Diagnostic Potential of Imaging Flow Cytometry

Affiliations

¹ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
² School of Science and Technology, Nazarbayev University, Kababnbay batyr avenue 53, 010000, Astana, Kazakhstan; A.N. Belozersky Institute of Physico-Chemical Biology/Biological Faculty, M.V. Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
³ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
⁴ Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
⁵ Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, 7600, South Africa.
⁶ Program in Cellular and Molecular Medicine, Boston Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
⁷ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany. Electronic address: holgerh@broadinstitute.org.

PMID: 29395345
DOI: 10.1016/j.tibtech.2017.12.008

Free article

Diagnostic Potential of Imaging Flow Cytometry

Minh Doan et al. Trends Biotechnol. 2018 Jul.

Free article

. 2018 Jul;36(7):649-652.

doi: 10.1016/j.tibtech.2017.12.008. Epub 2018 Jan 31.

Authors

Affiliations

¹ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
² School of Science and Technology, Nazarbayev University, Kababnbay batyr avenue 53, 010000, Astana, Kazakhstan; A.N. Belozersky Institute of Physico-Chemical Biology/Biological Faculty, M.V. Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
³ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
⁴ Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
⁵ Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, 7600, South Africa.
⁶ Program in Cellular and Molecular Medicine, Boston Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
⁷ Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany. Electronic address: holgerh@broadinstitute.org.

PMID: 29395345
DOI: 10.1016/j.tibtech.2017.12.008

Abstract

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.

Keywords: deep learning; disease diagnostics; high-content analysis; imaging flow cytometry; translational medicine.

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BB/N005163/Biotechnology and Biological Sciences Research Council/United Kingdom

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Diagnostic Potential of Imaging Flow Cytometry

Affiliations

Diagnostic Potential of Imaging Flow Cytometry

Authors

Affiliations

Abstract

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources