Multimodal FACED imaging for large-scale single-cell morphological profiling

被引:9
|
作者
Yip, Gwinky G. K. [1 ]
Lo, Michelle C. K. [1 ]
Yan, Wenwei [2 ,3 ]
Lee, Kelvin C. M. [1 ]
Lai, Queenie T. K. [1 ]
Wong, Kenneth K. Y. [1 ,4 ]
Tsia, Kevin K. [1 ,4 ]
机构
[1] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam Rd, Hong Kong, Peoples R China
[2] Columbia Univ, Dept Biomed Engn, Lab Funct Opt Imaging, New York, NY 10027 USA
[3] Columbia Univ, Mortimer B Zuckerman Mind Brain Behav Inst, New York, NY 10027 USA
[4] Hong Kong Sci Pk, Adv Biomed Instrumentat Ctr, Shatin, Hong Kong, Peoples R China
关键词
FLUORESCENCE; TOMOGRAPHY; CYTOMETRY;
D O I
10.1063/5.0054714
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Free-space angular-chirp-enhanced delay (FACED) is an ultrafast laser-scanning technique that allows for high imaging speed at the scale orders of magnitude greater than the current technologies. However, this speed advantage has only been restricted to bright-field and fluorescence imaging-limiting the variety of image contents and hindering its applicability in image-based bioassay, which increasingly demands rich phenotypic readout at a large scale. Here, we present a new high-speed quantitative phase imaging (QPI) based on time-interleaved phase-gradient FACED image detection. We further integrate this system with a microfluidic flow cytometer platform that enables synchronized and co-registered single-cell QPI and fluorescence imaging at an imaging throughput of 77 000 cells/s with sub-cellular resolution. Combined with deep learning, this platform empowers comprehensive image-based profiling of single-cell biophysical phenotypes that can offer not only sufficient label-free power for cell-type classification but also cell-cycle phase tracking with high accuracy comparable to the gold-standard fluorescence method. This platform further enables correlative, compartment-specific single-cell analysis of the spatially resolved biophysical profiles at the throughput inaccessible with existing QPI methods. The high imaging throughput and content given by this multimodal FACED imaging system could open new opportunities in image-based single-cell analysis, especially systematic analysis that correlates the biophysical and biochemical information of cells, and provide new mechanistic insights into biophysical heterogeneities in many biological processes.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Large-scale neurophysiology and single-cell profiling in human neuroscience
    Lee, Anthony T.
    Chang, Edward F.
    Paredes, Mercedes F.
    Nowakowski, Tomasz J.
    NATURE, 2024, 630 (8017) : 587 - 595
  • [2] Large-scale single-cell trapping and imaging using microwell arrays
    Rettig, JR
    Folch, A
    ANALYTICAL CHEMISTRY, 2005, 77 (17) : 5628 - 5634
  • [3] Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution
    Cheng, Junyun
    Liao, Jie
    Shao, Xin
    Lu, Xiaoyan
    Fan, Xiaohui
    ADVANCED SCIENCE, 2021, 8 (17)
  • [4] Immunology Driven by Large-Scale Single-Cell Sequencing
    Gomes, Tomas
    Teichmann, Sarah A.
    Talavera-Lopez, Carlos
    TRENDS IN IMMUNOLOGY, 2019, 40 (11) : 1011 - 1021
  • [5] Large-scale foundation model on single-cell transcriptomics
    Hao, Minsheng
    Gong, Jing
    Zeng, Xin
    Liu, Chiming
    Guo, Yucheng
    Cheng, Xingyi
    Wang, Taifeng
    Ma, Jianzhu
    Zhang, Xuegong
    Song, Le
    NATURE METHODS, 2024, 21 (08) : 1481 - 1491
  • [6] Large-scale arrays of picolitre chambers for single-cell analysis of large cell populations
    Lee, Won Chul
    Rigante, Sara
    Pisano, Albert P.
    Kuypers, Frans A.
    LAB ON A CHIP, 2010, 10 (21) : 2952 - 2958
  • [7] Highly Multiplexed Single-Cell Protein Profiling with Large-Scale Convertible DNA-Antibody Barcoded Arrays
    Zhao, Peng
    Bhowmick, Sirsendu
    Yu, Jianchao
    Wang, Jun
    ADVANCED SCIENCE, 2018, 5 (09)
  • [8] SCANPY: large-scale single-cell gene expression data analysis
    F. Alexander Wolf
    Philipp Angerer
    Fabian J. Theis
    Genome Biology, 19
  • [9] Generative pretraining from large-scale transcriptomes for single-cell deciphering
    Shen, Hongru
    Liu, Jilei
    Hu, Jiani
    Shen, Xilin
    Zhang, Chao
    Wu, Dan
    Feng, Mengyao
    Yang, Meng
    Li, Yang
    Yang, Yichen
    Wang, Wei
    Zhang, Qiang
    Yang, Jilong
    Chen, Kexin
    Li, Xiangchun
    ISCIENCE, 2023, 26 (05)
  • [10] SCANPY: large-scale single-cell gene expression data analysis
    Wolf, F. Alexander
    Angerer, Philipp
    Theis, Fabian J.
    GENOME BIOLOGY, 2018, 19