efficiently dissociates tissue and removes myelin debris through simple mechanical dissociation and density centrifugation and can be applied to rat and non-human primate tissues. We further describe the importance of including empty channels in downstream flow cytometry analyses of microglia single-cell suspensions to accurately assess the expression of protein targets in this highly autofluorescent cell type. This methodology ensures that observed fluorescence signals are not incorrectly attributed to the protein target of interest by appropriately taking into account the unique autofluorescence of this cell type, a phenomenon already present in young animals and that increases with aging to levels that are comparable to those observed with antibodies against highly abundant antigens.

Keywords: Microglia isolation (小胶质细胞分离), Flow cytometry (流式细胞仪), Neuroimmunology (神经免疫学), Autofluorescence (自体荧光), Neuroscience (神经科学)

Background

Microglia are a type of tissue-resident macrophages residing in the central nervous system (CNS) and account for 10% to 15% of all cells within the CNS. While displaying some canonical macrophage activities, such as the phagocytosis of debris and apoptotic bodies, microglia are also endowed with functions specific to the CNS microenvironment, such as synaptic remodeling, neuronal support, and oligodendrogenesis (Ransohoff and Khoury, 2016; Clayton et al., 2017; Li and Barres, 2018). This wide array of functions implies the existence of a diverse set of microglia phenotypes, states, and subsets well suited to characterization by single-cell analytical approaches. Fluorescence-based flow cytometry, a technique routinely used in immunological studies, allows for high-throughput, multiparametric analysis of single cells in suspension, isolated from blood or from dissociated tissues. However, the dissociation of CNS tissues generates a large amount of debris, principally myelin fragments, which must be removed before flow cytometry analysis. Commonly cited isolation techniques dissociate CNS tissue through enzymatic digestion and utilize magnetic bead-based strategies to remove myelin or isolate microglia. However, commonly used proteases in enzymatic tissue digestion, such as collagenase and trypsin, can lead to the unintended cleavage of surface antigens on microglia (Autengruber et al., 2012) and promote cellular transcriptional changes during the 37°C incubations required for enzymatic activity (O’Flanagan et al., 2019, Mattei et al., 2020). In addition, magnetic bead isolation is costly, limits study throughput, and, in our hands, does not improve the yield or viability compared to this protocol, which in young (~3 month) mice yields approximately 2.5 × 105 microglia for analysis by flow cytometry. In this study, we describe the preparation of CNS single-cell suspensions for flow cytometry by utilizing a simple mechanical tissue dissociation procedure followed by myelin removal via density centrifugation. Cost-efficient and easy to perform, all steps of this protocol are carried out on ice or at 4°C, limiting cellular changes that would otherwise occur during isolation at higher temperatures.


By utilizing two surface markers (CD45 and CD11b), this protocol yields a population of microglia with uniformly high expression of core the homeostatic markers CX3CR1, P2RY12, and TMEM119 (Burns et al., 2020, Figure 1D) and has also been successfully employed to isolate microglia in multiple states, both homeostatic and activated (Burns et al., 2020, Figure 6–figure supplement 1A), from mice as old as 24 months of age. Although this workflow is optimized for cell analysis by flow cytometry, we highly recommend utilizing an alternative 2-phase Percoll protocol (described in Burns et al., 2020) for fluorescence-activated cell sorting (FACS), as sorting instruments are more sensitive to the amount of debris remaining post-isolation, which can negatively impact FACS purity and yield.


While flow cytometry has become a routine method used in the analysis of immune cells, the analysis of microglia (minimally defined as CD45dim, CD11b+) presents a particular challenge, as they exhibit a uniquely intense level of autofluorescence compared to other cell types, including CNS-resident macrophages (CD45bright, CD11b+), which do not emit any detectable autofluorescence. In addition, the distribution of the autofluorescence signal in microglia is bimodal and biologically dynamic, with about two-thirds of the microglia showing a high autofluorescence signal (autofluorescence-positive) and the remaining third showing no or very low levels of autofluorescence (autofluorescence-negative) (Burns et al., 2020). Interestingly, both autofluorescence subsets are differentially impacted by aging and genetic perturbations, which adds further complexity to their analysis (Burns et al., 2020). In this protocol, we provide key considerations and analytical strategies to avoid the issues associated with microglia autofluorescence.

Materials and Reagents

  1. 25G x ¾” butterfly needle (EXELINT, catalog number: 26768)

  2. 20 ml luer-lock syringe (BD, catalog number: 302830)

  3. 7 ml dounce homogenizer (Wheaton, catalog number: 57542)

  4. 15 ml polypropylene conical tubes (ThermoFisher, catalog number: 339651)

  5. 96 well v-bottom assay plates (Corning, catalog number: 3897)

  6. 96 well 40 µm mesh filter plates (MilliporeSigma, catalog number: MANMN4010)

  7. Cluster tubes (Corning, catalog number: 4411)

  8. 10 cm plastic Petri dishes (Fisher Scientific, catalog number: FB0875713)

  9. Ultracomp eBeads Plus Compensation Beads (ThermoFisher, catalog number: 01-333-42)

  10. Fetal bovine serum (FBS) (ThermoFisher, catalog number: 26140079)

  11. 10% FBS/HBSS

  12. Flow staining buffer (ThermoFisher, catalog number: 00-4222-26)

  13. DAPI (ThermoFisher, catalog number: 62248)

  14. Percoll (GE Healthcare, catalog number: 17-0891-01)

  15. 10× Hanks Balanced Salt Solution (ThermoFisher, catalog number: 14185052)

  16. 1 M HEPES (ThermoFisher, catalog number: 15630080)

  17. 1× HBSS (ThermoFisher, catalog number: 14175095)

  18. 0.5 M EDTA (ThermoFisher, catalog number: 15575020)

  19. TruStain FcX (Biolegend, catalog number: 101320)

  20. Anti-CD45 BV785 (Biolegend, catalog number: 103149)

  21. Anti-CD11b BV510 (Biolegend, catalog number: 101263)

  22. 33% Isotonic Percoll (see Recipes)

  23. 1× Hanks Balanced Salt Solution (HBSS) with 25mM HEPES (see Recipes)

  24. Phosphate buffered solution (PBS) with 3mM EDTA (see Recipes)

  25. Fc receptor blocking solution (see Recipes)

  26. 2× Microglia Antibody Panel (see Recipes)

Equipment

  1. Disposable scalpels (Fisher Scientific, catalog: 3120032) or common single-edge razor blades

  2. 13 mm extra fine Bonn scissors (Fine Science Tools, catalog number: 14084-08)

  3. Iris forceps (Fine Science Tools, catalog number: 11370-31)

  4. 7 ml glass homogenizer (Wheaton, catalog number: 57542)

  5. Refrigerated tabletop centrifuge (ThermoFisher Sorvall Legend XTR, Rotor TX-1000)

  6. 5-laser LSR Fortessa X-20 (Becton Dickinson)

  7. Vacuum line for aspirating

Procedure

  1. Isolation of microglia

    1. Keep all solutions ice-cold through the procedure.

    2. Immediately following CO2 euthanasia, open the chest cavity to expose the heart. Insert the 25G butterfly needle into the left ventricle, make a small incision in the right atrium, and slowly perfuse the mouse with 20 ml of PBS with 3 mM EDTA.

    3. Cut and peel back the skin to expose the skull. Using scissors, cut the spine at the base of the skull. Starting from the brain stem, cut rostrally along the sagittal suture. Peel the two halves of the skull away to the side. Using tweezers, scoop out the brain and transfer into a 15 ml conical tube containing 5 ml of cold HBSS with 25 mM HEPES and keep on ice.

    4. Transfer the brain to a fresh Petri dish on ice and mince the tissue with a scalpel or razor blade into pieces approximately 1 mm in size.

