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ScienceDirect Unraveling cell populations in tumors by single-cell mass cytometry Serena Di Palma and Bernd Bodenmiller The development of new biotechnologies for the analysis of individual cells in heterogeneous populations is an important direction of life science research. This review provides a critical overview of relevant and recent advances in the field of singlecell mass cytometry, focusing on the latest applications in the study of cell heterogeneity. New approaches for multiparameter single-cell imaging, alongside advanced computational tools for deep mining of high-dimensional mass cytometric data, are facilitating the visualization of specific cell types and their interactions in complex cellular assemblies, such as tumors, potentially revealing new insights into cancer biology. Addresses Institute of Molecular Life Sciences, University of Zu¨rich, Zu¨rich, Switzerland Corresponding author: Bodenmiller, Bernd ([email protected])
correlates tumor cell phenotypes, tumor subtype and disease stage was possible until recently. To assess different cell phenotypes in a heterogeneous tumor specimen, including metastases, new technologies are required that allow analyses at the single-cell level, in a specific localization within the tumor itself, in the context of heterogeneous cellular assemblies (i.e. the microenvironment), and during dynamic processes such as phenotypic changes and metastasis [7]. Here, we review an emerging approach known as single-cell mass cytometry (MC) that enables characterization of protein expression and signaling activity in individual cells within heterogeneous populations. We expect that by delineating tumor heterogeneity and the elaborate cell-to-cell communication networks that regulate cancer by MC will help identify novel routes for patient classification and treatment.
A new single-cell analysis era Current Opinion in Biotechnology 2015, 31:122–129
Mass cytometry
This review comes from a themed issue on Analytical biotechnology
MC is a technology that allows detection and quantification of dozens of markers in a single cell simultaneously [8,9]. MC is therefore uniquely suited for multiparameter analyses of heterogeneous biological samples, such as tumors [10]. Before MC, cells are stained with antibodies in the way familiar to users of flow cytometry (FC), except that metal isotopes are employed as reporter groups rather than fluorophores [11]. Staining panels of 35 or more antibodies are designed to interrogate — in one analysis — cell surface phenotypes, cell cycle state, apoptosis, metabolism, proliferation, and activation states of intracellular signaling pathways, as depicted in Figure 1. Individual cells are analyzed at their basal state and after the exposure to exogenous stimuli (e.g. therapeutic agents). MC data from a single antibody panel can thus provide an overall representation of the network state of individual cells within complex cell populations without the need to compare or merge separate analyses from smaller antibody panels or sorted cell subpopulations [12].
Edited by Hadley D Sikes and Nicola Zamboni For a complete overview see the Issue and the Editorial Available online 11th August 2014 http://dx.doi.org/10.1016/j.copbio.2014.07.004 0958-1669/# 2014 Elsevier Ltd. All right reserved.
Introduction In many biological and clinical settings, cell populations are heterogeneous; this heterogeneity plays a critical role in cancer development, diagnosis and treatment. Although tumors typically derive from a single cell, at the time of clinical diagnosis intra-tumor heterogeneity is apparent on a macroscopic and microscopic scale. Different regions of a tumor can have a distinct morphology, with regions of high vascularity and invasiveness [1,2]. On the tumor cell level, the phenotypic heterogeneity arises from genetic, epigenetic, but also environmental influences. Cells within a tumor experience a variety of microenvironmental cues that can alter their phenotype with profound effects on cell behavior [3,4]. Due to technical limitations, most available methodologies only portray an average picture of cell populations, whereas information on potential cell heterogeneity is missed [5,6]. Thus, no comprehensive analysis that Current Opinion in Biotechnology 2015, 31:122–129
Each antibody in the panel is conjugated to a polymer chelated with a unique stable metal isotope of defined atomic mass; the isotope acts as the reporter [13]. When metal-labeled cells are introduced into the MC, the masses and abundances of the metal reporters bound to the cells via the antibodies are determined. These levels correlate to the level of epitope within individual cells [13,14]. Usually, four to five polymers bind to each antibody, and each polymer carries up to 30 metals atoms. The total number of metals that can be bound to an www.sciencedirect.com
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The simultaneous measurement capability of mass cytometry allows for in depth characterization of complex cell populations, including marker measurements for many cellular functions and hallmarks of cancer. Except for DNA content and cell viability (where no antibodies are employed, but iridium DNA intercalators and DNA-platinum adducts, respectively), all other readouts require staining of biomarkers with sets of metal-chelated antibodies.
antibody, combined with the level of sensitivity of MC, results in a limit of detection as low as hundreds copies for any given cellular molecule [15]. The ‘non-biological’ metals employed in MC are the 15 lanthanides, available in at least 38 pure isotopes of unique masses, which are suitable for metal chelating polymer chemistries [16–19]. In addition to the lanthanide reporters, other elements and their isotopes measurable by the mass cytometer are used to demarcate cells in terms of their DNA content (Iridium in the form of a metallointercalator) [20]; to determine cells in the S-phase of the cell cycle (Iodine in 5-iodo-2-deoxyuridine) and cell viability (Platinum in Cisplatin) [21,22]; to multiplex the measurements (Palladium chelated to bifunctional molecules that covalently bind to proteins) [23], bringing the total number of parameters that can be currently quantified simultaneously to 52, many more than traditional fluorescence-based technologies. Workflow and instrumentation
The Cytometry by Time Of Flight (CyTOF) mass cytometer is an adaptation of an inductively coupled plasma www.sciencedirect.com
mass spectrometer (ICP-MS) modified for high-dimensional cytometric applications [8]. Single-cell suspensions are nebulized into single-cell droplets, dried in a heated spray chamber and delivered as a stream into an argon plasma (7000 K), where the cells are atomized and ionized to generate single-cell ion clouds (Figure 2). In the mass cytometer, the ionization of each atom is similarly efficient, and the number of ions extracted from the plasma is directly related to the number of atoms introduced into the plasma, enabling quantitative elemental analysis. The atomic ion cloud is transferred into the highvacuum of the CyTOF, which is configured as a hybrid quadrupole-TOF instrument (Figure 2). The quadrupole acts as a mass filter allowing only ions with a mass-tocharge ratio (m/z) over 80 to enter the TOF mass analyzer; the TOF in turn measures the mass range of 100-200 m/z. A cell ion cloud can be detected for approximately 200– 300 ms, requiring rapid and simultaneous mass analysis. The TOF analyzer measures the complete mass spectrum in 13 ms pulses (76 400 Hz), scanning the entire ion cloud of a cell 20–30 times. The ion cloud volume and detector sampling frequency allow a sample throughput up to 1000 cells per second, while keeping individual cells Current Opinion in Biotechnology 2015, 31:122–129
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Schematic representation of the mass cytometry workflow. Cells of interest are cross-linked by formaldehyde, permeabilized using methanol or detergents, and labeled with antibodies conjugated to pure metal isotopes of defined mass. Subsequently, cells are nebulized, ionized and atomized in a 7000 K hot plasma and introduced into the mass cytometer, which is a hybrid quadrupole-time-of-flight mass spectrometer. Here, the composition and quantity of the reporter (metal) isotopes are determined. Signals corresponding to each isotope are then correlated with presence of the respective marker per single-cell event.
