Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches. (February 2019)
- Record Type:
- Journal Article
- Title:
- Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches. (February 2019)
- Main Title:
- Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches
- Authors:
- Leonavicius, Karolis
Nainys, Juozas
Kuciauskas, Dalius
Mazutis, Linas - Abstract:
- Graphical abstract: Highlights: Sequencing, mass spectrometry and microscopy are common techniques in single-cell biology. Microfluidic single-cell RNA-Seq techniques have created a need for computational data-fusion. Data-fusion uses canonical correlation analysis or similarity-based cell projection across datasets. Analysis of multi-omics datasets constitute a major challenge. Abstract : Biological samples are inherently heterogeneous and complex. Tackling this complexity requires innovative technological and analytical solutions. Recent advances in high-throughput single-cell isolation and nucleic acid barcoding methods are rapidly changing the technological landscape of biological sciences and now make it possible to measure the (epi)genomic, transcriptomic, or proteomic state of individual cells. In addition, few experimental approaches enable multi-omics measurements of the same cell. However, merging-omics data collected from different experiments remains a considerable challenge. Although several strategies for merging transcriptomics datasets have recently been introduced, cell-to-cell variability and heterogeneity remains one of the confounding factors limiting data fusion and integration. Here, we focus our discussion on the latest single-cell technological and analytical solutions to achieve high data dimensionality and resolution. Obtaining datasets with a wealth of multi-omics information will undoubtedly provide new avenues for researchers to unravel theGraphical abstract: Highlights: Sequencing, mass spectrometry and microscopy are common techniques in single-cell biology. Microfluidic single-cell RNA-Seq techniques have created a need for computational data-fusion. Data-fusion uses canonical correlation analysis or similarity-based cell projection across datasets. Analysis of multi-omics datasets constitute a major challenge. Abstract : Biological samples are inherently heterogeneous and complex. Tackling this complexity requires innovative technological and analytical solutions. Recent advances in high-throughput single-cell isolation and nucleic acid barcoding methods are rapidly changing the technological landscape of biological sciences and now make it possible to measure the (epi)genomic, transcriptomic, or proteomic state of individual cells. In addition, few experimental approaches enable multi-omics measurements of the same cell. However, merging-omics data collected from different experiments remains a considerable challenge. Although several strategies for merging transcriptomics datasets have recently been introduced, cell-to-cell variability and heterogeneity remains one of the confounding factors limiting data fusion and integration. Here, we focus our discussion on the latest single-cell technological and analytical solutions to achieve high data dimensionality and resolution. Obtaining datasets with a wealth of multi-omics information will undoubtedly provide new avenues for researchers to unravel the complexity of biological samples encountered in modern biological research and molecular diagnostics. … (more)
- Is Part Of:
- Current opinion in biotechnology. Volume 55(2019)
- Journal:
- Current opinion in biotechnology
- Issue:
- Volume 55(2019)
- Issue Display:
- Volume 55, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 55
- Issue:
- 2019
- Issue Sort Value:
- 2019-0055-2019-0000
- Page Start:
- 159
- Page End:
- 166
- Publication Date:
- 2019-02
- Subjects:
- Biotechnology -- Periodicals
660.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09581669 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.copbio.2018.09.012 ↗
- Languages:
- English
- ISSNs:
- 0958-1669
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3500.772500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9505.xml