    5. Transfer the minced tissue into the 7 ml dounce homogenizer and add 5 ml of HBSS with 25 mM HEPES.

    6. The 7 ml dounce homogenizer is supplied with two pestles of slightly different sizes and labeled by the manufacturer. Using the pestle marked “loose,” gently disrupt the tissue, on ice, for approximately 10 strokes. Repeat with the pestle marked “tight” for another 10 strokes.

    7. Pour the single-cell suspension into a fresh 15 ml conical tube. Rinse the homogenizer with 5 ml of HBSS with 25 mM HEPES and transfer to the same 15 ml conical tube.

    8. Centrifuge the single-cell suspension at 600 × g for 5 min at 4°C.

    9. Aspirate the supernatant and gently resuspend the cell pellet in 1 ml of 100% FBS.

    10. Add 9 ml of 33% isotonic Percoll solution and mix.

    11. Gently add 1 ml of 10% FBS/HBSS over the cell suspension.

    12. Centrifuge the cell suspension at 800 × g for 15 min at 4°C with full acceleration and no brake.

    13. Carefully aspirate the resulting myelin layer located at the interface and down to the cell pellet.



      Figure 1. Percoll isolation of microglia. A 30% Percoll cell-suspension overlaid with 1 ml 10% FBS/HBSS solution, A. before centrifugation and B. after centrifugation.


    14. Resuspend the cell pellet in 1 ml of HBSS with 25 mM HEPES.

    15. Add 9 ml of HBSS with 25 mM HEPES and centrifuge the single-cell suspension at 600 × g for 5 min at 4°C with full acceleration and brake on.

    16. Aspirate the cell pellet in a final volume of 1 ml of HBSS with 25 mM HEPES. Cells are now ready for antibody staining for flow cytometry.


  2. Fluorochrome-conjugated antibody staining for flow cytometry analysis of cell surface markers on microglia



    Figure 2. Example plate layout for anti-CD11c PE-Cy7 stained microglia and accompanying FMO controls


    1. Transfer 150 µl (approximately 1/7) of the single-cell suspension (approximately 300,000 live cells) from Step A16 to a well of a 96 well v-bottom plate. Repeat for any additional samples. Make sure to include wells for fluorescence-minus-one (FMO) controls for antigens of interest. For example, if the staining panel includes anti-CD11c PE-Cy7, include a well where cells are stained with all other antibodies EXCEPT for anti-CD11c PE-Cy7. This will account for background fluorescence in that specific channel for each sample.

    2. Centrifuge the plate at 300 × g for 3 min at 4°C.

    3. In one swift motion, decant the plate by flicking into a sink. Do not dab the plate dry. Do not flick the plate twice.

    4. Resuspend the cells in the plate with 25 µl of diluted FcBlock solution. Incubate at 4°C for 10 min.

    5. Add 25 µl of the 2× Microglia Antibody Panel to each well and mix well by pipetting. Incubate at 4°C for 30 min.

    6. Add 150 µl of Flow Staining Buffer to each well. Centrifuge the plate at 300 × g for 3 min at 4°C.

    7. In one swift motion, decant the plate by flicking into a sink. Do not dab the plate dry. Do not flick the plate twice.

    8. Resuspend the cells in 200 µl of Flow Staining Buffer. Centrifuge the plate at 300 × g for 3 min at 4°C.

    9. In one swift motion, decant the plate by flicking into a sink. Do not dab the plate dry. Do not flick the plate twice.

    10. Repeat Steps B8 and B9.

    11. Resuspend the cells in 200 µl of Flow Staining Buffer containing 0.1 µg/ml DAPI.

    12. Transfer the samples to a 40 µm mesh filter plate and centrifuge for 1 min at 100 × g to bring the samples to the lower chamber.

    13. Transfer the samples to cluster tubes and keep on ice, protected from light. Promptly proceed with Section C: single-color compensation controls.


  3. Preparation of single-color compensation controls



    Figure 3. Sample plate layout for compensation control beads


    1. For the number of fluorochromes used, add one drop of compensation beads to the same number of wells on a separate 96 well v-bottom plate. Include one extra well of beads for an unstained compensation control. Refer to Figure 3 for a sample layout.

    2. Add 150 µl of Flow Staining Buffer to each well.

    3. Add 1 µl of a single fluorochrome-conjugated antibody to a single well. Repeat for the remaining fluorochromes and wells. Do not add any antibody to the well containing the unstained compensation control. Incubate 15 min at room temperature, protected from light.

    4. Centrifuge the plate at 300 × g for 3 min at 4°C.

    5. In one swift motion, decant the plate by flicking into a sink. Do not dab the plate dry. Do not flick the plate twice.

    6. Resuspend the beads in 200 µl of flow staining buffer and transfer them to cluster tubes. Acquire promptly on the flow cytometer along with stained samples from Section B.


  4. Sample acquisition on a 5-laser LSR Fortessa X-20

    1. In Diva, create your experiment and select the detection channels corresponding to the fluorochrome panel used to stain your samples in Section B. For microglia, it is highly recommended to include an additional unoccupied detection channel (e.g., 488 nm laser, 710/50 nm bandpass) to record background autofluorescence.

    2. Create compensation controls for your experiment in Diva and acquire each individual single-color control, including the unstained beads, from Section C.

    3. Calculate the compensation matrix and then run the samples of interest from Section B.

Data analysis

  1. Identification of microglia from a CNS single-cell suspension

    After sample acquisition, data analysis is performed in FlowJo V10. If a compensation matrix was not generated on the flow cytometer during sample acquisition, complete the compensation wizard in FlowJo before continuing. By utilizing the hierarchical gating strategy illustrated in Figure 4, singlets are gated first, followed by live cells (DAPI-); lastly, microglia are identified by their relatively low CD45 expression and high CD11b expression.



    Figure 4. Hierarchical gating strategy to identify microglia from a CNS single-cell suspension. FSC, forward scatter. Reproduced from Burns et al., 2020.


  2. Analysis of protein target expression in microglia

    Microglia subsets were recently identified based on their high or low/negligible levels of autofluorescence (Burns et al., 2020). Because of the unusually high intensity, broad spectral properties, and bimodality of the autofluorescence signal in the microglia population (Figure 5), flow cytometry analysis of microglia poses specific challenges as experimental observations can be very easily confounded by background autofluorescence. Experiments should include “fluorescence-minus-one” controls (FMO) for antigen-fluorochrome combinations of interest to avoid this issue. In addition, when working with microglia, it is critical to include one or multiple unoccupied cytometer channels (i.e., with no fluorescent antibody/dyes in those channels) during sample acquisition, preferably a channel equivalent to PerCP-Cy5.5 (488 nm blue laser line, 710/50 nm bandpass filter), which is one of the most sensitive channels for microglia autofluorescence (Figure 5).



    Figure 5. Representative flow cytometry histograms of autofluorescence intensity of the entire microglia population, in a single sample, from multiple combinations of excitation lasers and emission filters. AF, autofluorescence. Reproduced from Burns et al., 2020.


      To illustrate these challenges, we present two examples where analysis of target protein expression in microglia can be easily confounded by autofluorescence if proper controls are not included to consider the fraction of the fluorescence signal that is attributable to autofluorescence rather than the target protein marker of interest.