fully resolved. The quantity of each metal reporter is determined for each cell ion cloud by integrating over all scans for that cell cloud [8]. Random variations in instrument responses, for instance due to unstable plasma or suboptimal instrument calibration, decrease the accuracy of quantitative determinations. Thus, for stringent quantitation, normalization with an internal standard is used to correct for those fluctuations [24,25]. Enhancing throughput: mass-tag cellular barcoding
Until recently, the number of samples that could be analyzed by MC was limited by low sample throughput via the MC fluidics systems and by sample preparation protocols that required an immuno-staining step for each individual sample to be analyzed. These caveats have been addressed with a new approach that has considerably enhanced throughput, called mass-tag cellular barcoding (MCB) [26,27]. In MCB, individual cell samples are labeled with a unique binary combination of mass-tags before combining the samples into a single (pooled) tube for antibody staining. As a result, the analysis time is decreased as fluidics purge steps, necessary between runs, are not required and as the pooled samples are stained in a single step. In addition, the staining among all samples is homogeneous and less antibody is needed. Furthermore, potential instrument variations during measurement Current Opinion in Biotechnology 2015, 31:122–129
affect all samples in the same way, greatly facilitating data normalization. Bodenmiller et al. [23] used seven lanthanide tags to multiplex entire 96-well plates and applied MCB to characterize human peripheral blood mononuclear cell signaling dynamics, intercellular communication, and variability among eight donors, defining the perturbation of the system by each of 27 kinase inhibitors. An additional refinement of MCB is the use of palladium isotopes, rather than lanthanides, for barcoding. The palladium masses are lower than those of the lanthanides, but still in the MC mass range; more lanthanide channels are therefore available for antibody tagging, further increasing multiplexity. Spatial mass cytometry for tissue analysis
MC, as described above, is applied to the analysis of cell suspensions; thereby, information on cell localization and interactions between cells is lost. However, this information is essential to the study of cell-to-cell interactions and intercellular communication within complex microenvironments. Recently, a new approach, called imaging mass cytometry, was developed to extend the applicability of MC to tissue analysis, allowing spatially resolved measurements of up to hundred biomarkers [28]. Imaging mass cytometry is based on the combination of MC with a high-resolution laser ablation system [29]. Tissue www.sciencedirect.com
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sections, stained with metal-conjugated antibodies using routine immunohistochemical (IHC) protocols, are ablated spot-by-spot and line-by-line using a laser beam at a spot size of 1 mm. The generated aerosol is transported and analyzed by MC. Reporting the isotope signals in the order of ablation yields a high-dimensional and high-parameter image of the tissue (Figure 3a). Giesen et al. [28] used this approach to analyze formalin-fixed, paraffin-embedded human breast cancer tissues (Figure 3b) with associated clinical information. Spatial analyses revealed the complexity of cell phenotypes and tumor microenvironment, and possibly will allow for a refined classification of patient subgroups. Angelo et al. [30] introduced a similar tissue imaging modality, referred to as multiplexed ion beam imaging (MIBI), which uses metal-chelated antibodies, as established for the MC pipeline, but employs secondary ion mass spectrometry for the analysis. Ultra high-resolution images with 50 nm pixel size were generated from MIBI experiments, revealing hidden phenotypic and morphological features of tumor biopsies. Currently, seven markers can be measured simultaneously. Repeated scans on the same tissue area have the potential to increase this number significantly. Thus, MIBI represents an excellent complementary technique to imaging mass cytometry. Computational tools for high-dimensional mass cytometry data
The ability to analyze high dimensional single-cell datasets and to identify cell (sub)populations in an unbiased, automated manner is critical for understanding cellular heterogeneity. However, despite improvements, most methods for analyzing high-dimensional single-cell data are still subjective, labor intensive, and require prior knowledge of the biological system [31,32]. MC output (fcs) files can be visualized by any FC software and often expert ‘supervised’ gating with two-parameter dotplots is used, although these fail to reveal all relationships that might exist in the high-multiparameter (>50) MC datasets. Therefore, different computational approaches have been developed to overcome this caveat (Figure 4a). A straightforward approach to reduce the dimensionality of multi-parameter datasets to (hopefully) biologically and clinically relevant observations is principle component analysis (PCA) [33]. PCA derives summary variables to capture and visualize the largest possible variations of the single cell data, but its ability to fully resolve subpopulations is limited. In another approach, cell subsets in heterogeneous samples are identified by clustering algorithms [34]. In general, clusters are identified by their proximity in n-dimensional space to their most similar characteristic and by their distance from other clusters of dissimilar characteristics.
