Methods such as phospho-proteomics1
, protein–protein interaction studies2
and gene expression profiling3
allow analysis of signaling networks via the simultaneous observation of every pathway component. However, these methods average information over many cells and do not take into account cell-to-cell heterogeneity, which has been shown to play key roles in the function of signaling networks4–6
. Flow cytometry allows the measurement of signaling at the single cell level7
but, similar to proteomic methods, temporal information is available only as a many-cell average, and spatial information is either completely absent or at very coarse levels, such as membrane and organelle compartmentalization.
A tool chest of fluorescent proteins emitting from the ultra violet (UV) to the near infrared have made it possible to simultaneously visualize the subcellular dynamics of multiple proteins in the same living cell — a technique referred to as experimental multiplexing. Such ‘multiplexed imaging’, which can reveal the coordination of two or more subcellular structures, is now routinely exploited to study, for example, the interactions of cytoskeletal fibers8, 9
, the transient coupling of adhesion molecules and cytoskeleton flows10
, and the choreography of protein recruitment to endocytic sites at the plasma membrane11
. In contrast, the subcellular coordination of the signaling events that regulate these dynamics has remained considerably more obscure. In some cases, a single signaling activity can be visualized together with the dynamics of subcellular structures, but this has until recently been restricted largely to second messengers, for example, fluorescent chelators that report ion concentrations12, 13
, or fluorescently tagged domains that bind accumulations of lipid second messengers14, 15
or mark cellular structures16
. The study of the coordinated activation of multiple signaling proteins has been hindered by formidable technical hurdles. These obstacles have been recently overcome by two very different but complementary approaches — the development of powerful new biosensors and the development of computational multiplexing.
To understand the spatiotemporal dynamics of signaling events, it is generally not sufficient to simply follow the changing localization of proteins. Signaling proteins interact with downstream targets only in specific ‘activated’ states. Therefore, to dissect the mechanism of signal transduction it is necessary to monitor protein localization and activation. Biosensors have been devised to report protein activation states, usually relying on Förster resonance energy transfer (FRET) readouts of a conformational change or protein interaction17, 18
. Due to the availability of fluorescent proteins spanning much of the visible spectrum, FRET-based biosensors can now be built using fluorescent proteins with orthogonal wavelengths, enabling the imaging of two biosensors in the same cell. Furthermore, a deeper understanding of fluorescent proteins has permitted the design of biosensors that respond directly to endogenous signaling molecules, without the need to use two fluorescent proteins. These experimental multiplexing approaches may ultimately enable us to visualize the activities of three and four signaling molecules at the same time.
However, even with these improved designs, only a limited number of biosensors can be introduced into one cell. In addition, multi-spectral imaging and introduction of multiple exogenous proteins is often accompanied by increased phototoxicity and the perturbation of cell physiology. Ideally one could study the dynamics of each protein species separately in different cells and subsequently relate them to each other via correlation with a common fiduciary event that occurs in each cell. Indeed, this approach has been applied, to study the timing of signaling events during phagocytosis and wound closure19–21
. However, many cellular functions display stochastic behaviors, resulting in a wide range of heterogeneous, less stereotypical signaling patterns. In this case, establishing the relationships between signaling activities that are not all observed in the same cell is quite challenging. Recent work by several labs has begun to break through this barrier. It has been shown that spatiotemporal relationships between any two co-observed cellular activities can be extracted from their constitutive, basal fluctuations22–24
. It has also been shown that, when measuring the relationships between two pairs of activities in different cells, with one activity in common between the pairs, it is possible to predict the spatiotemporal relationships among all of the individual activities, even when they are observed in different cells22
. This process has been referred to as computational multiplexing in order to distinguish it from experimental multiplexing. Although computational multiplexing is still in its very early days, it may ultimately become the technology of choice for reconstructing complex pathways with many component activities. Importantly, the concept of computational multiplexing relies on concurrent measurements of activities in pairs, triplets, or even quadruplets. The more relations that can be extracted through direct observation in single experiments, the more robust will be the computationally inferred relations between signaling components. Therefore, advances in experimental multiplexing will lay the groundwork for advances in computational multiplexing. The goal of this Innovation is to outline the current state of both experimental and computational multiplexing, and to project their joint potential for the analysis of signal transduction at the single cell level.