       In this first example, we selected the surface antigen CD11c, which is an integrin transcriptionally expressed at relatively high levels on a small subset of microglia during development, injury, disease, and aging. In Figure 6A, CD45dimCD11b+ microglia stained with anti-CD11c PE-Cy7 showed a clear bimodal distribution of signal in the PE-Cy7 channel, indicative at face value of positive CD11c staining in 57% of the microglia population. However, the histogram from the FMO control sample revealed a similar, bimodal pattern of fluorescence intensity with 44% of microglia gated as positive, reflective of the cellular autofluorescence in this cytometer channel (Figure 6A). When overlaid with the histogram from the CD11c PE-Cy7 stained sample, it was not possible to delineate an adequate gate to identify CD11c+ microglia given the bimodality of the autofluorescence signal in microglia and the existence of two subsets of microglia, one with high levels of autofluorescence and one with no or very low levels of autofluorescence (Figure 6B). Instead, visualizing the data in an XY dot plot format using an empty cytometer channel to measure autofluorescence (Blue laser, 710/50 nm) on one axis against the cytometer channel for the target antigen of interest on the other axis (CD11c PE-Cy7 in this example) allowed us to distinguish CD11c+ microglia previously confounded by the autofluorescence signal and use distinct thresholds for CD11c positivity for the autofluorescent negative and positive microglia populations (Figure 6C). Using this strategy, CD11c was found to be only expressed in 15% of the overall microglial population (Figure 6C). This result is markedly different from the one that mistakenly identified 57% of microglia as CD11c+ while ignoring the autofluorescence signal (Figure 6A). In imaging studies of young, healthy CD11c-YFP reporter mice, YFP+ cells were present as a minor population in the adult brain (Sato-Hashimoto et al., 2019), and in a recent review (Benmamar-Badel et al., 2020), it was estimated that approximately 2% of microglia are CD11c+ in these animals. Therefore, analyzing microglial CD11c expression by flow cytometry without taking AF into account could lead to misleading conclusions.



    Figure 6. Analysis of microglia from 5-month-old mice by flow cytometry. A. Histograms of fluorescence signal from FMO control and CD11c-stained microglia samples in the PE-Cy7 channel (Yellow-green laser, 780/60 nm). B. Overlay of the two previous histograms. C. Pseudocolor dot plots of signal in the PE-Cy7 fluorescence channel versus an unoccupied fluorescence channel (Blue laser, 710/50 nm). AF+, autofluorescence-positive; AF-, autofluorescence-negative; FMO, fluorescence-minus-one; Blue, 488 nm laser; YG, 561 nm yellow-green laser.


    As a second example, we analyzed the impact of microglia autofluorescence on the detection of proteins expressed in the entire microglia population (as opposed to a subset) with modest to high levels of expression at the single-cell level. For this, we specifically chose the receptor family for the Fc region of IgG immunoglobulins: CD16, CD32, and CD64. Fc-receptors comprise a large family of transmembrane proteins expressed at the plasma membrane of immune cells that bind the Fc region of antibodies and activate downstream signaling cascades. Microglia are known to express the gamma family of activating Fc receptors, which includes CD16/32 and CD64 (Pellerin et al., 2021). Fcer1g-deficient mice lack the common signaling FCER1G chain required for surface expression of all activating Fc receptors. When stained with FITC conjugated anti-CD16/32 antibody and without considering microglia autofluorescence, wild-type microglia showed a clear positive staining with a bi-modal distribution (Figure 7A). However, microglia from Fcer1g-/- mice retained a large fraction of this signal with a wider bimodal distribution, indicating that a significant fraction of the signal detected was not attributable to CD16/32 expression but to autofluorescence (Figure 7A). Accordingly, when the analytical strategy described in the prior example was used, and anti-CD16/32 stained samples were displayed on dot plots featuring CD16/32 expression on the y-axis against an empty channel on the x-axis, a large fraction of the signal associated with CD16/32 expression in Figure 7A became clearly attributable to the microglia autofluorescence signal in the Fcer1gKO mice (Figure 7B). Inclusion and overlay of each sample’s accompanying FMO control revealed the accurate expression levels of CD16/32 and the expected lack of CD16/32 staining in microglia from Fcer1gKO mice (Figure 7B). Gating autofluorescence-positive (AF+) and autofluorescence-negative (AF-) microglia subsets (Burns et al., 2020) using the autofluorescence channel further allowed the analysis of CD16/32 expression levels on these AF microglia subsets (Figure 7C).



    Figure 7. Analysis of Fc-receptor (CD16/32) surface expression levels in 6-month-old Fcer1g wild-type and knockout microglia. A. Individual and overlaid histograms of CD16/32 surface levels on microglia isolated from wild-type and Fcer1gKO animals. B. Individual and superimposed pseudocolor dot-plots of fluorescence signal detected in PerCP-Cy5.5 (B-710/50 nm, x-axis) and FITC (B-525/30 nm, y-axis) channels from anti-CD16/32 FITC stained and FMO control microglia isolated from wild-type or Fcer1gKO mice. C. Gating of microglia AF subsets by AF intensity in the B-710/50 nm channel. Histograms of FITC signal intensity in autofluorescence+ (AF+) and autofluorescence- (AF-) gated microglia subsets. Inset labels indicate geomean fluorescence intensity in the FITC channel for indicated population and the calculated difference between anti-CD16/32 FITC stained and FMO control. AF, autofluorescence; FMO, Fluorescence minus one; B, 488 nm blue laser.


    In contrast to the modest levels of expression of CD16/32, microglia express high levels of CD64, which exceed the autofluorescence intensity level seen in microglia. As a result, the detection of this highly expressed antigen is much less subject to autofluorescence confounding issues. This is highlighted by the bright, unimodal CD64 signal observed on wild-type microglia, which clearly surpasses the signal observed in Fcer1gKO microglia (Figure 8A). This is confirmed by displaying the data in a pseudocolor dot plot, as the signal observed in anti-CD64 stained samples exceeds that of background autofluorescence (Figure 8B). Gating AF+ and AF- microglia subsets using the autofluorescence channel further allowed the analysis of CD64 expression levels on these AF microglia subsets (Figure 8C). These results highlight that autofluorescence represents a major confounding factor for the detection of protein expression in microglia using fluorescence-based methods, such as flow cytometry, and that care should be taken to account for autofluorescence using empty cytometer channels to achieve accurate detection and quantification of protein expression in this cell type.



    Figure 8. Analysis of Fc receptor (CD64) surface expression levels in 6-month-old Fcerg1 wild-type and knockout microglia. A. Individual and overlaid histograms of CD64 surface levels on microglia isolated from wild-type and Fcer1gKO animals. B. Individual and superimposed pseudocolor dot-plots of fluorescence signal detected in PerCP-Cy5.5 (B-710/50 nm, x-axis) and APC (R-670/30 nm, y-axis) channels from anti-CD64 APC stained and FMO control microglia isolated from wild-type or Fcer1gKO mice. C. Gating of microglia AF subsets by AF intensity in the B-710/50 nm channel. Histograms of APC signal intensity in AF+ and AF- gated microglia subsets. Inset labels indicate geomean fluorescence intensity in the APC channel for indicated population and the calculated difference between anti-CD64 APC stained and FMO control. AF, autofluorescence; FMO, Fluorescence minus one; B, 488 nm blue laser; R, 640 nm red laser.

Notes

  1. During the wash steps of the flow cytometry staining procedure, flicking the plate twice or dabbing it dry after flicking may dislodge the cell pellet and increase cell loss.

  2. Although microglia AF is detectable in all flow cytometer excitation/emission combinations we have tested, we have found that fluorescence detection channels for red-laser (640 nm) excited fluorochromes exhibit relatively lower levels of AF signal, which may minimize the confounding effects of cell autofluorescence on protein marker detection. In contrast, we found that blue-laser (488 nm) fluorescence detection channels were the more sensitive to detect microglia autofluorescence, and PerCP-Cy5.5 (710/50 nm bandpass) provided the highest resolution between AF microglia subsets.

  3. If the microglial expression of an antigen of interest is expected to be low, it is advisable to test with brighter fluorochromes (e.g., AlexaFluor 647); however, relative fluorochrome intensity will vary depending on the cytometer instrument ultimately used.

