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Estimation of cancer cell migration in biomimetic random/oriented collagen fiber microenvironments |
| Jingru Yao(姚静如)1,†, Guoqiang Li(李国强)2,†,‡, Xiyao Yao(姚喜耀)1, Lianjie Zhou(周连杰)1, Zhikai Ye(叶志凯)3, Yanping Liu(刘艳平)4, Dongtian Zheng(郑栋天)1, Ting Tang(唐婷)2, Kena Song(宋克纳)5, Guo Chen(陈果)1, and Liyu Liu(刘雳宇)1,§ |
1 Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China;
2 Chongqing Key Laboratory for Resource Utilization of Heavy Metal Wastewater, Aquatic Ecosystems in the Three Gorges Reservoir Region of Chongqing Observation and Research Station, Chongqing University of Arts and Sciences, Yongchuan 402160, China;
3 Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China;
4 Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
5 College of Medical Technology and Engineering, Henan University of Science and Technology, Henan 471023, China} |
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Abstract Increasing data indicate that cancer cell migration is regulated by extracellular matrixes and their surrounding biochemical microenvironment, playing a crucial role in pathological processes such as tumor invasion and metastasis. However, conventional two-dimensional cell culture and animal models have limitations in studying the influence of tumor microenvironment on cancer cell migration. Fortunately, the further development of microfluidic technology has provided solutions for the study of such questions. We utilize microfluidic chip to build a random collagen fiber microenvironment (RFM) model and an oriented collagen fiber microenvironment (OFM) model that resemble early stage and late stage breast cancer microenvironments, respectively. By combining cell culture, biochemical concentration gradient construction, and microscopic imaging techniques, we investigate the impact of different collagen fiber biochemical microenvironments on the migration of breast cancer MDA-MB-231-RFP cells. The results show that MDA-MB-231-RFP cells migrate further in the OFM model compared to the RFM model, with significant differences observed. Furthermore, we establish concentration gradients of the anticancer drug paclitaxel in both the RFM and OFM models and find that paclitaxel significantly inhibits the migration of MDA-MB-231-RFP cells in the RFM model, with stronger inhibition on the high concentration side compared to the low concentration side. However, the inhibitory effect of paclitaxel on the migration of MDA-MB-231-RFP cells in the OFM model is weak. These findings suggest that the oriented collagen fiber microenvironment resembling the late-stage tumor microenvironment is more favorable for cancer cell migration and that the effectiveness of anticancer drugs is diminished. The RFM and OFM models constructed in this study not only provide a platform for studying the mechanism of cancer development, but also serve as a tool for the initial measurement of drug screening.
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Received: 02 February 2024
Revised: 02 March 2024
Accepted manuscript online: 13 March 2024
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PACS:
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87.85.dh
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(Cells on a chip)
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87.80.-y
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(Biophysical techniques (research methods))
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87.18.Gh
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(Cell-cell communication; collective behavior of motile cells)
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87.50.cf
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(Biophysical mechanisms of interaction)
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| Fund: We gratefully acknowledge support from the National Natural Science Foundation of China (Grant Nos. 11974066, 12174041, 12104134, T2350007, and 12347178) and the Fundamental and Advanced Research Program of Chongqing (Grant No. cstc2019jcyj-msxmX0477), and the Natural Science Foundation of Chongqing (Grant No. CSTB2022NSCQMSX1260), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202301333), the Scientific Research Fund of Chongqing University of Arts and Sciences (Grant Nos. R2023HH03 and P2022HH05), College Students’ Innovation and Entrepreneurship Training Program of Chongqing Municipal (Grant No. S202310642002). |
Corresponding Authors:
Guoqiang Li, Liyu Liu
