Pooling of acquisition data and submission to MegaClust Automated multidimensional identification of cell groups (i.e. n markers x m samples) Comparison of sample features in identified cell groups (i.e. cell count, median intensity of markers) Interpretation of results Pool ... Samples MegaClust Marker Cell group Sample MegaClust automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action Application Note MegaClust Enabling flow cytometry data analysis Key Words - CyTof - analysis of large datasets - Identification of cell groups - Mechanism of action Figure 1. Analysis workflow Introduction Flow Cytometry is able to measure multiple signals on each cell in a sample. It is therefore extensively used in drug development since it provides key insights on their mechanism of action. Flow cytometry datasets in drug development are often very large (millions of events) and typically consist of tens of samples. Conventional methods for analyzing these data are largely manual, which is both time-consuming and can introduce biases. MegaClust is a comprehensive platform (software, hardware and expertise) that provides a solution for the optimal and unbiased analysis of large flow cytometry datasets. This application note describes the analysis with MegaClust of a CyTof dataset of 2.25 million cells. It illustrates how the unique approach used by MegaClust to identify cell populations across samples provides a very powerful tool for analyses of drugs mechanism of action. Analysis workflow MegaClust performs an automated and unbiased identification of cell groups in flow cytometry datasets. MegaClust is designed to process simultaneously all markers and all samples of a dataset. This unique multi-dimensional approach results in a very accurate and thorough identification of the cell groups present in the samples of the dataset. Thus, identified cell groups act as common denominators between samples: MegaClust reports the cell distribution among the identified cell groups for each sample. This approach allows a very robust comparison between samples as required for pharmaceutical studies. The MegaClust analysis consists in 2 steps: 1. automated identification of cell groups present in a dataset (merged samples), based on their marker expression (i.e. intensity distribution). 2. quantitative comparisons of expression of markers of interests across samples
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Pooling of acquisition data and submission to MegaClust
Automated multidimensional identification of cell groups (i.e. n markers x m samples)
Comparison of sample features in identified cell groups (i.e. cell count, median intensity of markers)
Interpretation of results
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MegaClust automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action
Application
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Key Words
- CyTof
- analysis of large datasets
- Identification of cell groups
- Mechanism of action
Figure 1. Analysis workflow
Introduction
Flow Cytometry is able to measure multiple signals on each cell in a sample. It is therefore extensively used in drug development since it provides key insights on their mechanism of action. Flow cytometry datasets in drug development are often very large (millions of events) and typically consist of tens of samples. Conventional methods for analyzing these data are largely manual, which is both time-consuming and can introduce biases. MegaClust is a comprehensive platform (software, hardware and expertise) that provides a solution for the optimal and unbiased analysis of large flow cytometry datasets. This application note describes the analysis with MegaClust of a CyTof dataset of 2.25 million cells. It illustrates how the unique approach used by MegaClust to identify cell populations across samples provides a very powerful tool for analyses of drugs mechanism of action.
Analysis workflow
MegaClust performs an automated and unbiased identification of cell groups in flow cytometry datasets. MegaClust is designed to process simultaneously all markers and all samples of a dataset. This unique multi-dimensional approach
results in a very accurate and thorough identification of the cell groups present in the samples of the dataset. Thus, identified cell groups act as common denominators between samples: MegaClust reports the cell distribution among the identified cell groups for each sample. This approach allows a very robust comparison between samples as required for pharmaceutical studies.
The MegaClust analysis consists in 2 steps:
1. automated identification of cell groups present in a dataset (merged samples), based on their marker expression (i.e. intensity distribution).
2. quantitative comparisons of expression of markers of interests across samples
Figure 3. Phenotype of identified cell (sub)populations. CD11b low/mid/high Monocyte subpopulations (Mono.) are highlighted in orange. The bar graph shows the average size and standard deviation for each cell (sub)populations in the 17 samples
Cell (sub)populations
Overview of IL-7 stimulation : cell and intracellular markers
Figure 2. Bar graph and heat map showing the size and phenotype of the 74 identified cell groups, respectively. Cell group id are indicated below the heat map. Cell groups are ordered by phenotype similarity. Purple scale indicates % of Median Fluorescence Intensity (MFI).
Identification of cell groupsThe CyTof dataset used in this application consisted of 17 bone marrow samples (mononuclear cells) from one healthy human donor, i.e 5 resting basal states (replicates) and 12 distinct states perturbed by a set of ex vivo stimuli and inhibitors. Signals from 13 cell surface markers and 18 intracellular markers were
acquired on ∼133 K labeled cells per sample [1]. The
acquisition data for the 17 samples were merged resulting in a single dataset of 2.25 million cells that was processed with MegaClust (Fig.1). The MegaClust automated identification resulted in 74 distinct cell groups (Fig. 2).
Figure 3 summarizes the phenotypes of the cell (sub)populations identified in the merged dataset and obtained by merging similar cell groups. The size of the identified cell subpopulation is consistent with reported values [1]. Moreover the 17 samples have
similar cell distribution in the identified subpopulations. This is as expected since all samples come from the same donor and cells were perturbed for short period of times (< 1 hour).
