Tissue Imaging

"SLIM" or Spatial Light Interference Microscopy, can be used to monitor and track colon cancer progression.

Images of SLIM, or “Spatial Light Interference Microscopy, as they appear alongside traditional H&H stained samples at various stages of colon cancer progression.

Summary

This Application Note illustrates the use of Spatial Light Interference Microscopy (SLIM) for tissue imaging and diagnosis. SLIM is capable of automatically scanning and imaging microscopy slides with tissue samples. The quantitative data obtained from Phi Optics SLIM provides insight into the condition of the tissue, whether it is in benign or malignant condition, without staining. Therefore, it allows for an automatic diagnosis of cancer in the tissue sample. Moreover, phase imaging reveals certain structures that are not readily detected in simple staining methods that are current standards in clinics that can be used as an easier and faster indicator to diagnose the condition.

INTRODUCTION & PROCEDURE

SLIM-Zeiss-Andorv5Spatial Light Interference Microscopy (SLIM)

Phi Optics SLIM is a non-invasive phase imaging technology that quantifies the physical properties of live cells and tissues. The output is a live quantitative image (SLIM map) of the specimen on the microscope stage. The intensity of every pixel in the frame is a measure of the optical path length difference (in radians) through the sample, i.e. a phase shift map, which is measured with better than 0.5 nanometers

sensitivity (Figure 1) [1]. Moreover, SLIM is capable of automatically scanning a sample slide with tissue biopsies at high resolution, allowing for an automated tissue scanning and diagnosis [2]. The size of each frame depends on the objective and the detector. More detailed description of SLIM can be found in the reference pages in the “Technology” page of this website.

CellVista software phase shift map

Figure 1. Phi Optics CellVista acquisition program

Tissue scanning

Phi Optics CellVista software makes tissue scanning simple and quick by integrating microscope control and image acquisition. The focus correction implemented in the software keeps the sample in focus while the imaging system scans and images over a large area.

  1. Go to “Scanning” found on the bottom left corner of CellVista
  2. Setup experiment and focus points to cover the area of measurement
  3. Run experiment
SLIM in simple XY scanning mode.

Figure 2. CellVista SLIM software with a simple XY scan.

Mosaic

Stitching of the images obtained from Phi Optics SLIM and CellVista can easily be done using the ImageJ stitching plugin written by Preibisch et al. [3]. The plugin is included in the ImageJ in Stitching menu. For SLIM stitching for tissue imaging, which typically includes a large number of tiles, “Stitch Grid of Images” option is very valuable.

  1. Run “Stitch Grid of Images” in Stitching menu
  2. Set grid size x, y and overlap as they are configured in the measurement
  3. Select the directory that includes SLIM maps to be stitched, and configure the file names. The file name for unchanged SLIM maps should be {i}_0_1_0_SLIM.tif
  4. Other parameters can also be set to meet the details of the experiment. Click “OK” to run stitching.
Image of the ImageJ plugin set up window.

Figure 3. Stitch Grid of images plugin setup window.

APPLICATIONS

Quantitative phase as marker for diseases

The quantitative data from SLIM can be used as a marker for cancer diagnosis (Figure 4). The optical path lengths measured for the structures, such as stroma or red blood cells, present in a tissue biopsy are different for different structures, and thus, can be used as a marker for these structures. Moreover, some significant and indicative structures that are not detectible by staining, such as microcalcification in H&E staining, can be detected by SLIM (Figure 5) [4]. More recently, SLIM map based diagnosis carried out by pathologists showed a high correlation to the diagnosis on H&E stained biopsies [5].

The optical path lengths measured for the structures, such as stroma or red blood cells, present in a tissue biopsy are different for different structures, and thus, can be used as a marker for these structures. Moreover, some significant and indicative structures that are not detectable by staining, such as microcalcification in H&E staining, can be detected by SLIM

Figure 4. The quantitative data from SLIM can be used as a marker for cancer diagnosis

Figure 4. SLIM imaging signatures. Red blood cells with SLIM (a) and H&E (b). Red blood cells can be identified by their unique shape. Scale bar: 20 ?m. Lymphocytes with SLIM (c) and H&E (d). Lymphocytes were confirmed with CD45 staining. Stromal cells with SLIM (e) and H&E (f). (g) optical path length for the three different cells that feature high refractive index. Scale bar: 100 ?m. Color bar indicates optical path length in nanometers.

