Cellular heterogeneity arising from stochastic expression of genes, proteins and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of 'Omics' technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third-and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity. Single cell analysis: needs and applications Cellular heterogeneityCellular heterogeneity within an isogenic cell population is a widespread event [1,2]. Stochastic gene and protein expression at the single cell level has been clearly demonstrated in different systems using a variety of techniques [3][4][5]. Therefore, analyzing cell ensembles individually with high spatiotemporal resolutions will lead to a more accurate representation of cell-to-cell variations instead of the stochastic average masked by bulk measurements. Disconnect between single cell and average cell measurements is exemplified in Figure 1a. Using an integrated microfluidic bioprocessor for single cell gene expression analysis, the Mathies group showed that siRNA knockdown of GAPDH gene expression led to two distinct groups of individual Jurkat cells partial knockdown (~50%) and complete knockdown (~0%). The average result from 50 cells (~21%) was not representative of any one individual cell [6].To fully understand the cellular specificity and complexity of tissue microenvironments under physiological conditions, it is necessary to measure molecular signatures with single cell resolution. A clear example is provided by the recent work from Kim and colleagues, who analyzed single cell gene expression profiles using high-resolution confocal microscopy and correlated them with known cell lineages in Caenorhabditis elegans [7]. The group generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae. Cells were clustered into groups in a two-dimensional scatter plot according to their correlation in gene expression (Figure 1b). Two features of the scatter plot stand out: first, cells are diverse, but cluster with known fates such as muscles and neurons; second, cells from homogeneous tissue (e.g. intestinal cells) cluster more tightly than those from heterogeneous tissue (e.g. neurons).Corresponding author: Wang, D. (djwang@lbl.gov). Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form....

Single cell analysis: needs and applications Cellular heterogeneityCellular heterogeneity within an isogenic cell population is a widespread event [1,2].

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A single biomarker has an inherent specificity and sensitivity that cannot be improved, but multiple biomarkers can be combined to achieve improved clinical performances. This is the basis of multimarker strategies that integrate different biomarkers into a single score to support medical decisions. The simplest strategy determines ratios of different biomarkers or the number of different markers above their respective thresholds. A more advanced strategy employs similar biomarkers, but uses more sophisticated algorithms. The most advanced strategy employs large numbers of biomarkers that may or may not have been previously characterized and uses sophisticated algorithms.