A practical guide to intelligent image-activated cell sorting.
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
21
01
2019
accepted:
18
04
2019
pubmed:
7
7
2019
medline:
27
11
2019
entrez:
7
7
2019
Statut:
ppublish
Résumé
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.
Identifiants
pubmed: 31278398
doi: 10.1038/s41596-019-0183-1
pii: 10.1038/s41596-019-0183-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2370-2415Commentaires et corrections
Type : ErratumIn
Références
Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276 (2018).
pubmed: 30166209
Mikami, H. et al. Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit. Optica 5, 117–126 (2018).
Kanno, H., Mikami, H., Kaya, Y., Ozeki, Y. & Goda, K. Simple, stable, compact implementation of frequency-division-multiplexed microscopy by inline interferometry. Opt. Lett. 44, 467–470 (2019).
pubmed: 30702655
Shivhare, P. K., Bhadra, A., Sajeesh, P., Prabhakar, A. & Sen, A. K. Hydrodynamic focusing and interdistance control of particle-laden flow for microflow cytometry. Microfluid. Nanofluidics 20, 86 (2016).
Park, J. W. et al. Acoustofluidic harvesting of microalgae on a single chip. Biomicrofluidics 10, 034119 (2016).
pubmed: 27462380
pmcid: 4920807
Grenvall, C., Antfolk, C., Bisgaard, C. Z. & Laurell, T. Two-dimensional acoustic particle focusing enables sheathless chip Coulter counter with planar electrode configuration. Lab Chip 14, 4629–4637 (2014).
pubmed: 25300357
Sakuma, S., Kasai, Y., Hayakawa, T. & Arai, F. On-chip cell sorting by high-speed local-flow control using dual membrane pumps. Lab Chip 17, 2760–2767 (2017).
pubmed: 28685786
Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
pubmed: 26017442
Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep Learning (MIT Press, Cambridge, 2016).
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).
Krizhevesky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proc. 25th International Conference on Neural Information Processing Systems (NIPS 2012) (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 1097–1105 (Curran Associates, 2012).
Herzenberg, L. A. et al. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 48, 1819–1827 (2002).
Tung, J. W. et al. Modern flow cytometry: a practical approach. Clin. Lab. Med. 27, 453–468 (2007).
pubmed: 17658402
pmcid: 1994577
Liu, L., Cheung, T. H., Charville, G. W. & Rando, T. A. Isolation of skeletal muscle stem cells by fluorescence-activated cell sorting. Nat. Protoc. 10, 1612–1624 (2015).
pubmed: 26401916
pmcid: 4793971
Hayatsu, N. et al. Analyses of a mutant Foxp3 allele reveal BATF as a critical transcription factor in the differentiation and accumulation of tissue regulatory T cells. Immunity 47, 268–283 (2017).
pubmed: 28778586
de St Groth, B. F., Zhu, E. rhu., Asad, S. & Lee, L. Flow cytometric detection of human regulatory T cells. Methods Mol. Biol. 707, 263–279 (2011)
Shapiro, H. M. Practical Flow Cytometry (John Wiley & Sons, 2005).
Herzenberg, L. A., Gottlinger, C., Muller, W., Radbruch, A. & Recktenwald, D. Flow Cytometry and Cell Sorting (Springer, 1992).
Lindmo, T., Peters, D. C. & Sweet, R. G. Flow Cytometry and Sorting (Wiley-Liss, 1990).
Kawata, S., Hori, M., Kado, H. & Tamiya, E. Biological Imaging and Sensing (Springer, 2004).
Wang, P. & Wu, C. Micro/Nano Cell and Molecular Sensors (Springer, 2016).
Boutros, M., Heigwer, F. & Laufer, C. Microscopy-based high-content screening. Cell 163, 1314–1325 (2015).
pubmed: 26638068
Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).
pubmed: 28858338
Boutros, M. & Ahringer, J. The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9, 554–566 (2008).
pubmed: 18521077
Carpenter, A. E. Image-based chemical screening. Nat. Chem. Biol. 3, 461–465 (2007).
pubmed: 17637778
Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).
pubmed: 14764878
Lum, L. et al. Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 299, 2039–2045 (2003).
pubmed: 12663920
Kiger, A. et al. A functional genomic analysis of cell morphology using RNA interference. J. Biol. 2, 27 (2003).
pubmed: 14527345
pmcid: 333409
Liu, T., Sims, D. & Baum, B. Parallel RNAi screens across different cell lines identify generic and cell type-specific regulators of actin organization and cell morphology. Genome Biol. 10, R26 (2009).
pubmed: 19265526
pmcid: 2690997
Arpali, S. A., Arpali, C., Coskun, A. F., Chiang, H. H. & Ozcan, A. High-throughput screening of large volumes of whole blood using structured illumination and fluorescent on-chip imaging. Lab Chip 12, 4968–4971 (2012).
pubmed: 23047492
pmcid: 3485428
Zhang, Y. et al. High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging. ACS Photonics 5, 2081–2086 (2018).
Lei, C., Guo, B., Cheng, Z. & Goda, K. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev. 3, 011102 (2016).
Mikami, H. et al. High-speed imaging meets single-cell analysis. Chem 4, 2278–2300 (2018).
Mikami, H., Gao, L. & Goda, K. Ultrafast optical imaging technology: principles and applications of emerging methods. Nanophotonics 5, 497–509 (2016).
Porichis, F. et al. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014).
pubmed: 25472703
pmcid: 4256720
Wu, J. L. et al. Ultrafast laser-scanning time-stretch imaging at visible wavelengths. Light Sci. Appl. 6, e16196 (2017).
pubmed: 30167195
pmcid: 6061895
Mahjoubfar, A. et al. Time stretch and its applications. Nat. Photonics 11, 341–351 (2017).
Lai, Q. T. K. et al. High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton. Opt. Express 24, 28170–28184 (2016).
pubmed: 27958529
Han, Y. & Lo, Y. Imaging cells in flow cytometer using spatial-temporal transformation. Sci. Rep. 5, 13267 (2015).
pubmed: 26281956
pmcid: 4539609
Han, Y., Gu, Y., Zhang, A. C. & Lo, Y. H. Review: imaging technologies for flow cytometry. Lab Chip 16, 4639–4647 (2016).
pubmed: 27830849
pmcid: 5311077
Rane, A. S., Rutkauskaite, J., DeMello, A. & Stavrakis, S. High-throughput multi-parametric imaging flow cytometry. Chem 3, 588–602 (2017).
Miura, T. et al. On-chip light-sheet fluorescence imaging flow cytometry at a high flow speed of 1 m/s. Biomed. Opt. Express 9, 3424–3433 (2018).
pubmed: 29984107
pmcid: 6033546
Jiang, Y. et al. Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab Chip 17, 2426–2434 (2017).
pubmed: 28627575
George, T. C. et al. Distinguishing modes of cell death using the ImageStream
Kobayashi, H. et al. Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci. Rep. 7, 12454 (2017).
pubmed: 28963483
pmcid: 5622112
Muñoz, H. E. et al. Single-cell analysis of morphological and metabolic heterogeneity in Euglena gracilis by fluorescence-imaging flow cytometry. Anal. Chem. 90, 11280–11289 (2018).
pubmed: 30138557
Guo, B. et al. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy. Cytometry A 91A, 494–502 (2017).
George, T. C. et al. Quantitative measurement of nuclear translocation events using similarity analysis of multispectral cellular images obtained in flow. J. Immunol. Methods 311, 117–129 (2006).
pubmed: 16563425
Basiji, D. A., Ortyn, W. E., Liang, L., Venkatachalam, V. & Morrissey, P. Cellular image analysis and imaging by flow cytometry. Clin. Lab. Med. 27, 653–670 (2007).
pubmed: 17658411
pmcid: 2034394
Lee, D., Mehta, N., Shearer, A. & Kastner, R. A hardware accelerated system for high throughput cellular image analysis. J. Parallel Distrib. Comput. 113, 167–178 (2018).
Goda, K. & Jalali, B. Dispersive Fourier transformation for fast continuous single-shot measurements. Nat. Photonics 7, 102–112 (2013).
Wong, T. T. W. et al. Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow. Sci. Rep. 4, 3656 (2014).
pubmed: 24413677
pmcid: 3888978
Lau, A. K. S., Shum, H. C., Wong, K. K. Y. & Tsia, K. K. Optofluidic time-stretch imaging-an emerging tool for high-throughput imaging flow cytometry. Lab Chip 16, 1743–1756 (2016).
pubmed: 27099993
Lei, C. et al. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat. Protoc. 13, 1603–1631 (2018).
pubmed: 29976951
Guo, B. et al. Optofluidic time-stretch quantitative phase microscopy. Methods 136, 116–125 (2018).
pubmed: 29031836
Goda, K. et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. USA 109, 11630–11635 (2012).
pubmed: 22753513
Lei, C., Nitta, N., Ozeki, Y. & Goda, K. Optofluidic time-stretch microscopy: recent advances. Opt. Rev. 25, 464–472 (2018).
