Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level

被引:27
作者
Heaster, Tiffany M. [1 ]
Walsh, Alex J. [2 ,3 ]
Zhao, Yue [4 ]
Hiebert, Scott W. [4 ,5 ]
Skala, Melissa C. [1 ,6 ]
机构
[1] Univ Wisconsin, Dept Biomed Engn, Madison, WI 53715 USA
[2] CNR, JBSA Ft Sam Houston, Houston, TX 78234 USA
[3] JBSA Ft Sam Houston, Air Force Res Lab, Human Performance Wing 711, Human Effectiveness Directorate Bioeffects Div Op, Houston, TX 78234 USA
[4] Vanderbilt Univ Sch Med, Dept Biochem, Nashville, TN 37232 USA
[5] Vanderbilt Ingram Canc Ctr, Nashville, TN 37232 USA
[6] Morgridge Inst Res, Madison, WI 53715 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
Quiescence; tumor dormancy; fluorescence lifetime; metabolic imaging; cell-cycle status; single-cell analysis; FLUORESCENCE LIFETIME; INHIBITION; METABOLISM; HETEROGENEITY; APOPTOSIS; DORMANCY;
D O I
10.1002/jbio.201600276
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell-cycle status of tumor cells. Heterogeneity in tumor cell-cycle status (e.g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell-cycle status is closely linked to cellular metabolism. Thus, this study applies cell-level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two-photon microscopy and time-correlated single photon counting are used to measure optical redox ratio (NAD(P) H fluorescence intensity divided by FAD intensity), NAD(P) H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P) H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares - discriminant analysis (PLS-DA) are used to exploit all measurements together. Leave-one-out cross validation of the model yielded high classification accuracies (92.4 and 90.1% for two and three populations, respectively). OMI and PLS-DA also identifies each sub-population within heterogeneous samples. These results establish single-cell analysis with OMI and PLSDA as a label-free method to distinguish cell-cycle status within intact samples. This approach could be used to incorporate cell-level tumor heterogeneity in cancer drug development. [GRAPHICS] .
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页数:14
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