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Profiling Cell Signaling Networks at Single-cell Resolution*

  • Xiao-Kang Lun
    Footnotes
    Affiliations
    Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland

    Molecular Life Sciences PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zürich, Switzerland
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  • Bernd Bodenmiller
    Correspondence
    To whom correspondence should be addressed
    Footnotes
    Affiliations
    Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland
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  • Author Footnotes
    * This work was supported by an SNSF R'Equip grant, a SNSF Assistant Professorship grant (PP00P3-144874), the European Research Council (ERC) under the European Union's Seventh Framework Program (FP/2007–2013)/ERC Grant Agreement n. 336921, and an NIH grant (UC4 DK108132). The authors declare that they have no conflicts of interest with the contents of this article.
    ¶ Present address: Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115.
    ‖ Present address: Department of Quantitative Biomedicine, University of Zürich, 8057 Zürich, Switzerland.
Open AccessPublished:March 04, 2020DOI:https://doi.org/10.1074/mcp.R119.001790
      Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

      Graphical Abstract

      Cell-to-Cell Heterogeneity in the Signal Transduction Response

      Signaling pathways mediate cell communication and coordinate cellular functions such as proliferation, differentiation, and energy metabolism (
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      Figure thumbnail gr1
      Fig. 1Signaling network heterogeneity in cell populations. A, Mutated signaling proteins (e.g., kinases) may cause genetic heterogeneity in a population of cells and leads to differential signaling networks. B, Non-genetic signaling network heterogeneity may origin from extrinsic factors including stimulus concentration, matrix stiffness, local crowdedness, oxygen and nutrient gradients, as well as the intrinsic noise. C, Signaling network heterogeneity results in phenotypical variances in a population of cells. Bulk analysis averages these variances, resulting in misinterpretation of cell signaling network behaviors and cell phenotypes.
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      The Single-cell Era of Signaling Network Analysis

      Single-cell Analysis with High Multiplexity

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      • Dolinski K.
      • Botstein D.
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      • Raj A.
      • Eisen M.B.
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      • Brown P.O.
      • Botstein D.
      • Gehlenborg N.
      • O'Donoghue S.I.
      • Baliga N.S.
      • Goesmann A.
      • Hibbs M.A.
      • Kitano H.
      • Kohlbacher O.
      • Neuweger H.
      • Schneider R.
      • Tenenbaum D.
      • Gavin A.C.
      • Ashburner M.
      • Ball C.A.
      • Blake J.A.
      • Botstein D.
      • Butler H.
      • Cherry J.M.
      • Davis A.P.
      • Dolinski K.
      • Dwight S.S.
      • Eppig J.T.
      • Harris M.A.
      • Hill D.P.
      • Issel-Tarver L.
      • Kasarskis A.
      • Lewis S.
      • Matese J.C.
      • Richardson J.E.
      • Ringwald M.
      • Rubin G.M.
      • Sherlock G.
      • Yoshida H.
      • Nagaoka A.
      • Kusaka-Kikushima A.
      • Tobiishi M.
      • Kawabata K.
      • Sayo T.
      • Sakai S.
      • Sugiyama Y.
      • Enomoto H.
      • Okada Y.
      • Inoue S.
      • Lauffenburger D.A.
      • Horwitz A.F.
      • Rapoport T.A.
      • Jan C.H.
      • Williams C.C.
      • Weissman J.S.
      • Lawrence J.B.
      • Singer R.H.
      • Mingle L.A.
      • Okuhama N.N.
      • Shi J.
      • Singer R.H.
      • Condeelis J.
      • Liu G.
      • Babcock H.
      • Sigal Y.M.
      • Zhuang X.
      • Zhu L.
      • Zhang W.
      • Elnatan D.
      • Huang B.
      • Babcock H.P.
      • Moffitt J.R.
      • Cao Y.
      • Zhuang X.
      • Hell S.W.
      • Huang B.
      • Babcock H.
      • Zhuang X.
      • Xu Q.
      • Schlabach M.R.
      • Hannon G.J.
      • Elledge S.J.
      • Camacho C.
      • Coulouris G.
      • Avagyan V.
      • Ma N.
      • Papadopoulos J.
      • Bealer K.
      • Madden T.L.
      • Trapnell C.
      • Roberts A.
      • Goff L.
      • Pertea G.
      • Kim D.
      • Kelley D.R.
      • Pimentel H.
      • Salzberg S.L.
      • Rinn J.L.
      • Pachter L.
      • Dunham I.
      • Rouillard J.-M.
      • Zuker M.
      • Gulari E.
      • Buxbaum A.R.
      • Wu B.
      • Singer R.H.
      • Rasnik I.
      • McKinney S.A.
      • Ha T.
      • Shi X.
      • Lim J.
      • Ha T.
      RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.
      ,
      • Saka S.K.
      • Wang Y.
      • Kishi J.Y.
      • Zhu A.
      • Zeng Y.
      • Xie W.
      • Kirli K.
      • Yapp C.
      • Cicconet M.
      • Beliveau B.J.
      • Lapan S.W.
      • Yin S.
      • Lin M.
      • Boyden E.S.
      • Kaeser P.S.
      • Pihan G.
      • Church G.M.
      • Yin P.
      Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues.
      ) have made it possible to explore cellular phosphorylation landscapes and signaling regulatory network structure cell-by-cell in heterogeneous samples. Here we summarize currently available approaches for signaling network analysis at single-cell resolution (Fig. 2 and Table I).
      Figure thumbnail gr2
      Fig. 2Approaches to analyze cell signaling networks at single-cell resolution. Information on signaling network states in individual cells can be analyzed in cell suspension with mass cytometry, which allows simultaneous measurement of about 50 markers such as phosphorylation levels of signaling proteins and markers of cell phenotype. Single-cell RNA sequencing technologies allow transcriptomics profiling that can be used to infer cell signaling states. Multiplexed cell signaling profiling can be performed in situ with mass spectrometry-based imaging methods or with sequential immuno-based fluorescence imaging; these methods preserve spatial information. Live-cell imaging methods (e.g., kinase translocation reporters, FRET) can be used to monitor dynamic signaling behaviors in real time with single-cell resolution, although with lower multiplexing capability.
      Table IComparison of single-cell approaches for signaling network analysis
      TechniqueMultiplicityThrough putCostSample typeTarget of measurementSpatial resolutionSensitivity
      Flow cytometryUp to 30Very highLowSingle cells stained with fluorophore-conjugated antibodiesProteins and protein modifications. High number of additional assays available.N/AHigh
      Mass cytometryUp to 50HighLowSingle cells stained with metal isotope-conjugated antibodiesProteins, protein modifications and transcriptsN/AHigh
      Single-cell immuno-sequencing (CITE-seq and REAP-seq, etc.)UnlimitedMediumHighSingle cells stained with DNA oligonucleotide-labeled antibodiesProteins and protein modificationsN/AMedium
      Lab-on-chip and microfluidics (SCBC and scWesterns)10MediumLowSingle-cell lysisProteins and protein modificationsN/AHigh
      Single-cell proteomicsUnlimitedVery lowHighSingle-cell lysisProteins and protein modificationsN/ALow
      Single-cell RNA-seqUnlimitedMediumHighSingle-cell lysismRNAN/AMedium
      Multiplexed imaging based on sequential antibody staining (MELC, MxIF, CycIF, 4i, etc.)Up to 90MediumLowFixed cell or tissue slidesProteins and protein modificationsHighHigh
      Multiplexed imaging based on sequential antibody detection (immune-SABER and CODEX, etc.)30HighLowFixed cell or tissue slidesProteins and protein modificationsHighHigh
      Imaging mass cytometry (IMC)Up to 50MediumMediumFixed cell or tissue slidesProteins and protein modificationsMediumMedium
      Multiplexed ion beam imaging (MIBI)Up to 50MediumHighFixed cell or tissue slidesProteins and protein modificationsHighMedium
      MALDI-based imagingUnlimitedMediumHighFixed tissue slidesLipids and metabolitesLowLow
      In situ sequencing (FISSEQ)UnlimitedLowHighFixed cell or tissue slidesmRNAHighLow
      Fluorescence in situ hybridization (MERFISH and seqFISH, etc.)100sLowLowFixed cell or tissue slidesGenomic DNA and mRNAHighHigh
      Kinase translocation reporter3MediumLowLive cellsKinasesHighHigh
      FRETUp to 6MediumLowLive cellsKinases or interactive proteinsHighHigh