  4. When generating single-color compensation controls, microglia should not be used, as the brightness level and bimodal nature of autofluorescence will result in an aberrant compensation matrix. Ideally, compensation beads should be used, but if unavailable, a surrogate non-autofluorescent cell population, such as splenocytes, may be used instead.

  5. On the cytometer, PMT voltage levels should not be decreased to artificially minimize microglial autofluorescence, as this will also decrease the signal from antigens of interest.

  6. Although we do not explicitly cover the methods to probe intracellular antigens, such as LAMP1 and Ki-67, we have successfully used the eBioscience FoxP3 Transcription Factor (ThermoFisher, catalog number: 00-5523-00) staining kit, per manufacturer protocol. However, because this strong fixation/permeabilization protocol alters the detection of several key markers to identify microglia, we recommend using the nuclear antigen PU.1 to accurately identify microglia within these fixed and permeabilized single-cell suspensions.

  7. For murine samples, additional antibodies against microglia-specific cell surface markers have been successfully used (Burns et al., 2020, Figure 1), including anti-P2RY12 (Biolegend, catalog number: 848004), anti-TMEM119 (AbCam, catalog number: 225494), and anti-CX3CR1 (Biolegend, catalog number: 149023)

  8. Although this protocol has been applied primarily to mouse brain tissue, we have successfully used it for mouse spinal cord and brain tissue from rats and cynomolgus monkeys. When isolating CNS microglia from species other than mice, the tissue weight being processed should be kept under < 450 mg.

  9. Aging critically impacts levels of autofluorescence observed in microglia. Although autofluorescence can be detected with a bimodal distribution in microglia as early as postnatal day 30, autofluorescence levels dramatically increase with aging and become more and more confounding when mice get into mature adulthood and older (Burns et al., 2020).

Recipes

  1. 33% isotonic Percoll

    9 ml of Percoll

    10× Hanks Balanced Salt Solution

    20 ml of 1× HBSS with 25 mM HEPES

  2. HBSS with 25 mM HEPES

    12.5 ml 1 M HEPES 

    487.5 ml 1× HBSS 

  3. PBS with 3 mM EDTA

    6 ml 0.5 M EDTA 

    494 ml 1× HBSS

  4. Fc receptor blocking solution (for 10 samples)

    245 µl Flow Staining buffer 

    5 µl TruStain FcX 

  5. 2× microglia antibody panel (for 10 samples)

    2.5 µl of 0.2 mg/ml anti-CD45 BV785 

    2.5 µl of 0.2 mg/ml anti-CD11b BV510 

    245 µl Flow Staining buffer

  6. DAPI working solution

    1 µl of 1 mg/ml DAPI solution

    10 ml of Flow Staining buffer

Acknowledgments

We thank the authors of Burns et al. (2020), from which this protocol was originally derived from.

Competing interests

JCB and MM are full-time employees of Biogen and Biogen shareholders. RMR is a full-time employee of Third Rock Ventures. No authors were provided compensation or free products by any vendors utilized in this protocol.

Ethics

This study was performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Research animals at Biogen were housed in an AAALAC accredited facility and handled according to an approved institutional animal care and use committee (IACUC) protocol (#756).

References

  1. Autengruber, A., Gereke, M., Hansen, G., Hennig, C. and Bruder, D. (2012). Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur J Microbiol Immunol (Bp) 2(2): 112-120.
  2. Benmamar-Badel, A., Owens, T. and Wlodarczyk, A. (2020). Protective Microglial Subset in Development, Aging, and Disease: Lessons From Transcriptomic Studies. Front Immunol 11: 430.
  3. Burns, J. C., Cotleur, B., Walther, D. M., Bajrami, B., Rubino, S. J., Wei, R., Franchimont, N., Cotman, S. L., Ransohoff, R. M. and Mingueneau, M. (2020). Differential accumulation of storage bodies with aging defines discrete subsets of microglia in the healthy brain. Elife 9: e57495.
  4. Clayton, K. A., Van Enoo, A. A. and Ikezu, T. (2017). Alzheimer's Disease: The Role of Microglia in Brain Homeostasis and Proteopathy. Front Neurosci 11: 680.
  5. Li, Q. and Barres, B. A. (2018). Microglia and macrophages in brain homeostasis and disease. Nat Rev Immunol 18(4): 225-242.
  6. Mattei, D., Ivanov, A., van Oostrum, M., Pantelyushin, S., Richetto, J., Mueller, F., Beffinger, M., Schellhammer, L., Vom Berg, J., Wollscheid, B., Beule, D., Paolicelli, R. C. and Meyer, U. (2020). Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations. Int J Mol Sci 21(21).
  7. O'Flanagan, C. H., Campbell, K. R., Zhang, A. W., Kabeer, F., Lim, J. L. P., Biele, J., Eirew, P., Lai, D., McPherson, A., Kong, E., Bates, C., Borkowski, K., Wiens, M., Hewitson, B., Hopkins, J., Pham, J., Ceglia, N., Moore, R., Mungall, A. J., McAlpine, J. N., Team, C. I. G. C., Shah, S. P. and Aparicio, S. (2019). Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol 20(1): 210.
  8. Pellerin, K., Rubino, S. J., Burns, J. C., Smith, B. A., McCarl, C. A., Zhu, J., Jandreski, L., Cullen, P., Carlile, T. M., Li, A., Rebollar, J. V., Sybulski, J., Reynolds, T. L., Zhang, B., Basile, R., Tang, H., Harp, C. P., Pellerin, A., Silbereis, J., Franchimont, N., Cahir-McFarland, E., Ransohoff, R. M., Cameron, T. O. and Mingueneau, M. (2021). MOG autoantibodies trigger a tightly-controlled FcR and BTK-driven microglia proliferative response. Brain. doi: 10.1093/brain/awab231.
  9. Ransohoff, R. M. and El Khoury, J. J. (2016). Microglia in health and disease. Cold Spring Harb. Perspect. Biol 8(1): a020560.
  10. Sato-Hashimoto, M., Nozu, T., Toriba, R., Horikoshi, A., Akaike, M., Kawamoto, K., Hirose, A., Hayashi, Y., Nagai, H., Shimizu, W., Saiki, A., Ishikawa, T., Elhanbly, R., Kotani, T., Murata, Y., Saito, Y., Naruse, M., Shibasaki, K., Oldenborg, P. A., Jung, S., Matozaki, T., Fukazawa, Y. and Ohnishi, H. (2019). Microglial SIRPalpha regulates the emergence of CD11c(+) microglia and demyelination damage in white matter. Elife 8: e42025.

简介

[摘要]小胶质细胞是一种独特类型的大脑中发现的组织驻留先天免疫细胞,脊髓,和视网膜。在健康的神经系统中,它们的主要功能是保护组织免受传染性微生物的侵害,通过突触重塑支持神经元网络,并通过吞噬作用清除细胞外碎片和垂死的细胞。许多现有的小胶质细胞隔离协议需要使用酶促组织消化或基于磁珠的隔离步骤,这增加了这些程序的时间和成本,并为实验引入了可变性。在这里,我们报告了一个协议来生成单-从新鲜收获鼠脑或脊髓的细胞悬浮液,其中有效地解离组织,并通过简单的机械解离和密度离心去除髓鞘碎片和可以应用于大鼠和非人灵长类动物的组织。我们进一步描述了包括在小胶质细胞的下游流式细胞术分析单空信道的重要性-细胞悬浮液以准确评估的在这个高度自体荧光的细胞类型的蛋白质靶的表达。Ť他的方法,确保了小号所观察到的荧光信号小号是不正确通过适当地考虑到这种细胞类型的独特的自发荧光,已经存在于年轻动物的现象,并归因于感兴趣的蛋白质靶标是营养不良的人口增加ES与老化是水平与使用针对高丰度抗原的抗体观察到的结果相当。

