Diagnostic Potential of Imaging Flow Cytometry

Trends in Biotechnology

Volumn 36, Issue 7, 2018, Pages 649-652

  Doan, Minh ^a   Vorobjev, Ivan ^b,c   Rees, Paul ^a,d   Filby, Andrew ^e   Wolkenhauer, Olaf ^f,g   Goldfeld, Anne E ^h   Lieberman, Judy ^h   Barteneva, Natasha ^h   Carpenter, Anne E ^a   Hennig, Holger ^a,f  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b NAZARBAYEV UNIVERSITY   (Kazakhstan)

^c MOSCOW STATE UNIVERSITY   (Russian Federation)

^d SWANSEA UNIVERSITY   (United Kingdom)

^e NEWCASTLE UNIVERSITY   (United Kingdom)

^f UNIVERSITY OF ROSTOCK   (Germany)

^g STELLENBOSCH INSTITUTE FOR ADVANCED STUDY   (South Africa)

^h BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

deep learning; disease diagnostics; high content analysis; imaging flow cytometry; translational medicine

Indexed keywords

DIAGNOSIS; FLOW CYTOMETRY; LEARNING ALGORITHMS;

CLINICAL PRACTICES; DIAGNOSTIC POTENTIAL; DISEASE DIAGNOSTICS; HIGH CONTENT ANALYSIS; INFORMATION CONTENT ANALYSIS; MULTICHANNEL IMAGES; PARADIGM SHIFTS; TRANSLATIONAL MEDICINE;

DEEP LEARNING;

ALGORITHM; BODY FLUID; CIRCULATING TUMOR CELL; CONTENT ANALYSIS; DIAGNOSTIC VALUE; DIFFERENTIAL DIAGNOSIS; DIGITAL IMAGING; FEATURE EXTRACTION; FLOW CYTOMETRY; HEMATOLOGIC DISEASE; HUMAN; IMAGE PROCESSING; IMAGING FLOW CYTOMETRY; LIQUID BIOPSY; MACHINE LEARNING; PRIORITY JOURNAL; SHORT SURVEY; SINGLE CELL ANALYSIS; BLOOD; CLASSIFICATION; DATA ANALYSIS; DEVICES; DIAGNOSTIC IMAGING; FLUORESCENCE MICROSCOPY; MYELOID LEUKEMIA; PERSONALIZED MEDICINE; PROCEDURES; PROGNOSIS; SINGLE MOLECULE IMAGING; TUMOR EMBOLISM;

DATA ANALYSIS; DEEP LEARNING; FLOW CYTOMETRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEUKEMIA, MYELOID; MICROSCOPY, FLUORESCENCE; NEOPLASTIC CELLS, CIRCULATING; PRECISION MEDICINE; PROGNOSIS; SINGLE MOLECULE IMAGING; SINGLE-CELL ANALYSIS;

EID: 85041216958     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2017.12.008     Document Type: Short Survey

References (14)

1. Ogle, L.F., et al. Imagestream detection and characterisation of circulating tumour cells – a liquid biopsy for hepatocellular carcinoma?. J. Hepatol. 65 (2016), 305–313.
2. Blasi, T., et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun., 7, 2016, 10256.
3. Betters, D.M., Use of flow cytometry in clinical practice. J. Adv. Pract. Oncol. 6 (2015), 435–440.
4. Giesen, C., et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11 (2014), 417–422.
5. Han, Y., et al. Review: imaging technologies for flow cytometry. Lab Chip 16 (2016), 4639–4647.
6. Barteneva, N.S., Vorobjev, I.A., Imaging Flow Cytometry: Methods and Protocols. 2016, Springer.
7. Grimwade, L.F., et al. Applications of imaging flow cytometry in the diagnostic assessment of acute leukaemia. Methods 112 (2017), 39–45.
8. Zanella, F., et al. High content screening: seeing is believing. Trends Biotechnol. 28 (2010), 237–245.
9. Caicedo, J.C., et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14 (2017), 849–863.
10. Hennig, H., et al. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods 112 (2017), 201–210.
11. Angermueller, C., et al. Deep learning for computational biology. Mol. Syst. Biol., 12, 2016, 878.
12. Friedrich, M.J., Going with the flow: the promise and challenge of liquid biopsies. JAMA 318 (2017), 1095–1097.
13. Gerdtsson, E., et al. Multiplex protein detection on circulating tumor cells from liquid biopsies using imaging mass cytometry. Converg. Sci. Phys. Oncol., 4, 2018, 015002.
14. Chen, C.L., et al. Deep learning in label-free cell classification. Sci. Rep., 6, 2016, 21471.