      Non-spatial Single-Cell Analysis Based on Immunological Approaches

      Flow Cytometry

      Flow cytometry uses fluorophore-labeled antibodies to detect and quantify protein abundance in individual cells. It has been used to monitor relationships between multiple phosphorylation sites and correlations between phosphorylation states, functional readouts, and lineage-specific markers in complex populations of cells (
      • Perez O.D.
      • Nolan G.P.
      Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry.
      ). With the capability to simultaneously measure ∼10 (up to 30 in more advanced setups) phosphoproteins and phospholipids, flow cytometry-based single-cell analysis has recently been combined with inhibitor perturbation assays enabling the inference of signaling circuits and the reconstruction of signaling networks (
      • Sachs K.
      • Perez O.
      • Pe'er D.
      • Lauffenburger D.A.
      • Nolan G.P.
      Causal protein-signaling networks derived from multiparameter single-cell data.
      ). The development of fluorescent cell barcoding has greatly increased the throughput of flow cytometry-based intracellular signaling analysis. It is now routinely implemented as a screening tool to quantify cellular responses to kinase inhibitors in individual cell types in heterogeneous populations (
      • Krutzik P.O.
      • Nolan G.P.
      Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling.
      ,
      • Krutzik P.O.
      • Crane J.M.
      • Clutter M.R.
      • Nolan G.P.
      High-content single-cell drug screening with phosphospecific flow cytometry.
      ). However, because of the overlap of the fluorescent spectra of the fluorescent dyes used to label antibodies, the number of markers that can be analyzed simultaneously by flow cytometry remains limited, and signaling networks can only be sparsely or partially interrogated using this technique. Nevertheless, with the advantages of throughput and accessibility, flow cytometry is one of the most used methods for single-cell signaling assessments in research and diagnosis (
      • Davies R.
      • Sarkar I.
      • Hammenfors D.
      • Bergum B.
      • Vogelsang P.
      • Solberg S.M.
      • Gavasso S.
      • Brun J.G.
      • Jonsson R.
      • Appel S.
      Single cell based phosphorylation profiling identifies alterations in toll-like receptor 7 and 9 signaling in patients with primary Sjögren's Syndrome.
      ,
      • Kanegane H.
      • Hoshino A.
      • Okano T.
      • Yasumi T.
      • Wada T.
      • Takada H.
      • Okada S.
      • Yamashita M.
      • Yeh T.
      • Nishikomori R.
      • Takagi M.
      • Imai K.
      • Ochs H.D.
      • Morio T.
      Flow cytometry-based diagnosis of primary immunodeficiency diseases.
      ).

      Mass Cytometry

      Mass cytometry is based on inductively coupled plasma time-of-flight mass spectrometry and a single-cell sample introduction system (
      • Bandura D.R.
      • Baranov V.I.
      • Ornatsky O.I.
      • Antonov A.
      • Kinach R.
      • Lou X.
      • Pavlov S.
      • Vorobiev S.
      • Dick J.E.
      • Tanner S.D.
      Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.
      ). In mass cytometry, metal isotope-tagged antibodies are used to label proteins or protein modifications in cells. Metal tags allow multiplicity considerably higher than possible with flow cytometry. During the mass cytometry measurement, each stained single cell is vaporized, atomized, and ionized. The metals in the formed ion cloud are quantitatively analyzed by the mass spectrometer to yield a high-dimensional single-cell proteomic readout (Fig. 2, left panel) (
      • Bandura D.R.
      • Baranov V.I.
      • Ornatsky O.I.
      • Antonov A.
      • Kinach R.
      • Lou X.
      • Pavlov S.
      • Vorobiev S.
      • Dick J.E.
      • Tanner S.D.
      Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.
      ,
      • Bendall S.C.
      • Simonds E.F.
      • Qiu P.
      • Amir E.D.
      • Krutzik P.O.
      • Finck R.
      • Bruggner R.V.
      • Melamed R.
      • Trejo A.
      • Ornatsky O.I.
      • Balderas R.S.
      • Plevritis S.K.
      • Sachs K.
      • Pe'er D.
      • Tanner S.D.
      • Nolan G.P.
      Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.
      ). A mass cytometry analysis simultaneously quantifies up to 50 cell-surface or intracellular markers, including phosphorylation sites, with high analytical throughput of around 500 cells per second and millions of events per sample. A mass-tag barcoding strategy allows simultaneous measurement of hundreds of samples, eliminating batch effects that confound conventional measurements and reducing the workload (
      • Lun X.-K.
      • Szklarczyk D.
      • Gábor A.
      • Dobberstein N.
      • Zanotelli V.R.T.
      • Saez-Rodriguez J.
      • von Mering C.
      • Bodenmiller B.
      Analysis of the human kinome and phosphatome by mass cytometry reveals overexpression-induced effects on cancer-related signaling.
      ,
      • Bodenmiller B.
      • Zunder E.R.
      • Finck R.
      • Chen T.J.
      • Savig E.S.
      • Bruggner R.V.
      • Simonds E.F.
      • Bendall S.C.
      • Sachs K.
      • Krutzik P.O.
      • Nolan G.P.
      Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators.
      ,
      • Zunder E.R.
      • Finck R.
      • Behbehani G.K.
      • Amir E.-A.D.
      • Krishnaswamy S.
      • Gonzalez V.D.
      • Lorang C.G.
      • Bjornson Z.
      • Spitzer M.H.
      • Bodenmiller B.
      • Fantl W.J.
      • Pe'er D.
      • Nolan G.P.
      Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm.
      ).
      The mass cytometry does not have sensitivity superior to flow cytometry, but cell auto-fluorescence, which interferes with quantification of a fluorescently labeled marker in flow cytometry, is not an issue with mass cytometry (
      • Bandura D.R.
      • Baranov V.I.
      • Ornatsky O.I.
      • Antonov A.
      • Kinach R.
      • Lou X.
      • Pavlov S.
      • Vorobiev S.
      • Dick J.E.
      • Tanner S.D.
      Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.
      ). Although minor spill-over between channels of the mass cytometer occurs because of metal impurity, mass overlap, and oxidation (
      • Bendall S.C.
      • Nolan G.P.
      • Roederer M.
      • Chattopadhyay P.K.
      A deep profiler's guide to cytometry.
      ), these events are manageable with proper experimental design and can be removed computationally (
      • Chevrier S.
      • Crowell H.L.
      • Zanotelli V.R.T.
      • Engler S.
      • Robinson M.D.
      • Bodenmiller B.
      Compensation of signal spillover in suspension and imaging mass cytometry.
      ).
      Mass cytometry has been used in drug screening (
      • Bodenmiller B.
      • Zunder E.R.
      • Finck R.
      • Chen T.J.
      • Savig E.S.
      • Bruggner R.V.
      • Simonds E.F.
      • Bendall S.C.
      • Sachs K.
      • Krutzik P.O.
      • Nolan G.P.
      Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators.
      ). Relationships between all pairs of measured phosphorylation sites can be computed to infer network responses to a stimulus (
      • Krishnaswamy S.
      • Spitzer M.H.
      • Mingueneau M.
      • Bendall S.C.
      • Litvin O.
      • Stone E.
      • Pe'er D.
      • Nolan G.P.
      Conditional density-based analysis of T cell signaling in single-cell data.
      ) or to trace the network reshaping through a phenotypical transition (
      • Krishnaswamy S.
      • Zivanovic N.
      • Sharma R.
      • Pe'er D.
      • Bodenmiller B.
      Learning time-varying information flow from single-cell epithelial to mesenchymal transition data.
      ). When coupled to a transient overexpression technique, mass cytometry-based signaling profiling enables assessment of how intracellular signaling states and dynamics depend on protein abundance. These types of experiments have revealed novel signaling mechanisms involved in cancer progression and drug resistance (
      • Lun X.-K.
      • Szklarczyk D.
      • Gábor A.
      • Dobberstein N.
      • Zanotelli V.R.T.
      • Saez-Rodriguez J.
      • von Mering C.
      • Bodenmiller B.
      Analysis of the human kinome and phosphatome by mass cytometry reveals overexpression-induced effects on cancer-related signaling.
      ,
      • Lun X.-K.
      • Zanotelli V.R.T.
      • Wade J.D.
      • Schapiro D.
      • Tognetti M.
      • Dobberstein N.
      • Bodenmiller B.
      Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry.
      ).

      Single-cell Immuno-sequencing

      As only about 50 metal isotopes are routinely used in mass cytometry, deep profiling of phosphoprotein networks is not possible. Two recently developed techniques, REAP-seq and CITE-seq, barcode antibodies with oligonucleotides to increase multiplexing. These methods allow detection of targeted proteins by single-cell sequencing simultaneously with quantification of RNA transcriptomes in the same cells (
      • Peterson V.M.
      • Zhang K.X.
      • Kumar N.
      • Wong J.
      • Li L.
      • Wilson D.C.
      • Moore R.
      • McClanahan T.K.
      • Sadekova S.
      • Klappenbach J.A.
      Multiplexed quantification of proteins and transcripts in single cells.
      ,
      • Stoeckius M.
      • Hafemeister C.
      • Stephenson W.
      • Houck-Loomis B.
      • Chattopadhyay P.K.
      • Swerdlow H.
      • Satija R.
      • Smibert P.
      Simultaneous epitope and transcriptome measurement in single cells.
      ). More than 10 million distinct barcodes can be generated with a 12-mer oligonucleotide (412), making the measurable parameters in this type of methods virtually unlimited. REAP-seq and CITE-seq have been implemented for cell-surface marker staining, and it is expected that these techniques will soon be used at the intracellular level for comprehensive single-cell signal profiling. Yet, sequencing-based approaches suffer from high technical variance and are therefore less quantitative than flow and mass cytometry methods. Experimental cycles are also slower in sequencing methods compared with flow and mass cytometry, making optimizations more time-consuming.

      Lab-on-Chip and Microfluidics

      Lab-on-chip technologies, such as single-cell barcode chips (SCBCs) and single-cell Western blotting (scWesterns), are more sensitive than cytometric methods and allow detection of low-abundance proteins (
      • Hughes A.J.
      • Spelke D.P.
      • Xu Z.
      • Kang C.-C.
      • Schaffer D.V.
      • Herr A.E.
      Single-cell western blotting.
      ,
      • Wei W.
      • Shin Y.S.
      • Xue M.
      • Matsutani T.
      • Masui K.
      • Yang H.
      • Ikegami S.
      • Gu Y.
      • Herrmann K.
      • Johnson D.
      • Ding X.
      • Hwang K.
      • Kim J.
      • Zhou J.
      • Su Y.
      • Li X.
      • Bonetti B.
      • Chopra R.
      • James C.D.
      • Cavenee W.K.
      • Cloughesy T.F.
      • Mischel P.S.
      • Heath J.R.
      • Gini B.
      Single-cell phosphoproteomics resolves adaptive signaling dynamics and informs targeted combination therapy in glioblastoma.
      ,
      • Shi Q.
      • Qin L.
      • Wei W.
      • Geng F.
      • Fan R.
      • Shik Shin Y.
      • Guo D.
      • Hood L.
      • Mischel P.S.
      • Heath J.R.
      Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells.
      ). These approaches have been applied to resolve single-cell signaling network variations and functional heterogeneity (
      • Wei W.
      • Shin Y.S.
      • Xue M.
      • Matsutani T.
      • Masui K.
      • Yang H.
      • Ikegami S.
      • Gu Y.
      • Herrmann K.
      • Johnson D.
      • Ding X.
      • Hwang K.
      • Kim J.
      • Zhou J.
      • Su Y.
      • Li X.
      • Bonetti B.
      • Chopra R.
      • James C.D.
      • Cavenee W.K.
      • Cloughesy T.F.
      • Mischel P.S.
      • Heath J.R.
      • Gini B.
      Single-cell phosphoproteomics resolves adaptive signaling dynamics and informs targeted combination therapy in glioblastoma.
      ,
      • Shi Q.
      • Qin L.
      • Wei W.
      • Geng F.
      • Fan R.
      • Shik Shin Y.
      • Guo D.
      • Hood L.
      • Mischel P.S.
      • Heath J.R.
      Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells.
      ). Investigations of single-cell signaling kinetics can also be performed using microfluidic systems that allow fine time resolution and accurate dose control of the profiled stimulus (
      • Ng A.H.C.
      • Dean Chamberlain M.
      • Situ H.
      • Lee V.
      • Wheeler A.R.
      Digital microfluidic immunocytochemistry in single cells.
      ).

      Non-spatial Single-cell Analysis Based on 'Omics Approaches

      Immunostaining-based techniques allow multi-dimensional deep profiling of signaling networks at single-cell resolution, but also face three main limitations: First, the selection of measured targets is based on prior knowledge, so these methods are not suitable for exploratory studies. Second, not all targets of interest are measurable because of the high dependence on antibody availability. Third, given different antigen-binding affinities, quantifications cannot be compared across antibodies. Fortunately, the development of several antibody-free 'omics approaches has provided complementary techniques that do not suffer from these limitations.

