Intra- and extracellular influences, such as radioactive radiation during radioimmuntherapy, can cause mutations or DNA double-strand breaks (DSBs). After induction of DSBs, phosphorylation of the histone H2A variant H2AX occurs within a few minutes (gH2AX). It can pose potential dangers to the cell and thus to the entire organism. Depending on the extent of the damage, programmed cell death (apoptosis) can be triggered or the cell can undergo necrosis. Because of that laboratorys are searching for a substance to protect subsidiary organs during radioimmuntherapy. For the verification of the effect of these, the „gH2AX Analyzer“ in the HSA KIT can be used to quantify the gH2AX-Signals, which correlate to the DSBs.
The following sections describe a potential experiment in which the HSA KIT is applied in pharmaceutical research. The described steps were carried out exactly as outlined; however, the results have been distorted or inaccurately reported due to data privacy considerations.
Advantages of the HSA KIT
- Evidenced-based development: The HSA KIT is based on evidence from experiments. Projects are archived, allowing data to be traceable even after 2 years. This ensures that the settings and parameters used can be retraced, even after a long period of time.
- Easy modification: Modifying experiments is made simple with the Copy-Tool feature of the HSA KIT. It enables the quick copying of projects, preserving previous data. A new project with a new IP is created, ensuring that no previous data is lost. Subsequently, new processing and parameter adjustments can be made, facilitating easy comparison of data and potential evaluation.
These features of the HSA KIT enhance data traceability, enable efficient modifications, and facilitate effective evaluation of experimental results.
Background of this experiment
The suitable experimental animals were initially intravenously injected with a specific amount of the respective concentrations of the protective substance. Subsequently, a radioimmunotherapeutic agent targeted against prostate cancer was administered. In this case, a molecule directed against a prostate-specific antigen is coupled to a chelator via a linker. This chelator carries the radioactive alpha emitter Actinium-225. During therapy, the binding molecule accumulates Actinium-225 in the prostate tumor, causing the tumor cells to perish due to radiation-induced damage. As a side effect of Actinium-225-based therapy, kidney damage occurs. The second administered substance aims to suppress the uptake of radioactivity in the kidneys, thus protecting the organ. The employed radioimmunotherapeutic binds to an antigen expressed in both prostate tumors and kidneys, although the expression is significantly stronger in the tumor. The protective substance binds to the same antigen but much more weakly. The goal is to block the binding to the weakly antigen-expressing kidneys, allowing the radioimmunotherapeutic to still bind to the target antigen in the tumor. The Ac-225 coupled to the radioimmunotherapeutic is intended to be absorbed by the tumor cells and destroy them.
The HSA KIT provides an efficient solution for evaluating experimental results in research. After sample collection and performing immunohistochemical staining, the samples can be scanned. Subsequently, the HSA KIT enables quantification, allowing precise determination of when, at what concentration, and with which conjugates the damages occurred and their intensity. This helps identify the most effective approach to protect the kidney during targeted radiopharmaceutical therapy. Additionally, it provides insights into the effectiveness of the therapy. The experiment can be repeated, varying various parameters to assess their impact on the damages. This approach can also be applied to antibody-drug conjugates (ADCs), and different radioisotopes, such as Lutetium-177, can be employed, with their effects precisely determined using the HSA KIT.
Antibodies are proteins produced by the immune system to recognize and combat pathogens. In the laboratory, specific antibodies can be created to selectively bind to cancer cells. This antibody therapy aims to alert the immune system to the presence of cancer cells, enabling it to target and combat them effectively.
Neoadjuvant therapies can also be employed and easily evaluated using the HSA KIT.
Antibody-drug conjugates (ADC)
Antibody-drug conjugates represent an advanced therapeutic approach, consisting of a laboratory-manufactured antibody linked to a highly potent cytostatic agent. The unique feature is that the cytostatic agent remains inactive while attached to the antibody and only becomes active when absorbed by a cancer cell.