Lei, C. et al. GHz optical time-stretch microscopy by compressive sensing. IEEE Photonics J. 9, 1–8 (2017).
Hiraki, K. et al. All-IP-Ethernet architecture for real-time sensor-fusion processing. In Proc. SPIE BiOS 9720 97200D (2016). https://doi.org/10.1117/12.2212016
Inaba, M. & Hiraki, K. Network processing hardware. In Proc. Second Asian International Conference on Technologies for Advanced Heterogeneous Network (eds Cho, K. & Jacquet, P.) 103–112 (Springer, 2006).
Okada, K. et al. Protocol design for all-IP computer architecture. In Proc. International Conference on Information Networking 2008 (ICOIN2008) (eds Kaiser, B., Madden, S. & Suri, S.) 1–5 (IEEE, 2008).
Hao, N., Budnik, Ba & Gunawardena, J. Tunable signal processing through modular control of transcription factor translocation. Science 339, 460–464 (2013).
pubmed: 23349292
pmcid: 3746486
Von Erlach, T. C. et al. Cell-geometry-dependent changes in plasma membrane order direct stem cell signalling and fate. Nat. Mater. 17, 237–242 (2018).
Sarioglu, A. F. et al. A microfluidic device for label-free, physical capture of circulating tumor cell clusters. Nat. Methods 12, 685–691 (2015).
pubmed: 25984697
pmcid: 4490017
Moor, A. E. et al. Global mRNA polarization regulates translation efficiency in the intestinal epithelium. Science 357, 1299–1303 (2017).
pubmed: 28798045
pmcid: 5955215
Zenker, J. et al. A microtubule-organizing center directing intracellular transport in the early mouse embryo. Science 357, 925–928 (2017).
pubmed: 28860385
Pernas, L., Bean, C., Boothroyd, J. C. & Scorrano, L. Mitochondria restrict growth of the intracellular parasite Toxoplasma gondii by limiting its uptake of fatty acids. Cell Metab. 27, 886–897 (2018).
pubmed: 29617646
Cho, E. H. et al. Characterization of circulating tumor cell aggregates identified in patients with epithelial tumors. Phys. Biol. 9, 016001 (2012).
pubmed: 22306705
pmcid: 3387999
Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).
pubmed: 4149753
pmcid: 4149753
Molnar, B., Ladanyi, A., Tanko, L., Sréter, L. & Tulassay, Z. Circulating tumor cell clusters in the peripheral blood of colorectal cancer patients. Clin. Cancer Res. 7, 4080–4085 (2001).
pubmed: 11751505
pmcid: 11751505
Wang, L. et al. Chloroplast-mediated regulation of CO
pubmed: 27791081
Mackinder, L. C. M. et al. A spatial interactome reveals the protein organization of the algal CO
pubmed: 28938113
pmcid: 5616186
Ohnuki, S. & Ohya, Y. High-dimensional single-cell phenotyping reveals extensive haploinsufficiency. PLoS Biol. 16, 1–23 (2018).
Suzuki, G. et al. Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae. BMC Genomics 19, 149 (2018).
pubmed: 29458326
pmcid: 5819264
Iwaki, A., Ohnuki, S., Suga, Y., Izawa, S. & Ohya, Y. Vanillin inhibits translation and induces messenger ribonucleoprotein (mRNP) granule formation in Saccharomyces cerevisiae: application and validation of high-content, image-based profiling. PLoS ONE 8, e61748 (2013).
pubmed: 23637899
pmcid: 3634847
Treiser, M. D. et al. Cytoskeleton-based forecasting of stem cell lineage fates. Proc. Natl. Acad. Sci. USA 107, 610–615 (2010).
pubmed: 20080726
Thery, M. et al. Anisotropy of cell adhesive microenvironment governs cell internal organization and orientation of polarity. Proc. Natl. Acad. Sci. USA 103, 19771–19776 (2006).
pubmed: 17179050
Wu, C. Y. et al. Shaped 3D microcarriers for adherent cell culture and analysis. Microsyst. Nanoeng. 4, 21 (2018).
pubmed: 31057909
pmcid: 6220171
Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).
