Abstract

Recent studies have revealed the importance of outlier cells in complex cellular systems. Quantifying heterogeneity in such systems may lead to a better understanding of organ engineering, microtumor growth, and disease models, as well as more precise drug design. We used the ability of quantitative phase imaging to perform long-term imaging of cell growth to estimate the “influence” of cellular clusters on their neighbors. We validated our approach by analyzing epithelial and fibroblast cultures imaged over the course of several days. Interestingly, we found that there is a significant number of cells characterized by a medium correlation between their growth rate and distance (modulus of the Pearson coefficient between 0.25-.5). Furthermore, we found a small percentage of cells exhibiting strong such correlations, which we label as “influencer” cellular clusters. Our approach might find important applications in studying dynamic phenomena, such as organogenesis and metastasis.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2019 (1)

S. Sridharan Weaver, Y. Li, L. Foucard, H. Majeed, B. Bhaduri, A. J. Levine, K. A. Kilian, and G. Popescu, “Simultaneous cell traction and growth measurements using light,” J. Biophotonics 12(3), e201800182 (2019).
[Crossref]

2018 (5)

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

M. E. Kandel, M. Fanous, C. Best-Popescu, and G. Popescu, “Real-time halo correction in phase contrast imaging,” Biomed. Opt. Express 9(2), 623–635 (2018).
[Crossref]

J. J. Frost, K. J. Pienta, and D. S. Coffey, “Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem,” OncoTargets Ther. 9(14), 11429–11440 (2018).
[Crossref]

R. D. Kamm, R. Bashir, N. Arora, R. D. Dar, M. U. Gillette, L. G. Griffith, M. L. Kemp, K. Kinlaw, M. Levin, and A. C. Martin, “Perspective: The promise of multi-cellular engineered living systems,” APL Bioeng. 2(4), 040901 (2018).
[Crossref]

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12(10), 578–589 (2018).
[Crossref]

2017 (6)

J. W. Brown and J. C. Mills, “Implantable synthetic organoid matrices for intestinal regeneration,” Nat. Cell Biol. 19(11), 1307–1308 (2017).
[Crossref]

N. Gjorevski and M. P. Lutolf, “Synthesis and characterization of well-defined hydrogel matrices and their application to intestinal stem cell and organoid culture,” Nat. Protoc. 12(11), 2263–2274 (2017).
[Crossref]

V. Munnamalai and D. M. Fekete, “Building the human inner ear in an organoid,” Nat. Biotechnol. 35(6), 518–520 (2017).
[Crossref]

N. de Souza, “Stem cells: Organoid variability examined,” Nat. Methods 14(7), 655 (2017).
[Crossref]

M. E. Kandel, S. Sridharan, J. Liang, Z. Luo, K. Han, V. Macias, A. Shah, R. Patel, K. Tangella, A. Kajdacsy-Balla, G. Guzman, and G. Popescu, “Label-free tissue scanner for colorectal cancer screening,” J. Biomed. Opt. 22(6), 066016 (2017).
[Crossref]

G. Quadrato, T. Nguyen, E. Z. Macosko, J. L. Sherwood, S. Min Yang, D. R. Berger, N. Maria, J. Scholvin, M. Goldman, J. P. Kinney, E. S. Boyden, J. W. Lichtman, Z. M. Williams, S. A. McCarroll, and P. Arlotta, “Cell diversity and network dynamics in photosensitive human brain organoids,” Nature 545(7652), 48–53 (2017).
[Crossref]

2016 (1)

N. Cermak, S. Olcum, F. F. Delgado, S. C. Wasserman, K. R. Payer, M. A. Murakami, M. Knudsen, R. J. Kimmerling, M. M. Stevens, Y. Kikuchi, A. Sandikci, M. Ogawa, V. Agache, F. Baleras, D. M. Weinstock, and S. R. Manalis, “High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays,” Nat. Biotechnol. 34(10), 1052–1059 (2016).
[Crossref]

2014 (3)

M. Mir, A. Bergamaschi, B. S. Katzenellenbogen, and G. Popescu, “Highly Sensitive Quantitative Imaging for Monitoring Single Cancer Cell Growth Kinetics and Drug Response,” PLoS One 9(2), e89000 (2014).
[Crossref]

G. Popescu, K. Park, M. Mir, and R. Bashir, “New technologies for measuring single cell mass,” Lab Chip 14(4), 646–652 (2014).
[Crossref]