      Single-cell Proteomics by Mass Spectrometry

      A big challenge for single-cell mass spectrometry is the comparably low sensitivity of the technique, especially for low abundance proteins, which is because of sample loss during processing, the absence of amplification approaches for proteins, and limitations to instrument sensitivity. Advances in sample processing and alternative strategies to overcome these limitations have been introduced in the past few years. For instance, SCoPE-MS (Single Cell ProtEomics by mass spectrometry) uses labeling with tandem mass tags to embed single mammalian cells in hundreds of carrier cells to separate the identification (from multiple “carrier” cells) from quantification of proteins in single cells, enabling quantification of over 1000 proteins per single cell (
      • Budnik B.
      • Levy E.
      • Harmange G.
      • Slavov N.
      SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation.
      ). A second-generation version of this method SCoPE2 that includes optimized sample preparation steps was used to assess over 2,000 proteins in 356 single cells within 85 h (
      • Specht H.
      • Emmott E.
      • Perlman D.H.
      • Koller A.
      • Slavov N.
      High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity.
      ). This field of research is very active, and further advances in this type of approach and their adaptation to profile the phosphoproteome in single cells are expected to help push the boundaries of single-cell proteomics. Nevertheless, the low throughput and high cost are likely to remain significant limitations.

      Single-cell Transcriptomics and Epigenomics

      Single-cell sequencing techniques (
      • Tang F.
      • Barbacioru C.
      • Wang Y.
      • Nordman E.
      • Lee C.
      • Xu N.
      • Wang X.
      • Bodeau J.
      • Tuch B.B.
      • Siddiqui A.
      • Lao K.
      • Surani M.A.
      mRNA-Seq whole-transcriptome analysis of a single cell.
      ,
      • Buenrostro J.D.
      • Wu B.
      • Litzenburger U.M.
      • Ruff D.
      • Gonzales M.L.
      • Snyder M.P.
      • Chang H.Y.
      • Greenleaf W.J.
      Single-cell chromatin accessibility reveals principles of regulatory variation.
      ) do not directly measure protein abundance and cannot detect functional protein modifications that reflect signaling network activation. However, with the strength to quantify global RNA expression and identify whole-genome transcriptional regulation landscapes, these approaches can be used to infer transcriptional regulatory networks and the dynamics of signaling pathways in response to a stimulus (Fig. 2, left panel). For example, single-cell RNA-seq revealed a paracrine signaling-required repression of the inflammatory program (
      • Shalek A.K.
      • Satija R.
      • Shuga J.
      • Trombetta J.J.
      • Gennert D.
      • Lu D.
      • Chen P.
      • Gertner R.S.
      • Gaublomme J.T.
      • Yosef N.
      • Schwartz S.
      • Fowler B.
      • Weaver S.
      • Wang J.
      • Wang X.
      • Ding R.
      • Raychowdhury R.
      • Friedman N.
      • Hacohen N.
      • Park H.
      • May A.P.
      • Regev A.
      Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.
      ). Single-cell epigenomes can now be measured with ATAC-seq, which sequences transposase-accessible chromatin (
      • Buenrostro J.D.
      • Giresi P.G.
      • Zaba L.C.
      • Chang H.Y.
      • Greenleaf W.J.
      Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.
      ,
      • Cusanovich D.A.
      • Daza R.
      • Adey A.
      • Pliner H.A.
      • Christiansen L.
      • Gunderson K.L.
      • Steemers F.J.
      • Trapnell C.
      • Shendure J.
      Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing.
      ). By coupling single-cell transcriptomics and epigenomics analyses, the network of transcriptional regulation during stem cell differentiation was profiled, and crucial signaling pathways during the transition from quiescence to proliferation and differentiation were identified (
      • Guo J.
      • Grow E.J.
      • Yi C.
      • Mlcochova H.
      • Maher G.J.
      • Lindskog C.
      • Murphy P.J.
      • Wike C.L.
      • Carrell D.T.
      • Goriely A.
      • Hotaling J.M.
      • Cairns B.R.
      Chromatin and single-cell RNA-Seq profiling reveal dynamic signaling and metabolic transitions during human spermatogonial stem cell development.
      ,
      • Buenrostro J.D.
      • Corces M.R.
      • Lareau C.A.
      • Wu B.
      • Schep A.N.
      • Aryee M.J.
      • Majeti R.
      • Chang H.Y.
      • Greenleaf W.J.
      Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation.
      ).

      Spatial Single-cell Analysis Based on Immunological Approaches

      Spatial variables (e.g. cell contacts and protein localizations) might act as crucial determinants during the processing of cellular signaling information. These properties cannot be assessed with the single-cell analytical methods described above as cell detachment or tissue dissociation is required for sample acquisition. Imaging-based cytometry and 'omics techniques can preserve cellular spatial information and are also capable of resolving subcellular details of protein localization. The additional spatial dimension gained with these approaches provides clues to sources of cellular heterogeneity and facilitates the profiling of signaling network behaviors.

