Literature DB >> 23256674

Deciphering the single-cell omic: innovative application for translational medicine.

Ferdinando Mannello1, Daniela Ligi, Mauro Magnani.   

Abstract

Traditional technologies to investigate system biology are limited by the detection of parameters resulting from the averages of large populations of cells, missing cells produced in small numbers, and attempting to uniform the heterogeneity. The advent of proteomics and genomics at a single-cell level has set the basis for an outstanding improvement in analytical technology and data acquisition. It has been well demonstrated that cellular heterogeneity is closely related to numerous stochastic transcriptional events leading to variations in patterns of expression among single genetically identical cells. The new-generation technology of single-cell analysis is able to better characterize a cell's population, identifying and differentiating outlier cells, in order to provide both a single-cell experiment and a corresponding bulk measurement, through the identification, quantification and characterization of all system biology aspects (genomics, transcriptomics, proteomics, metabolomics, degradomics and fluxomics). The movement of omics into single-cell analysis represents a significant and outstanding shift.

Mesh:

Year:  2012        PMID: 23256674     DOI: 10.1586/epr.12.61

Source DB:  PubMed          Journal:  Expert Rev Proteomics        ISSN: 1478-9450            Impact factor:   3.940


  6 in total

Review 1.  Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium.

Authors:  Signe Altmäe; Francisco J Esteban; Anneli Stavreus-Evers; Carlos Simón; Linda Giudice; Bruce A Lessey; Jose A Horcajadas; Nick S Macklon; Thomas D'Hooghe; Cristina Campoy; Bart C Fauser; Lois A Salamonsen; Andres Salumets
Journal:  Hum Reprod Update       Date:  2013-09-29       Impact factor: 15.610

Review 2.  Everything is autoimmune until proven otherwise.

Authors:  Yehuda Shoenfeld
Journal:  Clin Rev Allergy Immunol       Date:  2013-10       Impact factor: 8.667

Review 3.  Regulation of gene expression in the genomic context.

Authors:  Taylor J Atkinson; Marc S Halfon
Journal:  Comput Struct Biotechnol J       Date:  2014-01-29       Impact factor: 7.271

Review 4.  We can't all be supermodels: the value of comparative transcriptomics to the study of non-model insects.

Authors:  Sara J Oppenheim; Richard H Baker; Sabrina Simon; Rob DeSalle
Journal:  Insect Mol Biol       Date:  2014-12-19       Impact factor: 3.585

Review 5.  Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome.

Authors:  Ferdinando Mannello; Daniela Ligi
Journal:  BMC Cancer       Date:  2013-07-12       Impact factor: 4.430

Review 6.  Understanding breast cancer stem cell heterogeneity: time to move on to a new research paradigm.

Authors:  Ferdinando Mannello
Journal:  BMC Med       Date:  2013-07-23       Impact factor: 8.775

  6 in total

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