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Imaging mass cytometry (figure and caption adapted from Giesen et al. [28]). (a) Workflow. Tissue sections are prepared for metal-chelated antibody labeling using IHC protocols. Then, tissue samples are positioned in a laser ablation chamber. The tissue is ablated and transported by a gas stream into the CyTOF for mass cytometry analysis. The measured isotope signals are plotted using the coordinates of each single laser shot, and a multidimensional tissue image is generated. Single-cell features and marker expression are determined, allowing the investigation of cell subpopulation properties within the analyzed tissue. (b) Example images generated by mass cytometry. Two luminal HER2+ breast cancer tissue samples are shown. 32 proteins and phosphorylation sites were measured simultaneously at 1 mm resolution. Tissue 1-a: Overlay of cytokeratin 8/18 (red), H3 (cyan) and vimentin (yellow). Tissue 1-b: Overlay of cytokeratin 7 (red), H3 (cyan) and CD44 (yellow). Tissue 1-c: Overlay of pan-actin (red), progesterone receptor (blue) and CD68 (yellow). Tissue 2-a: Overlay of HER2 (red), H3 (cyan) and vimentin (yellow). Tissue 2-b: Overlay of Ecadherin (red), cytokeratin 7 (yellow) and phosphorylation on S235/S236 on S6 (blue). Tissue 2-c: Overlay of b-catenin (red), estrogen receptor (blue) and CD68 (yellow).
There are a variety of clustering-based tools that have been applied to flow cytometry data, including FLAME, www.sciencedirect.com
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Computational workflow and tools implemented into the mass cytometrist’s toolbox. (a) Schematic workflow of the MC data analysis. MC raw data are transformed and analyzed by supervised gating approaches to obtain histograms, biaxial or 3D plots. The data can also be visualized by unsupervised methods to display n-dimensional parameters. This is performed by reducing dimensionality (e.g. Wanderlust, Gemstone, SPADE, viSNE) and/or by cluster identification (e.g. SPADE, FLAME, FlowCust). Additionally, statistical tools are employed to summarize many variables (heatmap, box plot, PCA). (b)–(c) Examples of cluster visualizations from SPADE and viSNE analysis, where combined populations of T and B cells, monocytes and dendritic cells are analyzed simultaneously by MC after applying cellular barcoding. In the legend, cell populations are indicated as TC (T cells); CD8+ TC (CD8+ T cells); CD4+ TC (CD4+ T cells); DC (dendritic cells); BC (B cells); CD27+ BC (CD27+ B cells); MC (monocytes); CD14+ MC (CD14+ monocytes). In B), the areas corresponding to each population are indicated. Typically, the median expression intensity of a selected set of markers is expressed for each node of the tree as a percentage of the maximum value in the dataset. In (c), each point of the viSNE map represents an individual cell, with each color representing the population it belongs to. SPADE and viSNE analyses reveal all the different cell populations and in different localization within the tree or the map, clustering monocytes, T and B cells together with their respective subpopulations (CD14+ MC; CD8+ and CD4+ T cells; CD27+ B cells).
FlowClust, SamSpectral, FlowMerge [35–38]. One of the first tools specifically developed to cluster MC data is SPADE (spanning-tree progression analysis of densitynormalized events), which uses a hierarchical clustering algorithm, after performing density-dependent downsampling, to organize high-dimensional cytometry data in an unsupervised manner [39]. Using SPADE, similar cells Current Opinion in Biotechnology 2015, 31:122–129
are clustered together and displayed in minimum spanning trees, facilitating the identification of cell subsets present in heterogeneous populations (an example of a SPADE tree is reported in Figure 4b). Through a simple 2D visualization, SPADE shows how measured markers behave across all cell types [12,40]. Amir et al. [41] developed a tool, called visual interactive stochastic www.sciencedirect.com
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neighbor embedding (viSNE), which allows visualization of high-dimensional data while preserving local and global geometry of cell relationships. A viSNE map provides a visual representation of the single-cell data, similar to a biaxial plot, where the positions of cells reflect their proximity in high-dimensional rather than two-dimensional space (Figure 4c). By integrating MC data obtained on leukemia and healthy bone marrow samples with viSNE, differences in cell populations between healthy and cancerous bone marrow samples have been visualized. By comparing leukemia diagnosis and relapse samples, the authors identified a rare leukemia subpopulation reminiscent of minimal residual disease [41]. Very recently, Bendall et al. [42] developed a new graphbased algorithm, called Wanderlust, which receives multiparameter single-cell events as input and maps them onto a one-dimensional developmental trajectory. Wanderlust is ideal for the exploration of any system undergoing a developmental process since it can be used to infer trajectories of regulatory events in healthy conditions and use these as foundations to understand their alteration in disease states. Wanderlust, in combination with MC data, has been used to investigate human B lymphopoiesis and to construct a general B-lineage trajectory representing, in chronological order, the in vivo development of cancerous B cells from primary human bone marrow. An automated, unsupervised data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets, called Citrus (cluster identification, characterization, and regression), has been published recently [43]. Citrus identifies clusters of phenotypically similar cells, calculates descriptive features of each cluster, and, requiring minimal inputs from users, applies conventional supervised learning methods to determine which cell subsets’ behaviors are the best predictors of a known experimental endpoint of interest.
therapeutic strategies in personalized medicine. Although mass cytometry is a new technology, it currently provides a level of information that is not attainable by other single-cell protein technologies and has already found application in cancer research and in many other fields including immunology, infectious disease, inflammation research and drug screening [12,44,45,46,47,48,49]. Further improvements are still needed to exploit the full potential of MC. These include: firstly, increase in the number of measurable markers (including RNA, DNA and metabolites) by employing additional stable isotopes and designing new chelation chemistries and affinity reagents; secondly, development of advanced computational tools to display in a comprehensive way sophisticated multidimensional data; thirdly, methods for more accurate quantitative analyses that will enable the estimation of absolute copy numbers of signaling molecules and their network structures, reaction rates, and signaling thresholds; fourthly, integration of MC data with other data types, such as single-cell genomic and transcriptomic data; fifthly, validation of quantified relationships with orthogonal biotechnologies and biological follow-ups. We anticipate that mass cytometry will become an invaluable player of the ‘omics’ arena, opening the avenue of ‘cytomics’, and complementing other single-cell genomics, transcriptomics, proteomics, and metabolomics methodologies.
