Skip to main content

Optimization of a widefield structured illumination microscope for non-destructive assessment and quantification of nuclear features in tumor margins of a primary mouse model of sarcoma.

Publication ,  Journal Article
Fu, HL; Mueller, JL; Javid, MP; Mito, JK; Kirsch, DG; Ramanujam, N; Brown, JQ
Published in: PLoS One
2013

Cancer is associated with specific cellular morphological changes, such as increased nuclear size and crowding from rapidly proliferating cells. In situ tissue imaging using fluorescent stains may be useful for intraoperative detection of residual cancer in surgical tumor margins. We developed a widefield fluorescence structured illumination microscope (SIM) system with a single-shot FOV of 2.1 × 1.6 mm (3.4 mm(2)) and sub-cellular resolution (4.4 µm). The objectives of this work were to measure the relationship between illumination pattern frequency and optical sectioning strength and signal-to-noise ratio in turbid (i.e. thick) samples for selection of the optimum frequency, and to determine feasibility for detecting residual cancer on tumor resection margins, using a genetically engineered primary mouse model of sarcoma. The SIM system was tested in tissue mimicking solid phantoms with various scattering levels to determine impact of both turbidity and illumination frequency on two SIM metrics, optical section thickness and modulation depth. To demonstrate preclinical feasibility, ex vivo 50 µm frozen sections and fresh intact thick tissue samples excised from a primary mouse model of sarcoma were stained with acridine orange, which stains cell nuclei, skeletal muscle, and collagenous stroma. The cell nuclei were segmented using a high-pass filter algorithm, which allowed quantification of nuclear density. The results showed that the optimal illumination frequency was 31.7 µm(-1) used in conjunction with a 4 × 0.1 NA objective (v=0.165). This yielded an optical section thickness of 128 µm and an 8.9 × contrast enhancement over uniform illumination. We successfully demonstrated the ability to resolve cell nuclei in situ achieved via SIM, which allowed segmentation of nuclei from heterogeneous tissues in the presence of considerable background fluorescence. Specifically, we demonstrate that optical sectioning of fresh intact thick tissues performed equivalently in regards to nuclear density quantification, to physical frozen sectioning and standard microscopy.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2013

Volume

8

Issue

7

Start / End Page

e68868

Location

United States

Related Subject Headings

  • Stromal Cells
  • Sarcoma, Experimental
  • Phantoms, Imaging
  • Muscle, Skeletal
  • Microscopy, Fluorescence
  • Mice
  • Lighting
  • Image Processing, Computer-Assisted
  • General Science & Technology
  • Disease Models, Animal
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Fu, H. L., Mueller, J. L., Javid, M. P., Mito, J. K., Kirsch, D. G., Ramanujam, N., & Brown, J. Q. (2013). Optimization of a widefield structured illumination microscope for non-destructive assessment and quantification of nuclear features in tumor margins of a primary mouse model of sarcoma. PLoS One, 8(7), e68868. https://doi.org/10.1371/journal.pone.0068868
Fu, Henry L., Jenna L. Mueller, Melodi P. Javid, Jeffrey K. Mito, David G. Kirsch, Nimmi Ramanujam, and J Quincy Brown. “Optimization of a widefield structured illumination microscope for non-destructive assessment and quantification of nuclear features in tumor margins of a primary mouse model of sarcoma.PLoS One 8, no. 7 (2013): e68868. https://doi.org/10.1371/journal.pone.0068868.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2013

Volume

8

Issue

7

Start / End Page

e68868

Location

United States

Related Subject Headings

  • Stromal Cells
  • Sarcoma, Experimental
  • Phantoms, Imaging
  • Muscle, Skeletal
  • Microscopy, Fluorescence
  • Mice
  • Lighting
  • Image Processing, Computer-Assisted
  • General Science & Technology
  • Disease Models, Animal