Single-cell mass cytometry adapted to measurements of the cell cycle G0/1 S G2
Method for cell cycle analysis – Markers of cell cycle phases S-phase G0 G1, G2, M – Validation with cycling T Cells System-level analysis of cell cycle in normal and malignant hematopoiesis – SPADE clustering – 35 parameter analysis of normal bone marrow cell cycle – Application to hematologic malignancies Cell cycle analysis by mass cytometry
Mass cytometry DNA staining G0 G1 G2 S HoechstIr Intercalator (pentamethylcyclopentadienyl)-Ir(III)-dipyridophenazine
IdU incorporates rapidly into S-phase cells 56.5% 47.6% Ir Intercalator Uridine Fluorescent Cytometry Mass Cytometry Hoechst Uridine
IdU incorporates rapidly into S-phase cells 56.5% 47.6% Ir Intercalator Uridine Fluorescent Cytometry Mass Cytometry Hoechst Uridine
Intercalator Ir193 IdU I127 No IdU5 min10 min15 min 30 min60 min120 min 0.3%21.9%20.6%24.2% 24.3% 26.6% 28.9% IdU incorporates rapidly into S-phase cells
-Andrew Hughes, Gene Ther Mol Biol., 2006; 10:41 p-histone H3 Additional markers allow for complete cell cycle state assignment
p-histone H3 Additional markers allow for complete cell cycle state assignment p-Rb (S807/S811) Uridine G0
p-histone H3 Additional markers allow for complete cell cycle state assignment p-histone H3 Uridine Cyclin B1 G0/G1 S G2
p-histone H3 Additional markers allow for complete cell cycle state assignment p-histone H3 Uridine p-Histone H3 (S28) M
B A C Cyclin B1 Dy164 IdU I127 p-Rb Ho165 IdU I127 p-Histone H3 Er168 IdU I127 human T cells G0 M S G2/M G0/G1 U937 cellsHL60 cellsNALM6 cells Cell cycle assessment is robust and consistent across multiple cell types
U937 cellsHL60 cellshuman T cells B A C Cyclin B1 Dy164 IdU I127 p-Rb Ho165 IdU I127 p-Histone H3 Er168 IdU I127 G0 M S G2/M G0/G1 NALM6 cells Cell cycle assessment is robust and consistent across multiple cell types
Pyronin Y Hoechst p-Rb Alexa 647 Pyronin Y IdU FITC p-Rb Alexa 647 p-Rb Ho165 IdU I127 p-Rb Ho165 Intercalator Ir193 A B C D 24.4% 75.6% 23.8% 76.3% 72.3% 27.6% Phosphorylated Rb (S807/S811) discriminates G0 and G1 phase cells
The same cell cycle markers can be used for fluorescence cytometry IdU Cyclin B1pRb Mass Cytometry S G0/G1 G2/M G0 Hoechst Cyclin B1pRb Fluorescence Cytometry IdU Pyronin Y Hoechst S S G0/G1 G2/M G0 IdU
0h 28h 48h Mass cytometry cell cycle analysis is equivalent to fluorescent methodologies Fluorescent cytometryMass cytometry
0h 28h 48h Mass cytometry cell cycle analysis is equivalent to fluorescent methodologies Fluorescent cytometryMass cytometry
0h 28h 48h Mass cytometry cell cycle analysis is equivalent to fluorescent methodologies Fluorescent cytometryMass cytometry
0h 28h 48h Mass cytometry cell cycle analysis is equivalent to fluorescent methodologies
Method for cell cycle analysis –Markers of cell cycle phases S-phase G0 G1, G2, M –Validation with cycling T Cells System-level analysis of cell cycle in normal and malignant hematopoiesis –SPADE clustering –35 parameter analysis of normal bone marrow cell cycle –Application to hematologic malignancies Cell cycle analysis by mass cytometry
Panel for analysis of cell cycle in human marrow
Biaxial plots are not a scalable solution Parameters: Plots: Sean Bendall, Erin Simonds. Science, May 2011
SPADE: Spanning-tree Progression Analysis of Density-normalized Events – Peng Qiu 1.Determine Tree Structure 1.Overlay regions with surface marker expression levels
SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD45
Naïve CD4 T CD4 T NK CD8 T Naïve CD8 T NKT SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD3 CD45
Early Mature Myeloid Meta- myelocytes Myelocytes Late Promyelocytes Myeloblasts CMP SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD15 CD45
Macrophages Mature Monocytes Early Monocytes PC-DCs Pro-monocytes Monoblasts SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD14 CD45
Mature B cells Immature B cells Pre-BII Plasma Pre-BI Pro-B SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD20 CD45
Erythroblasts Nucleated RBCs Pro- erythroblasts MEP SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD235 CD45
HSC Vasc. Progen? MPP Pre-BI Pro-B Pro- erythroblasts Monoblasts Myeloblasts CMP MEP GMP SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD34 CD45
HSC Macrophages Mature Monocytes Early Monocytes PC-DCs Pro-monocytes Naïve CD4 T CD4 T NK CD8 T Naïve CD8 T Vasc. Progen? MPP Mature B cells Immature B cells Pre-BII Plasma Pre-BI Pro-B Erythroblasts Nucleated RBCs Early Mature Myeloid Meta- myelocytes Myelocytes Late Promyelocytes Pro- erythroblasts Monoblasts Myeloblasts CMP MEP GMP NKT SPADE clustering of normal bone marrow mirrors immunophenotypic differentiation. CD45
B cell proliferation is concentrated in pre-BII population CD34 CD10 CD19 CD20 Pro-B Pre-BI Pre-BII Mature B Normal Human Bone Marrow Colored for CD45 G1 S G2 = 200 Cells = 67%
Erythroid cell proliferation is concentrated in erythroblast population Normal Human Bone Marrow Colored for CD45 G1SG2 CD34CD71CD235 MEP Pro- erythro- blast Erythro- blast Nucleated RBC = 200 Cells = 67%
Myelocyte proliferation peaks at early promyelocyte stage Normal Human Bone Marrow Colored for CD45 CD11b CD15 CD16 Early Promyelo- cytes Metamyelocyte Mature myelocyte Late Promyelo- cytes Myelocyte = 3500 Cells = 67% S G2 G1
SPADE analysis allows for identification of distinct AML immunophenotypes NK Cell T Cell Mature Myeloid Promyelocyte / Myelocyte HSC / Early Progenitor B Cell ?Pro-B / Pre-B Mature Monocytic Promonocyte Normal human bone marrow Clustered alongside AML samples Colored for CD45 Myleo/mono- blast
Cell cycle distribution varies across the immunophenotypic subsets within each AML sample AML9AML5 CD34 S = 20%
Conclusions Validated methodology for using mass cytometry to asses cell cycle state in combination with high-parameter immunophenotypic analysis System-wide analysis of proliferation across normal human hematopoiesis The ability to combine cell cycle state with multiple other variables in the monitoring of cellular responses at the single-cell level We intend to use this methodology to characterize the cell cycle within complex human cancer samples
Hematology –Bruno Medeiros –Peter Greenberg –Beverly Mitchell Aparna Raval Cytobank –Nikesh Kotecha Acknowledgements Nolan Lab –Garry Nolan –Wendy Fantl –Sean Bendall –Erin Simmonds –Rachel Fink –Matt Clutter –Angelica Trejo –Matt Hale Stanford –Michael Linderman –Sylvia Plevritis MD Anderson –Peng Qiu