CD11b low/mid/high monocyte subpopulations (groups 43, 34 and 29) at resting state (basal sample #1)
Manual gating versus MegaClust automated and unbiased delineation of subpopulations
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Figure 5. Cytogram CD11b/CD38. Cell groups #43, #34 and #29 shown in red. All cells are shown in blue.
CD11b low/mid/high monocyte subpopulations (groups 43, 34 and 29) at resting state (basal sample #1)
MegaClust multidimensional approach accurately captured the monocytes subpopulations
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Comparison with available manual gating information shows that MegaClust cell groups match currently admitted cell populations. As an example, cell group 29 (cf Fig. 2) is shown (red dots) on the 6 cytograms
used for manual gating of the Monocyte population (Fig. 4). Cell group 29 is well delineated and within the manual monocyte gates (blue polygons) [1].
MegaClust is able to identify subpopulations with a high level of accuracy, e.g. cell groups 43, 34 and 29 (cf Fig.
2) correspond to CD11b low, mid and high monocytes, respectively (Fig. 5 and 6).
Figure 4. Cell group #29 (red) shown on the cytograms used for the manual gating of monocyte population. Blue polygons indicate manual
gates. All cells are shown in blue.
Figure 6. % of cell counts of cell groups #43, #34 and #29. Manual gating (Monocyte CD11b low/mid/high) are indicated by vertical blue lines
CD11b high monocyte subpopulation (#29) at resting state (basal sample #1)
MegaClust generates well delineated cell groups in agreement with manual gating
Characterization of cell groups
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We show the effect on IL-7 stimulation on 18 intracellular markers in the 74 identified cell groups and corresponding cell subpopulations (Fig. 7). In
agreement with published data, IL-7 stimulation specifically increases pSTAT5 phosphorylation in T cells (Fig. 7 A,B) [2].
Figure 7. effect of IL-7 stimulation on 18 intracellular markers in (A) 74 cell groups and (B) in corresponding cell (sub) populations. Cell groups are ordered according to their phenotypic (surface marker) similarity (cf Fig. 2). Signaling induction is calculated as the difference of arcsinh median of the indicated ex vivo stimulus compared with the untreated control [1].
The intensity distributions of pSTAT5 (IL-7 stimulated versus basal) for the 3 cell subpopulations highlighted in orange in the previous figure (Native/Mature CD 8 T,
and NK cells) are shown on Fig 8. They confirm that the phosphorylation increase in T Cells relative to other cell populations shown on Fig. 7 is real.
Figure 8. pSTAT5 intensity distributions of naive CD8 T, mature CD8 T and NK cells (IL-7 stimulated vs basal).
Overview of IL-7 stimulation : cell and intracellular markers
Dasatinib is a BCR-ABL kinase inhibitor for imatinib-resistant chronic myelogenous leukemia (CML) [3]. In this application, we used dasatinib to illustrate the power of multiple sample comparisons allowed by the MegaClust approach. The following conclusions can be drawn from the analysis of intracellular signaling variations in mature B cells upon combined perturbations:
- dasatinib has an overall inhibition effect on most cell populations (Fig. 11 A)
- BCR cross-linking stimulates more strongly mature B cells (Fig. 11 B)
- dasatinib inhibits the BCR-induced stimulation on mature B cells (Fig. 11 A,B,C) [4]
Dasatinib however did not inhibit PMA/ionomycin activation of mature B cells suggesting that Dasatinib is a specific inhibitor of BCR-induced activation of B cells (Fig. 12 a,b). This result is in agreement with published data [5].
Figure 11. heat maps showing intracellular marker variations (a) dasatinib vs basal, (b) BCR vs basal and (c) dasatinib + BCR vs basal.
Analysis of dasatinib mechanism of action in mature B Cells
Overall impact of BCR vs dasatinib and BCR stimulation
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Figure 12. heat maps of intracellular marker variations in mature B Cells (CD38low/mid) upon stimulation: (a) PMA/ionomycin vs basal (b) dasatinib + PMA/ionomycin vs basal.
Conclusion
This application describes the analysis with MegaClust of a CyTOF dataset consisting of acquisition data for 17 samples, for a total of 2.2 M cells.
The goal of this note was to illustrate the power of the multidimensional and simultaneous identification approach used by MegaClust.
We first showed that the simultaneous automated processing of all markers performed by MegaClust resul ts in an unbiased and comprehensive identification of the cell groups present in the dataset.
We then showed how the unique ability of MegaClust to perform identification on a dataset resulting from the merging of multiple acquisition data enables studies of mechanism of action: it provides a very powerful tool to analyze variations in cell (sub)populations upon (combined) stimulations and candidate treatments. Applied to dasatinib, the MegaClust analysis confirms that dasatinib specifically affects BCR induced activation of mature B cells.
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References:
[1] S. C. Bendall, et al. Science 332, 687 (2011)[2] C. D. Surh, et al. Immunity 29, 848 (2008)[3] B. J. Druker, et al. Blood 112, 4808 (2008)[4] A. E. Schade, et al. Blood 111, 1366 (2008)[5] C. Yang, et al. Leukemia 22, 1755 (2008)
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