Abstract

The genetic and phenotypic heterogeneity of cancers can contribute to tumor aggressiveness, invasion, and resistance to therapy. Fluorescence imaging occupies a unique niche to investigate tumor heterogeneity due to its high resolution and molecular specificity. Here, heterogeneous populations are identified and quantified by combined optical metabolic imaging and subpopulation analysis (OMI-SPA). OMI probes the fluorescence intensities and lifetimes of metabolic enzymes in cells to provide images of cellular metabolism, and SPA models cell populations as mixed Gaussian distributions to identify cell subpopulations. In this study, OMI-SPA is characterized by simulation experiments and validated with cell experiments. To generate heterogeneous populations, two breast cancer cell lines, SKBr3 and MDA-MB-231, were co-cultured at varying proportions. OMI-SPA correctly identifies two populations with minimal mean and proportion error using the optical redox ratio (fluorescence intensity of NAD(P)H divided by the intensity of FAD), mean NAD(P)H fluorescence lifetime, and OMI index. Simulation experiments characterized the relationships between sample size, data standard deviation, and subpopulation mean separation distance required for OMI-SPA to identify subpopulations.

© 2015 Optical Society of America

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References

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    [Crossref] [PubMed]
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2014 (4)

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

K. Polyak, “Tumor Heterogeneity Confounds and Illuminates: A case for Darwinian tumor evolution,” Nat. Med. 20(4), 344–346 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

2013 (3)

R. Fisher, L. Pusztai, and C. Swanton, “Cancer heterogeneity: implications for targeted therapeutics,” Br. J. Cancer 108(3), 479–485 (2013).
[Crossref] [PubMed]

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

2012 (3)

I. Georgakoudi and K. P. Quinn, “Optical imaging using endogenous contrast to assess metabolic state,” Annu. Rev. Biomed. Eng. 14(1), 351–367 (2012).
[Crossref] [PubMed]

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

A. Walsh, R. S. Cook, B. Rexer, C. L. Arteaga, and M. C. Skala, “Optical imaging of metabolism in HER2 overexpressing breast cancer cells,” Biomed. Opt. Express 3(1), 75–85 (2012).
[Crossref] [PubMed]

2008 (1)

J. E. Visvader and G. J. Lindeman, “Cancer stem cells in solid tumours: accumulating evidence and unresolved questions,” Nat. Rev. Cancer 8(10), 755–768 (2008).
[Crossref] [PubMed]

2007 (2)

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

2002 (1)

W. Pan, J. Lin, and C. T. Le, “Model-based cluster analysis of microarray gene-expression data,” Genome Biol. 3(2), H0009 (2002).
[Crossref] [PubMed]

1992 (1)

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

1989 (1)

F. Tanaka, N. Tamai, and I. Yamazaki, “Picosecond-resolved fluorescence spectra of D-amino-acid oxidase. A new fluorescent species of the coenzyme,” Biochemistry 28(10), 4259–4262 (1989).
[Crossref] [PubMed]

1979 (1)

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

1974 (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automatic Control 19(6), 716–723 (1974).
[Crossref]

Akaike, H.

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automatic Control 19(6), 716–723 (1974).
[Crossref]

Almendro, V.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Ametller, E.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Arteaga, C. L.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

A. Walsh, R. S. Cook, B. Rexer, C. L. Arteaga, and M. C. Skala, “Optical imaging of metabolism in HER2 overexpressing breast cancer cells,” Biomed. Opt. Express 3(1), 75–85 (2012).
[Crossref] [PubMed]

Aurisicchio, L.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

Bird, D. K.

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

Borresen-Dale, A. L.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Boulineau, S.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Chance, B.

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

Cheng, Y. K.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Cheung, K. J.

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

Ciliberto, G.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

Cook, R. S.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

A. Walsh, R. S. Cook, B. Rexer, C. L. Arteaga, and M. C. Skala, “Optical imaging of metabolism in HER2 overexpressing breast cancer cells,” Biomed. Opt. Express 3(1), 75–85 (2012).
[Crossref] [PubMed]

Dowsett, M.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Duvall, C. L.

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

Eickhoff, J.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

Eliceiri, K. W.

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

Ewald, A. J.

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

Fisher, R.

R. Fisher, L. Pusztai, and C. Swanton, “Cancer heterogeneity: implications for targeted therapeutics,” Br. J. Cancer 108(3), 479–485 (2013).
[Crossref] [PubMed]

Gabrielson, E.

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

Gascon, P.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Gendron-Fitzpatrick, A.

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

Georgakoudi, I.

