Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum

Abstract

Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell "mass cytometry" to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.

Figures

Fig. 1

Mass cytometry profiling of immune cell response patterns. (A) Work-flow summary of mass cytometry analysis. Cells are stained with epitope-specific antibodies conjugated to transition element isotope reporters, each with a different mass. Cells are nebulized into single-cell droplets, and an elemental mass spectrum is acquired for each. The integrated elemental reporter signals for each cell can then be analyzed by using traditional flow cytometry methods as well as more advanced approaches such as heat maps of induced phosphorylation and tree plots. (B and C) Representative antibody surface-staining results and cell population definitions (“gating”) for (B) fluorescence and (C) mass cytometry analysis of fixed PBMCs from the same donor. Replicate analysis of a second donor is provided in (21) (Fig. S1A and S1B). *Pearson correlation between frequencies measured by fluorescence or mass cytometry, including both donors (r = 0.99, P < 0.000001, two-tailed t test) (table S1 and fig. S1C). (D) Induction of STAT3 and 5 phosphorylation by various ex vivo stimuli in naive CD4 +CD45RA+ T cells [(B) and (C), red boxes] as measured by (top) fluorescence and (bottom) mass cytometry. Red arrows indicate the expected shift along the STAT phosphorylation axes. (E) Heatmap summary of induced STAT phosphorylation in immune populations from the PBMC donor defined in (B) and (C) [column headers refer to blue polygons in (B) and (C)]. Responses to the indicated stimuli in each row were measured by (top) fluorescence and (bottom) mass cytometry. Color scale indicates the difference in log2 mean intensity of the stimulated condition compared with the unstimulated control. Signaling responses of a second donor are provided in (21) (fig. S1D). **Pearson correlation between signaling induction measured by fluorescence or mass cytometry, including both donors [pSTAT3: r = 0.92; P < 0.000001, two-tailed t test (fig. S1E); pSTAT5: r = 0.89, P < 0.000001, two-tailed t test] (figs. S1E and S1F).

Fig. 2

SPADE links related immune cell types in a multidimensional continuum of marker expression. (A) Summary of SPADE analysis. Single-cell data are sampled in a density-dependent fashion so as to reduce the total cell count while maintaining representation of all cell phenotypes. Neighboring cells are then grouped by unsupervised hierarchical clustering. Resulting nodes (defined as those cells within a boundary of an n-dimensional hull) are then linked by a minimum-spanning tree, which is flattened for 2D display.(B) Immunophenotypic progression in healthy human bone marrow. A tree plot was constructed by using 13 cell-surface antigens in healthy human bone marrow. 18 additional intracellular parameters were acquired concurrently but excluded from tree construction. The size of each circle in the tree indicates relative frequency of cells that fall within the 13-dimensional confines of the node boundaries. Node color is scaled to the median intensity of marker expression of the cells within each node, expressed as a percentage of the maximum value in the data set (CD45RA is shown). Putative cell populations were annotated manually (table S2) and are represented by colored lines encircling sets of nodes that have CD marker expression emblematic of the indicated subset designations. (C) Overlaid expression patterns of CD3, CD8, and CD4. Three markers, along with CD45RA (B), were used in clustering that helped define T cell lineages. Color scale is as described in (B). (D) Overlaid expression patterns of CD19, CD20, and CD38. Three markers were used in clustering that helped define B cell lineages. Color scale is as described in (B). (E) Overlaid expression patterns of CD34, CD123, and CD33. Three markers were used in clustering that helped define myeloid and progenitor cell lineages. Color scale is as described in (B). (F) Overlaid expression of complementary surface markers from a staining panel with 18 additional surface markers (fig. S4) by using the 13 core surface markers as landmarks (21). Overlaid expression patterns are shown for eight complementary surface markers that helped to further define the myeloid (CD13, CD14, and CD15), B cell (CD10), and NK/T cell (CD7, CD56, CD161, and CD16) portions of the SPADE representation. These markers were not used for tree construction. Color scale is as described in (B).

