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In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy
Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today’s best practice, manual annotation of select image events,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579269/ https://www.ncbi.nlm.nih.gov/pubmed/25867850 http://dx.doi.org/10.1038/nmeth.3363 |
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author | Chittajallu, Deepak R Florian, Stefan Kohler, Rainer H Iwamoto, Yoshiko Orth, James D Weissleder, Ralph Danuser, Gaudenz Mitchison, Timothy J |
author_facet | Chittajallu, Deepak R Florian, Stefan Kohler, Rainer H Iwamoto, Yoshiko Orth, James D Weissleder, Ralph Danuser, Gaudenz Mitchison, Timothy J |
author_sort | Chittajallu, Deepak R |
collection | PubMed |
description | Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today’s best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 days in HT-1080 fibrosarcoma xenografts in living mice using a dataset of 38,000 cells and compared the induced phenotypes. In contrast to 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo. |
format | Online Article Text |
id | pubmed-4579269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
record_format | MEDLINE/PubMed |
spelling | pubmed-45792692015-12-01 In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy Chittajallu, Deepak R Florian, Stefan Kohler, Rainer H Iwamoto, Yoshiko Orth, James D Weissleder, Ralph Danuser, Gaudenz Mitchison, Timothy J Nat Methods Article Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today’s best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 days in HT-1080 fibrosarcoma xenografts in living mice using a dataset of 38,000 cells and compared the induced phenotypes. In contrast to 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo. 2015-04-13 2015-06 /pmc/articles/PMC4579269/ /pubmed/25867850 http://dx.doi.org/10.1038/nmeth.3363 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Chittajallu, Deepak R Florian, Stefan Kohler, Rainer H Iwamoto, Yoshiko Orth, James D Weissleder, Ralph Danuser, Gaudenz Mitchison, Timothy J In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title | In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title_full | In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title_fullStr | In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title_full_unstemmed | In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title_short | In vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
title_sort | in vivo cell cycle profiling in xenograft tumors by quantitative intravital microscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579269/ https://www.ncbi.nlm.nih.gov/pubmed/25867850 http://dx.doi.org/10.1038/nmeth.3363 |
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