Acknowledgements We thank all members of the Bodenmiller lab for scientific support, discussion and feedback. SDP was supported by a Transition Postdoc fellowship (TPdF) from SystemsX and BB from a Swiss National Science Foundation (SNSF) project grant 31003A-143877, a SystemsX PhosphoNetPPM grant, a SNSF Assistant Professorship grant PP00P3-144874, a Swiss Cancer League grant and funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/ 2007-2013)/ERC Grant Agreement no. 336921.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:
Conclusions and future directions Tumor heterogeneity can only be understood by studying specialized cell types within the tumor itself in the context of the tumor microenvironment. Mass cytometry has proven to be a powerful technology to investigate complex assemblies of heterogeneous cell populations, such as those encountered in human cancer tissues. By combining information on cell phenotype, entangled cancer signaling circuitry, and spatially resolved pictures of intercellular communication, MC can help us elucidate processes occurring in the tumor microenvironment that are relevant to all aspects of tumor biology. These types of data have the potential to greatly improve our understanding of tumor progression. In concert with clinical data, MC could enable a finer classification of patients exploiting cell subpopulations and cell-to-cell relationships, and might aid the design of new potential www.sciencedirect.com
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39. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, Linderman MD et al.: Extracting a cellular hierarchy from highdimensional cytometry data with SPADE. Nat Biotechnol 2011, 29:886-891. 40. Linderman MD, Bjornson Z, Simonds EF, Qiu P, Bruggner RV, Sheode K et al.: CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics 2012, 28:2400-2401. 41. Amir E-aD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC et al.: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 2013, 31:545-552. viSNE is a tool that allows mapping high-dimensional cytometry data onto two dimensions, yet conserving the high-dimensional structure of the data. viSNE plots individual cells in a map similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell’s location in the plot. viSNE is applied to mass cytometry to map healthy versus cancerous bone marrow samples. Leukemia samples map into malformed shapes that are distinct from healthy bone marrow shapes and from each other. 42. Bendall Sean C, Davis Kara L, Amir E-ad D, Tadmor Michelle D, Simonds Erin F, Chen Tiffany J et al.: Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 2014, 157:714-725. Combination of single-cell mass cytometry with a new algorithm, called Wanderlust, to align single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. This approach is applied to human B cell lymphopoiesis, constructing trajectories that span from hematopoietic stem cells through to naive B cells. Wanderlust generates remarkably consistent trajectories across multiple individuals and determines the timing and order of key molecular and cellular events across developmental processes, such as human B cell development. 43. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP: Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 2014, 111: E2770-E2777. Citrus (cluster identification, characterization, and regression) is a new automated approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. Citrus’ methodology is demonstrated through the identification of known and novel pathways
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in stimulated peripheral blood mononuclear cells measured by mass cytometry, comparing its performance to that of existing methods. 44. Wolchinsky R, Hod-Marco M, Oved K, Shen-Orr SS, Bendall SC, Nolan GP et al.: Antigen-dependent integration of opposing proximal TCR-signaling cascades determines the functional fate of T lymphocytes. J Immunol 2014, 192:2109-2119. 45. Behbehani GK, Bendall SC, Clutter MR, Fantl WJ, Nolan GP: Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry Part A 2012, 81:552-566. 46. Newell EW, Sigal N, Nair N, Kidd BA, Greenberg HB, Davis MM: Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat Biotechnol 2013, 31:623-629. By combining mass cytometry with combinatorial peptide–MHC multimer staining approaches, a method is developed to simultaneously screen for T-cell epitopes in any protein of known sequence and to perform highdimensional phenotypic analysis of human T cells specific for those epitopes. This approach is used to screen up to 109 different peptide– MHC tetramers in a single human blood sample. Among 77 candidate rotavirus epitopes, six T-cell epitopes restricted to human leukocyte antigen (HLA)-A*0201 are identified in the blood of healthy individuals. 47. Newell Evan W, Sigal N, Bendall Sean C, Nolan Garry P, Davis Mark M: Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 2012, 36:142-152. Development of peptide–MHC tetramer staining in conjunction with mass cytometry to identify and profile antigen-specific T cells with a variety of phenotypic and functional markers. Computational methods are used that provided a picture of the functional and phenotypic diversity of the CD8+ T cell compartment. This analysis shows that CD8+ T cells from normal human donors display a broad continuum of phenotypic profiles with remarkable diversity in their abilities to produce various cytokines. 48. Fienberg H, Nolan G: Mass cytometry to decipher the mechanism of nongenetic drug resistance in cancer. In Highdimensional single cell analysis. Edited by Fienberg HG, Nolan GP. Berlin Heidelberg: Springer; 2014:85-94. 49. Bjornson ZB, Nolan GP, Fantl WJ: Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 2013, 25:484-494.