Fig. 3

Signaling functions mark developmental transitions in hematopoietic progression. (A) A heatmap summary, ordered developmentally by cell type and stimulation condition, of the status of 18 intracellular functional markers in cells treated with 1 of 13 biological and chemical stimuli. (Left) Abbreviations refer to recombinant human proteins, except BCR, B cell receptor cross-linking; LPS, lipopolysaccharide; PMA/Iono, phorbol-12-myristate-13-acetate with ionomycin; and PVO4, pervanadate. Single-cell data from healthy human bone marrow were manually divided (“gated”) into 24 conventional cell populations (fig. S5) according to 13 surface markers and DNA content. Signaling induction was calculated as the difference of inverse hyperbolic sine (arcsinh) medians of the indicated ex vivo stimulus compared with the untreated control for each manually assigned cell type (21). Each row within a given stimulus group (gray bars) indicates the signaling induction of 1 of 18 intracellular functional markers (bottom). A subset of conditions (red numbers) was highlighted for further discussion in (B). (B) Magnified view of the conditions marked in (A). A subset of these signaling responses (blue boxes) are shown as SPADE plots [(C) to (E)] to investigate correlations between signaling function and differences in immunophenotype as discussed in the text. (C) Canonical, cell type–specific signaling functions. Stimulation by IL-7, BCR, or LPS each induced phosphorylation of STAT5 in T cells, BLNK (SLP-65) [detected with an antibody raised against pSLP-76 (21)] in B cells, and p38 MAPK in monocytes, respectively. Signaling induction for each node in the SPADE diagram was calculated as the difference of arcsinh median intensity of the indicated ex vivo stimulus compared with the untreated control. (D) Correlation of IL-3–mediated induction of pSTAT3 and pSTAT5 with IL-3Ra expression [(Left) color scale as described in Fig. 2B] in myeloid and B cells. The B cell population that did not phosphorylate STAT3 in response to IL-3 stimulation is indicated (blue arrow). All nucleated cell subsets, including IL-3Rα+ B cells, exhibited pSTAT3 induction in response to IFNα stimulation. Signaling induction calculated as in (C). (E) Examples of phosphorylation responses that paralleled immunophenotypic progression identified by the SPADE algorithm. Changes in Btk/Itk, S6, CREB phosphorylation, and total IκBα are shown in response to IFNα, G-CSF, PVO4, and TNFα, respectively. Signaling induction is calculated as in (C).

Fig. 4

PCA confirms that cellular signaling potentially tracks with the immunophenotypic continuum in B cell subsets. (A) Using the SPADE representation (right), cells assigned to pre-B II, immature B, mature B, and IL-3Rα+ mature B cell populations were selected for PCA in 13 dimensions defined by the core immunophenotypic markers used in both panels. The relative frequencies of the four B cell populations are shown as stacked bars in 1% windows along the phenotypic progression axis (colors correspond to key at right); the number of cells in each window is expressed as a proportion of the sample subjected to PCA (gray line). (B) The measured intensities of five immunophenotypic markers (CD45RA, CD19, CD20, CD38, and CD123) along the phenotypic progression axis. These markers captured the majority of the phenotypic changes observed here during B cell maturation. Intracellular Ki67 expression, an indicator of cellular proliferation, was not used in defining the PCA axis but was among the 18 functional markers that were measured concurrently at thesingle-cell level.(C) Phosphorylation of ERK1/2, SLP-76(BLNK/SLP-65), PLCγ2, CREB, and SHP2 overlaid on the PCA progression axis. These and other functional epitopes were not used in the PCA axis construction. The top plot displays the basal levels (untreated) of these phosphorylated epitopes in the untreated sample. Subsequent plots display induced changes in phosphorylation in response to PVO4 and B cell receptor cross-linking relative to the untreated control.

Fig. 5

Multiplexed mass cytometry analysis reveals diverse signaling dynamics in response to the kinase inhibitor agent dasatinib. (A) SPADE plots of exemplary cell type–specific inhibitory effects of dasatinib. Phosphorylation of ERK1/2 was sensitive to dasatinib when induced with BCR cross-linking but not when induced with PMA/ionomycin. Signaling induction for each node in the SPADE diagram was calculated as the difference of arcsinh median of the indicated ex vivo stimulus compared with the untreated control. (B) T lymphocytes exhibited STAT5 phosphorylation in response to IL-7 in the presence of dasatinib with similar magnitudes as the response observed without drug. PVO4 induction of STAT5 phosphorylation was inhibited by dasatinib in all but plasmacytoid dendritic cells. Calculated as in (A). (C) Blymphocytes exhibited specific PLCγ2 phosphorylation in response to receptor cross-linking that was completely abolished in the presence of dasatinib. PLCγ2 phosphorylation was relatively large in all but B lymphocyte lineages in the presence of PVO4 but was inhibited completely by dasatinib treament in all cells. Calculated as in (A). (D) Using the SPADE representation, cells corresponding to HSC, MPP, pro-B, and pre-B I cell populations were selected for PCA of the 13 core immunophenotypic markers. The relative frequencies of the four progenitor cell populations are shown as stacked bars in 1% windows along the phenotypic progression axis (colors correspond to key at right); the number of cells in each window is expressed as a proportion of the sample subjected to PCA (gray line). (E) The measured intensities of six immunophenotypic markers (CD34, CD33, CD19, CD20, CD38, and CD123) along the progression axis. These markers captured the majority of the phenotypic changes observed here during progenitor cell maturation. (F) Basal (untreated) phosphorylation levels of p38, SrcFK, CREB, and SHP2 overlaid on the phenotypic progression axis. These and other functional epitopes were not used in the PCA axis construction. (G) Induced changes in phosphorylation of p38, SrcFK, CREB, and SHP2 in response to PVO4 compared with untreated control. Signaling induction is calculated as in (A). (H) Suppression of normal PVO4 response by dasatinib. Suppression index is calculated as the signaling induction by PVO4 with dasatinib pretreatment, minus the signaling induction by PVO4 alone.