I. Georgakoudi and K. P. Quinn, “Optical imaging using endogenous contrast to assess metabolic state,” Annu. Rev. Biomed. Eng. 14(1), 351–367 (2012).
[Crossref] [PubMed]

Gönen, M.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Gonzalez-Farre, X.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Helland, A.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Hicks, D. J.

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

Itshak, F.

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

Itzkovitz, S.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Johnson, M. L.

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

Jones, R. L.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Keely, P. J.

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

Kiviet, D. J.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Lafontant, A.

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

Lakowicz, J. R.

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

Le, C. T.

W. Pan, J. Lin, and C. T. Le, “Model-based cluster analysis of microarray gene-expression data,” Genome Biol. 3(2), H0009 (2002).
[Crossref] [PubMed]

Lin, J.

W. Pan, J. Lin, and C. T. Le, “Model-based cluster analysis of microarray gene-expression data,” Genome Biol. 3(2), H0009 (2002).
[Crossref] [PubMed]

Lindeman, G. J.

J. E. Visvader and G. J. Lindeman, “Cancer stem cells in solid tumours: accumulating evidence and unresolved questions,” Nat. Rev. Cancer 8(10), 755–768 (2008).
[Crossref] [PubMed]

Manning, H. C.

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

Marusyk, A.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Maruyama, R.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Michor, F.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Muñoz, M.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Nakase, Y.

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

Nghe, P.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Nowaczyk, K.

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

Oshino, R.

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

Pan, W.

W. Pan, J. Lin, and C. T. Le, “Model-based cluster analysis of microarray gene-expression data,” Genome Biol. 3(2), H0009 (2002).
[Crossref] [PubMed]

Polyak, K.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

K. Polyak, “Tumor Heterogeneity Confounds and Illuminates: A case for Darwinian tumor evolution,” Nat. Med. 20(4), 344–346 (2014).
[Crossref] [PubMed]

Poole, K. M.

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

Pusztai, L.

R. Fisher, L. Pusztai, and C. Swanton, “Cancer heterogeneity: implications for targeted therapeutics,” Br. J. Cancer 108(3), 479–485 (2013).
[Crossref] [PubMed]

Quinn, K. P.

I. Georgakoudi and K. P. Quinn, “Optical imaging using endogenous contrast to assess metabolic state,” Annu. Rev. Biomed. Eng. 14(1), 351–367 (2012).
[Crossref] [PubMed]

Ramanujam, N.

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

Randles, A.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Reis-Filho, J.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Rexer, B.

Riching, K. M.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

Russnes, H. G.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Rye, I. H.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Sanders, M. E.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

Schoener, B.

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

Skala, M. C.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

A. Walsh, R. S. Cook, B. Rexer, C. L. Arteaga, and M. C. Skala, “Optical imaging of metabolism in HER2 overexpressing breast cancer cells,” Biomed. Opt. Express 3(1), 75–85 (2012).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

Sunderlikova, V.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Swanton, C.

R. Fisher, L. Pusztai, and C. Swanton, “Cancer heterogeneity: implications for targeted therapeutics,” Br. J. Cancer 108(3), 479–485 (2013).
[Crossref] [PubMed]

Szmacinski, H.

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

Tamai, N.

F. Tanaka, N. Tamai, and I. Yamazaki, “Picosecond-resolved fluorescence spectra of D-amino-acid oxidase. A new fluorescent species of the coenzyme,” Biochemistry 28(10), 4259–4262 (1989).
[Crossref] [PubMed]

Tanaka, F.

F. Tanaka, N. Tamai, and I. Yamazaki, “Picosecond-resolved fluorescence spectra of D-amino-acid oxidase. A new fluorescent species of the coenzyme,” Biochemistry 28(10), 4259–4262 (1989).
[Crossref] [PubMed]

Tans, S. J.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

van Oudenaarden, A.

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Visvader, J. E.

J. E. Visvader and G. J. Lindeman, “Cancer stem cells in solid tumours: accumulating evidence and unresolved questions,” Nat. Rev. Cancer 8(10), 755–768 (2008).
[Crossref] [PubMed]

Walker, N.

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Walsh, A.

Walsh, A. J.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

Werb, Z.

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

White, J. G.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

Yamazaki, I.