Current Opinion in Biotechnology 2015, 31:122–129
ScienceDirect Unraveling cell populations in tumors by single-cell mass cytometry Serena Di Palma and Bernd Bodenmiller The development of new biotechnologies for the analysis of individual cells in heterogeneous populations is an important direction of life science research. This review provides a critical overview of relevant and recent advances in the field of singlecell mass cytometry, focusing on the latest applications in the study of cell heterogeneity. New approaches for multiparameter single-cell imaging, alongside advanced computational tools for deep mining of high-dimensional mass cytometric data, are facilitating the visualization of specific cell types and their interactions in complex cellular assemblies, such as tumors, potentially revealing new insights into cancer biology. Addresses Institute of Molecular Life Sciences, University of Zu¨rich, Zu¨rich, Switzerland Corresponding author: Bodenmiller, Bernd ([email protected])
correlates tumor cell phenotypes, tumor subtype and disease stage was possible until recently. To assess different cell phenotypes in a heterogeneous tumor specimen, including metastases, new technologies are required that allow analyses at the single-cell level, in a specific localization within the tumor itself, in the context of heterogeneous cellular assemblies (i.e. the microenvironment), and during dynamic processes such as phenotypic changes and metastasis [7]. Here, we review an emerging approach known as single-cell mass cytometry (MC) that enables characterization of protein expression and signaling activity in individual cells within heterogeneous populations. We expect that by delineating tumor heterogeneity and the elaborate cell-to-cell communication networks that regulate cancer by MC will help identify novel routes for patient classification and treatment.
A new single-cell analysis era Current Opinion in Biotechnology 2015, 31:122–129
Mass cytometry
This review comes from a themed issue on Analytical biotechnology
MC is a technology that allows detection and quantification of dozens of markers in a single cell simultaneously [8,9]. MC is therefore uniquely suited for multiparameter analyses of heterogeneous biological samples, such as tumors [10]. Before MC, cells are stained with antibodies in the way familiar to users of flow cytometry (FC), except that metal isotopes are employed as reporter groups rather than fluorophores [11]. Staining panels of 35 or more antibodies are designed to interrogate — in one analysis — cell surface phenotypes, cell cycle state, apoptosis, metabolism, proliferation, and activation states of intracellular signaling pathways, as depicted in Figure 1. Individual cells are analyzed at their basal state and after the exposure to exogenous stimuli (e.g. therapeutic agents). MC data from a single antibody panel can thus provide an overall representation of the network state of individual cells within complex cell populations without the need to compare or merge separate analyses from smaller antibody panels or sorted cell subpopulations [12].
Edited by Hadley D Sikes and Nicola Zamboni For a complete overview see the Issue and the Editorial Available online 11th August 2014 http://dx.doi.org/10.1016/j.copbio.2014.07.004 0958-1669/# 2014 Elsevier Ltd. All right reserved.
Introduction In many biological and clinical settings, cell populations are heterogeneous; this heterogeneity plays a critical role in cancer development, diagnosis and treatment. Although tumors typically derive from a single cell, at the time of clinical diagnosis intra-tumor heterogeneity is apparent on a macroscopic and microscopic scale. Different regions of a tumor can have a distinct morphology, with regions of high vascularity and invasiveness [1,2]. On the tumor cell level, the phenotypic heterogeneity arises from genetic, epigenetic, but also environmental influences. Cells within a tumor experience a variety of microenvironmental cues that can alter their phenotype with profound effects on cell behavior [3,4]. Due to technical limitations, most available methodologies only portray an average picture of cell populations, whereas information on potential cell heterogeneity is missed [5,6]. Thus, no comprehensive analysis that Current Opinion in Biotechnology 2015, 31:122–129
Each antibody in the panel is conjugated to a polymer chelated with a unique stable metal isotope of defined atomic mass; the isotope acts as the reporter [13]. When metal-labeled cells are introduced into the MC, the masses and abundances of the metal reporters bound to the cells via the antibodies are determined. These levels correlate to the level of epitope within individual cells [13,14]. Usually, four to five polymers bind to each antibody, and each polymer carries up to 30 metals atoms. The total number of metals that can be bound to an www.sciencedirect.com
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The simultaneous measurement capability of mass cytometry allows for in depth characterization of complex cell populations, including marker measurements for many cellular functions and hallmarks of cancer. Except for DNA content and cell viability (where no antibodies are employed, but iridium DNA intercalators and DNA-platinum adducts, respectively), all other readouts require staining of biomarkers with sets of metal-chelated antibodies.
antibody, combined with the level of sensitivity of MC, results in a limit of detection as low as hundreds copies for any given cellular molecule [15]. The ‘non-biological’ metals employed in MC are the 15 lanthanides, available in at least 38 pure isotopes of unique masses, which are suitable for metal chelating polymer chemistries [16–19]. In addition to the lanthanide reporters, other elements and their isotopes measurable by the mass cytometer are used to demarcate cells in terms of their DNA content (Iridium in the form of a metallointercalator) [20]; to determine cells in the S-phase of the cell cycle (Iodine in 5-iodo-2-deoxyuridine) and cell viability (Platinum in Cisplatin) [21,22]; to multiplex the measurements (Palladium chelated to bifunctional molecules that covalently bind to proteins) [23], bringing the total number of parameters that can be currently quantified simultaneously to 52, many more than traditional fluorescence-based technologies. Workflow and instrumentation
The Cytometry by Time Of Flight (CyTOF) mass cytometer is an adaptation of an inductively coupled plasma www.sciencedirect.com
mass spectrometer (ICP-MS) modified for high-dimensional cytometric applications [8]. Single-cell suspensions are nebulized into single-cell droplets, dried in a heated spray chamber and delivered as a stream into an argon plasma (7000 K), where the cells are atomized and ionized to generate single-cell ion clouds (Figure 2). In the mass cytometer, the ionization of each atom is similarly efficient, and the number of ions extracted from the plasma is directly related to the number of atoms introduced into the plasma, enabling quantitative elemental analysis. The atomic ion cloud is transferred into the highvacuum of the CyTOF, which is configured as a hybrid quadrupole-TOF instrument (Figure 2). The quadrupole acts as a mass filter allowing only ions with a mass-tocharge ratio (m/z) over 80 to enter the TOF mass analyzer; the TOF in turn measures the mass range of 100-200 m/z. A cell ion cloud can be detected for approximately 200– 300 ms, requiring rapid and simultaneous mass analysis. The TOF analyzer measures the complete mass spectrum in 13 ms pulses (76 400 Hz), scanning the entire ion cloud of a cell 20–30 times. The ion cloud volume and detector sampling frequency allow a sample throughput up to 1000 cells per second, while keeping individual cells Current Opinion in Biotechnology 2015, 31:122–129
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Schematic representation of the mass cytometry workflow. Cells of interest are cross-linked by formaldehyde, permeabilized using methanol or detergents, and labeled with antibodies conjugated to pure metal isotopes of defined mass. Subsequently, cells are nebulized, ionized and atomized in a 7000 K hot plasma and introduced into the mass cytometer, which is a hybrid quadrupole-time-of-flight mass spectrometer. Here, the composition and quantity of the reporter (metal) isotopes are determined. Signals corresponding to each isotope are then correlated with presence of the respective marker per single-cell event.
