| dc.creator | Li,Biaoru | |
| dc.date | 2005-04-01 | |
| dc.date.accessioned | 2019-05-03T12:44:11Z | |
| dc.date.available | 2019-05-03T12:44:11Z | |
| dc.identifier | https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0717-34582005000100011 | |
| dc.identifier.uri | http://revistaschilenas.uchile.cl/handle/2250/84812 | |
| dc.description | Recent advances in functional genomics allow us to estimate the expression of several thousands of genes in the mammalian genome. Techniques such as microarrays, expressed tag sequencing (EST), serial analysis of gene expression (SAGE), subtractive cloning and differential display (DD), and two-dimensional electrophoresis gel have been extensively used to screen and analyze parallel gene expression. Some pathological processes, for example, tumorigenesis and solid tumour growth, in which the former is derived from a single-cell and the latter has a mixed-cell problem, present new challenges to the limit of these functional genomic techniques. To fully understand the functions of cells in tumorigenesis or in heterogeneous solid tumour masses, it is essential for scientists and physicians to develop a strategy to identify genomic expression profiles for a single-cell or for small numbers of cells. In this article, we review recently developed methods that enable functional genomic analysis at the single-cell or multi-cell level. In addition, the paper will review different techniques of single-cell genomic expression at the level of DNA, mRNA, protein and post-translational modifications. | |
| dc.format | text/html | |
| dc.language | en | |
| dc.publisher | Pontificia Universidad Católica de Valparaíso | |
| dc.relation | 10.4067/S0717-34582005000100011 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | Electronic Journal of Biotechnology v.8 n.1 2005 | |
| dc.subject | genome | |
| dc.subject | genomic expression | |
| dc.subject | genomic expression analysis at single cell | |
| dc.subject | single cell | |
| dc.title | A strategy to identify genomic expression at single-cell level or a small number of cells | |