When administered as a medication in the body, the antibody selectively binds to cancer cells. This binding results in the complete internalization of the antibody-drug conjugate into the cancer cell. Inside the cell, the cytostatic agent is separated from the antibody. This detachment activates the cytostatic agent, aiming to selectively destroy the cancer cell.
The HSA kit can recognize and analyze digitally generated microscopy images from slide scanners (such as the Axio Scan Z.1). This includes supporting image formats like .tiff, .czi, and .jpg.
To optimize the detection of gH2AX signals, two deep learning models were trained. The first model, called „HyperNonVesselNet,“ is designed to detect erythrocytes and blood vessels. It divides the entire kidney into non-vessel and vessel areas. The goal is to identify the relevant areas or objects in an image and separate them from the background and/or other objects.
The second model, „HypergH2AXNet“, is applied as a substructure within the non-vessel area.
Signal detection with the HSA KIT
In order to verify the plausibility of the software results, the sections were individually examined. All structures automatically identified by the HSA KIT were considered, including the non-vessel area, erythrocytes or vessels, and gH2AX signals. Initially, the entire kidney was examined to rule out major errors.
The base ROI (Region of Interest), mentioned earlier, is represented by the black outline of the kidney in Figure Xa. This defines the area to be quantified, and all detected and analyzed signals are located within this marking. The area outside this marking is filled with black diagonal lines, indicating that this region is excluded from quantification. The red-marked structures represent erythrocytes or vessels. Due to the automatic detection of these structures, the exclusion of blood vessels and red blood cells does not need to be performed manually. This allows for the definition of the non-vessel area, depicted in yellow (Figure Xb). Within this area, the gH2AX signals are quantified, represented in black (Figure Xc). For result calculation, the non-vessel area serves as the basis for the entire kidney, as it contains only cells where potential DNA damage can occur.
Signaldetection in whole kidney; highest opacity
gH2AX-Signaldetection; lowest opacity
Additionally, to assess the quality of the results, the staining detected as signals is displayed and hidden, and examined. The latter was done to facilitate a better comparison to determine whether these locations represent signals or not. Image sections were captured at a 40x magnification in the HSA KIT and later compared in the course of the study.
The structures detected as signals are highlighted in green in Figure Xa. The red-marked area was identified by the software as an erythrocyte. The non-vessel area, which would typically appear in yellow, has been hidden in both image sections for a better overview. It can be observed that no signals are detected within the erythrocytes. As an example, red blood cells within the image are circled in purple. These structures are characterized by a hollow space filled with yellowish, irregular structures. Both the hollow spaces and the structures within them can vary in size and shape. The model was trained to only detect larger blood vessels and blood cells (>100 μm), as smaller ones are often present between cells (purple) and do not have a negative impact on the quality of the results, as the model does not falsely detect them as positives. Further details on this and the training process were explained in section „Training“. As can be seen in the image section, these structures were not detected as signals. Moreover, no structures in this image section were falsely identified as gH2AX signals, indicating that no false-positive results were generated at this location. Additionally, the image section shows that no false-negative signal was detected. Every distinct signal, regardless of its size, present in Figure X was correctly detected by the software. Therefore, the error rate at this point is 0% for both false positives and false negatives.
Automated area calculation of the gH2AX signals with the HSA KIT
The file names in the following section were created fictitiously and do not reflect the real outcomes of the experiment, but rather the actual process.
The scanned OTs were analyzed using the HSA KIT. The parameters were optimized and adjusted according to the staining intensity by applying and verifying various settings, which will be explained in more detail later. The goal was to capture the entire area of each distinct signal and minimize the detection of unspecific staining and red blood cells. Through comparing the results of different settings, a Confidence value of 0.70 was found to be optimal. This parameter indicates the software’s level of confidence in the signals it detects, ranging from 0 to 1. Additionally, a range of 0 to 5,000,000 μm2 was set for the size range in which signals should be detected. Since the areas of DNA double-strand breaks can vary greatly, this option was not further considered.