      Sequential Fluorescence Imaging

      Spatial information on protein localization and tissue organization can be acquired through fluorescence microscopic measurements of cell monolayers or tissue sections. Fluorescence spectrum overlap limits the number of channels that can be detected in a simultaneous measurement, however. To achieve the high multiplicity required for signaling network profiling, technologies have been developed that allow sequential imaging of the same specimen without influencing antigen abundance or tissue structure (Fig. 2, middle panel). The first generation of sequential imaging approaches applies fluorophore-labeled antibodies to detect targets of interest (
      • Lin J.-R.
      • Fallahi-Sichani M.
      • Sorger P.K.
      Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.
      ,
      • Gerdes M.J.
      • Sevinsky C.J.
      • Sood A.
      • Adak S.
      • Bello M.O.
      • Bordwell A.
      • Can A.
      • Corwin A.
      • Dinn S.
      • Filkins R.J.
      • Hollman D.
      • Kamath V.
      • Kaanumalle S.
      • Kenny K.
      • Larsen M.
      • Lazare M.
      • Li Q.
      • Lowes C.
      • McCulloch C.C.
      • McDonough E.
      • Montalto M.C.
      • Pang Z.
      • Rittscher J.
      • Santamaria-Pang A.
      • Sarachan B.D.
      • Seel M.L.
      • Seppo A.
      • Shaikh K.
      • Sui Y.
      • Zhang J.
      • Ginty F.
      Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue.
      ,
      • Schubert W.
      • Bonnekoh B.
      • Pommer A.J.
      • Philipsen L.
      • Böckelmann R.
      • Malykh Y.
      • Gollnick H.
      • Friedenberger M.
      • Bode M.
      • Dress A.W.M.
      Analyzing proteome topology and function by automated multidimensional fluorescence microscopy.
      ,
      • Gut G.
      • Herrmann M.D.
      • Pelkmans L.
      Multiplexed protein maps link subcellular organization to cellular states.
      ). Specifically, MELC implements photo-bleaching after each round of antibody staining and imaging cycle to remove the residual fluorescence (
      • Schubert W.
      • Bonnekoh B.
      • Pommer A.J.
      • Philipsen L.
      • Böckelmann R.
      • Malykh Y.
      • Gollnick H.
      • Friedenberger M.
      • Bode M.
      • Dress A.W.M.
      Analyzing proteome topology and function by automated multidimensional fluorescence microscopy.
      ). Alkaline oxidation chemistry is used in a recently developed method called MxIF to chemically inactivate the fluorescent dyes after imaging (
      • Gerdes M.J.
      • Sevinsky C.J.
      • Sood A.
      • Adak S.
      • Bello M.O.
      • Bordwell A.
      • Can A.
      • Corwin A.
      • Dinn S.
      • Filkins R.J.
      • Hollman D.
      • Kamath V.
      • Kaanumalle S.
      • Kenny K.
      • Larsen M.
      • Lazare M.
      • Li Q.
      • Lowes C.
      • McCulloch C.C.
      • McDonough E.
      • Montalto M.C.
      • Pang Z.
      • Rittscher J.
      • Santamaria-Pang A.
      • Sarachan B.D.
      • Seel M.L.
      • Seppo A.
      • Shaikh K.
      • Sui Y.
      • Zhang J.
      • Ginty F.
      Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue.
      ,
      • Li C.
      • Ma H.
      • Wang Y.
      • Cao Z.
      • Graves-Deal R.
      • Powell A.E.
      • Starchenko A.
      • Ayers G.D.
      • Washington M.K.
      • Kamath V.
      • Desai K.
      • Gerdes M.J.
      • Solnica-Krezel L.
      • Coffey R.J.
      Excess PLAC8 promotes an unconventional ERK2-dependent EMT in colon cancer.
      ). CycIF combines oxidative inactivation and enzymatic antibody cleavage for sequential imaging (
      • Lin J.-R.
      • Fallahi-Sichani M.
      • Sorger P.K.
      Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.
      ). Multiplexed imaging can be also performed with indirect immunofluorescence, which does not require special antibody conjugation and allows amplification of signal from low-abundance markers using secondary antibodies (
      • Gut G.
      • Herrmann M.D.
      • Pelkmans L.
      Multiplexed protein maps link subcellular organization to cellular states.
      ). Experiments that rely on sequential staining and bleaching can take several days (
      • Lin J.-R.
      • Fallahi-Sichani M.
      • Sorger P.K.
      Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.
      ,
      • Gerdes M.J.
      • Sevinsky C.J.
      • Sood A.
      • Adak S.
      • Bello M.O.
      • Bordwell A.
      • Can A.
      • Corwin A.
      • Dinn S.
      • Filkins R.J.
      • Hollman D.
      • Kamath V.
      • Kaanumalle S.
      • Kenny K.
      • Larsen M.
      • Lazare M.
      • Li Q.
      • Lowes C.
      • McCulloch C.C.
      • McDonough E.
      • Montalto M.C.
      • Pang Z.
      • Rittscher J.
      • Santamaria-Pang A.
      • Sarachan B.D.
      • Seel M.L.
      • Seppo A.
      • Shaikh K.
      • Sui Y.
      • Zhang J.
      • Ginty F.
      Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue.
      ,
      • Gut G.
      • Herrmann M.D.
      • Pelkmans L.
      Multiplexed protein maps link subcellular organization to cellular states.
      ), tissue properties may change and sample handling can introduce error.
      Second-generation sequential imaging approaches employ DNA-labeled antibodies (
      • Jungmann R.
      • Avendaño M.S.
      • Woehrstein J.B.
      • Dai M.
      • Shih W.M.
      • Yin P.
      Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT.
      ,
      • Goltsev Y.
      • Samusik N.
      • Kennedy-Darling J.
      • Bhate S.
      • Hale M.
      • Vazquez G.
      • Black S.
      • Nolan G.P.
      Deep profiling of mouse splenic architecture with CODEX multiplexed imaging.
      ). Unlike methods that require time-consuming rounds of antibody staining, DNA-labeled antibodies are simultaneously applied to the specimen. The DNA oligonucleotides conjugated to the antibodies serve as barcodes that can be sequentially detected by fluorophore-labeled dNTPs in CODEX (
      • Goltsev Y.
      • Samusik N.
      • Kennedy-Darling J.
      • Bhate S.
      • Hale M.
      • Vazquez G.
      • Black S.
      • Nolan G.P.
      Deep profiling of mouse splenic architecture with CODEX multiplexed imaging.
      ) or by fluorescent probes directly and indirectly linked to complementary DNA sequence in Exchange-PAINT (
      • Jungmann R.
      • Avendaño M.S.
      • Woehrstein J.B.
      • Dai M.
      • Shih W.M.
      • Yin P.
      Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT.
      ) and immune-SABER (
      • Saka S.K.
      • Wang Y.
      • Kishi J.Y.
      • Zhu A.
      • Zeng Y.
      • Xie W.
      • Kirli K.
      • Yapp C.
      • Cicconet M.
      • Beliveau B.J.
      • Lapan S.W.
      • Yin S.
      • Lin M.
      • Boyden E.S.
      • Kaeser P.S.
      • Pihan G.
      • Church G.M.
      • Yin P.
      Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues.
      ). These approaches allow profiling of spatial signaling heterogeneity and reveal tissue organization-related network variations (
      • Gerdes M.J.
      • Sevinsky C.J.
      • Sood A.
      • Adak S.
      • Bello M.O.
      • Bordwell A.
      • Can A.
      • Corwin A.
      • Dinn S.
      • Filkins R.J.
      • Hollman D.
      • Kamath V.
      • Kaanumalle S.
      • Kenny K.
      • Larsen M.
      • Lazare M.
      • Li Q.
      • Lowes C.
      • McCulloch C.C.
      • McDonough E.
      • Montalto M.C.
      • Pang Z.
      • Rittscher J.
      • Santamaria-Pang A.
      • Sarachan B.D.
      • Seel M.L.
      • Seppo A.
      • Shaikh K.
      • Sui Y.
      • Zhang J.
      • Ginty F.
      Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue.
      ,
      • Graf J.F.
      • Zavodszky M.I.
      Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures.
      ). The capability for multiplexed super-resolution imaging in Exchange-PAINT enables the assessment of signaling protein interactions and clustering effects (
      • Werbin J.L.
      • Avendaño M.S.
      • Becker V.
      • Jungmann R.
      • Yin P.
      • Danuser G.
      • Sorger P.K.
      Multiplexed exchange-PAINT imaging reveals ligand-dependent EGFR and Met interactions in the plasma membrane.
      ) but is time-consuming.
      Challenges shared by all fluorescence-based methods are potential sample autofluorescence, which can be especially high in formalin-fixed, paraffin-embedded samples.

