Note: CHPs are a research use only (RUO) reagent, and not intended to be used as a diagnostic.   

Introduction                                                                   Download PDF File

The fields of histology and immunohistochemistry have been extremely helpful for clinical diagnosis and staging of numerous diseases such as cancer, fibrosis, and cirrhosis. Scientists and clinicians have relied on having a wide array of tissue stains to help identify important anatomical features and aspects within the tissue biopsies that enable them make an accurate diagnosis [1,2]. However, an experienced pathologist with a practiced eye must make decisions based on subtle differences in the staining, such as Masson’s Trichrome, which leads to inter- and intra-observer sampling variability [1]. Thus, a reliable, sensitive, and easy to read staining procedure can reduce errors by removing the human variability associated with subjective scoring. Collagen hybridizing peptides (CHPs) have proven to be a unique tool for specifically recognizing denatured collagen molecules in a variety of tissues, species, and disease models [3]. Determining the collagen content in a histology sample is the key to assessing fibrotic disease progression. Here, we describe a fluorescent image quantification protocol for use with Image-J/FIJI that can be used to objectively measure collagen content. This measurement is based on CHP fluorescent intensity within the tissue sample, which has been readily treated to completely denatured its collagen content on purpose. This protocol is designed to work with formalin-fixed paraffin-embedded (FFPE) that have undergone heat-mediated antigen retrieval as well as on frozen tissue sections.

Total Collagen Content

To use CHPs for evaluating the total collagen content within a tissue section, the collagen must be fully denatured to allow for CHP binding on all available collagen. We recommend heating the tissue section to thermally denature the collagen. Our tests indicate that the long heating periods used in heat-induced epitope retrieval (HIER) methods are sufficient to completely denature the collagen in the sample (regardless of the buffer used). The tissue sections shown in this application note were placed in a tissue steamer for 45 minutes at 95-100 °C. Alternatively, a water bath can be used to heat up a sealed 50 mL tube containing DI water to temperatures over 85 °C. After heating, pipette the hot DI water onto the tissue samples and allow to sit for 5 minutes (repeat 5X).

Experimental Protocol

1. FFPE Sections: perform deparaffinization prior to CHP staining by submerging sections for 2 x 5-minute washes in xylenes, 100% ethanol, 95% ethanol, 50% ethanol, and DI water in this order.
2. Perform HIER or purposefully heat denature the tissue for the determination of total collagen content.
3. Create a working solution for CHP staining by dissolving CHP powder in 1× PBS so that the concentration is within a 5-20 μM range. The exact concentration depends on the optimized parameters for the tissue section and the volume needed.
4. Heat CHP solution to 80 °C. Since CHP can self-assemble into homotrimers in solution over time (e.g., during storage at 4 °C) and lose its driving force for collagen hybridization, the trimeric CHP needs to be thermally dissociated to single strands by heating briefly at 80°C.
5. Quickly quench hot CHP solution in an ice water bath (~30 sec) before using it to bind to unfolded collagen in the tissue.
6. Apply quenched CHP solution to tissue section. Completely cover tissue section with CHP staining solution and allow overnight binding at 4 °C in a humidity chamber.
7. Wash sections using 3 x 5-minute washes of 1× PBS or 1× Tween-20.
8. Mount coverslips and image.

For a detailed protocol on tissue preparation please see our User Manual.
After washing off excess CHP solution, the tissue can be imaged, and imported into ImageJ/FIJI for signal quantification (See below). The signal intensity correlates with the amount of collagen within the sample. As the tissue sections get thicker, they contain more collagen and therefore we expect to see higher signal intensity from CHP binding to denatured collagen strands. In Figure 1, CHP signal is more intense as the tissue gets thicker, confirming that CHP signal correlates to total collagen content. The intensity was determined by measuring the average pixel intensity of the total area imaged after background subtraction. This method will allow researchers to easily visualize and quantify the total collagen content within a tissue section.


