Abstract

Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on human samples at single-cell resolution, but instruments process only one sample at a time. Here we describe mass-tag cellular barcoding (MCB), which increases mass cytometry throughput by using n metal ion tags to multiplex up to 2n samples. We used seven tags to multiplex an entire 96-well plate, and applied MCB to characterize human peripheral blood mononuclear cell (PBMC) signaling dynamics and cell-to-cell communication, signaling variability between PBMCs from eight human donors, and the effects of 27 inhibitors on this system. For each inhibitor, we measured 14 phosphorylation sites in 14 PBMC types, resulting in 18,816 quantified phosphorylation levels from each multiplexed sample. This high-dimensional, systems-level inquiry allowed analysis across cell-type and signaling space, reclassified inhibitors and revealed off-target effects. High-content, high-throughput screening with MCB should be useful for drug discovery, preclinical testing and mechanistic investigation of human disease.

Experiment Overview

Purpose
The goal of this study was to develop a cell multiplexing method to increase the sample throughput of mass cytometry, and to apply this method to human peripheral blood mononuclear cells (PBMCs) for the study of cell signaling dynamics, variability between PBMC donors, and high-content kinase inhibitor profiling.

Samples were gated into the following cell-types
IgM+ B cells, IgM- B cells, CD4+ T cells, CD8+ T cells, CD14-HLA-DR- Monocytes, CD14- HLA-DRmid Monoctyes, CD14- HLA-DRhigh Monocytes, CD14+ HLA-DR- Monocytes, CD14+ HLA-DRmid Monoctyes, CD14+ HLA-DRhigh Monocytes, CD14- Surface-, CD14+ Surface-, NK cells, Dendritic cells

Samples were stimulated with one of the following conditions:
Orthovanadate, IL-2, IL-3, G-CSF, GM-CSF, BCR/FcR-XL, IFN-g, IFN-a, LPS, PMA/Ionomycin

Samples were treated with one of the following inhibitors
AKT-1/2, Sorafenib, BTK Inhib. III, Crassin, Dasatinib, GDC-0941, Go-6983, H89, IKK Inhib., Imatinib, JAK(pan) Inhib., JAK2 Inhib., JAK3 Inhib., Lck Inhib., Lestaurtinib, PP2, Rapamycin, Ruxolitinib, SB-202190, SP600125, Staurosporine, Streptonigrin, Sunitinib, Syk Inhib. IV, Tofacitinib, U0126, VX680

Surface and intracellular markers measured
CD3, CD45, pNFkB, pP38, CD4, CD20, CD33, pSTAT5, CD123, pAKT, pSTAT1, pSHP2, pZAP70, pSTAT3, CD14, pSLP76, pBTK, pPLCg2, pERK, pLAT, IgM, pS6, HLA-DR, CD7

Cytometer Used
DVS Sciences, Inc. CyTOF™ Mass Cytometer

Figure 1A: Mass-tag cell barcoding

Figure 1a: Cells were covalently labeled with a bifunctional compound, maleimido-mono-amide-DOTA (mDOTA). This compound can be loaded with a lanthanide(III) isotope ion, and reacts covalently with cellular thiol groups through the maleimide moiety.

Figure 1B: Mass-tag cell barcoding

Figure 1b: Seven unique lanthanide isotopes were used to generate 128 combinations, enough to barcode each sample in a 96-well plate. The seven lanthanide isotopes, their masses and their locations on the 96-well plate are shown.

Figure 1C: Mass-tag cell barcoding

Figure 1c: A density dot plot of barcoded cells is shown with the y-axis and x-axis plot showing barcoding channel (BC) 1 (lanthanum 139) versus barcoding channel 2 (praseodymium 141). Cells positive and negative for a given channel are indicated.

Figure 1D: Mass-tag cell barcoding

Figure 1d: K562 cells were stimulated with orthovanadate, placed in a 96-well plate as geometrical patterns (checkerboard or striped pattern), barcoded, analyzed by mass cytometry and subsequently deconvoluted using Boolean gating to validate the accuracy of the de-barcoding. The two resulting heat maps of the measured SLP76-Tyr696 phosphorylation levels are shown.

Figure 2A: PBMC signaling time-course experiment

Figure 2a: Twelve conditions and 8 different time points were used to capture time-resolved PBMC signal transduction from 0 to 240 min.

Figures 2B-C: PBMC signaling time-course experiment

Figure 2b-c: (b) The expression and localization of cell surface markers within the SPADE tree is shown. (c) Fourteen unique PBMC cell types were distinguished by SPADE analysis based on surface marker expression shown in b.

Figure 2D: PBMC signaling time-course experiment

Figure 2d: The time-resolved response of the PBMC continuum of subpopulations to IFN-α stimulation by STAT1 phosphorylation, as visualized by SPADE.