      Mass Spectrometry-based Immunological Imaging Approaches

      In imaging mass cytometry (IMC), all antibodies are applied simultaneously to stain tissue samples. A laser is then used to ablate antibody-stained samples spot by spot. A mixed argon and helium stream then transports the ablated materials into a mass cytometer. Proteins and protein modifications, such as phosphorylation, are quantified, preserving subcellular level (1 μm2) spatial information (Fig. 2, middle panel) (
      • Giesen C.
      • Wang H.A.O.
      • Schapiro D.
      • Zivanovic N.
      • Jacobs A.
      • Hattendorf B.
      • Schüffler P.J.
      • Grolimund D.
      • Buhmann J.M.
      • Brandt S.
      • Varga Z.
      • Wild P.J.
      • Günther D.
      • Bodenmiller B.
      Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.
      ,
      • Bodenmiller B.
      Multiplexed epitope-based tissue imaging for discovery and healthcare applications.
      ). IMC can be used to analyze proteins (including phosphoproteins) and RNAs simultaneously enabling, for example, analysis of correlations between transcriptional control and spatial signaling properties (
      • Schulz D.
      • Zanotelli V.R.T.
      • Fischer J.R.
      • Schapiro D.
      • Engler S.
      • Lun X.-K.
      • Jackson H.W.
      • Bodenmiller B.
      Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry.
      ). Multiplexed ion beam imaging (MIBI), like IMC, uses metal-labeled antibodies for tissue staining. In MIBI, an oxygen duoplasmatron primary ion beam is used to liberate the antibodies to generate the secondary ion beam. Subsequently, a magnetic sector mass spectrometer or time-of-flight is used to detect the isotope abundances from the second ion beam from every pixel of analyzed sample (
      • Angelo M.
      • Bendall S.C.
      • Finck R.
      • Hale M.B.
      • Hitzman C.
      • Borowsky A.D.
      • Levenson R.M.
      • Lowe J.B.
      • Liu S.D.
      • Zhao S.
      • Natkunam Y.
      • Nolan G.P.
      Multiplexed ion beam imaging of human breast tumors.
      ,
      • Keren L.
      • Bosse M.
      • Thompson S.
      • Risom T.
      • Vijayaragavan K.
      • McCaffrey E.
      • Marquez D.
      • Angoshtari R.
      • Greenwald N.F.
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      • Nolan G.
      • Montine T.J.
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      • West R.
      • Bendall S.C.
      • Angelo M.
      MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure.
      ). The advantages of MIBI are that the same sample can be scanned multiple times and that the resolution can achieve 10 nm. The benefits of all mass spectrometry-based immunological imaging approaches are that samples can be stored indefinitely, that sample autofluorescence does not interfere with quantification, and that the dynamic range is orders of magnitude higher than in fluorescent-based approaches.

      Spatial 'Omics in Single-cell Analysis

      MALDI-based Imaging

      MALDI-based imaging mass spectrometry can be used to detect biomolecules, including lipids, metabolites, peptides, and proteins (
      • Schwamborn K.
      • Caprioli R.M.
      MALDI Imaging mass spectrometry – painting molecular pictures.
      ). Although MALDI-based imaging is mainly applied at tissue-level resolution, it has been used for unbiased quantitative and spatial profiling of the signal-mediating lipidome and metabolome (
      • Sugiura Y.
      • Honda K.
      • Suematsu M.
      Development of an imaging mass spectrometry technique for visualizing localized cellular signaling mediators in tissues.
      ) and in systemic assessments of disease states and drug responses (
      • Schwamborn K.
      • Caprioli R.M.
      MALDI Imaging mass spectrometry – painting molecular pictures.
      ,
      • Nielsen M.M.B.
      • Lambertsen K.L.
      • Clausen B.H.
      • Meyer M.
      • Bhandari D.R.
      • Larsen S.T.
      • Poulsen S.S.
      • Spengler B.
      • Janfelt C.
      • Hansen H.S.
      Mass spectrometry imaging of biomarker lipids for phagocytosis and signalling during focal cerebral ischaemia.
      ). A novel MALDI-based tissue imaging platform was recently developed that, because of optimized ionization efficiency, has a resolution at the subcellular level of 5 μm per pixel (
      • Soltwisch J.
      • Kettling H.
      • Vens-Cappell S.
      • Wiegelmann M.
      • Müthing J.
      • Dreisewerd K.
      Mass spectrometry imaging with laser-induced postionization.
      ). Using a transmission geometry ion source, 1-μm resolution can be achieved with MALDI-based imaging systems, although at compromised sensitivity (
      • Zavalin A.
      • Yang J.
      • Hayden K.
      • Vestal M.
      • Caprioli R.M.
      Tissue protein imaging at 1 μm laser spot diameter for high spatial resolution and high imaging speed using transmission geometry MALDI TOF MS.
      ).

      Spatial Transcriptomics

      Several spatial transcriptomics approaches have been established based on various techniques, including fluorescent in situ sequencing (FISSEQ) (
      • Lee J.H.
      • Daugharthy E.R.
      • Scheiman J.
      • Kalhor R.
      • Yang J.L.
      • Ferrante T.C.
      • Terry R.
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      • Aach J.
      • Church G.M.
      Highly multiplexed subcellular RNA sequencing in situ.
      ), multiplexed MERFISH (
      • Chen K.H.
      • Boettiger A.N.
      • Moffitt J.R.
      • Wang S.
      • Zhuang X.
      • Crosetto N.
      • Bienko M.
      • Oudenaarden A.van
      • Femino A.M.
      • Fay F.S.
      • Fogarty K.
      • Singer R.H.
      • Raj A.
      • Bogaard P.van den
      • Rifkin S.A.
      • Oudenaarden A.van
      • Tyagi S.
      • Rodriguez A.J.
      • Czaplinski K.
      • Condeelis J.S.
      • Singer R.H.
      • Balagopal V.
      • Parker R.
      • Jung H.
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      • Holt C.E.
      • Gregor T.
      • Garcia H.G.
      • Little S.C.
      • Buxbaum A.R.
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      • Singer R.H.
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      • Singer R.H.
      • Zenklusen D.
      • Raj A.
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      • Munsky B.
      • Neuert G.
      • Oudenaarden A.van
      • Lagha M.
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      • Levine M.
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      • Choi P.J.
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      • Yoshida H.
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      • Tobiishi M.
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      • Sayo T.
      • Sakai S.
      • Sugiyama Y.
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      • Okada Y.
      • Inoue S.
      • Lauffenburger D.A.
      • Horwitz A.F.
      • Rapoport T.A.
      • Jan C.H.
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      • Weissman J.S.
      • Lawrence J.B.
      • Singer R.H.
      • Mingle L.A.
      • Okuhama N.N.
      • Shi J.
      • Singer R.H.
      • Condeelis J.
      • Liu G.
      • Babcock H.
      • Sigal Y.M.
      • Zhuang X.
      • Zhu L.
      • Zhang W.
      • Elnatan D.
      • Huang B.
      • Babcock H.P.
      • Moffitt J.R.
      • Cao Y.
      • Zhuang X.
      • Hell S.W.
      • Huang B.
      • Babcock H.
      • Zhuang X.
      • Xu Q.
      • Schlabach M.R.
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      • Camacho C.
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      • Avagyan V.
      • Ma N.
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      • Bealer K.
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      • Trapnell C.
      • Roberts A.
      • Goff L.
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      • Kim D.
      • Kelley D.R.
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      • Wu B.
      • Singer R.H.
      • Rasnik I.
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      • Ha T.
      • Shi X.
      • Lim J.
      • Ha T.
      RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.
      ), and spatial barcoding (
      • Ståhl P.L.
      • Salmén F.
      • Vickovic S.
      • Lundmark A.
      • Navarro J.F.
      • Magnusson J.
      • Giacomello S.
      • Asp M.
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      • Huss M.
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      • Codeluppi S.
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      • Mulder J.
      • Bergmann O.
      • Lundeberg J.
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      Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.
      ). Data from these experiments can be used to infer signaling pathway activation and cell-to-cell communication. Spatial transcriptomics are also powerful methods for evaluating remote cell-signaling control mechanisms because mRNAs are used as expression readouts for secreted ligands (e.g. cytokines and chemokines) that are difficult to detect in proteome-based analyses (
      • Schulz D.
      • Zanotelli V.R.T.
      • Fischer J.R.
      • Schapiro D.
      • Engler S.
      • Lun X.-K.
      • Jackson H.W.
      • Bodenmiller B.
      Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry.
      ).