T. Kim, R. J. Zhou, M. Mir, S. D. Babacan, P. S. Carney, L. L. Goddard, and G. Popescu, “White-light diffraction tomography of unlabeled live cells,” Nat. Photonics 8(3), 256–263 (2014).
[Crossref]

2013 (1)

K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. Jo, H. Park, and Y. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors 13(4), 4170–4191 (2013).
[Crossref]

2012 (1)

R. J. DeBerardinis and C. B. Thompson, “Cellular Metabolism and Disease: What Do Metabolic Outliers Teach Us?” Cell 148(6), 1132–1144 (2012).
[Crossref]

2011 (2)

Z. Wang, L. J. Millet, M. Mir, H. Ding, S. Unarunotai, J. A. Rogers, M. U. Gillette, and G. Popescu, “Spatial light interference microscopy (SLIM),” Opt. Express 19(2), 1016 (2011).
[Crossref]

M. Mir, Z. Wang, Z. Shen, M. Bednarz, R. Bashir, I. Golding, S. G. Prasanth, and G. Popescu, “Optical measurement of cycle-dependent cell growth,” Proc. Natl. Acad. Sci. U. S. A. 108(32), 13124–13129 (2011).
[Crossref]

2010 (4)

Z. Wang, I. S. Chun, X. L. Li, Z. Y. Ong, E. Pop, L. Millet, M. Gillette, and G. Popescu, “Topography and refractometry of nanostructures using spatial light interference microscopy,” Opt. Lett. 35(2), 208–210 (2010).
[Crossref]

K. Park, L. Millet, J. Huan, N. Kim, G. Popescu, N. Aluru, K. J. Hsia, and R. Bashir, “Measurement of Adherent Cell Mass and Growth,” Proc. Natl. Acad. Sci. U. S. A. 107(48), 20691–20696 (2010).
[Crossref]

A. K. Bryan, A. Goranov, A. Amon, and S. R. Manalis, “Measurement of mass, density, and volume during the cell cycle of yeast,” Proc. Natl. Acad. Sci. U. S. A. 107(3), 999–1004 (2010).
[Crossref]

M. Godin, F. F. Delgado, S. Son, W. H. Grover, A. K. Bryan, A. Tzur, P. Jorgensen, K. Payer, A. D. Grossman, M. W. Kirschner, and S. R. Manalis, “Using buoyant mass to measure the growth of single cells,” Nat. Methods 7(5), 387–390 (2010).
[Crossref]

2009 (2)

A. Tzur, R. Kafri, V. S. LeBleu, G. Lahav, and M. W. Kirschner, “Cell growth and size homeostasis in proliferating animal cells,” Science 325(5937), 167–171 (2009).
[Crossref]

J. P. Thiery, H. Acloque, R. Y. Huang, and M. A. Nieto, “Epithelial-mesenchymal transitions in development and disease,” Cell 139(5), 871–890 (2009).
[Crossref]

2008 (7)

G. Reshes, S. Vanounou, I. Fishov, and M. Feingold, “Cell shape dynamics in Escherichia coli,” Biophys. J. 94(1), 251–264 (2008).
[Crossref]

G. Popescu, Y. Park, N. Lue, C. Best-Popescu, L. Deflores, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Optical imaging of cell mass and growth dynamics,” Am. J. Physiol. Cell Physiol. 295(2), C538–C544 (2008).
[Crossref]

P. Goss, A. L. Allan, D. I. Rodenhiser, P. J. Foster, and A. F. Chambers, “New clinical and experimental approaches for studying tumor dormancy: does tumor dormancy offer a therapeutic target?” APMIS 116(7-8), 552–568 (2008).
[Crossref]

K. W. Hunter, N. P. Crawford, and J. Alsarraj, “Mechanisms of metastasis,” Breast Cancer Res. 10(S1), S2 (2008).
[Crossref]

R. A. Weinberg, “The many faces of tumor dormancy,” APMIS 116(7-8), 548–551 (2008).
[Crossref]

H. Wikman, R. Vessella, and K. Pantel, “Cancer micrometastasis and tumour dormancy,” APMIS 116(7-8), 754–770 (2008).
[Crossref]

A. K. Croker and A. L. Allan, “Cancer stem cells: implications for the progression and treatment of metastatic disease,” J. Cell. Mol. Med. 12(2), 374–390 (2008).
[Crossref]

2007 (1)