Once the parameters were optimized, the model was applied to the samples, and the area of the gH2AX signals was automatically calculated by the software. The total areas of the kidneys were also automatically calculated, allowing for a comparison between the total damage areas and the kidney areas. In addition, the percentage of damage within the triple determinations was averaged, and the standard deviation was calculated (Table 1).
To visualize these values, the HSA KIT created a bar chart representing the percentage of DNA damage relative to the total area of the kidney. The averaged values of damages from the 3 treated animals per group and time point were used. The standard deviations were also included.
In addition to the total damage area relative to the total kidney area, the HSA KIT automatically generates a column chart that indicates the total number of γH2AX signals per mm2 for each animal.
It can be observed that the animals from group 1 incubated with the substance for only two hours (darkblue bars) have incurred a less amount of damage. The bar representing the 15-hour values of group 1 (orange) shows the lowest level of damage. The animals from group 2, which were incubated for 2 hours (grey) indicate a similar level of damage as group 3 (green). The group 3, which shows the highest damage, was incubated for 2 hours (light blue).
The column chart suggests that the group number has an impact on the extent of DNA damage. When the incubation period remains constant (2h or 15h) and only the group number changes, altering the concentration of the substance, the number of damages significantly increases.
The inserted standard deviations do not show a clear pattern of increase or decrease.
Based on the column chart presented in the results section, it can be inferred that the concentration of the substance is the decisive factor. Two time points and three different concentrations were examined. The results indicated that the number of damages, both in terms of area and quantity, increased with higher substance concentration.
With the HSA KIT, it is possible to adjust certain parameters according to the sections. Care was taken to choose these parameters in a way that allowed for the automatic detection of as many manually identified γH2AX signals as possible. Additionally, the entire area of these signals was considered, as later in the study, the calculation of the signal area was intended. A confidence level of 0.70 was found to be optimal. Both small and large γH2AX signals were recognized as such, and their entire areas were detected. During the manual examination of individual sections, it was observed that some tissue sections exhibited particularly intense brown background staining. This may have occurred because endogenous peroxidase activity was not completely blocked, leading to the co-staining of white and red blood cells. Therefore, these sections were specifically checked for false-positive results since the signals to be detected were also stained brown. It was found that, except for a few signals, no significantly high number of false detections was obtained. The error rate was less than 5% for both false-positive and false-negative detections. Additionally, nonspecific staining may have originated from incomplete removal of paraffin, as it can mask the specific staining. Furthermore, inadequate rinsing of the tissue sections or overdevelopment of the substrate reaction due to an excessive amount of chromogen in the solution could lead to nonspecific staining. Since this was a rapid staining method, highly concentrated reagents and relatively short incubation times were used. As a result, even every additional second during incubation caused significant changes, such as drying out of the sections, which mainly occurred at the edges of the kidneys. These nonspecific stainings were also not detected as false positives.
The recall value was 0.995, and the precision value was 0.966, indicating that there were more false-positive results (i.e., a signal being detected when it wasn’t present) than false-negative detections. This could be observed from the number of false negatives (FN) and false positives (FP). However, none of the calculated values deviated significantly from 1, suggesting that the model was suitable for the accurate quantification of γH2AX signals. The harmonic mean, F1-score, was 0.980, further confirming that this was an appropriate deep learning model.
To further investigate the quality of the model, metrics were calculated. For this purpose, a region of a kidney was selected, and gH2AX signals were manually detected.
Ground Truth data (GTD): manual annotation
A total of 434 structures were annotated. Subsequently, the HyperH2AXNet model under investigation was applied to the same region of the section.
same region detected by the model HypergH2AXNet
With the DL model, a total of 447 objects were detected as signals. In the next step, a comparison was made with HSA KIT to determine which manually annotated signals were automatically detected by the model and which ones were not. Based on this, the values for the number of FN (false negatives), TN (true negatives), FP (false positives), and TP (true positives) were counted. The following values were obtained: FN: 2 TN: indeterminable FP: 15 TP: 432 Using these values and equations (1.1) to (1.4), the metrics accuracy, precision, recall, and F1-score were calculated. Since the TN values are indeterminable, they were not further considered. The following values were determined: Accuracy = 0.962; Precision = 0.966; Recall = 0.995; F1-score = 0.980 The recall value is the highest among all the values, followed by the F1-score. The precision value is slightly higher than the accuracy.