E-mail: guoqiangli1989@126.com;lyliu@cqu.edu.cn
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Cite this article:
Jingru Yao(姚静如), Guoqiang Li(李国强), Xiyao Yao(姚喜耀), Lianjie Zhou(周连杰), Zhikai Ye(叶志凯), Yanping Liu(刘艳平), Dongtian Zheng(郑栋天), Ting Tang(唐婷), Kena Song(宋克纳), Guo Chen(陈果), and Liyu Liu(刘雳宇) Estimation of cancer cell migration in biomimetic random/oriented collagen fiber microenvironments 2024 Chin. Phys. B 33 058706
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[1] Spill F, Reynolds D S, Kamm R D and Zaman M H 2016 Curr. Opin. Biotech. 40 41
[2] Klein-Goldberg A, Maman S and Witz I P 2014 Cancer Lett. 352 54
[3] Anderson N M and Simon M C 2020 Curr. Biol. 30 R905
[4] Gordon-Weeks A and Yuzhalin A E 2020 Cancers 12 3331
[5] Patwardhan S, Mahadik P, Shetty O and Sen S 2021 Biomaterials 279 121185
[6] Vasudevan J, Jiang K, Fernandez J G and PhD C T L 2023 Acta Biomater. 163 351
[7] Sun X, Wu B, Chiang H C, Deng H, Zhang X, Xiong W, Liu J, Rozeboom A M, Harris B T, Blommaert E, Gomez A, Garcia R E, Zhou Y, Mitra P, Prevost M, Zhang D, Banik D, Isaacs C, Berry D, Lai C, Chaldekas K, Latham P S, Brantner C A, Popratiloff A, Jin V X, Zhang N, Hu Y, Pujana M A, Curiel T J, An Z and Li R 2021 Nature 599 673
[8] Riching K M, Cox B L, Salick M R, Pehlke C, Riching A S, Ponik S M, Bass B R, Crone W C, Jiang Y, Weaver A M, Eliceiri K W and Keely P J 2014 Biophys. J 107 2546
[9] Tomko L A, Hill R C, Barrett A, Szulczewski J M, Conklin M W, Eliceiri K W, Keely P J, Hansen K C and Ponik S M 2018 Sci. Rep. 8 12941
[10] Conklin M W, Eickhoff J C, Riching K M, Pehlke C A, Eliceiri K W, Provenzano P P, Friedl A and Keely P J 2011 Am. J. Pathol. 178 1221
[11] Locker J and Segall J E 2011 Am. J Pathol. 178 966
[12] Li G, Liu Y, Yao J, Song K, Wang G, Zhou L, Chen G and Liu L 2022 Chin. Phys. B 31 028703
[13] Uzel S G M, Amadi O C, Pearl T M, Lee R T, So P T C and Kamm R D 2016 Small 12 612
[14] Shimizu A, Goh W H, Itai S, Karyappa R, Hashimoto M and Onoe H 2020 Biomicrofluidics 14 044106
[15] Morrow C M, Mukherjee A, Traore M A, Leaman E J, Kim A, Smith E M, Nain A S and Behkam B 2019 Lab Chip 19 3641
[16] Tong X, Jiang J, Zhu D and Yang F 2016 ACS Biomater. Sci. Eng. 2 845
[17] Shen S, Zhang F, Gao M and Niu Y 2020 Micromachines 11 493
[18] Tian C, Tu Q, Liu W and Wang J 2019 Trend. Anal. Chem. 117 146
[19] Mao M, He J, Lu Y, Li X, Li T, Zhou W and Li D 2018 Biofabrication 10 025008
[20] Zhang X, Karim M, Hasan M M, Hooper J, Wahab R, Roy S and AlHilal T A 2022 Cancers 14 648
[21] Liu Y, Liu R, Liu H, Lyu T, Chen K, Jin K and Tian Y 2023 Analyst 148 5822
[22] Nagaraju S, Truong D, Mouneimne G and Nikkhah M 2018 Adv. Health. Mater. 7 1701257
[23] Jeon J S, Zervantonakis I K, Chung S, Kamm R D and Charest J L 2013 PLoS One 8 e56910
[24] Truong D D, Kratz A, Park J G, Barrientos E S, Saini H, Nguyen T, Pockaj B, Mouneimne G, LaBaer J and Nikkhah M 2019 Cancer Res. 79 3139
[25] Graf F, Horn P, Ho A D, Boutros M and Maercker C 2021 FASEB J. 35 e21692
[26] Gurrala R, Byrne C E, Brown L M, Tiongco R F P, Matossian M D, Savoie J J, Collins-Burow B M, Burow M E, Martin E C and Lau F H 2021 Front. Bioeng. Biotech. 9 618448
[27] Liu C, Chiang B, Mejia D L, Luker K E, Luker G D and Lee A 2019 Acta Biomater. 83 221
[28] Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein L J, Davidson N E, Glasgow A, Harbhajanka A, Gilmore H and Madabhushi A 2021 npj Breast Cancer 7 104
[29] Yao J, Li G, Jiao Y, Zheng Y, Liu Y, Wang G, Zhou L, Zhang H, Zhang X, Shuai J, Fan Q, Ye F, Lou S, Chen G, Song K, Liao Y and Liu L 2021 Lab Chip 21 3004
[30] Han W, Chen S, Yuan W, Fan Q, Tian J, Wang X, Chen L, Zhang X, Wei W, Liu R, Qu J, Jiao Y, Austin R H and Liu L 2016 Proc. Natl. Acad. Sci. USA 113 11208
[31] Hai M, Liu Y, Xiong L, Li G, Wang G, Zhang H, Shuai J, Chen G and Liu L 2020 Chin. Phys. B 29 098702
[32] Aizel K, Clark A G, Simon A, Geraldo S, Funfak A, Vargas P, Bibette J, Vignjevic D M and Bremond N 2017 Lab Chip 17 3851
[33] Liu H, Wang Y, Cui K, Guo Y, Zhang X and Qin J 2019 Adv. Mater. 31 1902042
[34] Patt D, Gauthier M and Giordano S 2006 Womens Health 2 11
[35] Samaan T M A, Samec M, Liskova A, Kubatka P and Büsselberg D 2019 Biomolecules 9 789
[36] Murray S, Briasoulis E, Linardou H, Bafaloukos D and Papadimitriou C 2012 Cancer Treat. Rev. 38 890
[37] Krtolica A, Solorzano C O, Lockett S and Campisi J 2002 Cytometry 49 73
[38] Caffrey T A M, Agarwal L A and Weksler B B 1988 In Vitro. Cell. Dev. 24 247 |
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