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].
Ultraviolet resonance Raman [UVRR] spectra of Cu, Zn superoxide dismutase [SOD] contain bands arising from vibrations of metal-bound histidine ligands. Spectra in H 2 O solution reveal several modes of the His61 side chain, which bridges the Cu 2+ and Zn 2+ ions as imidazolate. The disappearance of these bands signals disruption of the bridge when the pH is lowered to 3.0, or the Cu 2+ is reduced to Cu + . Binding of hydroxide [pH 12] or cyanide to the Cu 2+ perturbs the imidazolate modes, reflecting geometry changes induced by these strong-field ligands. In D 2 O solution several additional bands become enhanced which arise from histidine ligands that have undergone NH/D exchange. Some of these are attributed to Cu-bound ligands and others to Zn-bound ligands, on the basis of selective changes accompanying removal and replacement of the metals. Excitation profiles are similar for these bands, and for the bridging imidazolate bands; they are redshifted relative to nonligating histidine. The detection of site-specific histidine ligand modes gives promise for wide applicability of UVRR spectroscopy in studying histidine ligation in metalloproteins. The single tyrosine residue of SOD, which is a target of active-site-catalyzed nitration by peroxynitrite, is found to have an elevated pK a , 11.4, despite being exposed to solvent.
Human mesenchymal stem cells (hMSC) are critical for tissue regeneration. How hMSC respond to genotoxic stresses and potentially contribute to aging and cancer remain underexplored. We showed that ionizing radiation induced cellular senescence of hMSC over a period of 10 days, showing a critical transition between days 3 and 6. This was confirmed by senescence-associated B-galactosidase staining, protein expression profiles of key cell cycle regulators (retinoblastoma protein, p53, p21 waf1/Cip1 , and p16 INK4A ), and senescenceassociated secretory phenotypes (interleukin-8, interleukin-12, GRO, and MDC). We observed dramatic cytoskeletal reorganization of hMSC through reduction of myosin-10, redistribution of myosin-9, and secretion of profilin-1. Using a SILAC-based phosphoproteomics method, we detected significant reduction of myosin-9 phosphorylation at Ser 1943 , coinciding with its redistribution. Importantly, through treatment with cell-permeable inhibitors (4,5,6,7-tetrabromo-1H-benzotriazole and 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole) and gene knockdown using RNA interference, we identified CK2, a kinase responsible for myosin-9 phosphorylation at Ser 1943 , as a key factor contributing to the radiation-induced senescence of hMSC. We showed that individual knockdown of CK2 catalytic subunits CK2A and CK2A ¶ induced hMSC senescence. However, only knockdown of CK2A resulted in morphologic phenotypes resembling those of radiation-induced senescence. These results suggest that CK2A and CK2A ¶ play differential roles in hMSC senescence progression, and their relative expression might represent a novel regulatory mechanism for CK2 activity. [Cancer Res 2009;69(20):8200-7]
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