MegaClust. MegaClust. MegaClust automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action

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1 Application Note automated and multidimensional processing of flow cytometry data enables the analysis of drugs mechanism of action Introduction Key Words - CyTof - analysis of large datasets - Identification of cell groups - Mechanism of action Enabling flow cytometry data analysis 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. 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 of a CyTof dataset of 2.2 million cells. It illustrates how the unique approach used by to identify cell populations across samples provides a very powerful tool for analyses of drugs mechanism of action. Analysis workflow performs an automated and unbiased identification of cell groups in flow cytometry datasets. is designed to process simultaneously all markers and all samples of a dataset. This unique multi-dimensional approach Sample Marker 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: 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 analysis consists in 2 steps:. 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 Cell group Samples Pool Pooling of acquisition data and submission to 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 Figure. Analysis workflow

2 Identification of cell groups The CyTof dataset used in this application consisted of acquired on K labeled cells per sample []. The bone marrow samples (mononuclear cells) from acquisition data for the samples were merged one healthy human donor, i.e resting states resulting in a single dataset of 2.2 million cells that (replicates) and 2 distinct states cell groups perturbed resulting by a set of from was processed identification with (Fig.). The ex vivo stimuli and inhibitors. Signals from cell automated identification resulted in distinct cell surface markers and intracellular markers were groups (Fig. 2). events [log] max min HSC / MEP 6 6 Mature B Cell (sub)populationsoverview of cell (sub)populations diff. IL- stimulated vs (untreated) lo low mid hi high mid cytoid DC hi cytoid DC DC B CD B2 group_il_basal lo B mid B Pre-B I Mature CD T T Naive CD T Pre-B II Naive T T Mature CD T cytoid DC Mature Naive T T T 2 6 Overview of IL- Size stimulation of cell (sub)populations : cell and intracellular are: markers - in agreement with experimental data of times (< hour). - consistent across samples % events events [log] 6... mid hi low Mono. cytoid DC mid Mono. high Mono. cytoid DC group_il_basal lo B lo low B B mid B CD mid mid B B low B CD mid B Naive CD T Mature CD T Naive T Mature T Mature T CMP / MEP CD CD CD CDRA CD perk_2 CD perk_2 perk.2 pzap -2 LP 6 pzap CD2 total IkBa LP 6 LP-6 p p CD p p p-p CD CD CDRA CD CD CD2 CD Figure 2. Bar graph and heat map showing the size and phenotype of the 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)... Figure summarizes the phenotypes of the cell (sub)populations identified in the merged dataset and 2 6 obtained by merging similar cell groups. The size of the identified cell subpopulation is consistent with reported values []. Moreover the samples have max min cell groups resulting from identification HSC / MEP Mature B Focus on group CD CD2 CD 2 6 mid 2 6 Figure. Phenotype of identified cell (sub)populations. low/mid/high subpopulations (Mono.) are highlighted in orange. The bar graph shows the average size and standard deviation for each cell (sub)populations in the samples CD Naive CD T Naive CD T T Mature CD T Naive T Mature T CD CDRA CD CD CD2 CD Pre-B I Pre-B II cytoid DC CMP / MEP asinh diff. vs. unstim. - CD CD CD 2 CDRA CD 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

3 Characterization of cell groups Comparison with available manual gating information used for manual gating of the population shows that cell groups match currently (Fig. ). Cell group is well delineated and within the admitted cell populations. As an example, cell group(#) at manual monocyte gates (blue polygons) high monocyte subpopulation resting state ( sample #) []. (cf Fig. 2) is shown (red dots) on the 6 cytograms CD CD generates well delineated cell groups in agreement with manual gating CD CD CD low/mid/high monocyte subpopulations (groups, and ) at resting state ( sample #) Figure. Cell group # (red) shown on the cytograms used for the manual gating of monocyte population. Blue polygons indicate manual gates. All cells are shown in blue. is able to identify subpopulations with a high 2) correspond to low, mid and high monocytes, level of accuracy, e.g. cell groups, and (cf automated Fig. respectively (Fig. and 6). Manual gating versus and unbiased delineation of subpopulati mid high low mid/high monocyte subpopulations (groups, nd ) at resting state ( sample #) mensional approach accurately captured the monocytes subpopulations Figure. Cytogram /. Cell groups #, # and # shown in red. All cells are shown in blue Figure 6. % of cell counts of cell groups #, # and #. Manual gating ( low/mid/high) are indicated by vertical blue lines low mid high low () mid () high ().. % of cell counts mid high Manual low..... high.6 mid. w. hi - CD- - CD- Manual