[背景]小胶质细胞是一种类型的组织的-驻留在中枢神经系统(CNS)和占了CNS内的所有单元的10%至15%定居巨噬细胞。在显示一些典型的巨噬细胞活动的同时,如碎片和凋亡小体的吞噬作用,小胶质细胞还具有特定于 CNS 微环境的功能,如突触重塑、神经元支持和少突胶质细胞生成(Ransohoff 和 Khoury,2016 年;Clayton等人。 , 2017; Li 和 Barres, 2018)。这种广泛的功能阵列意味着存在一组不同的小神经胶质细胞的表型,状态,和子集由单个非常适合于表征-细胞分析方法。基于荧光的流式细胞术是免疫学研究中常用的一种技术,可以对悬浮液中的单个细胞进行高通量、多参数分析,从血液中分离出来或从分离的组织中分离出来。然而,CNS 组织的解离会产生大量碎片,主要是髓鞘碎片,必须在流式细胞术分析之前将其去除。常用的分离技术通过酶消化分离中枢神经系统组织,并利用基于磁珠的策略去除髓鞘或分离小胶质细胞。然而,酶促组织消化中常用的蛋白酶,如胶原酶和胰蛋白酶,会导致小胶质细胞表面抗原的意外裂解(Autengruber等,2012),并在酶活性所需的 37 ° C 孵育期间促进细胞转录变化(O'Flanagan等人,2019 年,Mattei等人,2020 年)。此外,磁珠分离是昂贵,限制小号研究吞吐量,并且,在我们手中,并不能提高的相比,这协议,产率或生存力,其在年轻(约3月)的小鼠的产量约为2.5 × 10 5小胶质细胞用于流式细胞术分析。在这项研究中,我们描述了CNS单的制备-细胞悬浮液用于流式细胞术通过利用一个简单的机械组织离解过程,接着通过密度离心除去髓磷脂。经济高效且易于执行,该协议的所有步骤均在冰上或4°C 下进行,限制了在较高温度下隔离期间可能发生的细胞变化。

通过利用两个表面标记(CD45和CD11b),该协议产生的小胶质细胞的群体与核心的均匀的高表达的稳态标记CX3CR1,P2RY12 ,和TMEM119(伯恩斯等人,2020年,图1D)和也已成功地采用从 24 个月大的小鼠中分离处于多种状态的小胶质细胞,包括稳态和激活状态(Burns等,2020,图 6-图补充件 1A)。虽然此工作流是通过流式细胞术对细胞分析而优化的,强烈建议采用一种替代的2相的Percoll协议(在伯恩斯描述等人对荧光,2020)-激活细胞分选(FACS),作为分选仪器更敏感分离后残留的碎片量,这会对FACS纯度和产量产生负面影响。

虽然流式细胞术已成为免疫细胞分析的常规方法,但小胶质细胞(最低限度定义为 CD45暗淡、CD11b + )的分析提出了一个特殊的挑战,因为与其他细胞类型相比,它们表现出独特的强自发荧光水平,包括中枢神经系统驻留巨噬细胞(CD45亮、CD11b + ),它们不发出任何可检测的自发荧光。此外,自发荧光信号的小胶质细胞中的分布是双峰的和生物的动态,用约2 -没有显示出高自发荧光信号的小胶质细胞(自体荧光阳性)和剩余第三放映的三分之二或自发荧光的非常低的水平(autofluorescence-负)(Burns等人,2020 年)。有趣的是,这两个自发荧光子集都受到衰老和遗传扰动的不同影响,这进一步增加了其分析的复杂性(Burns等,2020)。在该协议中,我们提供了关键考虑因素和分析策略,以避免与小胶质细胞自体荧光相关的问题。

关键字:小胶质细胞分离, 流式细胞仪, 神经免疫学, 自体荧光, 神经科学


材料和试剂

1. 25G x ¾”蝴蝶针(EXELINT,目录号:26768)     
2. 20 ml l luer-lock 注射器(BD,目录号:302830)     
3. 7 ml dounce均质机(Wheaton,目录号:57542)     
4. 15 ml 聚丙烯锥形管(ThermoFisher,目录号:339651)     
5. 96 孔 v 底测定板(Corning,目录号:3897)     
6. 96 孔 40 µm 网孔过滤板(MilliporeSigma,目录号:MANMN4010)     
7.集束管(Corning,目录号:4411)     
8. 10 cm塑料培养皿(Fisher Scientific,目录号:FB0875713)     
9. Ultracomp eBeads Plus Compensation Beads(ThermoFisher,目录号:01-333-42)     
10.胎牛血清(FBS)(ThermoFisher,目录号:26140079) 
11. 10% FBS/HBSS 
12.流动染色缓冲液(ThermoFisher,目录号:00-4222-26) 
13. DAPI(ThermoFisher,目录号:62248) 
14. Percoll(GE Healthcare,目录号:17-0891-01) 
15. 10 × Hanks平衡盐溶液(ThermoFisher,目录号:14185052) 
16. 1 M HEPES(ThermoFisher,目录号:15630080) 
17. 1 × HBSS(ThermoFisher,目录号:14175095) 
18. 0.5 M EDTA(ThermoFisher,目录号:15575020) 
19. TruStain FcX(Biolegend,目录号:101320) 
20.抗CD45 BV785(Biolegend,目录号:103149) 
21.抗CD11b BV510(Biolegend,目录号:101263) 
22. 33% 等渗 Percoll(见配方) 
23. 1 × Hanks 平衡盐溶液 (HBSS) 和 25mM HEPES(见配方) 
24.含 3mM EDTA 的磷酸盐缓冲溶液 (PBS)(参见配方) 
25. Fc受体阻断液(见配方) 
26. 2 ×小胶质细胞抗体组(见食谱) 

设备

一次性手术刀(Fisher Scientific,目录:3120032)或普通单刃剃须刀片
13 mm超细波恩剪刀(Fine Science Tools,目录号:14084-08)
虹膜钳(Fine Science Tools,目录号:11370-31)
7 ml玻璃均质器(Wheaton,目录号:57542)
冷冻台式离心机(ThermoFisher Sorvall Legend XTR,Rotor TX-1000)。
5 激光 LSR Fortessa X-20(Becton Dickinson)
用于抽吸的真空管线

程序

小胶质细胞的分离
在整个过程中保持所有溶液冰冷。
在 CO 2安乐死之后立即打开胸腔以暴露心脏。将 25G 蝴蝶针插入左心室,在右心房做一个小切口,然后用3 mM EDTA 的20 ml PBS缓慢灌注小鼠。
切开并剥开皮肤以暴露头骨。使用剪刀,在头骨底部剪开脊柱。从脑干开始,沿矢状缝合口切开。将头骨的两半剥开到一边。使用镊子,舀出大脑并转移到含有 5 ml 冷 HBSS和25 mM HEPES的 15 ml 锥形管中,并保持在冰上。
脑转移到新鲜P ETRI菜在冰上,用切碎组织一个解剖刀或剃刀刀片成片大约1mm的尺寸。
将切碎的组织转移到 7 ml dounce 匀浆器中,并加入 5 ml HBSS和25 mM HEPES。
7 ml dounce 均质器配有两个尺寸略有不同的研杵,并由制造商标记。使用标记的杵“松,”轻轻地破坏组织,在冰上,约10个冲程。用标记为“紧”的杵重复 10 次。
倾单-细胞悬浮液到一个新的15ml锥形管中。用 5 ml HBSS和25 mM HEPES冲洗匀浆器,并转移到相同的 15 ml 锥形管中。
离心机的单-在600细胞悬液×克5分钟,在4℃。
吸出上清液,轻轻地将细胞沉淀重悬在 1 ml 100% FBS 中。
加入 9 ml 33% 等渗 Percoll 溶液并混合。
在细胞悬液上轻轻加入 1 ml 10% FBS/HBSS。
将细胞悬液在4°C 下以800 × g 的速度离心15 分钟,全加速且无刹车。
C完全将位于界面处的髓鞘层吸入并向下至细胞颗粒。

