NTRODUCTION
Huntington's disease (HD) is a progressive neurodegenerative genetic disorder, caused by an autosomal dominant mutation on the Huntingtin gene (HTT). The mutation of HTT means that the length of a repeated section of the gene exceeds a normal range, resulting in an abnormal long poly-glutamine repeat on one part of the protein, causing the proteins to create hydrogen bonds and to form protein aggregates. The presence of the mutant proteins results in gradual damage to specific areas of the brain but the exact way this happens is not fully understood. The pathologic changes in the brain of HD patients affect muscle coordination and leads to cognitive decline, dementia and psychiatric problems. HD is also the most common genetic cause of chorea which is abnormal involuntary writhing movements.
Research into the mechanism of HD is primarily focused on the brain pathology the disease produces. Modeling the disease in animal models is critical for understanding the fundamental mechanisms of the disease and for supporting the early stages of drug development. The size and amount of the aggregates combined with the large area of the ROIs (Cerebral Cortex, Striatum, and Hippocampus) has previously made quantification very difficult.
By using digital image analysis based on virtual slides to quantify the presence of protein aggregates for evaluating e.g. effect of a drug in HD treatment studies, it is possible to obtain high-throughput quantification of aggregates without compromising the accuracy and quality of the results.

KEYWORDS
Huntington’s, protein aggregate, Striatum, Cerebral Cortex, Hippocampus, Htt, IHC, quantitative, digital pathology, image analysis.

METHODS
The right and left hemisphere of each brain must first be extracted as individual images by using the auxiliary APP for Arrayimager, to allow you to report the results per animal. When the images from each hemisphere have been extracted, the ROIs must be outlined manually as shown in Figure 1. Finally the APP for quantification can be applied.
The mutant Htt aggregates have a set of very distinct characteristics and can be classified based on their properties of shape, size and color. The aggregates appear distinct and are readily identified as compared to the artifacts observed in some sections. Representatives of the Htt aggregates versus artifacts are depicted in Figure 2.
In pre-processing, the Polynomial Blob filter is applied to enhance the aggregates. The diameter of the aggregates that we would like to include varies and for this reason, two Polynomial Blob filters of different size are applied. Both are used on the blue color band (RGB-B). Furthermore, a third feature image is applied to identify dark artifact areas. The feature images are illustrated in Figure 3.
Since the preprocessing steps will make the aggregates stand out as bright signals on a dark background, a threshold classifier will be sufficient to identify the aggregate class. The value of the threshold for each feature image is fine-tuned by examining a set of images that are representative for the entire dataset. See Figure 4 for an example of the classification before post processing.
Finally, a series of post processing steps is included, where the detected aggregates are evaluated on size (classified object that are too large or too small is discarded), shape (aggregates are evaluated based on the irregularity, i.e. how circular it is), and position relative to artifacts. The final result of the segmentation after post processing is shown in Figure 5.

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

- Cortex Dist, Striatum Dist and Hippocampus Dist.: Aggregate area distribution

- #Aggregate Cortex, #Aggregate Striatum and #Aggregate Hippocampus: Number of aggregates within each ROI. 

- Area Fraction Cortex, Area Fraction Striatum and Area Fraction Hippocampus: Percentage area occupied by aggregates per ROI per animal.


AUXILIARY APPS (included)
Auxiliary APPs are used for additional process steps, e.g. finding Region of Interest (ROI).

ARRAYIMAGER GRID CONFIGURATION
Arrayimager grid configuration: By using the Arrayimager™ a grid can be fitted on each slide to obtain a separate high magnification image for each hemisphere while uniquely identifying each hemisphere section with a Donor ID as well as core coordinates. This way each brain/hemisphere can easily be tracked during result reporting.


STAINING PROTOCOL
There is no staining protocol available


ADDITIONAL INFORMATION
The APP utilizes the EngineTM and Viewer software modules, where EngineTM offers an execution platform to expand processing capability and speed of image analysis. Viewer gives 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 AuthorTM module the APP can be customized to fit other purposes. AuthorTM offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.

The APP also utilizes the module TissuearrayTM, which offers an integrated automated Tissue Micro Array (TMA) analysis. Add the TissuealignTM module to allow for automated alignment, VirtualDoubleStainingTM and VirtualMultiplexingTM to be seamlessly integrated in the TMA workflow.


REFERENCES

There are currently no references.

INTRODUCTION

The formation of new blood vessels by angiogenesis is an integral part of both normal developmental processes and numerous pathologies, ranging from tumor growth and metastasis to inflammation and ocular disease. Preclinical studies of disease mechanisms and the search for efficient treatments therefore includes assays for quantification of the effects of stimulators and inhibitors of angiogenesis. In certain diseases of the eye, angiogenesis is one of the fundamental processes where new blood vessels originating from the corneal veins will extend into the corneal stroma. This process, which is a biological consequence of inflammation, can also be used as an angiogenesis assay e.g., for testing agents with cancer treatment potential. The in vivo corneal neovascularization assay is based on the introduction of an angiogenic factor, such as vascular endothelial growth factor (VEGF) into the cornea of experimental animals, and then monitoring the effect of an inhibitor administered locally or systemically. The capillary growth, which takes place in a uniform direction towards the VEGF implant [See EXAMPLES: figure 1], can then be monitored and quantified over time.

KEYWORDS

Angiogenesis, bioassay, antiangiogenic, VEGF, quantitative, digital pathology, image analysis.