M. Brackstone, J. L. Townson, and A. F. Chambers, “Tumour dormancy in breast cancer: an update,” Breast Cancer Res. 9(3), 208 (2007).
[Crossref]

2004 (2)

B. A. Teicher, D. L. Selwood, and P. A. Andrews, “Anticancer drug development: preclinical screening, clinical trials and approval (vol 91, pg 1000, 2004),” Br. J. Cancer 91(11), 1977 (2004).
[Crossref]

K. Pantel and R. H. Brakenhoff, “Dissecting the metastatic cascade,” Nat. Rev. Cancer 4(6), 448–456 (2004).
[Crossref]

2003 (1)

J. B. Weitzman, “Growing without a size checkpoint,” J. Biol. 2(1), 3 (2003).
[Crossref]

2002 (3)

A. F. Chambers, A. C. Groom, and I. C. MacDonald, “Dissemination and growth of cancer cells in metastatic sites,” Nat. Rev. Cancer 2(8), 563–572 (2002).
[Crossref]

I. C. MacDonald, A. C. Groom, and A. F. Chambers, “Cancer spread and micrometastasis development: quantitative approaches for in vivo models,” BioEssays 24(10), 885–893 (2002).
[Crossref]

G. N. Naumov, I. C. MacDonald, P. M. Weinmeister, N. Kerkvliet, K. V. Nadkarni, S. M. Wilson, V. L. Morris, A. C. Groom, and A. F. Chambers, “Persistence of solitary mammary carcinoma cells in a secondary site: a possible contributor to dormancy,” Cancer Res 62, 2162–2168 (2002).

2000 (1)

M. D. Cameron, E. E. Schmidt, N. Kerkvliet, K. V. Nadkarni, V. L. Morris, A. C. Groom, A. F. Chambers, and I. C. MacDonald, “Temporal progression of metastasis in lung: cell survival, dormancy, and location dependence of metastatic inefficiency,” Cancer Res 60, 2541–2546 (2000).

1999 (1)

P. Hahnfeldt, D. Panigrahy, J. Folkman, and L. Hlatky, “Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy,” Cancer Res 59, 4770–4775 (1999).

1996 (1)

B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Process 5(8), 1266–1271 (1996).
[Crossref]

1995 (1)

G. A. Dunn and D. Zicha, “Dynamics Of Fibroblast Spreading,” J. Cell Sci. 108, 1239–1249 (1995).

1952 (1)

H. G. Davies and M. H. Wilkins, “Interference microscopy and mass determination,” Nature 169(4300), 541 (1952).
[Crossref]

1951 (1)

R. Barer, ““Phase-contrast” methods and birefringence,” Nature 167(4251), 642–643 (1951).
[Crossref]

Acloque, H.

J. P. Thiery, H. Acloque, R. Y. Huang, and M. A. Nieto, “Epithelial-mesenchymal transitions in development and disease,” Cell 139(5), 871–890 (2009).
[Crossref]

Agache, V.

N. Cermak, S. Olcum, F. F. Delgado, S. C. Wasserman, K. R. Payer, M. A. Murakami, M. Knudsen, R. J. Kimmerling, M. M. Stevens, Y. Kikuchi, A. Sandikci, M. Ogawa, V. Agache, F. Baleras, D. M. Weinstock, and S. R. Manalis, “High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays,” Nat. Biotechnol. 34(10), 1052–1059 (2016).
[Crossref]

Allan, A. L.

P. Goss, A. L. Allan, D. I. Rodenhiser, P. J. Foster, and A. F. Chambers, “New clinical and experimental approaches for studying tumor dormancy: does tumor dormancy offer a therapeutic target?” APMIS 116(7-8), 552–568 (2008).
[Crossref]

A. K. Croker and A. L. Allan, “Cancer stem cells: implications for the progression and treatment of metastatic disease,” J. Cell. Mol. Med. 12(2), 374–390 (2008).
[Crossref]

Alsarraj, J.

K. W. Hunter, N. P. Crawford, and J. Alsarraj, “Mechanisms of metastasis,” Breast Cancer Res. 10(S1), S2 (2008).
[Crossref]

Aluru, N.

K. Park, L. Millet, J. Huan, N. Kim, G. Popescu, N. Aluru, K. J. Hsia, and R. Bashir, “Measurement of Adherent Cell Mass and Growth,” Proc. Natl. Acad. Sci. U. S. A. 107(48), 20691–20696 (2010).
[Crossref]

Amon, A.