 图片包含 文字, 瓶子 描述已自动生成
图 1. Percoll 分离小胶质细胞。甲30%的Percoll细胞-悬浮液覆盖1毫升10%FBS / HBSS溶液,甲。离心前和 B. 离心后。

用25 mM HEPES在 1 ml HBSS 中重悬细胞沉淀。
添加9毫升HBSS的用25mM的HEPES和离心单-细胞悬浮液以600 ×克在5分钟4℃下用全加速和制动上。
用25 mM HEPES在1 ml HBSS的最终体积中吸出细胞沉淀。细胞现在已准备好用于流式细胞术的抗体染色。

用于流式细胞术分析小胶质细胞表面标志物的荧光染料偶联抗体染色
 
图表、气泡图 描述已自动生成
图 2.抗 CD11c PE-Cy7 染色的小胶质细胞和伴随的 FMO 对照的示例板布局

转移150微升(约单一的1/7)-细胞悬浮液(约imately从步骤A16 300000活细胞)到井的96孔v -底板。对任何其他样品重复此操作。确保包括用于感兴趣抗原的荧光减一 (FMO) 控制的孔。例如,如果染色面板包括抗 CD11c PE-Cy7,则包括一个孔,其中细胞被所有其他抗体染色,除了抗 CD11c PE-Cy7。这将解释每个样本在该特定通道中的背景荧光。
将板在4°C 下以 300 × g离心3 分钟。
在一个快速的动作中,通过轻弹到水槽中来倒出盘子。不要将盘子擦干。不要轻弹盘子两次。
重悬细胞与25中的板微升的稀释溶液FcBlock。在 4°C 下孵育 10 分钟。
向每个孔中加入 25 µl 2 × Microglia Antibody Panel,并通过移液充分混合。在 4°C 下孵育 30 分钟。
向每个孔中加入 150 µl Flow Staining Buffer。在4°C 下以 300 x g离心板3 分钟。
在一个快速的动作中,通过轻弹到水槽中来倒出盘子。不要将盘子擦干。不要轻弹盘子两次。
在 200 µl流动染色缓冲液中重悬细胞。将板在4°C 下以 300 x g离心3 分钟。
在一个快速的动作中,通过轻弹到水槽中来倒出盘子。不要将盘子擦干。不要轻弹盘子两次。
重复步骤 B8 和 B9。
重悬200细胞μ流染色缓冲液升含有0.1微克/毫升DAPI。
将样品转移到 40 µm 滤板和离心机上,以 100 x g离心 1 分钟,将样品带到下室。
将样品转移到簇管中并保存在冰上,避光。单:及时与C部分进行-色补偿控制。

单色补偿控制的制备
 
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图 3.补偿控制珠的样品板布局

对于使用的荧光染料的数量,在单独的 96 孔 v 底板上的相同数量的孔中添加一滴补偿珠。包括一口额外的珠子,用于未染色的补偿控制。有关示例布局,请参阅图 3。
向每个孔中加入 150 µl Flow Staining Buffer。
将 1 µl 的单一荧光染料偶联抗体添加到单个孔中。对剩余的荧光染料和孔重复上述操作。不要在含有未染色补偿控制的孔中添加任何抗体。在室温下孵育 15 分钟,避光。
将板在4°C 下以 300 × g离心3 分钟。
在一个快速的动作中,通过轻弹到水槽中来倒出盘子。不要将盘子擦干。不要轻弹盘子两次。
在 200 µl 流动染色缓冲液中重悬珠子并将它们转移到簇管中。在流式细胞仪上迅速获取 B 部分的染色样本。

5 激光 LSR Fortessa X-20 上的样品采集
在 Diva 中,创建您的实验并选择与用于对 B 部分中的样品进行染色的荧光染料面板相对应的检测通道。对于小胶质细胞,强烈建议包括一个额外的未占用检测通道(例如,488 nm 激光,710/50 nm 带通)以记录背景自发荧光。
在 Diva 中为您的实验创建补偿控制,并从 C 部分获取每个单独的单色控制,包括未染色的珠子。
计算补偿矩阵,然后从 B 部分运行感兴趣的样本。

数据分析

1.从小胶质细胞的鉴定一个CNS单-细胞悬浮液     
样品采集后,在 FlowJo V10 中进行数据分析。如果在样品采集期间流式细胞仪上未生成补偿矩阵,请在继续之前完成 FlowJo 中的补偿向导。通过利用图 4 中所示的分层门控策略,单线态首先被门控,然后是活细胞 (DAPI - ) ;最后,小胶质细胞通过其相对低的 CD45 表达和高 CD11b 表达来识别。

手机屏幕的截图描述已自动生成
图4.分层门控策略以从CNS单一识别的小胶质细胞-细胞悬浮液。FSC ,前向散射。转载自 Burns等人,2020 年。

2.小胶质细胞蛋白靶点表达分析     
最近根据其高或低/可忽略的自发荧光水平确定了小胶质细胞亚群(Burns等,2020)。由于异常高的强度,宽的光谱特性,并在小胶质细胞群体(图5)的自发荧光信号的双峰,流动的小胶质细胞的姿势作为实验观察可通过背景自发荧光可以非常容易地混淆具体挑战的流式细胞分析。Ë xperiments应包括对感兴趣抗原荧光组合“荧光减一”控件(FMO)牛逼o避免这个问题。此外,在处理小胶质细胞时,在样品采集过程中包含一个或多个未占用的细胞计数器通道(即,这些通道中没有荧光抗体/染料)至关重要,最好是相当于 PerCP-Cy5.5(488 nm 蓝色)的通道激光线,710/50 nm 带通滤波器),它是小胶质细胞自发荧光最敏感的通道之一(图 5)。

图片包含 形状 描述已自动生成
图 5.单个样本中整个小胶质细胞群的自发荧光强度的代表性流式细胞术直方图,来自激发激光器和发射滤波器的多种组合。AF ,自发荧光。转载自 Burns等人,2020 年。

                                                                                                                  为了说明这些挑战,我们提出了两个例子,如果不包括适当的控制来考虑可归因于自发荧光而不是目标蛋白标记的荧光信号的分数,那么自发荧光很容易混淆小胶质细胞中目标蛋白表达的分析.
  在该第一示例中,我们选择了表面抗原的CD11c,其是在小胶质细胞上的一个小的子集相对高水平的发展,损伤,疾病过程中转录表达的整联,并老化。在图 6A 中,CD45暗淡CD11b +用抗 CD11c PE-Cy7 染色的小胶质细胞在 PE-Cy7 通道中显示出清晰的双峰信号分布,表明在 57% 的小胶质细胞群中阳性 CD11c 染色的面值。然而,来自 FMO 控制样本的直方图显示了类似的双峰荧光强度模式,44% 的小胶质细胞门控为阳性,反映了该细胞仪通道中的细胞自发荧光(图 6A)。当与来自 CD11c PE-Cy7 染色样本的直方图重叠时,鉴于小胶质细胞中自发荧光信号的双峰性和两个小胶质细胞亚群的存在,一个具有高水平的小胶质细胞亚群的存在,不可能描绘出足够的门来识别 CD11c +小胶质细胞自发荧光和一个没有或非常低水平的自发荧光(图 6B)。相反,使用空的细胞仪通道以XY 点图格式可视化数据,以测量一个轴上的自发荧光(蓝色激光,710/50 nm)与另一轴上感兴趣的目标抗原的细胞仪通道(CD11c PE-Cy7)在这个例子中)允许我们区分之前被自发荧光信号混淆的CD11c +小胶质细胞,并使用不同的阈值来确定自发荧光阴性和阳性小胶质细胞群的 CD11c 阳性(图 6C)。使用这种策略,CD11c的被发现在整体的小胶质细胞的15%,只表示升人口(图6C)。这个结果是从所述一个显着不同的是错误地识别的小胶质细胞的57%作为表面CD11c +而忽略了自发荧光信号(图6A)。在年轻,健康的CD11c YFP记者小鼠的影像学检查,YFP +细胞存在作为未成年人群体在成人大脑(佐藤太郎等人,2019) ,并在最近进行的检讨(Benmamar-BADEL等人。,2020) ,估计这些动物中大约 2% 的小胶质细胞是 CD11c + 。因此,在不考虑 AF 的情况下通过流式细胞术分析小胶质细胞 CD11c 表达可能会导致误导性结论。

