Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/14/7/1330 |
| _version_ | 1797589184151552000 |
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| author | Michele Massimino Federica Martorana Stefania Stella Silvia Rita Vitale Cristina Tomarchio Livia Manzella Paolo Vigneri |
| author_facet | Michele Massimino Federica Martorana Stefania Stella Silvia Rita Vitale Cristina Tomarchio Livia Manzella Paolo Vigneri |
| author_sort | Michele Massimino |
| collection | DOAJ |
| description | Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell <i>omics</i> has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell–cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional <i>omics</i> at single-cell resolution. |
| first_indexed | 2024-03-11T01:02:49Z |
| format | Article |
| id | doaj.art-8b181914a4d3404d9ec397943bfb5458 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-11T01:02:49Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-8b181914a4d3404d9ec397943bfb54582023-11-18T19:28:50ZengMDPI AGGenes2073-44252023-06-01147133010.3390/genes14071330Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in CancerMichele Massimino0Federica Martorana1Stefania Stella2Silvia Rita Vitale3Cristina Tomarchio4Livia Manzella5Paolo Vigneri6Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyCancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell <i>omics</i> has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell–cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional <i>omics</i> at single-cell resolution.https://www.mdpi.com/2073-4425/14/7/1330single-cell analysisCTCsomicsbioinformatic approachescancerprecision medicine |
| spellingShingle | Michele Massimino Federica Martorana Stefania Stella Silvia Rita Vitale Cristina Tomarchio Livia Manzella Paolo Vigneri Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer Genes single-cell analysis CTCs omics bioinformatic approaches cancer precision medicine |
| title | Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer |
| title_full | Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer |
| title_fullStr | Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer |
| title_full_unstemmed | Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer |
| title_short | Single-Cell Analysis in the <i>Omics</i> Era: Technologies and Applications in Cancer |
| title_sort | single cell analysis in the i omics i era technologies and applications in cancer |
| topic | single-cell analysis CTCs omics bioinformatic approaches cancer precision medicine |
| url | https://www.mdpi.com/2073-4425/14/7/1330 |
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