      Live-cell Imaging

      It is important to note that cell signaling transduction is a dynamic process that cannot be fully understood from snapshot measurements of transient network states. Information along the time dimension, in addition to the multiplexed signaling profiling, is therefore necessary to systematically decode the causality of signaling behaviors and to characterize network kinetics (
      • Koseska A.
      • Bastiaens P.I.
      Cell signaling as a cognitive process.
      ). As signaling events are mainly present intracellularly, they can be detected only after a fixation and permeabilization procedure that disrupts the signaling dynamics through time. Conventionally, serial snapshot information is acquired to enable the rebuilding of time dimension and the computational reconstruction of signaling trajectories (
      • Lun X.-K.
      • Zanotelli V.R.T.
      • Wade J.D.
      • Schapiro D.
      • Tognetti M.
      • Dobberstein N.
      • Bodenmiller B.
      Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry.
      ,
      • Zi Z.
      • Feng Z.
      • Chapnick D.a.
      • Dahl M.
      • Deng D.
      • Klipp E.
      • Moustakas A.
      • Liu X.
      Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics.
      ). Technically, these approaches do not fully resolve the transient events of signaling processing, and the computation inference becomes complicated when measured signaling behaviors that are highly heterogeneous. Several live-cell imaging methods exploit protein physical properties (e.g. kinase subcellular localization and protein proximity) to monitor signaling events through time (
      • Ryu H.
      • Chung M.
      • Dobrzyński M.
      • Fey D.
      • Blum Y.
      • Lee S.S.
      • Peter M.
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      • Jeon N.L.
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      • Ahmed S.
      • Grant K.
      • Edwards L.
      • Rahman A.
      • Cirit M.
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      • Haugh J.
      • Albeck J.
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      • Aoki K.
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      • Komatsu N.
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      • Kida K.
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      • Anderson K.
      • Kolch W.
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      • Rivard N.
      • Chen J.
      • Lin J.
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      • Meyer T.
      • Cohen-Saidon C.
      • Cohen A.
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      ,
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      ,
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      ,
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      High-content imaging platform for profiling intracellular signaling network activity in living cells.
      ,
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      ,
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      ,
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      Live Cell Imaging of ERK and MEK: simple binding equilibrium explains the regulated nucleocytoplasmic distribution of ERK.
      ). Although these methods are not yet highly multiplexed, capturing information on central signaling nodes through time allows tracing the pathway and network behaviors.

      Fluorescence Resonance Energy Transfer

      Fluorescence resonance energy transfer (FRET) experiments are based on energy transfer between two proximate fluorophores that leads to a shift of the emission spectrum that is captured by microscopy. FRET can be used to monitor the proximity of interactive signaling proteins (
      • Burack W.R.
      • Shaw A.S.
      Live Cell Imaging of ERK and MEK: simple binding equilibrium explains the regulated nucleocytoplasmic distribution of ERK.
      ) or as a biosensor for phosphorylation events to indicate pathway activity in real-time (Fig. 2, right panel) (
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      • Kolch W.
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      • Brusch L.
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      • Kholodenko B.
      • Patterson K.
      • Brummer T.
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      • Daly R.
      • Purvis J.
      • Lahav G.
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      • Gayer S.
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      • St-Denis N.
      • Pasculescu A.
      • Taylor L.
      • Tate S.
      • Hardy W.
      • Colwill K.
      • Dai A.
      • Bagshaw R.
      • Dennis J.
      • Gingras A.
      • Daly R.
      • Pawson T.
      Frequency modulation of ERK activation dynamics rewires cell fate.
      ,
      • Aoki K.
      • Kumagai Y.
      • Sakurai A.
      • Komatsu N.
      • Fujita Y.
      • Shionyu C.
      • Matsuda M.
      Stochastic ERK activation induced by noise and cell-to-cell propagation regulates cell density-dependent proliferation.
      ). FRET-based analysis characterizes single-cell temporal signaling states that can be correlated with functional readouts such as proliferation and differentiation. Given the broad fluorescent spectrum occupancy from each FRET sensor, multiplexing of FRET experiments to study complex signaling network behaviors is challenging. Several approaches to increase FRET multiplexing have been developed that rely on careful selection of fluorophores or image decoding and error propagation schemes. Up to six protein interaction/phosphorylation events have been measured simultaneously in a multiplexed FRET setup (
      • Bunt G.
      • Wouters F.S.
      FRET from single to multiplexed signaling events.
      ,
      • Hoppe A.D.
      • Scott B.L.
      • Welliver T.P.
      • Straight S.W.
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      N-Way FRET microscopy of multiple protein-protein interactions in live cells.
      ,
      • Geiβler D.
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      • Löhmannsröben H.-G.
      • Hildebrandt N.
      Six-color time-resolved Förster resonance energy transfer for ultrasensitive multiplexed biosensing.
      ). FRET biosensors used in combination with a multi-parameter imaging platform have been used to separately monitor the activities of 40 signaling proteins in individual cells; the data generated were used to infer network dynamics comprehensively (
      • Kuchenov D.
      • Laketa V.
      • Stein F.
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      • Klingmüller U.
      • Schultz C.
      High-content imaging platform for profiling intracellular signaling network activity in living cells.
      ).

      Activity-based Reporters

      Many kinases, such as ERK, are translocated to the nucleus once activated. Thus, fluorescently-labeled versions of these proteins can be used to track signaling activities in real-time (
      • Lee T.K.
      • Denny E.M.
      • Sanghvi J.C.
      • Gaston J.E.
      • Maynard N.D.
      • Hughey J.J.
      • Covert M.W.
      A noisy paracrine signal determines the cellular NF-kappaB response to lipopolysaccharide.
      ,
      • Kellogg R.A.
      • Tay S.
      Noise facilitates transcriptional control under dynamic inputs.
      ,
      • Lidke D.S.
      • Huang F.
      • Post J.N.
      • Rieger B.
      • Wilsbacher J.
      • Thomas J.L.
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      ERK nuclear translocation is dimerization-independent but controlled by the rate of phosphorylation.
      ). Studies of kinase nuclear translocation at single-cell resolution revealed considerable heterogeneity in signaling dynamics (
      • Lee T.K.
      • Denny E.M.
      • Sanghvi J.C.
      • Gaston J.E.
      • Maynard N.D.
      • Hughey J.J.
      • Covert M.W.
      A noisy paracrine signal determines the cellular NF-kappaB response to lipopolysaccharide.
      ) and noise-facilitated transcription output (
      • Kellogg R.A.
      • Tay S.
      Noise facilitates transcriptional control under dynamic inputs.
      ). A novel category of biosensors, known as kinase translocation reporters, was developed to convert phosphorylation into a nucleocytoplasmic shuttling event that allows monitoring of the activities of key signaling mediators including JNK, p38, and ERK simultaneously to identify temporal signaling crosstalk between the pathways (Fig. 2, right panel) (
      • Regot S.
      • Hughey J.J.
      • Bajar B.T.
      • Carrasco S.
      • Covert M.W.
      High-sensitivity measurements of multiple kinase activities in live single cells.
      ). An important strength of these live-cell imaging technologies is the preservation of natural cellular states. The same imaged samples can be re-analyzed using other compatible single-cell methods. For instance, a study has coupled NFκB nuclear translocation analysis with single-cell RNA-sequencing to reveal three distinct cell subpopulations with different transcription profiles (
      • Lane K.
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      Measuring signaling and RNA-Seq in the same cell links gene expression to dynamic patterns of NF-κB activation.
      ).
      Each of the approaches discussed above has its advantages and limitations, as summarized in Table I. When selecting a single-cell method to study cell signaling networks, we suggest that experimentalists first accurately phrase their question and then assess whether it is necessary to acquire spatial or dynamics information, and then consider the factors of multiplexing, sensitivity, throughput, and cost.