fully resolved. The quantity of each metal reporter is determined for each cell ion cloud by integrating over all scans for that cell cloud [8]. Random variations in instrument responses, for instance due to unstable plasma or suboptimal instrument calibration, decrease the accuracy of quantitative determinations. Thus, for stringent quantitation, normalization with an internal standard is used to correct for those fluctuations [24,25]. Enhancing throughput: mass-tag cellular barcoding
Until recently, the number of samples that could be analyzed by MC was limited by low sample throughput via the MC fluidics systems and by sample preparation protocols that required an immuno-staining step for each individual sample to be analyzed. These caveats have been addressed with a new approach that has considerably enhanced throughput, called mass-tag cellular barcoding (MCB) [26,27]. In MCB, individual cell samples are labeled with a unique binary combination of mass-tags before combining the samples into a single (pooled) tube for antibody staining. As a result, the analysis time is decreased as fluidics purge steps, necessary between runs, are not required and as the pooled samples are stained in a single step. In addition, the staining among all samples is homogeneous and less antibody is needed. Furthermore, potential instrument variations during measurement Current Opinion in Biotechnology 2015, 31:122–129
affect all samples in the same way, greatly facilitating data normalization. Bodenmiller et al. [23] used seven lanthanide tags to multiplex entire 96-well plates and applied MCB to characterize human peripheral blood mononuclear cell signaling dynamics, intercellular communication, and variability among eight donors, defining the perturbation of the system by each of 27 kinase inhibitors. An additional refinement of MCB is the use of palladium isotopes, rather than lanthanides, for barcoding. The palladium masses are lower than those of the lanthanides, but still in the MC mass range; more lanthanide channels are therefore available for antibody tagging, further increasing multiplexity. Spatial mass cytometry for tissue analysis
MC, as described above, is applied to the analysis of cell suspensions; thereby, information on cell localization and interactions between cells is lost. However, this information is essential to the study of cell-to-cell interactions and intercellular communication within complex microenvironments. Recently, a new approach, called imaging mass cytometry, was developed to extend the applicability of MC to tissue analysis, allowing spatially resolved measurements of up to hundred biomarkers [28]. Imaging mass cytometry is based on the combination of MC with a high-resolution laser ablation system [29]. Tissue www.sciencedirect.com
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sections, stained with metal-conjugated antibodies using routine immunohistochemical (IHC) protocols, are ablated spot-by-spot and line-by-line using a laser beam at a spot size of 1 mm. The generated aerosol is transported and analyzed by MC. Reporting the isotope signals in the order of ablation yields a high-dimensional and high-parameter image of the tissue (Figure 3a). Giesen et al. [28] used this approach to analyze formalin-fixed, paraffin-embedded human breast cancer tissues (Figure 3b) with associated clinical information. Spatial analyses revealed the complexity of cell phenotypes and tumor microenvironment, and possibly will allow for a refined classification of patient subgroups. Angelo et al. [30] introduced a similar tissue imaging modality, referred to as multiplexed ion beam imaging (MIBI), which uses metal-chelated antibodies, as established for the MC pipeline, but employs secondary ion mass spectrometry for the analysis. Ultra high-resolution images with 50 nm pixel size were generated from MIBI experiments, revealing hidden phenotypic and morphological features of tumor biopsies. Currently, seven markers can be measured simultaneously. Repeated scans on the same tissue area have the potential to increase this number significantly. Thus, MIBI represents an excellent complementary technique to imaging mass cytometry. Computational tools for high-dimensional mass cytometry data
The ability to analyze high dimensional single-cell datasets and to identify cell (sub)populations in an unbiased, automated manner is critical for understanding cellular heterogeneity. However, despite improvements, most methods for analyzing high-dimensional single-cell data are still subjective, labor intensive, and require prior knowledge of the biological system [31,32]. MC output (fcs) files can be visualized by any FC software and often expert ‘supervised’ gating with two-parameter dotplots is used, although these fail to reveal all relationships that might exist in the high-multiparameter (>50) MC datasets. Therefore, different computational approaches have been developed to overcome this caveat (Figure 4a). A straightforward approach to reduce the dimensionality of multi-parameter datasets to (hopefully) biologically and clinically relevant observations is principle component analysis (PCA) [33]. PCA derives summary variables to capture and visualize the largest possible variations of the single cell data, but its ability to fully resolve subpopulations is limited. In another approach, cell subsets in heterogeneous samples are identified by clustering algorithms [34]. In general, clusters are identified by their proximity in n-dimensional space to their most similar characteristic and by their distance from other clusters of dissimilar characteristics.