F. Tanaka, N. Tamai, and I. Yamazaki, “Picosecond-resolved fluorescence spectra of D-amino-acid oxidase. A new fluorescent species of the coenzyme,” Biochemistry 28(10), 4259–4262 (1989).
[Crossref] [PubMed]

Annu. Rev. Biomed. Eng. (1)

I. Georgakoudi and K. P. Quinn, “Optical imaging using endogenous contrast to assess metabolic state,” Annu. Rev. Biomed. Eng. 14(1), 351–367 (2012).
[Crossref] [PubMed]

Biochemistry (1)

F. Tanaka, N. Tamai, and I. Yamazaki, “Picosecond-resolved fluorescence spectra of D-amino-acid oxidase. A new fluorescent species of the coenzyme,” Biochemistry 28(10), 4259–4262 (1989).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Br. J. Cancer (1)

R. Fisher, L. Pusztai, and C. Swanton, “Cancer heterogeneity: implications for targeted therapeutics,” Br. J. Cancer 108(3), 479–485 (2013).
[Crossref] [PubMed]

Cancer Res. (2)

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref] [PubMed]

A. J. Walsh, R. S. Cook, H. C. Manning, D. J. Hicks, A. Lafontant, C. L. Arteaga, and M. C. Skala, “Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer,” Cancer Res. 73(20), 6164–6174 (2013).
[Crossref] [PubMed]

Cell (1)

K. J. Cheung, E. Gabrielson, Z. Werb, and A. J. Ewald, “Collective invasion in breast cancer requires a conserved basal epithelial program,” Cell 155(7), 1639–1651 (2013).
[Crossref] [PubMed]

Cell Rep (1)

V. Almendro, Y. K. Cheng, A. Randles, S. Itzkovitz, A. Marusyk, E. Ametller, X. Gonzalez-Farre, M. Muñoz, H. G. Russnes, A. Helland, I. H. Rye, A. L. Borresen-Dale, R. Maruyama, A. van Oudenaarden, M. Dowsett, R. L. Jones, J. Reis-Filho, P. Gascon, M. Gönen, F. Michor, and K. Polyak, “Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity,” Cell Rep 6(3), 514–527 (2014).
[Crossref] [PubMed]

Genome Biol. (1)

W. Pan, J. Lin, and C. T. Le, “Model-based cluster analysis of microarray gene-expression data,” Genome Biol. 3(2), H0009 (2002).
[Crossref] [PubMed]

IEEE Trans. Automatic Control (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automatic Control 19(6), 716–723 (1974).
[Crossref]

J. Biol. Chem. (1)

B. Chance, B. Schoener, R. Oshino, F. Itshak, and Y. Nakase, “Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,” J. Biol. Chem. 254(11), 4764–4771 (1979).
[PubMed]

J. Biomed. Opt. (2)

A. J. Walsh, K. M. Poole, C. L. Duvall, and M. C. Skala, “Ex vivo optical metabolic measurements from cultured tissue reflect in vivo tissue status,” J. Biomed. Opt. 17(11), 116015 (2012).
[Crossref] [PubMed]

M. C. Skala, K. M. Riching, D. K. Bird, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, P. J. Keely, and N. Ramanujam, “In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia,” J. Biomed. Opt. 12(2), 024014 (2007).
[Crossref] [PubMed]

Nat. Med. (1)

K. Polyak, “Tumor Heterogeneity Confounds and Illuminates: A case for Darwinian tumor evolution,” Nat. Med. 20(4), 344–346 (2014).
[Crossref] [PubMed]

Nat. Rev. Cancer (1)

J. E. Visvader and G. J. Lindeman, “Cancer stem cells in solid tumours: accumulating evidence and unresolved questions,” Nat. Rev. Cancer 8(10), 755–768 (2008).
[Crossref] [PubMed]

Nature (1)

D. J. Kiviet, P. Nghe, N. Walker, S. Boulineau, V. Sunderlikova, and S. J. Tans, “Stochasticity of metabolism and growth at the single-cell level,” Nature 514(7522), 376–379 (2014).
[Crossref] [PubMed]

Proc. Natl. Acad. Sci. U.S.A. (2)

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U.S.A. 104(49), 19494–19499 (2007).
[Crossref] [PubMed]

J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson, “Fluorescence Lifetime Imaging of Free and Protein-Bound NADH,” Proc. Natl. Acad. Sci. U.S.A. 89(4), 1271–1275 (1992).
[Crossref] [PubMed]

Other (1)

J. Lakowicz, Principles of fluorescence spectroscopy (Plenum Publishers, New York, 1999).

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Figures (11)