图表描述自动生成
图 6. 通过流式细胞术分析 5 个月大小鼠的小胶质细胞。A. PE-Cy7 通道(黄绿色激光,780/60 nm)中FMO 对照和 CD11c 染色的小胶质细胞样品的荧光信号直方图。B. 前两个直方图的叠加。C. PE-Cy7 荧光通道与未占用荧光通道(蓝色激光,710/50 nm)中信号的伪彩色点图。AF+ ,自体荧光阳性;AF- ,自体荧光阴性;FMO ,荧光减一;蓝色,488 nm 激光;YG , 561 nm 黄绿色激光。

  作为第二个例子,我们分析了小胶质细胞的自体荧光的对检测的影响在整个小胶质细胞群体表达的蛋白质的(而不是一个子集)具有适度在以高水平表达的单-细胞水平。为此,我们专门选择了 IgG 免疫球蛋白 Fc 区的受体家族:CD16、CD32 和 CD64。Fc -受体包括一大类跨膜蛋白,它们在免疫细胞的质膜上表达,这些蛋白结合抗体的 Fc 区并激活下游信号级联。已知小胶质细胞表达激活 Fc 受体的 γ 家族,其中包括 CD16/32 和 CD64(Pellerin 等人,已提交)。Fcer1g 缺陷小鼠缺乏所有激活 Fc 受体表面表达所需的通用信号 FCER1G 链。当用 FITC 共轭抗 CD16/32 抗体染色且不考虑小胶质细胞自发荧光时,野生型小胶质细胞显示出双峰分布的清晰阳性染色(图 7A)。然而,来自Fcer1g -/-小鼠的小胶质细胞保留了该信号的很大一部分,具有更宽的双峰分布,表明检测到的信号的很大一部分不是由 CD16/32 表达而是由自体荧光引起的(图 7A)。因此,当使用前面例子中描述的分析策略时,抗 CD16/32 染色的样本显示在点图上,y 轴上的 CD16/32 表达对 x 轴上的空通道,很大一部分图 7A 中与 CD16/32 表达相关的信号明显归因于Fcer1g KO小鼠中的小胶质细胞自发荧光信号(图 7B)。每个样品随附的 FMO 控制的包含和叠加揭示了 CD16/32 的准确表达水平以及Fcer1g KO小鼠小胶质细胞中预期缺乏 CD16/32 染色(图 7B)。使用自发荧光通道门控自发荧光阳性 (AF + ) 和自发荧光阴性 (AF - ) 小胶质细胞子集(Burns等人,2020 年)进一步允许分析这些 AF 小胶质细胞子集上的 CD16/32 表达水平(图 7C)。           

图表描述以低置信度自动生成
图 7. 6 个月大的Fcer1g野生型和敲除小胶质细胞中 Fc 受体 (CD16/32) 表面表达水平的分析。A.从野生型和Fcer1g KO动物中分离的小胶质细胞上CD16/32 表面水平的单个和叠加直方图。B.在 PerCP-Cy5.5(B-710/50 nm,x 轴)和 FITC(B-525/30 nm,y 轴)通道中检测到的来自抗 CD16的荧光信号的单个和叠加伪彩色点图/32 从野生型或Fcer1g KO小鼠中分离的 FITC 染色和 FMO 对照小胶质细胞。C. 通过 B-710/50 nm 通道中的 AF 强度对小胶质细胞 AF 子集进行门控。自发荧光+ (AF+) 和自发荧光- (AF-) 门控小胶质细胞亚群中 FITC 信号强度的直方图。插图标签表示 FITC 通道中指定人群的几何平均荧光强度以及抗 CD16/32 FITC 染色和 FMO 控制之间的计算差异。AF ,自发荧光;FMO ,荧光减一;B , 488 nm 蓝色激光。

  与中等水平的 CD16/32 表达相反,小胶质细胞表达高水平的 CD64,超过了小胶质细胞中看到的自发荧光强度水平。因此,这种高度表达的抗原的检测较少受到自发荧光混淆问题的影响。在野生型小胶质细胞上观察到的明亮的单峰 CD64 信号突出了这一点,它明显超过了在Fcer1g KO小胶质细胞中观察到的信号(图 8A)。这通过在伪彩色点图中显示数据得到证实,因为在抗 CD64 染色样品中观察到的信号超过了背景自发荧光的信号(图 8B)。门控AF +和AF -使用自发荧光信道子集的小胶质进一步允许在这些AF小胶质细胞亚群(图8C)CD64的表达水平的分析。Ť HESE结果突出显示的自体荧光代表小号一主要混杂因子的使用基于荧光的方法检测在小神经胶质细胞的蛋白质表达,如流式细胞仪,以及护理应使用空血细胞计数通道,以实现准确的检测采取帐户自发荧光和这种细胞类型中蛋白质表达的定量。

图片包含图表 描述已自动生成
图 8. 6 个月大的Fcerg1野生型和敲除小胶质细胞中 Fc 受体 (CD64) 表面表达水平的分析。A.从野生型和Fcer1g KO动物中分离的小胶质细胞上CD64 表面水平的单个和叠加直方图。B.在 PerCP-Cy5.5(B-710/50 nm,x 轴)和 APC(R-670/30 nm,y 轴)通道中检测到的来自抗 CD64 的荧光信号的单个和叠加伪彩色点图从野生型或Fcer1g KO小鼠中分离的APC 染色和 FMO对照小胶质细胞。C. 通过 B-710/50 nm 通道中的 AF 强度对小胶质细胞 AF 子集进行门控。AF+ 和 AF 门控小胶质细胞亚群中 APC 信号强度的直方图。插图标签表示 APC 通道中指定人群的几何平均荧光强度以及抗 CD64 APC 染色和 FMO 控制之间的计算差异。AF ,自发荧光;FMO ,荧光减一;B 、488nm蓝色激光;R , 640 nm 红色激光。