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Imaging mass cytometry (figure and caption adapted from Giesen et al. [28]). (a) Workflow. Tissue sections are prepared for metal-chelated antibody labeling using IHC protocols. Then, tissue samples are positioned in a laser ablation chamber. The tissue is ablated and transported by a gas stream into the CyTOF for mass cytometry analysis. The measured isotope signals are plotted using the coordinates of each single laser shot, and a multidimensional tissue image is generated. Single-cell features and marker expression are determined, allowing the investigation of cell subpopulation properties within the analyzed tissue. (b) Example images generated by mass cytometry. Two luminal HER2+ breast cancer tissue samples are shown. 32 proteins and phosphorylation sites were measured simultaneously at 1 mm resolution. Tissue 1-a: Overlay of cytokeratin 8/18 (red), H3 (cyan) and vimentin (yellow). Tissue 1-b: Overlay of cytokeratin 7 (red), H3 (cyan) and CD44 (yellow). Tissue 1-c: Overlay of pan-actin (red), progesterone receptor (blue) and CD68 (yellow). Tissue 2-a: Overlay of HER2 (red), H3 (cyan) and vimentin (yellow). Tissue 2-b: Overlay of Ecadherin (red), cytokeratin 7 (yellow) and phosphorylation on S235/S236 on S6 (blue). Tissue 2-c: Overlay of b-catenin (red), estrogen receptor (blue) and CD68 (yellow).
There are a variety of clustering-based tools that have been applied to flow cytometry data, including FLAME, www.sciencedirect.com
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Computational workflow and tools implemented into the mass cytometrist’s toolbox. (a) Schematic workflow of the MC data analysis. MC raw data are transformed and analyzed by supervised gating approaches to obtain histograms, biaxial or 3D plots. The data can also be visualized by unsupervised methods to display n-dimensional parameters. This is performed by reducing dimensionality (e.g. Wanderlust, Gemstone, SPADE, viSNE) and/or by cluster identification (e.g. SPADE, FLAME, FlowCust). Additionally, statistical tools are employed to summarize many variables (heatmap, box plot, PCA). (b)–(c) Examples of cluster visualizations from SPADE and viSNE analysis, where combined populations of T and B cells, monocytes and dendritic cells are analyzed simultaneously by MC after applying cellular barcoding. In the legend, cell populations are indicated as TC (T cells); CD8+ TC (CD8+ T cells); CD4+ TC (CD4+ T cells); DC (dendritic cells); BC (B cells); CD27+ BC (CD27+ B cells); MC (monocytes); CD14+ MC (CD14+ monocytes). In B), the areas corresponding to each population are indicated. Typically, the median expression intensity of a selected set of markers is expressed for each node of the tree as a percentage of the maximum value in the dataset. In (c), each point of the viSNE map represents an individual cell, with each color representing the population it belongs to. SPADE and viSNE analyses reveal all the different cell populations and in different localization within the tree or the map, clustering monocytes, T and B cells together with their respective subpopulations (CD14+ MC; CD8+ and CD4+ T cells; CD27+ B cells).
FlowClust, SamSpectral, FlowMerge [35–38]. One of the first tools specifically developed to cluster MC data is SPADE (spanning-tree progression analysis of densitynormalized events), which uses a hierarchical clustering algorithm, after performing density-dependent downsampling, to organize high-dimensional cytometry data in an unsupervised manner [39]. Using SPADE, similar cells Current Opinion in Biotechnology 2015, 31:122–129
are clustered together and displayed in minimum spanning trees, facilitating the identification of cell subsets present in heterogeneous populations (an example of a SPADE tree is reported in Figure 4b). Through a simple 2D visualization, SPADE shows how measured markers behave across all cell types [12,40]. Amir et al. [41] developed a tool, called visual interactive stochastic www.sciencedirect.com
Studying cell heterogeneity by single-cell cytometry Di Palma and Bodenmiller 127
neighbor embedding (viSNE), which allows visualization of high-dimensional data while preserving local and global geometry of cell relationships. A viSNE map provides a visual representation of the single-cell data, similar to a biaxial plot, where the positions of cells reflect their proximity in high-dimensional rather than two-dimensional space (Figure 4c). By integrating MC data obtained on leukemia and healthy bone marrow samples with viSNE, differences in cell populations between healthy and cancerous bone marrow samples have been visualized. By comparing leukemia diagnosis and relapse samples, the authors identified a rare leukemia subpopulation reminiscent of minimal residual disease [41]. Very recently, Bendall et al. [42] developed a new graphbased algorithm, called Wanderlust, which receives multiparameter single-cell events as input and maps them onto a one-dimensional developmental trajectory. Wanderlust is ideal for the exploration of any system undergoing a developmental process since it can be used to infer trajectories of regulatory events in healthy conditions and use these as foundations to understand their alteration in disease states. Wanderlust, in combination with MC data, has been used to investigate human B lymphopoiesis and to construct a general B-lineage trajectory representing, in chronological order, the in vivo development of cancerous B cells from primary human bone marrow. An automated, unsupervised data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets, called Citrus (cluster identification, characterization, and regression), has been published recently [43]. Citrus identifies clusters of phenotypically similar cells, calculates descriptive features of each cluster, and, requiring minimal inputs from users, applies conventional supervised learning methods to determine which cell subsets’ behaviors are the best predictors of a known experimental endpoint of interest.
therapeutic strategies in personalized medicine. Although mass cytometry is a new technology, it currently provides a level of information that is not attainable by other single-cell protein technologies and has already found application in cancer research and in many other fields including immunology, infectious disease, inflammation research and drug screening [12,44,45,46,47,48,49]. Further improvements are still needed to exploit the full potential of MC. These include: firstly, increase in the number of measurable markers (including RNA, DNA and metabolites) by employing additional stable isotopes and designing new chelation chemistries and affinity reagents; secondly, development of advanced computational tools to display in a comprehensive way sophisticated multidimensional data; thirdly, methods for more accurate quantitative analyses that will enable the estimation of absolute copy numbers of signaling molecules and their network structures, reaction rates, and signaling thresholds; fourthly, integration of MC data with other data types, such as single-cell genomic and transcriptomic data; fifthly, validation of quantified relationships with orthogonal biotechnologies and biological follow-ups. We anticipate that mass cytometry will become an invaluable player of the ‘omics’ arena, opening the avenue of ‘cytomics’, and complementing other single-cell genomics, transcriptomics, proteomics, and metabolomics methodologies.