HSA KIT vs. ImageJ vs. QuPath
The same region with Image J
The same region with QuPath
To compare the Results better we used a zoomlevel of 80.
GTD HSA KIT (the signals were set manually)
HypergH2AXNet (model in HSA KIT):
The following confusionmatrix is a template and were used to create one for each of the softwaresolutions.
After that the solutions were summarized in a table.
With these data a column chart was created where the blue column is the HSA HypergH2AXNet, the black column ImageJ and the last one (yellow) QuPath. The metrices precision, recall and F1-score were calculated for each of them to compare the methods better.
If you compare all of the columns you can see that the model „HypergH2AXNet“ the column with the highest average score of all the models have. ImageJ and QuPath are approximately on the same level.
A manual plausibility analysis was conducted to assess whether the numerical values obtained using the automated quantification software (HSA KIT) appeared plausible compared to the sections. Each section was manually examined in the HSA KIT, and comparisons were made both within the respective groups and across different groups. Since the damages had comparable sizes, the number of damages could be equated with size, allowing the plausibility analysis of size values based on the number of damages to be conducted. To make qualitative statements regarding the extent of damages, sections from the same region of the kidney were used for comparison. The gH2AX signals were counted within an area of 200 μm x 200 μm at 20x magnification, and the number of signals in the sections within the groups was compared. In order to maintain the scope of this study, only one animal or area of the kidney was represented and compared for each group and time point. The entire kidney section is depicted below them, and the chosen area is indicated by the blue cross.
The kidney sections a) and d) do not show any nonspecific staining at first glance, while this is the case for the other two time points. A brownish, hazy staining extends over the majority of the kidney. Looking at the kidney a), it is evident that this sample has the fewest damages. Only one signal with a diameter of approximately 3 μm was observed. The 6th time point b), exhibits around 25 distinct signals. In terms of size, these signals range between 3 to 5 μm. The section c) shows 39 damages. Here, occasional signal complexes can also be found, resulting in a size spectrum ranging from 3 to 10 μm. In the last section d), 54 signals were counted. Larger complexes can also be found here, ranging in size around 10 μm.
Except for the kidney c), no nonspecific staining is noticeable. The 14th time point a) shows a relatively small DNA damage of approximately 2 μm. The section from b) also exhibits only one signal, but this signal consists of multiple nuclear damages, resulting in roughly five times the total size, namely 9.5 μm. The next time point displays 8 damages, among which there is a signal comparable in size to the previous time point. The remaining signals have a size of approximately 5 μm.. In section d) significantly more signals are present, forming larger complexes with a diameter of about 5 to 10 μm. A total of 74 signals were counted.
Nonspecific brownish staining is also only visible in the section c). The section a) shows 4 distinct signals with a size of approximately 3 μm. The next section exhibits 7 signals in the same size range as the signals from a). The c) section shows 11 gH2AX signals, including several larger signals measuring about 5 to 6 μm. In section d) 34 signals are observed, some of which have a diameter twice as large, namely 8 to 10 μm, compared to those generated in a) or b).
An immunohistochemical staining of gH2AX signals was performed, with a positive control and a negative control for each section supporting the success of this staining. Subsequently, quantification was carried out using the HS Analysis study management software (HSA KIT), and the results passed a plausibility test. The models used and trained in this study were trained specifically for this purpose, namely the „HyperNonVesselNet“ and „HypergH2AXNet“. The first model was designed for automated detection of vessels or erythrocytes, excluding them from the total kidney area to determine the non-vessel area. The second model was trained to detect gH2AX signals, with its application limited to the non- vessel area as a substructure. This approach allowed achieving an error rate of less than 5 % and enabled the quantification of both small, weak signals and large, intense signals.