Quantitative Phase Imaging Flow Cytometry for Ultra‐Large‐Scale Single‐Cell Biophysical Phenotyping

Cellular biophysical properties are the effective label‐free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell‐based assays that involve large‐scale single‐cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single‐cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label‐free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi‐ATOM, that enables label‐free single‐cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s—a capability that is otherwise inaccessible in current QPI. With the aim to translate multi‐ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high‐dimensional single‐cell biophysical phenotypic profiles at ultra‐large‐scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label‐free statistical power to classify multiple human leukemic cell types at high accuracy (~92–97%). This system could substantiate the significance of high‐throughput QPI flow cytometry in enabling next frontier in large‐scale image‐derived single‐cell analysis applied in biological discovery and cost‐effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry

[1]  Alberto Orfao,et al.  Minimal residual disease diagnostics in acute lymphoblastic leukemia: need for sensitive, fast, and standardized technologies. , 2015, Blood.

[2]  Edmund Y Lam,et al.  Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm , 2014, Journal of biomedical optics.

[3]  Lassi Paavolainen,et al.  Data-analysis strategies for image-based cell profiling , 2017, Nature Methods.

[4]  Jürgen Popp,et al.  Making a big thing of a small cell--recent advances in single cell analysis. , 2014, The Analyst.

[5]  Antoni Ribas,et al.  Single-cell analysis tools for drug discovery and development , 2015, Nature Reviews Drug Discovery.

[6]  YoungJu Jo,et al.  Quantitative Phase Imaging Techniques for the Study of Cell Pathophysiology: From Principles to Applications , 2013, Sensors.

[7]  Cheng Lei,et al.  Exploring the Deep Feature Space of a Cell Classification Neural Network , 2018, ArXiv.

[8]  J. Pollack,et al.  Molecular profiling reveals myeloid leukemia cell lines to be faithful model systems characterized by distinct genomic aberrations , 2006, Leukemia.

[9]  Makoto Yamada,et al.  High-throughput imaging flow cytometry by optofluidic time-stretch microscopy , 2018, Nature Protocols.

[10]  U. Keyser,et al.  Real-time deformability cytometry: on-the-fly cell mechanical phenotyping , 2015, Nature Methods.

[11]  Dino Di Carlo,et al.  Single-Cell Analysis of Morphological and Metabolic Heterogeneity in Euglena gracilis by Fluorescence-Imaging Flow Cytometry. , 2018, Analytical chemistry.

[12]  K. Pantel,et al.  Challenges in circulating tumour cell research , 2014, Nature Reviews Cancer.

[13]  Dino Di Carlo,et al.  Multiparameter mechanical and morphometric screening of cells , 2016, Scientific Reports.

[14]  Gabriel Popescu,et al.  Quantitative phase imaging for medical diagnosis , 2017, Journal of biophotonics.

[15]  Valentina Proserpio,et al.  Single-cell technologies are revolutionizing the approach to rare cells , 2015, Immunology and cell biology.

[16]  Pasquale Memmolo,et al.  Tomographic flow cytometry by digital holography , 2016, Light: Science & Applications.

[17]  M. Elowitz,et al.  Challenges and emerging directions in single-cell analysis , 2017, Genome Biology.

[18]  H. Greenspan,et al.  Quantitative phase microscopy spatial signatures of cancer cells , 2017, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[19]  Vadim Backman,et al.  Network signatures of nuclear and cytoplasmic density alterations in a model of pre and postmetastatic colorectal cancer , 2014, Journal of biomedical optics.

[20]  Edmund Y. Lam,et al.  Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow , 2013, Scientific Reports.

[21]  Anson H L Tang,et al.  Time-stretch microscopy on a DVD for high-throughput imaging cell-based assay. , 2017, Biomedical optics express.

[22]  Fumihito Arai,et al.  Intelligent Image-Activated Cell Sorting , 2018, Cell.

[23]  Kenneth K. Y. Wong,et al.  Optical Time Stretch for High-Speed and High-Throughput Imaging—From Single-Cell to Tissue-Wide Scales , 2016, IEEE Journal of Selected Topics in Quantum Electronics.

[24]  Oliver Otto,et al.  Mechanical phenotyping of primary human skeletal stem cells in heterogeneous populations by real-time deformability cytometry. , 2016, Integrative biology : quantitative biosciences from nano to macro.

[25]  B. Jalali,et al.  Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena , 2009, Nature.

[26]  Bahram Jalali,et al.  Performance of serial time-encoded amplified microscopy , 2010, CLEO/QELS: 2010 Laser Science to Photonic Applications.

[27]  Cheng Lei,et al.  Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning , 2017, Scientific Reports.

[28]  Barry R. Masters,et al.  Quantitative Phase Imaging of Cells and Tissues , 2012 .

[29]  Chwee Teck Lim,et al.  Cancer diagnosis: from tumor to liquid biopsy and beyond. , 2018, Lab on a chip.

[30]  Hayden Kwok-Hay So,et al.  Multi‐ATOM: Ultrahigh‐throughput single‐cell quantitative phase imaging with subcellular resolution , 2019, Journal of biophotonics.

[31]  Ho Cheung Shum,et al.  Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry. , 2016, Lab on a chip.

[32]  Hayden Kwok-Hay So,et al.  Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution , 2019, bioRxiv.

[33]  Pinhas Girshovitz,et al.  Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization , 2012, Biomedical optics express.

[34]  K. Goda,et al.  Optofluidic time-stretch quantitative phase microscopy. , 2017, Methods.

[35]  S. S. Gorthi,et al.  Phase imaging flow cytometry using a focus-stack collecting microscope. , 2012, Optics letters.

[36]  Hayden Kwok-Hay So,et al.  Large-Scale Multi-Class Image-Based Cell Classification With Deep Learning , 2019, IEEE Journal of Biomedical and Health Informatics.

[37]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[38]  Kenneth K Y Wong,et al.  High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton. , 2016, Optics express.

[39]  Bahram Jalali,et al.  High-throughput single-microparticle imaging flow analyzer , 2012, Proceedings of the National Academy of Sciences.

[40]  Tom Misteli,et al.  High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease. , 2017, Trends in genetics : TIG.

[41]  Yo Sup Moon,et al.  Quantitative Diagnosis of Malignant Pleural Effusions by Single-Cell Mechanophenotyping , 2013, Science Translational Medicine.

[42]  Hyun-seok Min,et al.  Quantitative Phase Imaging and Artificial Intelligence: A Review , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[43]  A. Saliba,et al.  Single-cell RNA-seq: advances and future challenges , 2014, Nucleic acids research.

[44]  Steven M Kornblau,et al.  Design, development, and validation of a high‐throughput drug‐screening assay for targeting of human leukemia , 2014, Cancer.

[45]  Ata Mahjoubfar,et al.  Deep Learning in Label-free Cell Classification , 2016, Scientific Reports.