Once the appropriate imaging settings are found, CHP staining allows for easy determination of total collagen content. This is valuable for examining total collagen content in fibrotic tissues, and it may prove beneficial for evaluating wound healing or disease progression as collagen remodeling is associated with numerous pathological diseases and healthy organ upkeep. The fluorescent intensity can be quantified using the ImageJ/FIJI platform.

Instrument Parameters

Instrument: EVOS Auto-FL Imager (LifeTechnologies)
Detection: Fluorescence (R-CHP: 548/563 nm; F-CHP: 494/512 nm)
Magnification: 4X up to 40X (images in this application note were taken at 4X)
Light Settings: Choose an acceptable exposure time, light brightness, and gain then apply these settings to all sections imaged. Ensure that images do not contain saturated pixels, adjust settings accordingly.
Image-J/FIJI Macro Steps
Please follow the steps below to complete the image analysis correctly, you may also choose to copy and paste the code into the ImageJ Macro editor.
1. Import image; **Choose an image to import into ImageJ/FIJI
2. run("16-bit"); **Converts image to 16-bit format. Image -> type -> 16-bit
3. run("HiLo"); **Uses LUT to convert pixels to a high/low format
limit = pixcount/10;
threshold = pixcount/AUTO_THRESHOLD;
nBins = 256;
getHistogram(values, histA, nBins);
i = -1;
found = false;
do {
counts = histA[++i];
if (counts > limit) counts = 0;
found = counts > threshold;
}while ((!found) && (i < histA.length-1))
hmin = values[i];
i = histA.length;
do {
counts = histA[--i];
if (counts > limit) counts = 0;
found = counts > threshold;
} while ((!found) && (i > 0))
hmax = values[i];
setMinAndMax(hmin, hmax);
5. print(hmin, hmax);
6. run(“Apply LUT”); **Applies changes to brightness/contrast
7. run("mpl-viridis"); **Converts pixels to mpl-viridis format in LUT
8. run("Measure"); **Measures the pixel intensities in the image/ROI. Analyze -> Measure.
9. run("HiLo"); **Converts pixels back to HiLo from LUT
10. run("Subtract Background...", "rolling=1 sliding"); **subtracts background signal from image. Process-> Subtract background -> Check sliding paraboloid -> Pixel 1
11. run("Measure"); ** Measures the pixel intensity of the image/ROI after subtracting the background. Analyze -> Measure.

This process was designed based of previous literature that focused on the ImageJ/FIJI image quantification tools and methods [4,5]. The images below are a visual representation of each step of the ImageJ/FIJI macro. Special thanks to Dr. Kota Miura for developing the code used in step 4 of the macro [6]. We want to provide the most effective and efficient protocol for our customers. As such, we ask that if you have optimized these steps or found an alternative tool within ImageJ/FIJI that works better, please contact us at: and we will update this application note and acknowledge your contribution!

Step 1.

Step 2.

Step 3.

Step 4 & 5.

Step 6 & 7.

Step 8 & 9.

Note: CHPs are a research use only (RUO) reagent, and not intended to be used as a diagnostic.

(1) Weiskirchen, R.; Weiskirchen, S.; Tacke, F. Organ and tissue fibrosis: Molecular signals, cellular mechanisms and translational implications. Mol. Aspects Med. 2019, 65 (June 2018), 2–15.
(2) Goodman, Z. D. Grading and staging systems for inflammation and fibrosis in chronic liver diseases. J. Hepatol. 2007, 47, 598–607.
(3) Hwang, J.; Huang, Y.; Burwell, T. J.; Peterson, N. C.; Connor, J.; Weiss, S. J.; Yu, S. M.; Li, Y. In situ imaging of tissue remodeling with collagen hybridizing peptides. ACS Nano 2017, 11, 9825–9835.
(4) Bankhead, P. Analyzing fluorescence microscopy images with ImageJ. 2014, No. May.
(5) Jensen, E. C. Quantitative Analysis of Histological Staining and Fluorescence Using ImageJ. Anat. Rec. 2013, 296, 378–381.
(6) Miura, K. Python + ImageJ, FIJI Cookbook (accessed Dec 2, 2019).

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