SLIM imaging signatures. Red blood cells with SLIM (a) and H&E (b). Red blood cells can be identified by their unique shape. Scale bar: 20 ?m. Lymphocytes with SLIM (c) and H&E (d). Lymphocytes were confirmed with CD45 staining. Stromal cells with SLIM (e) and H&E (f). (g) optical path length for the three different cells that feature high refractive index. Scale bar: 100 ?m. Color bar indicates optical path length in nanometers.

SLIM map based diagnosis carried out by pathologists showed a high correlation to the diagnosis on H&E stained biopsies.

Figure 5. SLIM map based diagnosis carried out by pathologists showed a high correlation to the diagnosis on H&E stained biopsies.

Figure 5. SLIM imaging of breast microcalcifications. Breast tissue with calcium phosphate: SLIM image (a), color bar in nanometers; H&E image (b). The whole slice is 2.2 cm × 2.4 cm. The SLIM image is stitched by 4785 images and the H&E is stitched by 925 images. Scale bar: 100 µm. Breast tissue with calcium oxalate: SLIM image (c), color bar in nanometers; H&E image (d). The entire slice is 1. 6 cm × 2.4 cm. The SLIM image is stitched by 2840 images and the H&E is stitched by 576 images. Scale bar: 200 µm.

APPLICATIONS

Scattering analysis

SLIM, by imaging the phase shift, can reconstruct the field scattered by the tissue sample (Figure 6). Therefore, the scattering parameters, such as scattering mean free path (ls) and anisotropy (g), can be calculated from the SLIM maps [6]. These parameters have been used for predicting prostate cancer recurrence, and outperformed some of the widely accepted clinical tools, such as CAPRA-S (Figure 7) [2].

SLIM can be used calculate an optical anisotropy in the single layer of stroma immediately adjoining multiple glands in each core.

Figure 6. SLIM image of a stromal tissue region in the prostate.

Figure 6. (A) Optical anisotropy (g) was calculated in the single layer of stroma immediately adjoining multiple glands in each core. (B) The histograms show the distribution of anisotropy values among the 89 non-recurrent and 92 recurrent cases. The bin-size on the histogram was set at 0.01. (C) SLIM image of a stromal tissue region in the prostate imaged using the 40X/0.75NA objective. Optical anisotropy value calculated using the scattering phase theorem in this tissue region was g=0.932. (D) Anisotropy calculation using Henyey-Greenstein phase function fit of the scattering angular distribution yields g=0.928.

SLIM data for Anisotropy as seen plotted with CAPRA-S to predict recurrence rates for prostate cancer.

Figure 7. SLIM data can be used to help predict recurrence rates for prostate cancer.

Figure 7. Single layers of stroma immediately adjoining 12–16 glands were isolated in SLIM images from each of the 92 recurrent and 89 non- recurrent patients who underwent prostatectomy. The patients in the two groups were matched based on age at prostatectomy, Gleason grade and clinical stage. The optical anisotropy parameter was calculated for each region, as described in Materials and Methods. This parameter separates cases of recurrence from non-recurrent twins with an AUC of 0.72, as shown. Lower values of this index correspond to a greater probability of biochemical recurrence. By using a cut-off value of g=0.938, we can predict recurrence with a sensitivity of 77% and specificity of 62%. CAPRA-S scores corresponding to 161 patients, 83 recurrent and 78 non-recurrent, showed poor discrimination (AUC 0.54). Twenty cases were excluded in CAPRA-S analysis due to one or more missing parameters for CAPRA-S calculation.

References

  1. G. Popescu (2011) Quantitative phase imaging of cells and tissues (McGrow-Hill, New York)
  2. S. Sridharan, V. Macias, K. Tangella, A. Balla and G. Popescu, Prediction of prostate cancer recurrence using quantitative phase imaging, Scientific Reports, 5, 9976 (2015)
  3. S. Preibisch, S. Saalfeld and P. Tomancak, Globally optimal stitching of tiled 3D microscopic image acquisitions, Bioinformatics, 25 (11), 1463-1465 (2009)
  4. Z. Wang, K. Tangella, A. Balla and G. Popescu, Tissue refractive index as marker of disease, Journal of Biomedi- cal Optics, 16 (11), 116017 (2011)
  5. H. Majeed, M. Kandel, K. Han, Z. Luo, V. Macias, K. Tangella, A. Balla and G. Popescu, Breast cancer diagnosis using spatial light interference microscopy, Journal of Biomedical Optics, 20 (11), 111210 (2015)
  6. Z. Wang, H. Ding and G. Popescu, Scattering-phase theorem, Optics Letters, 36 (7), 1215-1217 (2011)