Here, we present a protocol to process fresh bone marrow (BM) isolated from mouse or human for high-dimensional mass cytometry (Cytometry by Time-Of-Flight, CyTOF) analysis of neutrophil-lineage cells.
In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a myeloid-biased 39-antibody CyTOF panel to evaluate the hematopoietic system with a focus on the neutrophil-lineage cells by using this protocol. The CyTOF result was analyzed with an open-resource dimensional reduction algorithm, viSNE, and the data was presented to demonstrate the outcome of this protocol. We have discovered new neutrophil-lineage cell populations based on this protocol. This protocol of fresh whole BM preparation may be used for 1), CyTOF analysis to discover unidentified cell populations from whole BM, 2), investigating whole BM defects for patients with blood disorders such as leukemia, 3), assisting optimization of fluorescence-activated flow cytometry protocols that utilize fresh whole BM.
In the past few decades, cytometry methods have been a powerful tool to investigate the hematopoietic system in the BM. These methods include fluorescence-activated flow cytometry and the new method of CyTOF using heavy metal-labeled antibodies. They have led to discoveries of many cell types in a heterogeneous biological specimen by identification of their unique surface marker expression profiles. Increased spectrum overlaps that’s associated with more channels leads to higher data inaccuracy in fluorescence-activated flow cytometry applications. Therefore, unwanted cells are routinely removed in order to enrich cell populations of interest for fluorescence-activated flow cytometry analysis. For example, Ly6G (or Gr-1) and CD11b are considered mature myeloid cell markers and Ly6G+ (or Gr-1+) and CD11b+ cells are routinely removed from BM samples by using magnetic enrichment kits prior to flow cytometry analysis of hematopoietic stem and progenitor cells (HSPCs) or by combining these markers in one dump cocktail channel1,2,3. Another example is that neutrophils are routinely removed from human blood specimen to enrich peripheral blood mononuclear cells (PBMC) for immunological studies. Whole bone marrow isolated from mouse or human, however, is rarely investigated intact for cytometry analysis.
Recently, CyTOF has become a revolutionary tool to investigate the hematopoietic system4,5,6. With CyTOF, the fluorophore-labeled antibodies are replaced by heavy element reporter-labeled antibodies. This method allows for the measurement of over 40 markers simultaneously without the concern of spectrum overlap. It has enabled the analysis of intact biological specimen without pre-depletion steps or a dump channel. Therefore, we can view the hematopoietic system comprehensively with high-content dimensionality from conventional 2-D flow cytometry plots. Cell populations omitted in the past during depletion or gating process can now be brought into light with the high-dimensional data generated by CyTOF4,5. We have designed an antibody panel that simultaneously measures 39 parameters in the hematopoietic system with a focus on the myeloid linage7. Compared to the conventional flow cytometry data, the interpretation and visualization of the unprecedented single-cell high-dimensional data generated by CyTOF is challenging. Computational scientists have developed dimensionality reduction techniques for the visualization of high-dimensional datasets. In this article, we used the algorithm, viSNE, which uses t-Distributed Stochastic Neighbor Embedding (t-SNE) technique to analyze the CyTOF data and to present the high-dimensional result on a 2-dimensional map while conserving the high-dimensional structure of the data8,9,10. On the tSNE plot, similar cells are clustered into subsets and the color is used to highlight the feature of the cells. For example, on Figure 1 the myeloid cells are distributed into several cell subsets based on the similarities of their expression patterns of 33 surface markers resulted from CyTOF (Figure 1)4. Here we investigated mouse bone marrow with our previously reported 39-marker CyTOF panel by viSNE analysis7. viSNE analysis of our CyTOF data revealed an unidentified cell population that showed both HSPC (CD117+) and neutrophil (Ly6G+) characteristics (Figure 2)7.
In conclusion, we present a protocol to process fresh whole bone marrow for CyTOF analysis. In this article, we used mouse bone marrow as an example, while this protocol can also be used to process human bone marrow samples. The details specific to human bone marrow samples are also noted in the protocol as well. The advantage of this protocol is that it contains details such as incubation time and temperature that were optimized to preserve neutrophil-lineage cells in the whole bone marrow to enable investigation on the intact whole bone marrow. This protocol may also be easily modified for fluorescence-activated flow cytometry applications.
