Literature DB >> 27730745

Autofluorescence flow sorting of breast cancer cell metabolism.

Amy T Shah1, Taylor M Cannon1, James N Higginbotham2, Robert J Coffey2, Melissa C Skala3,4.   

Abstract

Clinical cancer treatment aims to target all cell subpopulations within a tumor. Autofluorescence microscopy of the metabolic cofactors NAD(P)H and FAD has shown sensitivity to anti-cancer treatment response. Alternatively, flow cytometry is attractive for high throughput analysis and flow sorting. This study measures cellular autofluorescence in three flow cytometry channels and applies cellular autofluorescence to sort a heterogeneous mixture of breast cancer cells into subpopulations enriched for each phenotype. Sorted cells were grown in culture and sorting was validated by morphology, autofluorescence microscopy, and receptor expression. Ultimately, this method could be applied to improve drug development and personalized treatment planning.
© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  autofluorescence; breast cancer; cellular metabolism; flow cytometry; tumor heterogeneity

Mesh:

Substances:

Year:  2016        PMID: 27730745      PMCID: PMC5547001          DOI: 10.1002/jbio.201600128

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  18 in total

1.  Autofluorescence spectroscopy of epithelial tissues.

Authors:  Yicong Wu; Jianan Y Qu
Journal:  J Biomed Opt       Date:  2006 Sep-Oct       Impact factor: 3.170

2.  Biodegradable nanoparticles for targeted ultrasound imaging of breast cancer cells in vitro.

Authors:  Jun Liu; Jie Li; Thomas J Rosol; Xueliang Pan; Jeffrey L Voorhees
Journal:  Phys Med Biol       Date:  2007-07-24       Impact factor: 3.609

3.  Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma.

Authors:  M E Prince; R Sivanandan; A Kaczorowski; G T Wolf; M J Kaplan; P Dalerba; I L Weissman; M F Clarke; L E Ailles
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-08       Impact factor: 11.205

4.  Prospective identification of tumorigenic breast cancer cells.

Authors:  Muhammad Al-Hajj; Max S Wicha; Adalberto Benito-Hernandez; Sean J Morrison; Michael F Clarke
Journal:  Proc Natl Acad Sci U S A       Date:  2003-03-10       Impact factor: 11.205

5.  Autofluorescence-activated cell sorting of pancreatic islet cells: purification of insulin-containing B-cells according to glucose-induced changes in cellular redox state.

Authors:  M Van De Winkel; D Pipeleers
Journal:  Biochem Biophys Res Commun       Date:  1983-07-29       Impact factor: 3.575

6.  Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features.

Authors:  Chad J Creighton; Xiaoxian Li; Melissa Landis; J Michael Dixon; Veronique M Neumeister; Ashley Sjolund; David L Rimm; Helen Wong; Angel Rodriguez; Jason I Herschkowitz; Cheng Fan; Xiaomei Zhang; Xiaping He; Anne Pavlick; M Carolina Gutierrez; Lorna Renshaw; Alexey A Larionov; Dana Faratian; Susan G Hilsenbeck; Charles M Perou; Michael T Lewis; Jeffrey M Rosen; Jenny C Chang
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-03       Impact factor: 11.205

Review 7.  Links between metabolism and cancer.

Authors:  Chi V Dang
Journal:  Genes Dev       Date:  2012-05-01       Impact factor: 11.361

8.  Differentiation state and invasiveness of human breast cancer cell lines.

Authors:  C L Sommers; S W Byers; E W Thompson; J A Torri; E P Gelmann
Journal:  Breast Cancer Res Treat       Date:  1994       Impact factor: 4.872

9.  Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer.

Authors:  Alex J Walsh; Rebecca S Cook; H Charles Manning; Donna J Hicks; Alec Lafontant; Carlos L Arteaga; Melissa C Skala
Journal:  Cancer Res       Date:  2013-10-15       Impact factor: 12.701

Review 10.  Targeting cellular metabolism to improve cancer therapeutics.

Authors:  Y Zhao; E B Butler; M Tan
Journal:  Cell Death Dis       Date:  2013-03-07       Impact factor: 8.469

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  4 in total

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Authors:  Jonathan Hira; Deanna Wolfson; Aaron John Christian Andersen; Tor Haug; Klara Stensvåg
Journal:  Sci Rep       Date:  2020-01-24       Impact factor: 4.379

2.  Natural NADH and FAD Autofluorescence as Label-Free Biomarkers for Discriminating Subtypes and Functional States of Immune Cells.

Authors:  Sarah Lemire; Oana-Maria Thoma; Lucas Kreiss; Simon Völkl; Oliver Friedrich; Markus F Neurath; Sebastian Schürmann; Maximilian J Waldner
Journal:  Int J Mol Sci       Date:  2022-02-20       Impact factor: 5.923

3.  Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer.

Authors:  Minfeng Yang; Arpan Mahanty; Chunjing Jin; Alex Ngai Nick Wong; Jung Sun Yoo
Journal:  J Immunother Cancer       Date:  2022-09       Impact factor: 12.469

4.  Fluorescence lifetime shifts of NAD(P)H during apoptosis measured by time-resolved flow cytometry.

Authors:  Faisal Alturkistany; Kapil Nichani; Kevin D Houston; Jessica P Houston
Journal:  Cytometry A       Date:  2018-10-19       Impact factor: 4.355

  4 in total

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