Acknowledgements We thank all members of the Bodenmiller lab for scientific support, discussion and feedback. SDP was supported by a Transition Postdoc fellowship (TPdF) from SystemsX and BB from a Swiss National Science Foundation (SNSF) project grant 31003A-143877, a SystemsX PhosphoNetPPM grant, a SNSF Assistant Professorship grant PP00P3-144874, a Swiss Cancer League grant and funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/ 2007-2013)/ERC Grant Agreement no. 336921.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:
Conclusions and future directions Tumor heterogeneity can only be understood by studying specialized cell types within the tumor itself in the context of the tumor microenvironment. Mass cytometry has proven to be a powerful technology to investigate complex assemblies of heterogeneous cell populations, such as those encountered in human cancer tissues. By combining information on cell phenotype, entangled cancer signaling circuitry, and spatially resolved pictures of intercellular communication, MC can help us elucidate processes occurring in the tumor microenvironment that are relevant to all aspects of tumor biology. These types of data have the potential to greatly improve our understanding of tumor progression. In concert with clinical data, MC could enable a finer classification of patients exploiting cell subpopulations and cell-to-cell relationships, and might aid the design of new potential www.sciencedirect.com
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33. Ringner M: What is principal component analysis? Nat Biotechnol 2008, 26:303-304. 34. Nugent R, Meila M: An overview of clustering applied to molecular biology. In Statistical methods in molecular biology. Edited by Bang H, Zhou XK, van Epps HL, Mazumdar M. Humana Press; 2010:369-404. 35. Zare H, Shooshtari P, Gupta A, Brinkman R: Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinform 2010, 11:403. 36. Pyne S, Hu X, Wang K, Rossin E, Lin T-I, Maier LM et al.: Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci 2009, 106:8519-8524. 37. Lo K, Hahne F, Brinkman R, Gottardo R: flowClust: a bioconductor package for automated gating of flow cytometry data. BMC Bioinform 2009, 10:145. 38. Finak G, Bashashati A, Brinkman R, Gottardo R: Merging mixture components for cell population identification in flow cytometry. Adv Bioinform 2009, 2009:12. www.sciencedirect.com
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39. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, Linderman MD et al.: Extracting a cellular hierarchy from highdimensional cytometry data with SPADE. Nat Biotechnol 2011, 29:886-891. 40. Linderman MD, Bjornson Z, Simonds EF, Qiu P, Bruggner RV, Sheode K et al.: CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics 2012, 28:2400-2401. 41. Amir E-aD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC et al.: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 2013, 31:545-552. viSNE is a tool that allows mapping high-dimensional cytometry data onto two dimensions, yet conserving the high-dimensional structure of the data. viSNE plots individual cells in a map similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell’s location in the plot. viSNE is applied to mass cytometry to map healthy versus cancerous bone marrow samples. Leukemia samples map into malformed shapes that are distinct from healthy bone marrow shapes and from each other. 42. Bendall Sean C, Davis Kara L, Amir E-ad D, Tadmor Michelle D, Simonds Erin F, Chen Tiffany J et al.: Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 2014, 157:714-725. Combination of single-cell mass cytometry with a new algorithm, called Wanderlust, to align single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. This approach is applied to human B cell lymphopoiesis, constructing trajectories that span from hematopoietic stem cells through to naive B cells. Wanderlust generates remarkably consistent trajectories across multiple individuals and determines the timing and order of key molecular and cellular events across developmental processes, such as human B cell development. 43. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP: Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 2014, 111: E2770-E2777. Citrus (cluster identification, characterization, and regression) is a new automated approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. Citrus’ methodology is demonstrated through the identification of known and novel pathways
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in stimulated peripheral blood mononuclear cells measured by mass cytometry, comparing its performance to that of existing methods. 44. Wolchinsky R, Hod-Marco M, Oved K, Shen-Orr SS, Bendall SC, Nolan GP et al.: Antigen-dependent integration of opposing proximal TCR-signaling cascades determines the functional fate of T lymphocytes. J Immunol 2014, 192:2109-2119. 45. Behbehani GK, Bendall SC, Clutter MR, Fantl WJ, Nolan GP: Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry Part A 2012, 81:552-566. 46. Newell EW, Sigal N, Nair N, Kidd BA, Greenberg HB, Davis MM: Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat Biotechnol 2013, 31:623-629. By combining mass cytometry with combinatorial peptide–MHC multimer staining approaches, a method is developed to simultaneously screen for T-cell epitopes in any protein of known sequence and to perform highdimensional phenotypic analysis of human T cells specific for those epitopes. This approach is used to screen up to 109 different peptide– MHC tetramers in a single human blood sample. Among 77 candidate rotavirus epitopes, six T-cell epitopes restricted to human leukocyte antigen (HLA)-A*0201 are identified in the blood of healthy individuals. 47. Newell Evan W, Sigal N, Bendall Sean C, Nolan Garry P, Davis Mark M: Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 2012, 36:142-152. Development of peptide–MHC tetramer staining in conjunction with mass cytometry to identify and profile antigen-specific T cells with a variety of phenotypic and functional markers. Computational methods are used that provided a picture of the functional and phenotypic diversity of the CD8+ T cell compartment. This analysis shows that CD8+ T cells from normal human donors display a broad continuum of phenotypic profiles with remarkable diversity in their abilities to produce various cytokines. 48. Fienberg H, Nolan G: Mass cytometry to decipher the mechanism of nongenetic drug resistance in cancer. In Highdimensional single cell analysis. Edited by Fienberg HG, Nolan GP. Berlin Heidelberg: Springer; 2014:85-94. 49. Bjornson ZB, Nolan GP, Fantl WJ: Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 2013, 25:484-494.
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