pubmed: 25035496
Orange, J. S. Formation and function of the lytic NK-cell immunological synapse. Nat. Rev. Immunol. 8, 713–725 (2008).
pubmed: 19172692
pmcid: 2772177
Dustin, M. L., Chakraborty, A. K. & Shaw, A. S. Understanding the structure and function of the immunological synapse. Cold Spring Harb. Perspect. Biol. 2, a002311 (2010).
pubmed: 20843980
pmcid: 2944359
Ingham, P. W. The molecular genetics of embryonic pattern formation in Drosophila. Nature 335, 25–34 (1988).
pubmed: 2901040
Mullins, M. C., Hammerschmidt, M., Haffter, P. & Nüsslein-Volhard, C. Large-scale mutagenesis in the zebrafish: in search of genes controlling development in a vertebrate. Curr. Biol. 4, 189–202 (1994).
pubmed: 7922324
Fabritius, A. et al. Imaging-based screening platform assists protein engineering. Cell Chem. Biol. 25, 1554–1561 (2018).
pubmed: 30220597
Környei, Z. et al. Cell sorting in a Petri dish controlled by computer vision. Sci. Rep. 3, 1–10 (2013).
Das, A. et al. Adaptive from innate: human IFN-γ
pubmed: 28754679
pmcid: 5563168
Jin, A. et al. A rapid and efficient single-cell manipulation method for screening antigen-specific antibody-secreting cells from human peripheral blood. Nat. Med. 15, 1088–1092 (2009).
pubmed: 19684583
Yoshimoto, N. et al. An automated system for high-throughput single cell-based breeding. Sci. Rep. 3, 1191 (2013).
pubmed: 23378922
pmcid: 3561619
Dura, B. et al. Longitudinal multiparameter assay of lymphocyte interactions from onset by microfluidic cell pairing and culture. Proc. Natl. Acad. Sci. USA 113, E3599–E3608 (2016).
pubmed: 27303033
Ogunniyi, A. O., Story, C. M., Papa, E., Guillen, E. & Love, J. C. Screening individual hybridomas by microengraving to discover monoclonal antibodies. Nat. Protoc. 4, 767–782 (2009).
pubmed: 19528952
pmcid: 4034573
Yao, X. et al. Tumor cells are dislodged into the pulmonary vein during lobectomy. J. Thorac. Cardiovasc. Surg. 148, 3224–3231 (2014).
pubmed: 25172322
pmcid: 4356533
Piatkevich, K. D. et al. A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat. Chem. Biol. 14, 352–360 (2018).
pubmed: 29483642
pmcid: 5866759
Brasko, C. et al. Intelligent image-based in situ single-cell isolation. Nat. Commun. 9, 1–7 (2018).
Grys, B. T. et al. Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 216, 65–71 (2017).
pubmed: 27940887
pmcid: 5223612
Hennig, H. et al. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods 112, 201–210 (2017).
pubmed: 27594698
pmcid: 5231320
Abadi, M. et al. Tensorflow: a system for large-scale machine learning. In Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) 265–283 (USENIX, 2016).
Chollet, F. Keras: the Python deep learning library. https://keras.io (2015).
Kasai, Y., Sakuma, S. & Arai, F. On-chip multi-sorting using high-speed and high-accuracy flow control. In Proc. 22nd International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS2018) (eds Tseng, F.-G. & Lee, G.-B.) 1237–1238 (Chemical and Biological Microsystems Society, 2018).
Paszke, A. et al. Automatic differentiation in PyTorch. In Proc. 31st Conference on Neural Information Processing Systems (NIPS 2017) (eds Guyon, I. et al.)1–4 (Curran Associates, 2017).
Tokui, S., Oono, K., Hido, S. & Clayton, J. Chainer: a next-generation open source framework for deep learning. In Proc. Conference on Neural Information Processing Systems (NIPS 2015) (eds Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 1–4 (Curran Associates, 2015).
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100.1–R100.11 (2006).
Abrams, C. S. et al. Direct detection of activated platelets and platelet-derived microparticles in humans. Blood 75, 128–138 (1990).
pubmed: 2294986
Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).
pubmed: 4193940
pmcid: 4193940
Kalisky, T. & Quake, S. R. Single-cell genomics. Nat. Methods 8, 311–314 (2011).
pubmed: 21451520
Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).
pubmed: 26458175
pmcid: 4636926
Yamano, T. et al. Light and low-CO
pubmed: 20660228