Figure 2E: PBMC signaling time-course experiment

Figure 2e: Time-resolved response of the PBMC continuum of subpopulations to LPS stimulation by NFκB, STAT3 and STAT1 phosphorylation, as visualized by SPADE. Putative intercellular communication is indicated by black arrows.

Figure 3A: Signaling response comparison of PBMCs from eight donors

Figure 3a: Twelve conditions were used to compare signaling responses of PBMCs from eight different donors after 15 min exposure to the inhibitor and subsequent 30 min stimulation.

Figure 3BCDF: Signaling response comparison of PBMCs from eight donors

Figure 3bcdf: (b) The expression of the CD3 cell surface marker within the SPADE tree for all donors is shown. (c) The expression of the CD33 cell surface markers within the SPADE tree for all donors. (d) Comparison of the response to 30 min BCR/FcR-XL stimulation of the PBMC continua of subpopulations of the analyzed donors as visualized by SPADE shown by the median phosphorylation levels of S6 protein. (f) As d, but the median of phosphorylation on STAT5 after 30 min IFN-α stimulation is shown.

Figure 3E: Signaling response comparison of PBMCs from eight donors

Figure 3e: Correlation plot of the fold-change induction over all stimuli, phosphorylation site and cell type pairs between donors after 30-min stimulation.

Download 8-Donor IC50 Spreadsheet

Figure 4A-B: Analysis of PBMC response to kinase inhibition

Figure 4ab: (a) The effect of 27 inhibitors on PBMC signaling was quantified by MCB, including the IC50 value and percent inhibition of phosphorylation levels. (b) Experimental set-up for each inhibitor experiment. Twelve stimulation conditions were applied for 30 min in conjunction with an eight-point, fourfold dilution series of each inhibitor.

Download Plate Setup Spreadsheet

Figure 4C: Analysis of PBMC response to kinase inhibition

Figure 4c: Gating scheme. Ten cell surface markers were combined to define 14 cell types.

Figure 4D: Analysis of PBMC response to kinase inhibition

Figure 4d: For each cell type, 14 phosphorylation sites covering many immune signaling pathways were quantified by mass cytometry. Examples of dose-response curves are shown for staurosporine treatment in CD4+ T cells.

View All Dose Response Curves

Figure 5: Overview of inhibitor impact

Figure 5: (a) A miniaturized signaling network, guided by canonical pathways, including vertical ordering of nodes from membrane-proximal signaling proteins to nuclear-localized transcription, is used here to depict the effect of a stimulus or inhibitor on each quantified phosphorylation site after 15-min incubation with the inhibitor and subsequent 30-min cell stimulation. As some antibodies recognize different proteins in different cell types, three cell type–specific signaling networks are shown. In the absence of inhibitor (“No inhibitor”), the response to each stimulus relative to the untreated state is represented as fold change by a sized red or black circle (for induction and reduction of phosphorylation levels, respectively). For example, activation of B cells by IFN-α caused an approximately onefold induction of phosphorylated STAT1 and STAT3. To visualize the effects of inhibitors (‘inhibition’), circles were sized inversely to the IC50 and colored by the amount of percent inhibition (‘inhibition’). For example, in the presence of ruxolitinib, inhibition of phosphorylation of STAT1 (IC50 = 23 nM, 93% inhibition) and STAT3 (IC50 = 4 nM, 147% inhibition) was observed (Fig. 5a, ‘inhibition’), whereas without activation of the B cells, no observable effects of ruxolitinib on the quantified signaling nodes were visible (Fig. 5b, yellow box). Fold-change induction before inhibition and confidence intervals for IC50 values and percent inhibition are not shown (Supplementary Results 3). (b) The impact of all inhibitors under all stimulation conditions is shown for IgM+ B cells. (c) The impact of all inhibitors on all cell types after 30 min IFN-α stimulation is shown. Sections highlighted by color are detailed in the main text.

Download IC50 Spreadsheet

Figure 6A-D: Principle component analysis of cell type and drug response

Figure 6abcd: (a) Cell type PCA across all inhibitors, phosphorylation sites and conditions. (b) Cell type PCA for streptonigrin across all phosphorylation sites and conditions. (c) Inhibitor PCA across all cell types, phosphorylation sites, and conditions. (d) Inhibitor PCA for monocytes after IFN-α stimulation across all phosphorylation sites.

Download PCA Spreadsheet

Figure 6E: Principle component analysis of cell type and drug response

Figure 6e: Pairwise distance correlation plot to show the agreement between in vivo data generated by MCB and previously generated16 in vitro kinome inhibition profiles. Distances shown were scaled as a fraction of the maximum distance.

Download in vitro comparison spreadsheet

Figure 6F: Principle component analysis of cell type and drug response

Figure 6f: (f) As e, but pairwise distance correlation plot between in vivo data generated by MCB and a different set of previously generated17 in vitro kinome inhibition profiles.

Download in vitro comparison spreadsheet

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