All experiments followed approved guidelines of the La Jolla Institute for Allergy and Immunology Animal Care and Use Committee, and approval for the use of rodents was obtained from the La Jolla Institute for Allergy and Immunology according to criteria outlined in the Guide for the Care and Use of Laboratory Animals from the National Institutes of Health.
1. Harvest Mouse Bone Marrow (BM)
2. Stain BM Cells for CyTOF
3. Prepare Cells for CyTOF Acquisition
Figure 1 is presented as an example result from CyTOF experiments. On this tSNE plot the cells across multiple mouse tissues were clustered into subsets based on the similarity of their surface marker expression profiles measured by a 33-parameter CyTOF panel. Cells with more similar properties were automatically clustered together such as the neutrophils, macrophages, or the DCs based on the expression of the 33 markers on each cell.
Figure 2 is presented as an example result for the mouse BM CyTOF experiment using the protocol presented in this study. The protocol preserved the integrity of the whole BM, leading to the discovery of a previously unknown cell population that co-expresses neutrophil (Ly6G+, Figure 2A) and HSPC (CD117+, Figure 2B) signature surface markers simultaneously. This cell population shows a distinct pattern of the surface marker expression measured by our CyTOF panel (Figure 2C) and was omitted by previous myeloid progenitor research due to Ly6G+ cell depletion1,2,3. More importantly, this result led to the discovery of the small subset of the cluster to the left side of the viSNE map that doesn’t express Ly6G however was clustered closely to the CD117+Ly6G+ cells, which suggests the similarity of these cells to the neutrophil-lineage based on the expression of the 39 markers used for this CyTOF experiment.
We used the marker expression profile from the CyTOF data shown in Figure 2C to build a 13-color FACS (fluorescence-activated cell sorting) panel that allows us to isolate the neutrophil progenitors by flow cytometry for downstream functional assays (Figure 3).
Figure 1: Example of the tSNE plot resulted from CyTOF. Aggregate tSNE dimensionality reduced single-cell data from all mice tissues analyzed were plotted and color coded by the 28 'unsupervised' clusters. The coarse identities of each cluster were annotated based on various published analyses. This figure has been modified from reference4. Please click here to view a larger version of this figure.
Figure 2: Automated single-cell analysis of Lin–CD117+Ly6A/E– HSPC cells in bone marrow identifies a distinct neutrophil progenitor population. viSNE maps of Lin–CD117+Ly6A/E– HSPC cells are shown as dot overlays to display the 5 automated clusters. (A) Ly6G and (B) CD117 expression pattern is shown on viSNE map of Lin–CD117+Ly6A/E– HSPC cells as spectrum colored dots. (C) The expression patterns of the indicated markers are shown as histogram overlays of each cluster. This figure has been modified from reference7. Please click here to view a larger version of this figure.
Figure 3: FACS gating strategy demonstrated with mass cytometry (CyTOF) dataset. Manually gated target population was back gated to automated viSNE map for validation. This figure has been modified from reference7. Please click here to view a larger version of this figure.
In past decades, fluorescence-based flow cytometry was used as the main method to study cellular lineages and heterogeneity1,2,3. Although flow cytometry has provided multi-dimensional data, this method is limited by choices of parameters and spectral overlap. To overcome the weakness of flow cytometry we took advantage of CyTOF, which uses heavy metal isotopes instead of fluorophores to label antibodies that eliminates crosstalk between detector channels or autofluorescence of the cells, therefore, can measure many more parameters simultaneously at the single-cell level and generate a much deeper assessment of cellular diversity12. Cell populations omitted in the past during depletion or gating process, especially important immune cells like neutrophil-lineage cells which were for long considered homogeneous13,14, can be better characterized by CyTOF 2,3,7.
To accommodate this powerful cytometry tool of CyTOF to study cell heterogeneity and characterize rare cell populations, protocols that preserve the integrity of biological specimens are extremely practical for heterogeneity studies. Here we described a protocol to process whole BM for CyTOF that enables comprehensive characterization of new cell populations in intact whole BM, which may be used for mouse or human studies. Whole bone marrow isolated from mouse and human contains cell populations, especially neutrophil-lineage cells that are highly fragile and sensitive to environmental changes such as temperature and culture conditions. In this protocol, we have optimized the temperature and incubation conditions for each step in order to preserve these sensitive populations to the maximum level. By doing so the integrity of the whole bone marrow cells is well protected. With personalized marker panel incorporated to this protocol, researchers may identify more cells of interest in different research fields.