      Computational Methods for Signaling Network Analysis Using Single-cell Information

      Multiplexed measurements allow systematic assessment of network states and dynamics in one single experiment in which the multivariate dependences and high-dimensional distributions are precisely preserved. Network responses to perturbations can be visualized at the single-cell level using single-cell signaling fold changes (
      • O'Gorman W.E.
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      • Hernandez J.D.
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      Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus.
      ), although the interpretation of signaling causality can be indirect. Recently developed computational approaches apply statistical inference to reconstruct signaling network structure (
      • Sachs K.
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      • Pe'er D.
      • Lauffenburger D.A.
      • Nolan G.P.
      Causal protein-signaling networks derived from multiparameter single-cell data.
      ,
      • Krishnaswamy S.
      • Spitzer M.H.
      • Mingueneau M.
      • Bendall S.C.
      • Litvin O.
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      Conditional density-based analysis of T cell signaling in single-cell data.
      ,
      • Chan T.E.
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      Gene regulatory network inference from single-cell data using multivariate information measures.
      ,
      • Huang X.-T.
      • Zhu Y.
      • Hang Chan L.L.
      • Zhao Z.
      • Yan H.
      Inference of cellular level signaling networks using single-cell gene expression data in C. elegans reveals mechanisms of cell fate specification.
      ) and use mechanistic models to characterize network dynamics (
      • Hasenauer J.
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      ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
      ,
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      A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability.
      ).
      For the reconstruction of signaling networks, Bayesian modeling has been applied in flow cytometry measurement of 11 intracellular phosphorylation sites with individual treatments of nine small-molecule inhibitors. Exploiting natural cellular variability and the re-shaping of multivariate distributions upon perturbations, a probabilistic network was assembled that replicates known pathway relationships and predicts novel network causalities (
      • Sachs K.
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      • Nolan G.P.
      Causal protein-signaling networks derived from multiparameter single-cell data.
      ). Alternatively, correlation-based statistics can be used to quantify relationships and dependences between measured parameters and are therefore widely used to assess the strength of signaling circuits and infer network structure and dynamics in both flow cytometry and transcriptomics data (
      • Redell M.S.
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      ,
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      Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.
      ).
      In complex signaling regulatory networks, relationships between pairs of signaling proteins are often dependent on multiple parameters and non-monotonic in shape. Correlation analysis often fails to reflect the true strength of these relationships. Based on information theory, methods have been developed that use mutual information (MI) and maximal information coefficients (MIC) to quantify the relationships between two variables independently of their linearity and continuity (
      • Kraskov A.
      • Stögbauer H.
      • Grassberger P.
      Estimating mutual information.
      ,
      • Reshef D.N.
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      Detecting novel associations in large data sets.
      ). A more advanced measure, termed DREMI, has been recently developed to quantify mutual information in a density-independent manner; this removes the bias of cell distribution. Networks reconstructed and quantified by DREMI recapitulate well-known signaling processes (
      • Krishnaswamy S.
      • Spitzer M.H.
      • Mingueneau M.
      • Bendall S.C.
      • Litvin O.
      • Stone E.
      • Pe'er D.
      • Nolan G.P.
      Conditional density-based analysis of T cell signaling in single-cell data.
      ). In combination with experimental methods for tracing biological time during a cell transition (
      • Bendall S.C.
      • Davis K.L.
      • Amir E.D.
      • Tadmor M.D.
      • Simonds E.F.
      • Chen T.J.
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      Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.
      ), DREMI revealed signaling network reprogramming during cellular phenotypical shifts (
      • Krishnaswamy S.
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      Learning time-varying information flow from single-cell epithelial to mesenchymal transition data.
      ). Another density-independent measure, called binned pseudo-R2 (BP-R2), applies classical R2 statistics. The BP-R2 score reflects the strengths of signaling relationships in steady-state and dynamic studies with high accuracy (
      • Lun X.-K.
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      Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry.
      ).
      Mechanistic models can reveal biochemical insights into a given signaling network and the functional heterogeneity within a cell population. Ordinary differential equations (ODEs) are commonly applied when mass action kinetics analyses are used to determine the concentration of signaling nodes over time. ODE models have been used to study network features such as feedback loops (
      • Hughey J.J.
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      Computational modeling of mammalian signaling networks.
      ). A pilot single-cell analysis used ODE-constrained mixture modeling to study the variability of the response of phosphorylated ERK to stimulation with NGF in PC12 cells; two cell subpopulations with differential signaling responses caused by varied receptor abundance were identified (
      • Hasenauer J.
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      ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
      ). In another study, a hierarchal population model was developed, in combination with the single-cell modeling, to explain multiple levels of heterogeneity in NGF-treated PC12 cells (
      • Loos C.
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      • Fröhlich F.
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      A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability.
      ).

      Accounting for Confounding Factors

      Single-cell technologies have enabled characterization of differential signaling behaviors in cell populations that are masked by conventional batch measurements. However, these advantages also come with the challenge that multiple levels of confounding factors can bias the single-cell readouts (
      • Rapsomaniki M.A.
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      CellCycleTRACER accounts for cell cycle and volume in mass cytometry data.
      ,
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      Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells.
      ,
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      ,
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      The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.
      ,
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      Single-cell mass cytometry adapted to measurements of the cell cycle.
      ,
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      Conclusion and Perspective

      Signaling networks are centrally involved in information processing necessary for proper control of cell functions and cell fate. Deregulated signaling often leads to the emergence of disease. Recent advances in systems biology research have identified multiple layers of variability that contribute to heterogeneous signaling network states and dynamics. Importantly, the essential role of signaling network heterogeneity in the initiation and development of diseases, such as cancer, has been revealed. Many recently developed techniques are now capable of quantifying signaling events and network behaviors at the single-cell level.
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      ).
      Integration of single-cell signaling characterization with multi-omics profiling will lead to an understanding of signaling circuits as well as feedback mechanisms between signaling pathways and transcriptional and epigenomic programs (
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      ) and can be applied to answer questions regarding crosstalk between the regulators of the phosphoprotein network and transcription and the involvement of spatial factors, such as cell-to-cell contacts and protein localization, in such networks.

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