After After
Before Before

10120 - RNAscope, Hepatocellular Carcinoma

INTRODUCTION
Hepatocellular carcinoma (HCC) is a primary cancer of the liver and a leading cause of cancer-related death worldwide[1]. Important risk factors include chronic liver disease and cirrhosis, and the incidence of HCC is expected to continue to escalate. HCC is an aggressive cancer and early diagnosis is critical for the survival of patients. In situ hybridization (ISH) is used for the detection of markers of HCC. Presented in 2012, RNAscope is one of the more recent RNA in situ hybridization techniques that allows visualization of multiple target genes. RNAscope uses a probe design strategy that amplifies the signal and suppresses the background, which results in increased sensitivity and specificity[2].

The APP works on dual probe RNAscope images. Cells and probe signals are automatically detected and cells are classified according to the number of probes associated with the cell. Both probe signals and cells are quantified.

KEYWORDS
RNAscope, dual probe, liver, cancer, digital pathology, image analysis, in situ hybridization, ISH, hepatocellular carcinoma, HCC

METHODS
Initially, nuclei and signals from two different probes (red and teal) are detected inside a manually outlined region of interest (ROI). Both nucleus and probe signal detection use a polynomial blob filter to enhance their features. The nucleus detection is based on a combination of the red, green, and hematoxylin color-deconvolution bands, whereas the detection of red and teal probe signals are based on the eosin and chromaticity red color-deconvolution bands. Large areas of probe signal are separated into smaller sized probe signals. Cytoplasm is simulated by dilating nucleus areas until another cell or white space is reached, or until a maximum cell diameter of 4 µm is reached (see figure 2). Probe signals are further classified as coming from inside a nucleus, inside cytoplasm, or outside cells. Probe signal count and area are quantified for each class. Cells (nuclei + cytoplasm) are classified based on the number of red and teal probe signals coming from each cell (see figure 3). All cell classes are quantified.

QUANTITATIVE OUTPUT VARIABLES
The output variables obtained from this protocol are:

  • Red probes in nuclei (#): Total number of red probe signals inside nuclei
  • Red probes in cyt (#): Total number of red probe signals inside cytoplasm
  • Red probes outside (#): Total number of red probe signals outside cells
  • Teal probes in nuclei (#): Total number of teal probe signals inside nuclei
  • Teal probes in cyt (#): Total number of teal probe signals inside cytoplasm
  • Teal probes outside (#): Total number of teal probe signals outside cells
  • Red probe area in nuclei (µm2): Total area of red probe signals inside nuclei
  • Red probe area in cyto (µm2): Total area of red probe signals inside cytoplasm
  • Teal probe area in nuclei (µm2): Total area of teal probe signals inside nuclei
  • Teal probe area in cyto (µm2): Total area of teal probe signals inside cytoplasm

Each cell is classified ranging from 0-4+ based on the amount of red and turquoise signal as [red, teal] as follows:

  0 1+ 2+ 3+ 4+
0 0,0 1,0 2,0 3,0 4,0
1+ 0,1 1,1 2,1 3,1 4,1
2+ 0,2 1,2  2,2 3,2 4,2
3+ 0,3 1,3 2,3 3,3 4,3
4+ 0,4 1,4 2,4 3,4 4,4

0: 0 probe signals, 1+: 1-3 probe signals, 2+: 4-10 probe signals, 3+: 11-14 probe signals, 4+: >15 probe signals

AUXILIARY APPS (included) 
No Auxiliary APPs are available.

WORKFLOW
Step 1: Manually outline one or multiple ROIs in an image
Step 2: Load and run the APP “10120 - RNAscope, Hepatocellular Carcinoma” for the quantification of cells and probe signals

STAINING PROTOCOL
RNAscope® 2.5 LS Duplex Assay (Advanced Cell Diagnostics, Inc.)

ADDITIONAL INFORMATION
The APP utilizes the Visiopharm Engine™ and Viewer software modules, where Engine™ offers an execution platform to expand processing capability and speed of image analysis. The Viewer allows a fast review together with several types of image adjustment properties ex. outlining of regions, annotations and direct measures of distance, curve length, radius, etc.

By adding the Author™ module the APP can be customized to fit other purposes. Author™ offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.

REFERENCES
1. Hepatocellular Carcinoma: A Review. Balogh, Julius et al. Journal of Hepatocellular Carcinoma 3 (2016): 41–53. PMC. Web. 16 Oct. 2017.
2. RNAscope – A Novel in Situ RNA Analysis Platform for Formalin-Fixed Paraffin-Embedded Tissues, F. Wang, J. Flanagan, N. Su, L.C. Wang, S. Bui, A. Nielsen, X. Wu, H.T. V, X.J. Ma and Y. Luo, Journal of Molecular Diagnosis (2012), Jan; 14(1): 22-29. Doi: 10.1016/j.jmoldx.2011.08.002.

RUO
Figure 1
Figure 1
Field-of-view of human hepatocellular carcinoma (HCC) with dual probe signals.
Figure 2
Figure 2
Analyzed image showing identified nuclei (blue) and simulated cytoplasm (yellow).
Figure 3
Figure 3
Analyzed image showing cells classified based on contained probes.