Abstract
Each year, about 7–8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.







Similar content being viewed by others
References
Bancroft JD (2008) Theory and practice of histological techniques. Elsevier Health Sciences, Amsterdam
Basiji DA, Ortyn WE, Liang L, Venkatachalam V, Morrissey P (2007) Cellular image analysis and imaging by flow cytometry. Clin Lab Med 27(3):653–670. doi:10.1016/j.cll.2007.05.008
Caprio GD, Stokes C, Higgins JM, Schonbrun E (2015) Single-cell measurement of red blood cell oxygen affinity. Proc Natl Acad Sci USA 112(32):9984–9989. doi:10.1073/pnas.1509252112
Cohen SJ, Punt CJA, Iannotti N, Saidman BH, Sabbath KD, Gabrail NY, Picus J, Morse M, Mitchell E, Miller MC, Doyle GV, Tissing H, Terstappen LWMM, Meropol NJ (2008) Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol 26(19):3213–3221. doi:10.1200/JCO.2007.15.8923
Dey P (2010) Cancer nucleus: morphology and beyond. Diagn Cytopathol 38(5):382–390. doi:10.1002/dc.21234
Goda K, Ayazi A, Gossett DR, Sadasivam J, Lonappan CK, Sollier E, Fard AM, Hur SC, Adam J, Murray C, Wang C, Brackbill N, Carlo DD, Jalali B (2012) High-throughput single-microparticle imaging flow analyzer. Proc Natl Acad Sci USA 109(29):11630–11635. doi:10.1073/pnas.1204718109
Gopakumar G, Subrahmanyam GRKS, Siva GS (2014) Morphology based classification of leukemia cell lines: K562 and MOLT in a microfluidics based imaging flow cytometer. In: Proceedings of the 2014 Indian conference on computer vision, graphics and image processing, ICVGIP’14, Bangalore, India, December 14–18, 2014, pp 34:1–34:7. doi:10.1145/2683483.2683517
Gopakumar G, Jagannadh VK, Gorthi SS, Subrahmanyam GRKS (2016) Framework for morphometric classification of cells in imaging flow cytometry. J Microsc 261(3):307–319. doi:10.1111/jmi.12335
Gorthi SS, Schonbrun E (2012) Phase imaging flow cytometry using a focus-stack collecting microscope. Opt Lett 37(4):707–709. doi:10.1364/OL.37.000707
Gorthi SS, Schaak D, Schonbrun E (2013) Fluorescence imaging of flowing cells using a temporally coded excitation. Opt Express 21(4):5164–5170. doi:10.1364/OE.21.005164
Kachel V, Benker G, Lichtnau K, Valet G, Glossner E (1979) Fast imaging in flow: a means of combining flow-cytometry and image analysis. J Histochem Cytochem 27(1):335–341. doi:10.1177/27.1.374598
Kay DB, Cambier JL, Wheeless LL (1979) Imaging in flow. J Histochem Cytochem 27(1):329–334. doi:10.1177/27.1.374597
Kirby M, Sirovich L (1990) Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1):103–108. doi:10.1109/34.41390
Nayar R (2014) Cytopathology in oncology. Springer, Berlin
Ng EX, Miller MA, Jing T, Lauffenburger DA, Chen CH (2015) Low-volume multiplexed proteolytic activity assay and inhibitor analysis through a pico-injector array. Lab Chip 15(4):1153–1159. doi:10.1039/C4LC01162G
Ng EX, Miller MA, Jing T, Chen CH (2016) Single cell multiplexed assay for proteolytic activity using droplet microfluidics. Biosens Bioelectron 81:408–414. doi:10.1016/j.bios.2016.03.002
Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, Girardo S, Pagliara S, Ekpenyong A, Jacobi A, Wobus M, Tpfner N, Keyser UF, Mansfeld J, Fischer-Friedrich E, Guck J (2015) Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods 12(3):199–202. doi:10.1038/nmeth.3281, 4 p following 202
Schonbrun E, Gorthi SS, Schaak D (2012) Microfabricated multiple field of view imaging flow cytometry. Lab Chip 12(2):268–273. doi:10.1039/c1lc20843h
Schonbrun E, Di Caprio G, Schaak D (2013) Dye exclusion microfluidic microscopy. Opt Express 21(7):8793–8798. doi:10.1364/OE.21.008793
Schonbrun E, Malka R, Di Caprio G, Schaak D, Higgins JM (2014) Quantitative absorption cytometry for measuring red blood cell hemoglobin mass and volume. Cytometry A 85(4):332–338. doi:10.1002/cyto.a.22450
Solomon D (2003) Chapter 14: role of triage testing in cervical cancer screening. JNCI Monogr 2003(31):97–101
Stewart BW (2014) Wild C: World cancer report 2014. World Health Organization, Geneva
Sung Y, Lue N, Hamza B, Martel J, Irimia D, Dasari RR, Choi W, Yaqoob Z, So P (2014) Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel. Phys Rev Appl 1(1):014002. doi:10.1103/PhysRevApplied.1.014002
Terstappen LW, Johnsen S, Segers-Nolten IM, Loken MR (1990) Identification and characterization of plasma cells in normal human bone marrow by high-resolution flow cytometry. Blood 76(9):1739–1747
Terstappen LW, Safford M, Knemann S, Loken M, Zurlutter K, Bchner T, Hiddemann W, Wrmann B (1992) Flow cytometric characterization of acute myeloid leukemia. Part II. Phenotypic heterogeneity at diagnosis. Leukemia 6(1):70–80
Walts AE, Thomas P (2002) Endometrial cells and the AutoPap system for primary screening of cervicovaginal Pap smears. Diagn Cytopathol 27(4):232–237. doi:10.1002/dc.10175
Wu J, Li J, Chan RK (2013) A light sheet based high throughput 3D-imaging flow cytometer for phytoplankton analysis. Opt Express 21(12):14474–14480. doi:10.1364/OE.21.014474
Xia Y, Whitesides GM (1998) Soft lithography. Annu Rev Mater Sci 28(1):153–184. doi:10.1146/annurev.matsci.28.1.153
Yan Y, Boey D, Ng LT, Gruber J, Bettiol A, Thakor NV, Chen CH (2016) Continuous-flow C. elegans fluorescence expression analysis with real-time image processing through microfluidics. Biosens Bioelectron 77:428–434. doi:10.1016/j.bios.2015.09.045
Yang J, Zhang D, Frangi A, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137. doi:10.1109/TPAMI.2004.1261097
Acknowledgments
Sai Siva Gorthi would like to acknowledge funding from pilot grant on cancer biology of Department of Biotechnology (DBT), Government of India.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jagannadh, V.K., Gopakumar, G., Subrahmanyam, G.R.K.S. et al. Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening. Med Biol Eng Comput 55, 711–718 (2017). https://doi.org/10.1007/s11517-016-1549-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-016-1549-y