Fig. 1
Fig. 1 Three dimensional graphs of the behavior of OMI-SPA to model simulated data using the redox ratio mean and standard deviation of SKBr3 and MDA-MB-231 cells. (a) Difference in the AIC values of 1 and 2 component Gaussian models of the simulated cell populations as a function of varying sample sizes and varying proportions (0% to 100%) of SKBr3 cells. AIC1-AIC2 > 0 indicates two component model is a better fit than the one-component model. (b) Error of the mean redox ratio computed for the SKBr3 subpopulation, and (c) MDA-MB-231 population. (d) Error of the estimated proportion of the SKBr3 subpopulation.
Fig. 2
Fig. 2 The behavior of OMI-SPA to model data simulated from the mean and standard deviations of NAD(P)H τm of SKBr3 and MDA-MB-231 cells. (a) Difference in the AIC values of 1 and 2 component Gaussian models of the simulated cell populations as a function of varying sample sizes and varying proportion of SKBr3 cells. (b) Error of the mean NAD(P)H τm computed for the SKBr3 subpopulation, and (c) MDA-MB-231 population. (d) Error of the estimated proportion of the SKBr3 subpopulation.
Fig. 3
Fig. 3 The behavior of OMI-SPA to model data simulated from the mean and standard deviations of the FAD τm of SKBr3 and MDA-MB-231 cells. (a) Difference in the AIC values of 1 and 2 component Gaussian models to the simulated cell populations as a function of varying sample sizes (N) and varying proportion of SKBr3 cells. (b) Error of the mean FAD τm computed for the SKBr3 subpopulation and (c) MDA-MB-231 population. (d) Error of the estimated proportion of the SKBr3 subpopulation.
Fig. 4
Fig. 4 Representative images of the redox ratio (NAD(P)H/FAD), NAD(P)H mean lifetime, and FAD mean lifetime of MDA-MB-231 cells, SKBr3 cells, and mixed populations.
Fig. 5
Fig. 5 Histograms of redox ratios, quantified per cell, of a population of approximately 200 cells of varying percentages of MDA-MB-231 cells and SKBr3 cells measured experimentally. The solid blue line represents best mixed-model Gaussian distribution fit. The red dashed curves represent the two component contributions, if a two component model is optimal. Histograms are normalized to have a total area of 1.
Fig. 6
Fig. 6 Histograms of NAD(P)H τm from populations of approximately 200 cells of varying percentages of MDA-MB-231 cells and SKBr3 cells. The solid blue line represents the best mixed-model Gaussian distribution fit. The red dashed curves represent the two component contributions, if a two component model is optimal. Histograms are normalized to have a total area of 1.
Fig. 7
Fig. 7 Representative images with cells color coded red if the cell mean NAD(P)H τm value is greater than 1.06 ns and blue if the NAD(P)H τm value is less than 1.06 ns.
Fig. 8
Fig. 8 Histograms of FAD τm from populations of approximately 200 cells of varying percentages of MDA-MB-231 cells and SKBr3 cells. The solid blue line represents the best mixed-model Gaussian distribution fit. The red dashed curves represent the two component contributions, if a two component model is optimal. Histograms are normalized to have a total area of 1.
Fig. 9
Fig. 9 Histograms of OMI Index from populations of approximately 200 cells of varying percentages of MDA-MB-231 cells and SKBr3 cells. The solid blue line represents the best mixed-model Gaussian distribution fit. The red dashed curves represent the two component contributions, if a two component model is optimal. Histograms are normalized to have a total area of 1.
Fig. 10
Fig. 10 Behavior of OMI-SPA for generalized data. Simulation initial conditions include a population of size N = 300 cells, each population proportion was 0.5, and all populations had a normalized mean of 1. The distance between the means was varied from 0 to 2. The standard deviation of the populations varied from 0 to 1. (a) AIC difference for the 1 and 2 component models to fit the simulated data. (b) Error of the mean, (c) error of the variance, and (d) error of the subpopulation proportions computed for the two-component models of the simulated data.
Fig. 11
Fig. 11 Minimum sample size (N), if less than 10,000 cells, required to resolve two populations with minimal population mean and proportion error (AIC2<AIC1-20) at varied mean distances (0-2) and population proportions (0-1). Standard deviation of the populations was varied: (a) 0.05, (b) 0.1, (c) 0.25, and (d) 0.5. The red, dashed circle in (b) encompasses the majority of normalized OMI endpoint mean distances and proportions of subpopulations observed in patient-derived organoids. The red, dashed line (d) represents the normalized distance (1.16) and standard deviation between two HER2 positive cell lines, one responsive to trastuzumab and one resistant.

Tables (2)

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Table 1 Experimental groups for the SKBr3 and MDA-MB-231 co-culture experiments.

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Table 2 Mean, proportion (P), and standard deviation and % errors computed from the optimal fitting Gaussian distribution model of the co-culture experimental data.

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