Although CyTOF is a powerful end-point analytical tool for discovery of new cell populations and is able to predict the new populations’ function by their marker characteristics, this technology is limited for further downstream functional studies. To enable downstream functional studies, we used viSNE to comprehensively characterize each cluster by examine their expression level of each 39 markers and found the distinct marker combinations as shown in Figure 2C. Although CyTOF data could not be applied to current sorting technologies, its advantage on the measurement of many parameters simultaneously could assist us to identify the best combination of markers within limited parameters. This critical step of data analysis assisted us to take full advantage of CyTOF results to design a FACS-compatible fluorescence-based sorting panel. For example, in this experiment, we used this information in Figure 2C to build a 13-color FACS panel that allows us to isolate the neutrophil progenitor (NeP) by flow cytometry for downstream functional assays (Figure 3). By using CyTOF and FACS in a pipeline, these methods together could enable the isolation of newly discovered cell types for functional studies such as morphology, versatility, in vitro assays, and in vivo studies.
The authors have nothing to disclose.
We would like to thank the LJI Flow Cytometry core for assistance with mass cytometry procedure. This work was supported by NIH grants R01HL134236, P01HL136275, and R01CA202987 (all to C.C.H) and ADA7-12-MN-31 (04) (to C.C.H. and Y.P.Z).
CyTOF Antibodies (mouse) | |||
Anti-Mouse CD45 (Clone 30-F11) -89Y | Fluidigm | Cat# 3089005B | |
Anti-Human/Mouse CD45R/B220 (Clone RA36B2)-176Yb | Fluidigm | Cat# 3176002B | |
Anti-mouse CD105 (Clone MJ7/18)-Purified | Biolegend | Cat# 120402; RRID:AB_961070 | |
Anti-mouse CD115 (CSF-1R) (Clone AFS98)-Purified | Biolegend | Cat# 135502; RRID:AB_1937293 | |
Anti-Mouse CD117/c-kit (Clone 2B8)-166Er | Fluidigm | Cat# 3166004B | |
Anti-mouse CD11a (Clone M17/4)-Purified | Biolegend | Cat# 101101; RRID:AB_312774 | |
Anti-Mouse CD11b (Clone M1/70)-148Nd | Fluidigm | Cat# 3148003B | |
Anti-Mouse CD11c (Clone N418)-142Nd | Fluidigm | Cat# 3142003B | |
Anti-mouse CD127 (IL-7Rα) (Clone A7R34)-MaxPar Ready | Biolegend | Cat# 133919; RRID:AB_2565433 | |
Anti-Mouse CD150 (Clone TC1512F12.2)-167Er | Fluidigm | Cat# 3167004B | |
Anti-mouse CD16.2 (FcγRIV) (Clone 9E9)-Purified | Biolegend | Cat# 149502; RRID:AB_2565302 | |
Anti-Mouse CD162 (Clone 4RA10 (RUO))-Purified | BD Biosciences | Cat# 557787; RRID:AB_647340 | |
Anti-mouse CD169 (Siglec-1) (Clone 3D6.112)-Purified | Biolegend | Cat# 142402; RRID:AB_10916523 | |
Anti-mouse CD182 (CXCR2) (Clone SA044G4)-Purified | Biolegend | Cat# 149302; RRID:AB_2565277 | |
Anti-mouse CD183 (Clone CXCR3-173)-Purified | Biolegend | Cat# 126502; RRID:AB_1027635 | |
Anti-mouse CD335 (NKp46) (Clone 29A1.4)-MaxPar Ready | Biolegend | Cat# 137625; RRID:AB_2563744 | |
Anti-mouse CD34 (Clone MEC14.7)-Purified | Biolegend | Cat# 119302; RRID:AB_345280 | |