笔记

1.在的洗涤步骤的流式细胞术染色过程中,轻拍板两次或轻拍其轻拂可以逐出细胞沉淀并增加细胞损失后干燥。     
2.虽然小胶质细胞AF是在我们已经测试了所有的流式细胞仪的激发/发射的组合可检测的,我们已经发现,对于红色激光(640纳米)激发的荧光染料表现出AF信号的相对较低的水平,这可以最小化荧光检测通道的混杂影响细胞自发荧光对蛋白质标记检测的影响。相比之下,我们发现蓝色激光 (488 nm) 荧光检测通道对检测小胶质细胞自发荧光更敏感,而 PerCP-Cy5.5(710/50 nm 带通)在 AF 小胶质细胞亚群之间提供了最高分辨率。     
3.如果所关注的抗原的小胶质表达预期要低,最好是用荧光染料亮测试(例如,的AlexaFluor 647); 然而,相对荧光染料的强度会因最终使用的细胞仪仪器而异。     
4.当生成单-色补偿控制,不应该被用来小胶质细胞,作为亮度水平和自发荧光的双峰性质将导致异常的补偿矩阵。理想情况下,应使用补偿珠,但如果不可用,则可以使用替代的非自体荧光细胞群,例如脾细胞。     
5.在细胞仪上,不应通过降低 PMT 电压水平来人为地减少小胶质细胞自发荧光,因为这也会降低来自感兴趣抗原的信号。     
6.虽然我们没有明确涵盖探测细胞内抗原的方法,例如 LAMP1 和 Ki-67,但我们已经成功地使用了 eBioscience FoxP3 转录因子(ThermoFisher,目录号:00-5523-00)染色试剂盒,按照制造商的协议. 然而,由于这种强烈的固定/透化协议改变的几个关键标志物检测以识别小胶质细胞,我们建议使用核抗原PU.1到准确识别这些固定和透化单内的小胶质细胞-细胞悬浮液。     
7.对于鼠类样本,已成功使用针对小胶质细胞特异性细胞表面标志物的其他抗体(Burns等人,2020,图 1),包括抗 P2RY12(Biolegend,目录号:848004)、抗 TMEM119(AbCam,目录号:225494)和抗CX3CR1(Biolegend,目录号:149023)     
8.虽然该协议主要应用于小鼠脑组织,但我们已成功将其用于大鼠和食蟹猴的小鼠脊髓和脑组织。当从除小鼠其它物种分离的CNS小胶质细胞,正被处理的组织重量应在<450毫克被保持。     
9.老化严重影响小号自发荧光的水平在小胶质细胞观察。尽管早在出生后第 30 天就可以在小胶质细胞中以双峰分布检测到自发荧光,但自发荧光水平会随着年龄的增长而显着增加,并且在小鼠进入成熟期和更老时变得越来越令人困惑(Burns等人,2020 年)。     
食谱

1. 33% 等渗 Percoll     
9 ml Percoll(GE Healthcare,目录号:17-0891-01)
10 × Hanks平衡盐溶液(ThermoFisher,目录号:14185052)
20 ml 1 × HBSS和25 mM HEPES
2. HBSS与25 mM HEPES     
12.5 ml 1 M HEPES(ThermoFisher,目录号:15630080)
487.5 ml 1 × HBSS(ThermoFisher,目录号:14175095)
3.含3 mM EDTA 的PBS     
6 ml 0.5 M EDTA(ThermoFisher,目录号:15575020)
494 毫升 1 × HBSS
4. Fc受体封闭液(10个样品)     
245 μ升流染色缓冲液(赛默飞,目录号:00-4222-26)
5 μ升TruStain FCX(Biolegend公司,目录号:101320)
5. 2 ×小胶质细胞抗体板(用于 10 个样品)     
2.5 μ的0.2升毫克/ ml抗CD45 BV785(Biolegend公司,目录号:103149)
2.5 μ的0.2升毫克/毫升的抗CD11b BV510(Biolegend公司,目录号:101263)
245 μ升流染色缓冲液
6. DAPI 工作解决方案                   
1 µl 1 mg/ml DAPI 溶液
10 毫升流动染色缓冲液

致谢

我们感谢伯恩斯等人的作者。( 2020 ) ,该协议最初源自该协议。

利益争夺

JCB和MM是全-生物遗传和生物遗传股东全职员工。RMR 是 Third Rock Ventures 的全职员工。本协议中使用的任何供应商均未向作者提供补偿或免费产品。

伦理

这项研究是根据美国国立卫生研究院实验室动物护理和使用指南进行的。Biogen 的研究动物被安置在 AAALAC 认可的设施中,并根据批准的机构动物护理和使用委员会 (IACUC) 协议 (#756) 进行处理。

参考

Autengruber, A.、Gereke, M.、Hansen, G.、Hennig, C. 和 Bruder, D. (2012)。酶促组织分解对表面分子表达水平和免疫细胞功能的影响。Eur J Microbiol Immunol (Bp) 2(2): 112-120。
Benmamar-Badel, A.、Owens, T. 和 Wlodarczyk, A.(2020 年)。发育、衰老和疾病中的保护性小胶质细胞亚群:转录组学研究的经验教训。前免疫学11: 430。              
Burns, JC, Cotleur, B., Walther, DM, Bajrami, B., Rubino, SJ, Wei, R., Franchimont, N., Cotman, SL, Ransohoff, RM 和 Mingueneau, M. (2020)。随着年龄的增长,存储体的差异积累定义了健康大脑中小胶质细胞的离散子集。Elife 9:e57495 。
Clayton, KA、Van Enoo, AA 和 Ikezu, T.(2017 年)。阿尔茨海默病:小胶质细胞在脑稳态和蛋白质病中的作用。前神经科学11:680。
Li, Q. 和 Barres, BA (2018)。脑稳态和疾病中的小胶质细胞和巨噬细胞。Nat Rev Immunol 18(4): 225-242。
Mattei, D., Ivanov, A., van Oostrum, M., Pantelyushin, S., Richetto, J., Mueller, F., Beffinger, M., Schellhammer, L., Vom Berg, J., Wollscheid, B ., Beule, D., Paolicelli, RC 和 Meyer, U. (2020)。酶解诱导脑细胞群中的转录和蛋白质型偏差。Int J Mol Sci 21(21)。
O'Flanagan, CH, Campbell, KR, Zhang, AW, Kabeer, F., Lim, JLP, Biele, J., Eirew, P., Lai, D., McPherson, A., Kong, E., Bates, C., Borkowski, K., Wiens, M., Hewitson, B., Hopkins, J., Pham, J., Ceglia, N., Moore, R., Mungall, AJ, McAlpine, JN, Team, CIGC, Shah, SP 和 Aparicio, S. (2019)。用于单细胞 RNA-seq 的实体肿瘤组织与冷活性蛋白酶的分离使保守的胶原酶相关应激反应最小化。基因组生物学20(1):210。              
Pellerin, K., Rubino, SJ, Burns, JC, Smith, BA, Zhu, J. , Jandreski, L., Cullen, P., Carlisle, T., Reynolds, T.,, Zhang, B., Basile, R., Tang, H., Parker-Harp, C., Franchimont, N., Cahir-McFarland, E., Ransohoff, R., Cameron, TO 和 Mingueneau, M. (2020) 。抗 MOG 自身抗体会触发严格控制的 FcR 和 BTK 依赖性小胶质细胞增殖反应。(已提交)
Ransohoff, RM 和 El Khoury, JJ (2016)。小胶质细胞在健康和疾病中。冷泉港。透视。生物学8(1):a020560。              
Sato-Hashimoto, M., Nozu, T., Toriba, R., Horikoshi, A., Akaike, M., Kawamoto, K., Hirose, A., Hayashi, Y., Nagai, H., Shimizu, W ., Saiki, A., Ishikawa, T., Elhanbly, R., Kotani, T., Murata, Y., Saito, Y., Naruse, M., Shibasaki, K., Oldenborg, PA, Jung, S. , Matozaki, T., Fukazawa, Y. 和 Ohnishi, H. (2019)。小胶质细胞 SIRPalpha 调节 CD11c(+) 小胶质细胞的出现和白质脱髓鞘损伤。Elife 8:e42025。
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Copyright Burns et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Burns, J. C., Ransohoff, R. M. and Mingueneau, M. (2021). Isolation of Microglia and Analysis of Protein Expression by Flow Cytometry: Avoiding the Pitfall of Microglia Background Autofluorescence. Bio-protocol 11(14): e4091. DOI: 10.21769/BioProtoc.4091.
  2. Burns, J. C., Cotleur, B., Walther, D. M., Bajrami, B., Rubino, S. J., Wei, R., Franchimont, N., Cotman, S. L., Ransohoff, R. M. and Mingueneau, M. (2020). Differential accumulation of storage bodies with aging defines discrete subsets of microglia in the healthy brain. Elife 9: e57495.
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