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Analytical Technology for Single-Cancer-Cell Analysis

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Handbook of Single-Cell Technologies

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Abstract

Traditional analysis in cancer initiates an empirical screening on bulk tumors based on standard equipment. Nonetheless, the tumors inherently exhibit distinct characteristics, including heterogeneity/plasticity/morphology of cells, cell-matrix or cell-cell interactions, and in-depth mass transport. Such traditional analysis may not acquire the invaluable yield since the critical cells of interest are in the minority as well as their activities could be altered during preparation. In recent years, emerging techniques in single-cell analysis have opened a new avenue targeting precise cancer medicine, including single-cell genome/transcriptome, next-generation sequencing, tumor spheroid formation targeting cancer stem cells, and microfluidic approach for the physical assay of cell size (mass/volume/density), deformability, and electrokinetic properties from single cells. In this chapter, an overall view of current techniques and commercial equipment for single-cell analysis in cancer and their potential translation into clinic will be present.

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References

  • Ahmmed S, Bithi S, Pore A et al (2018) Multi-sample deformability cytometry of cancer cells. APL Bioeng 2:032002

    Article  Google Scholar 

  • Amir E-A, Davis K, Tadmor M et al (2013) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31:545–552

    Article  Google Scholar 

  • Antfolk M, Kim S, Koizumi S et al (2017) Label-free single-cell separation and imaging of cancer cells using an integrated microfluidic system. Sci Rep 7:46507

    Article  Google Scholar 

  • Bacher R, Kendziorski C (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17:63

    Article  Google Scholar 

  • Barer R, Ross K, Tkaczyk S (1953) Refractometry of living cells. Nature 171:720–724

    Article  Google Scholar 

  • Baslan T, Hicks J (2017) Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer 17:557–569

    Article  Google Scholar 

  • Bendall S, Simonds E, Qiu P, Amir E-A, Krutzik P, Finck R, Bruggner R, Melamed R, Trejo A, Ornastsky O, Balderas R et al (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 6:687–696

    Article  Google Scholar 

  • Borgstrom E, Paterlini M, Mold J et al (2017) Comparison of whole genome amplification techniques for human single cell exome sequencing. PLoS One 12:e0171566

    Article  Google Scholar 

  • Bryan A, Hecht V, Shen W et al (2014) Measuring single cell mass, volume, and density with dual suspended microchannel resonators. Lab Chip 14:569–576

    Article  Google Scholar 

  • Chaffer C, Juan BS, Lim E et al (2016) EMT, cell plasticity and metastasis. Cancer Metastasis Rev 35:645–654

    Article  Google Scholar 

  • Datlinger P, Rendeiro A, Schmidl C et al (2017) Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods 14:297–301

    Article  Google Scholar 

  • Dean F, Hosono S, Fang L et al (2002) Comprehensive human genome amplification using multiple displacement amplification. PNAS 99:5261–5266

    Article  Google Scholar 

  • Dou M, Clari G, Tsai C et al (2019) High-throughput single cell proteomics enabled by multiplex isobaric labelling in a Nanodroplet sample preparation platform. Anal Chem. https://doi.org/10.1021/acs.analchem.9b03349

  • Emmert-Buck MR, Bonner RF, Smith PD et al (1996) Laser capture microdissection. Science 274:998–1001

    Article  Google Scholar 

  • Filipovic N, Djukic T, Radovic M et al (2014) Electromagnetic field investigation on different cancer cell lines. Cancer Cell Int 14:84

    Article  Google Scholar 

  • Fröhlich J, König H (2000) New techniques for isolation of single prokaryotic cells. FEMS Microbiol 24:567–572

    Article  Google Scholar 

  • Gawad C, Koh W, Quake S (2014) Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. PNAS 111:17947–17952

    Article  Google Scholar 

  • Gill N, Nyberg K, Lee L et al (2019) A scalable filtration method for high throughput screening based on cell deformability. Lab Chip 19:343–357

    Article  Google Scholar 

  • Goding JW (1980) Antibody production by hybridomas. J Immunol Methods 185:285–308

    Article  Google Scholar 

  • Gross A, Schoendube J, Zimmermann S et al (2015) Techniques for single-cell isolation. Int J Mol Sci 16:16897–16919

    Article  Google Scholar 

  • Grover W, Bryan A, Diez-Silva M et al (2011) Measuring single-cell density. PNAS 108:10992–10996

    Article  Google Scholar 

  • Guo G, Luc S, Marco E et al (2013) Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell 13:492–505

    Article  Google Scholar 

  • Hang C, Chen H, Yen M et al (2011) Gene expression of human lung Cancer cell line CL1–5 in response to a direct current electric field. PLoS One 6:e25928

    Article  Google Scholar 

  • Hierahn T, Wadsworth MH, Hughes T et al (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14:395–398

    Article  Google Scholar 

  • Hirsch J, Gallian E (1968) Methods for the determination of adipose cell size in man and animals. J Lipid Res 9:110–119

    Article  Google Scholar 

  • Hong Y, Fang F, Zhang Q (2016) Circulating tumor cell clusters: what we know and what we expect. Int J Oncol 49:2206–2216

    Article  Google Scholar 

  • Hou H, Chang H, Cheng J (2015) Electrotaxis studies of lung cancer cells using a multichannel dual-electric-field microfluidic chip. J Vis Exp 106:53340

    Google Scholar 

  • Jaitin D, Kenigsberg E, Keren-Shaul H et al (2014) Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types. Science 343:776–779

    Article  Google Scholar 

  • Justus C, Leffler N, Ruiz-Echevarria M et al (2014) In vitro cell migration and invasion assays. J Vis Exp 88:51046

    Google Scholar 

  • Khamenehfar A, Li P (2016) Microfluidic devices for circulating tumor cells isolation and subsequent analysis. Curr Pharm Biotechnol 17:810–821

    Article  Google Scholar 

  • Kimmerling R, Prakadan S, Gupta A et al (2018) Linking single-cell measurements of mass, growth rate, and gene expression. Genome Biol 19:207

    Article  Google Scholar 

  • Kramer N, Walzl A, Unger C et al (2013) In vitro cell migration and invasion assays. Mutat Res 752:10–24

    Article  Google Scholar 

  • Kuo C, Chiang C, Chang C et al (2014) Modeling of cancer metastasis and drug resistance via biomimetic nano-cilia and microfluidics. Biomaterials 35:1562–1571

    Article  Google Scholar 

  • Kuo C, Wang J, Lin Y et al (2017a) Three-dimensional spheroid culture targeting versatile tissue bioassays using a PDMS-based hanging drop array. Sci Rep 7:4363

    Article  Google Scholar 

  • Kuo C, Wang J, Wo A et al (2017b) ParaStamp and its applications to cell patterning, drug synergy screening, and rewritable devices for droplet storage. Adv Biosyst 1:1700048

    Article  Google Scholar 

  • Kuo C, Lu S, Chen W et al (2018) Facilitating tumor spheroid-based bioassays and in vitro blood vessel modeling via bioinspired selfformation microstructure devices. Lab Chip 18:2453–2465

    Article  Google Scholar 

  • Kuo C, Wang J, Lu S et al (2019) A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments. Sci Rep 9:10120

    Article  Google Scholar 

  • Kyrochristos I, Roukos D (2019) Comprehensive intra-individual genomic and transcriptional heterogeneity: evidence-based colorectal cancer precision medicine. Cancer Treat Rev 80:101894

    Article  Google Scholar 

  • Landwehr G, Kristof A, Rahman S et al (2018) Biophysical analysis of fluid shear stress induced cellular deformation in a microfluidic device. Biomicrofluidics 12:054109

    Article  Google Scholar 

  • Lee J, Mhawech-Fauceglia P, Lee N et al (2013) A three-dimensional microenvironment alters protein expression and chemosensitivity of epithelial ovarian cancer cells in vitro. Lab Investig 93:528–542

    Article  Google Scholar 

  • Li L, Lu M, Fan Y et al (2019a) High-throughput and ultra-sensitive single-cell profiling of multiple microRNAs and identification of human cancer. Chem Commun (Camb) 55:10404–10407

    Article  Google Scholar 

  • Li R, Jia F, Zhang W et al (2019b) Device for whole genome sequencing single circulating tumor cells from whole blood. Lab Chip. https://doi.org/10.1039/c9lc00473d

  • Liu D, Paczkowski P, Mackay S et al (2020) Single-cell multiplexed proteomics on the IsoLight resolves cellular functional heterogeneity to reveal clinical responses of cancer patients to immunotherapies. Methods Mol Biol 2055:413–431

    Article  Google Scholar 

  • Ma W, Hsiung L, Wang C et al (2015) A novel 96well-formatted micro-gap plate enabling drug response profiling on primary tumour samples. Sci Rep 5:9656

    Article  Google Scholar 

  • Mani S, Guo W, Liao M et al (2008) The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133:704–715

    Article  Google Scholar 

  • Milardi D, Grande G, Vincerzoni F et al (2019) Proteomics for the identification of biomarkers in testicular cancer-review. Front Endocrinol (Lausanne) 10:462

    Article  Google Scholar 

  • Moldavan A (1934) Photo-electric technique for the counting of microscopial cells. Science 80:188–189

    Article  Google Scholar 

  • Nagrath S, Sequist L, Maheswaran S et al (2007) Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450:1235–1239

    Article  Google Scholar 

  • Picelli S (2017) Single-cell RNA-sequencing: the future of genome biology is now. RNA Biol 14:637–660

    Article  Google Scholar 

  • Punjiya M, Nejad H, Mathews J et al (2019) A flow through device for simultaneous dielectrophoretic cell trapping and AC electroporation. Sci Rep 9:11988

    Article  Google Scholar 

  • Rantalainen M (2018) Application of single-cell sequencing in human cancer. Brief Funct Genomics 17:273–282

    Article  Google Scholar 

  • Riethdorf S, Fritsche H, Muller V et al (2007) Detection of circulating tumor cells in peripheral blood of patients with metastatic breast cancer: a validation study of the cell search system. Clin Cancer Res 13:920–928

    Article  Google Scholar 

  • Saadatpour A, Lai S, Guo G et al (2015) Single-cell analysis in cancer genomics. Trends Genet 31:576–586

    Article  Google Scholar 

  • Stegle O, Teichmann S, Marioni J (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16:133–145

    Article  Google Scholar 

  • Stoechius M, Hafemeister C, Stephenson W et al (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14:865–868

    Article  Google Scholar 

  • Stubbington M, Rozenblatt-Rosen O, Regev A et al (2017) Single-cell transcriptomics to explore the immune system in health and disease. Science 358:58–63

    Article  Google Scholar 

  • Telenius H, Carter N, Bebb C et al (1992) Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics 13:718–725

    Article  Google Scholar 

  • Tung Y, Hsiao A, Allen S et al (2011) High-throughput 3D spheroid culture and drug testing using a 384 hanging drop array. Analyst 136:473–478

    Article  Google Scholar 

  • Tzur A, Moore J, Jorgensen P et al (2011) Optimizing optical flow cytometry for cell volume-based sorting and analysis. PLoS One 6:e16053

    Article  Google Scholar 

  • Vitak S, Torkenczy K, Rosenkrantz J et al (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods 14:302–308

    Article  Google Scholar 

  • Wills QF, Mead AJ (2015) Application of single-cell genomics in cancer: promise and challenges. Hum Mol Genet 24:R74–R84

    Article  Google Scholar 

  • Wu P, Aroush D, Asnacios A et al (2018) A comparison of methods to assess cell mechanical properties. Nat Methods 15:491–498

    Article  Google Scholar 

  • Yin Y, Jiang Y, Lam K et al (2019) High-throughput single-cell sequencing with linear amplification. Mol Cell S1097-2765(19):30618–30615

    Google Scholar 

  • Yoon C, Till J, Cho S et al (2019) KRAS activation in gastric adenocarcinoma stimulates epithelial-to-mesenchymal transition to cancer stem-like cells and promotes metastasis. Mol Cancer Res 17:1945–1957

    Article  Google Scholar 

  • Yu M, Bardia A, Aceto N et al (2014) Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science 345:216–220

    Article  Google Scholar 

  • Zhang K, Gao M, Chong Z et al (2016) Single-cell isolation by a modular single-cell pipette for RNA-sequencing. Lab Chip 16:4742–4748

    Article  Google Scholar 

  • Zhou W, Ji Z, Fang W et al (2019) Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq. Nucleic Acids Res 47(19):e121

    Google Scholar 

  • Zhu Y, Piehowski P, Zhao R et al (2018) Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nat Commun 9:882

    Article  Google Scholar 

  • Zhu L, Pan R, Zhou D et al (2019) BCL11A enhances stemness and promotes progression by activating Wnt/β-catenin signaling in breast cancer. Cancer Manag Res 11:2997–3007

    Article  Google Scholar 

Download references

Acknowledgments

Financial support from the Ministry of Science and Technology (MOST), Taiwan, under the grant 107-2622-8-002-018 is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Life Science, National Taiwan University, Taipei, Taiwan

    Hsinyu Lee

  2. Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

    Ching-Te Kuo

Authors
  1. Ching-Te Kuo
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  2. Hsinyu Lee
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Corresponding authors

Correspondence to Ching-Te Kuo or Hsinyu Lee .

Editor information

Editors and Affiliations

  1. Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India

    Tuhin Subhra Santra

  2. Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan

    Fan-Gang Tseng

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Kuo, CT., Lee, H. (2022). Analytical Technology for Single-Cancer-Cell Analysis. In: Santra, T.S., Tseng, FG. (eds) Handbook of Single-Cell Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-10-8953-4_33

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