Anti-mouse CD41 (Clone MWReg30)-MaxPar Ready | Biolegend | Cat# 133919; RRID:AB_2565433 | |
Anti-Mouse CD43 (Clone S11)-146Nd | Fluidigm | Cat# 3146009B | |
Anti-Mouse CD48 (Clone HM48.1)-156Gd | Fluidigm | Cat# 3156012B | |
Anti-mouse CD62L (Clone MEL-14)-MaxPar Ready | ThermoFisher | Cat# 14-1351-82; RRID:AB_467481 | |
Anti-mouse CD71 (Clone RI7217)-Purified | Biolegend | Cat# 113802; RRID:AB_313563 | |
Anti-mouse CD90 (Clone G7)-Purified | Biolegend | Cat# 105202; RRID:AB_313169 | |
Anti-Mouse F4/80 (Clone BM8)-159Tb | Fluidigm | Cat# 3159009B | |
Anti-mouse FcεRIα (Clone MAR-1)-MaxPar Ready | Biolegend | Cat# 134321; RRID:AB_2563768 | |
Anti-mouse GM-CSF (MP1-22E9 (RUO))-Purified | BD Biosciences | Cat# 554404; RRID:AB_395370 | |
Anti-Mouse I-A/I-E (Clone M5/114.15.2)-174Yb | Fluidigm | Cat# 3174003B | |
Anti-Mouse Ki67 (Clone B56 (RUO))-Purified | BD Biosciences | Cat# 556003; RRID:AB_396287 | |
Anti-Mouse Ly-6A/E (Sca-1) (Clone D7)-169Tm | Fluidigm | Cat# 3169015B | |
Anti-Mouse Ly6B (Clone 7/4)-Purified | abcam | Cat# ab53457; RRID:AB_881409 | |
Anti-mouse Ly-6G (Clone 1A8)-MaxPar Ready | Biolegend | Cat# 127637; RRID:AB_2563784 | |
Anti-Mouse NK1.1 (Clone PK136)-165Ho | Fluidigm | Cat# 3165018B | |
Anti-Mouse Siglec-F (Clone E50-2440 (RUO))-Purified | BD Biosciences | Cat# 552125; RRID:AB_394340 | |
Anti-Mouse TCRβ (Clone H57-597)-143Nd | Fluidigm | (Clone H57-597)-143Nd | |
Anti-mouse TER-119/Erythroid Cells (Clone TER-119)-MaxPar Ready | Biolegend | Cat# 116241; RRID:AB_2563789 | |
Chemicals, Peptides and Recombinant Proteins | |||
Antibody Stabilizer | CANDOR Bioscience | Cat# 130050 | |
Bovine Serum Albumin | Sigma-Aldrich | Cat# A4503 | |
Cisplatin-194Pt | Fluidigm | Cat# 201194 | |
eBioscience 1X RBC Lysis Buffer | ThermoFisher | Cat# 00-4333-57 | |
eBioscience Foxp3 / Transcription Factor Staining Buffer Set | ThermoFisher | Cat# 00-4333-57 | |
EQ Four Element Calibration Beads | Fluidigm | Cat# 201078 | |
Ethylenediaminetetraacetic acid (EDTA) | ThermoFisher | Cat# AM9260G | |
Fetal Bovine Serum | Omega Scientific | Cat# FB-02 | |
HyClone Phosphate Buffered Saline solution | GE Lifesciences | Cat#SH30256.01 | |
Intercalator-Ir | Fluidigm | Cat# 201192B | |
MAXPAR Antibody Labeling Kits | Fluidigm | http://www.dvssciences.com/product-catalog-maxpar.php | |
Paraformaldehyde | Sigma-Aldrich | Cat# 158127 | |
Sodium azide | Sigma-Aldrich | Cat# S2002 | |
Triton X-100 | Sigma-Aldrich | Cat# X100 | |
Trypsin EDTA 1X | Corning | Cat# 25-053-Cl | |
Experimental Model: Organism/Strains | |||
Mouse: C57BL/6J | The Jackson Laboratory | Stock No: 000664 | |
Software Alogrithm | |||
Bead-based Normalizer | Finck et al., 2013 | https://med.virginia.edu/flow-cytometry-facility/wp-content/uploads/sites/170/2015/10/3_Finck-Rachel_CUGM_May2013.pdf | |
Cytobank | Cytobank | https://www.cytobank.org/ | |
Cytofkit v1.r.0 | Chen et al., 2016 | https://bioconductor.org/packages/release/bioc/html/cytofkit.html | |
t-SNE | van der Maaten and Hinton, 2008 | https://cran.r-project.org/web/packages/Rtsne/index.html | |