A. K. Bryan, A. Goranov, A. Amon, and S. R. Manalis, “Measurement of mass, density, and volume during the cell cycle of yeast,” Proc. Natl. Acad. Sci. U. S. A. 107(3), 999–1004 (2010).
[Crossref]

Andrews, P. A.

B. A. Teicher, D. L. Selwood, and P. A. Andrews, “Anticancer drug development: preclinical screening, clinical trials and approval (vol 91, pg 1000, 2004),” Br. J. Cancer 91(11), 1977 (2004).
[Crossref]

Arlotta, P.

G. Quadrato, T. Nguyen, E. Z. Macosko, J. L. Sherwood, S. Min Yang, D. R. Berger, N. Maria, J. Scholvin, M. Goldman, J. P. Kinney, E. S. Boyden, J. W. Lichtman, Z. M. Williams, S. A. McCarroll, and P. Arlotta, “Cell diversity and network dynamics in photosensitive human brain organoids,” Nature 545(7652), 48–53 (2017).
[Crossref]

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Supplementary Material (3)

NameDescription
» Visualization 1       HeLa cell growth
» Visualization 2       HeLa cell growth rate
» Visualization 3       HeLa cell "influence" map

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Figures (7)

Fig. 1.
Fig. 1. Time-lapse quantitative phase imaging identifies influential cells by analyzing spatial variations in growth rates. (a) QPI images were acquired using SLIM (CellVista SLIM Pro, Phi Optics, Inc.). The SLIM module is attached to a commercial phase contrast microscope and introduces controlled phase-shifts with an SLM located at the position conjugate to the objective’s back focal plane. Four such images are combined to form a phase map. (b) We acquire time-lapse QPI data that is then segmented into clusters. To estimate the influence of each cluster among its neighbors, we assign a growth rate to each cluster and analyze the correlation between growth rate and distance. Strong correlations hint that the cell may influence the growth of its neighbors. (c) “Influence” is understood as the ability of a cluster to promote or suppress the growth of its neighbors, so that cells that promote growth are likely to be surrounded by neighbors with higher growth rates.
Fig. 2.
Fig. 2. Cell growth resembles a genealogical tree when time is taken as the 3rd dimension, with two daughter cells after the first division (red), and four daughter cells (purple) after the second division.
Fig. 3.
Fig. 3. Quantitative phase image of 3T3 fibroblasts. The phase map of the whole petri dish was reconstructed from mosaic tiles assembled by an in-house software. Data was acquired with a 5X/0.15NA objective.
Fig. 4.
Fig. 4. Dry-mass and segmentation. (a) Cellular dry mass is proportional to the phase of halo-corrected slim images. A representative position of the sample at increasing zoom levels. (b) Segmentation is performed by a series of morphological operations as discussed in Results.
Fig. 5.
Fig. 5. Estimation of dry mass doubling time from time-lapse SLIM images. (a) Phase maps of a single cluster at the times indicated. (b) Relative dry mass change vs. time for a given cluster normalized by the first time point (Calculated Dry Mass). To account for phase-wrapping in dividing cells, we compensate the dry mass so that the total never decreases over time. The final doubling time coefficient is estimated form this compensated curve.
Fig. 6.
Fig. 6. Timelapse cell growth with projected growth rates. (a) Calculated growth coefficient, b, is projected onto the black and white segmentation to produce a time-lapse sequence showing the spatial distribution of cellular growth. (b) Histogram of growth coefficients.
Fig. 7.
Fig. 7. Projection of cellular influence (see also Visualization 1, Visualization 2, and Visualization 3). (a) Correlation coefficients for the 3T3 culture are projected onto the segmentation map at the end of the experiment. This gives a spatial distribution of correlation coefficients. (b) Histogram of the correlation coefficient for all 3T3 clusters. (c) Correlation coefficients for the HeLa culture are projected onto the segmentation map at the end of the experiment. (d) Histogram of the correlation coefficient for all HeLa clusters. (e) When comparing the population statistics, as a percentage of the population described by the influence value, it is apparent that 3T3 cells composed of a population with weakly coupled (low p) clusters, while HeLa cells have a larger tail distribution indicating more, highly coupled clusters.

Equations (3)

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m ( x , y ) = λ 2 π γ ϕ ( x , y )
ρ = cov ( r , b 1 ) σ r σ b 1
m ( t ) / m 0 = 2 b t

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