4 2 2 2 HSC / MEP Mature B CD Pre-B I Pre-B II cytoid DC CMP / MEP Change in signaling upon IL- stimulation events [log] Diff. IL- stimulated vs untreated _IL_Basal lo B group_il_basal 2 2 lo B diff. IL- stimulated vs (untreated) mid B We show the effect on IL- stimulation on intracellular markers in the identified cell groups and corresponding cell subpopulations (Fig. ). In agreement with published data, IL- stimulation IL- specifically increases phosphorylation in T cells (Fig. A,B) [2]. Basal perk_2 perk.2 Overview of IL- stimulation : cell and intracellular markers perk_2 Color pzap Key pzap LP 6 MFI LP-6 LP 6 (%) CD total IkBa CDRA p p p p CD 2 p-p 6 CD group_il_basal CD2 A CD asinh diff. vs. unstim. CD -2-2 asinh diff. vs. unstim. perk_2 perk perk_2 pzap LP 6 LP-6 p p p-p pzap LP 6 p p Increase of in + T Cells upon IL- Stimulation B asinh diff. vs. unstim. Figure. effect of IL- stimulation on intracellular markers in (A) 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 -2 - is calculated 2 as the perk_2 difference of arcsinh median of the indicated ex vivo stimulus compared with the untreated control [] mid B is selectively activated in T Cells cluster_il_basal cluster_il_basal Signaling variations upon IL- stimulation lo low mid mid hi hi high cytoid DC cytoid DC The intensity distributions of (IL- stimulated versus ) for the cell subpopulations highlighted in orange in the previous figure (Native/Mature CD T, % of cell counts Naive CD T Mature CD T IL IL lo B lo low B B mid B CD mid mid B B Naive T and cells) are shown on Fig. They confirm that the phosphorylation increase in T Cells relative to pzap other cell populations shown on Fig. is real. Figure. intensity distributions of naive T, mature T and cells (IL- stimulated vs ). Mature T Native T Mature T 2 6 Naive CD T Mature CD T Naive T Mature T Naive CD T Naive CD T T Mature CD T Mature CD T T Naive T Naive T T Mature T Mature T T IL Intensity () Intensity () Intensity () LP 6 p p IL IL 2 6

5 Investigating the effects of dasatinib Analysis of dasatinib mechanism of action in mature B Cells Dasatinib is a BCR-ABL kinase inhibitor for imatinibresistant chronic myelogenous leukemia (CML) []. In this application, we used dasatinib to illustrate the power of multiple sample comparisons allowed by the approach. The following conclusions can be drawn from the analysis of intracellular signaling variations in mature B cells upon combined perturbations: diff. dasatinib vs Diff. BCR vs Diff. dasatinib + BCR vs diff. dasatinib vs lo low low 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. 2 a,b). 2 This result is 2 in agreement 2 2 with published data []. mid mid mid hi hi high high cytoid DC cytoid DC cytoid DC low B CD mid B CD CDRA dasatinib has an overall inhibition effect on most cell CD populations (Fig. A) - BCR cross-linking stimulates more CDstrongly mature B cells (Fig. B) - dasatinib inhibits the BCR-induced CD2 stimulation on mature B cells (Fig. A,B,C) [] CD CD A asinh diff. vs. unstim Focus on impact on mature B Cells B B2 group_dasatinib+basal_na lo B lo low B B mid B CD mid mid B B perk.2 pzap LP-6 p-p perk.2 CD CDRA 2 6 CD pzap LP-6 CD p-p CD2 CD CD asinh diff. vs. unstim. asinh diff. vs. unstim. perk_2 perk.2 group_dasatinib+pmaiono_na pzap LP 6 LP-6 Dasatinib has no impact on PMA/ionomycin stimulation of mature B cel Figure. heat maps showing intracellular marker variations (a) dasatinib vs, (b) BCR vs and (c) dasatinib + BCR vs. lo low mid mid hi hi high cytoid DC cytoid DC B B2 group_dasatinib+basal_na lo B lo low B B mid B CD mid mid B B Naive CD T Naive CD T T Diff. PMA/ionomycin Mature vs CD T T Naive CD T Naive CD T T Mature CD T Naive CD T Naive T Naive T T Mature CD T Mature CD T T Mature CD T Mature T Mature T T Naive T Naive T T Investigating the effects of dasatinib group_pmaiono_na A Naive T Mature T Mature T T Mature T p p p-p Diff. dasatinib + PMA/ionomycin vs perk_2 perk.2 pzap LP 6 LP-6 p p p-p B B C -2 perk.2 perk/2 pmapkap K2 pzap LP-6 p-p - pp pe Ki pm pz pn to ph p pb PS pc pc mid hi cytoid DC mid hi lo low B B cytoid DC mid mid B B lo low B B Naive CD T mid mid B B Mature CD T Naive T Mature T Naive CD T Figure 2. heat maps of intracellular marker variations in mature B Cells A. E. Schade, et al. Blood, 6 (2) (low/mid) upon stimulation: (a) PMA/ionomycin vs (b) dasatinib + PMA/ionomycin vs Mature CD T asinh diff. vs. uns

6 Conclusion This application describes the analysis with of a CyTOF dataset consisting of acquisition data for 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. We first showed that the simultaneous automated processing of all markers performed by results in an unbiased and comprehensive identification of the cell groups present in the dataset. We then showed how the unique ability of 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 analysis confirms that dasatinib specifically affects BCR induced activation of mature B cells. Headquarters Japanese Branch Geneva Bioinforma cs (GeneBio) SA 2 avenue de Champel CH- Geneva Switzerland Geneva Bioinforma c #, 2- - Nagata- Chiyoda- ku, Tokyo - Japan Tel: + Fax: + Tel: + () 6 Fax: + () References: [] [2] [] [] [] S. C. B. A. C. C. Bendall, et al. Science, 6 (2) D. Surh, et al. Immunity, (2) J. Druker, et al. Blood 2, (2) E. Schade, et al. Blood, 6 (2) Yang, et al. Leukemia, (2) Vital-IT High Performance Computing Center genebio Geneva Bioinformatics SA For more information visit megaclust.vital-it.ch