Proteins are central to all cellular processes - including providing structure to cells, transporting molecules across cell membranes, controlling cell growth and adhesion, catalyzing biochemical processes by functioning as enzymes and regulating signal transduction. Characterizing the quantity and activity of proteins is therefore critical for understanding molecular mechanisms of cellular processes including those involved in disease progression, cell differentiation and fate, and for targeted discovery and development of novel therapeutics, vaccines and diagnostics. Measuring DNA and RNA can provide qualitative information on gene-products (proteins) but cannot provide information on protein location, post-translational modifications (PTMs) or interactions with other proteins and hence, we need tools and assays to directly measure proteins, their modifications and interactions. Numerous analytical methods have been developed to analyze proteins such as gel electrophoresis, immunoassays, chromatography and mass spectrometry. However, these methods require a large number of cells for analysis, resulting in a population-averaged measurement. Cells are heterogeneous in nature and hence, population-averaged data can mask the underlying molecular mechanisms; more desirable data in many instances could be data at the level of single cells[1
]. A well-known example is response of bacteria to antibiotics, at certain doses many die but some survive. Similarly, one of the unanswered questions in cancer therapy has been why essentially identical cells respond differently to a drug. Single-cell level measurement of proteins (and other molecules) has provided valuable insight into mechanisms that dictate heterogeneity in cellular response to drugs and other internal and external stimuli. For example, it was reported that dynamic response of tumor suppressor protein p53 network derived from population studies was misleading [4
]. Instead of damped oscillations seen in population-averaged data, individual cells show series of undamped p53 pulses with fixed amplitude and duration, independent of the amount of γ-irradiation. Similarly, real-time imaging of transcription factor RelA translocation in single cells revealed variability in the oscillatory dynamics of RelA translocation among single cells, and that RelA translocation dynamics determined the degree and timing of downstream gene expression[3
]. Usefulness of single cell measurements is obvious for stem cell research as decisions in individual cells determines their fate. For haematopoietic stem cells, studying the varying levels of Sca-1 protein in individual cells revealed that Sca-1 protein abundance determines the timing and type of differentiation[6
]. In a clinical context, single-cell level examination of T cell populations previously thought to be homogeneous were found to contain subpopulations with different cytokine profiles [25
], and these differences may serve to predict patient immune response to drug therapy.