METHODS

Prior to the image analysis, it is important to outline the Region of Interest (ROI), thereby limiting the analysis from including baseline vessels and the vessels closest to the VEGF. 

To enhance the vessels, while suppressing the background variation, several preprocessing steps are included. The more important steps include doing a median filtering to remove the variation of the background and applying a polynomial linear filter to enhance the blood vessels which have a very linear structure. The polynomial linear filter implementation is proprietary to Visiopharm and can effectively enhance most linear structures.

The classification identifies the entire vessel area within the region of interest. However, there is no straightforward way to translate the area for this complex structure into an estimate of length. Instead a skeletonization is applied after discarding all objects smaller than a (user-) defined limit. The skeletonize-step transforms the overlaying label to a 1-pixel wide structure by removing all pixels that do not belong to the midline [See EXAMPLES: figure 2]. From this, the total vessel length and individual segment lengths are calculated.


QUANTITATIVE OUTPUT VARIABLES

The output variables obtained from this protocol include:

- Segment Lengths: The distribution of vessel segment lengths

- Total Vessel Length: The total vessel segment length


AUXILIARY APPS (included)

Auxiliary APPs are used for additional process steps, e.g. finding Region of Interest (ROI).

There are no auxiliary APPs.


STAINING PROTOCOL

There is no staining protocol available.


ADDITIONAL INFORMATION

The APP utilizes the EngineTM and Viewer software modules, where EngineTM offers an execution platform to expand processing capability and speed of image analysis. Viewer gives 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 AuthorTM module the APP can be customized to fit other purposes. AuthorTMoffers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.


REFERENCES

LITERATURE

1. Angiogenesis: Quantification of Corneal Vascular Outgrowth, C. Aa. Johnsen:
http://visiopharm.com/pdf/pdf-0010.pdf

2. The Pathophysiology of Angiogenesis, P. J. Polverini, Critical Reviews in Oral Biology and Medicine 6: 230, 1995

INTRODUCTION

Surgically-induced knee joint instability in rats by transection of the Anterior Cruciate Ligament (ACLT), partial medial Meniscectomy (pMx) or a combination of both (ACLTpMx), is one of the most commonly used experimental animal models of osteoarthritis (OA) [See REFERENCES: 1,2]. It shows very similar anatomical location and histopathological features as the human disease. The model is suitable for studying basic pathophysiological aspects of OA and for the pharmaceutical development of disease-modifying drugs.

The severity of joint destruction can best be demonstrated in coronal histological sections of the whole knee joint; stained with Hematoxilin-Eosin (or Safranin-O-Fast Green, see related APP no. 10027). The most important histopathological hallmarks are surface fibrillation and erosion of the articular cartilage, decrease in chondrocyte number, loss of proteoglycan staining and a sclerosis (thickening) of the subchondral bone plate.

Traditionally, the evaluation of the severity of joint damage is done by subjective semi-quantitative grading of the different histopathological features using defined scoring systems [See REFERENCES: 3]. However, even with 'blinding' of the observers and sample randomization, this methodology is prone to bias, suffers from low sensitivity to change and requires considerable experience in histopathology. On the other hand, quantitative digital histomorphometry of cartilage destruction and subchondral bone sclerosis can offer an objective, less time consuming and more sensitive assessment of OA histopathology [See REFERENCES: 4].

This APP can be used for quantifying a number of parameters relating to joint damage: Cartilage volume/area, surface fibrillation, cellularity, and bone sclerosis. The APP has been verified in both age-dependency studies and treatment studies. 

KEYWORDS

Osteoarthritis, Joint, knee, Rat, Hematoxilin-Eosin, ACLT, pMx, ACLTpMx, Cartilage, Chondrocytes, Fibrillation, Bone sclerosis, quantitative, digital pathology, image analysis.

METHODS

This APP can be used on virtual slides with several sections on each slide. Initially an Analysis Box with a predefined size is placed manually at the medial tibia cartilage, including potential lesion area and excluding osteophytes. 

Cartilage, bone and chondrocytes are located inside the Analysis Box.

On the cartilage surface towards the femur, the surface length and Euclidean length between measurement points can be obtained for calculation of the Fibrillation Index.

QUANTITATIVE OUTPUT VARIABLES

The output variables obtained from this APP include:

- Cartilage area in BOX: Area of cartilage, within Analysis Box

- Number of chondrocytes in BOX: Number of Chondrocyte profiles, within Analysis Box

- Solid Bone percent: Percent of solid bone, within Analysis Box

- Cartilage Surface Length: Length of cartilage border towards the femur, within Analysis Box

- Cartilage Fibrillation Index = Euclidean Distance/Cartilage Surface Length, within Analysis Box


AUXILIARY APPS (included)

Auxiliary APPs are used for additional process steps, e.g. finding Region of Interest (ROI).

There are no Auxiliary APPs available.



STAINING PROTOCOL

Execution of this APP is dependent on the standardized staining protocol '10026 - Rat knee joint, Hematoxilin-Eosin, Joint Damage - Staining Protocol', developed by, Dr. Karl Rudolphi and Rolf Keiffer, Sanofi Deutschland GmbH, R&D Aging, Quality of Life.

The protocol is made available electronically upon purchase of the APP.


ADDITIONAL INFORMATION

The APP utilizes the EngineTM and Viewer software modules, where EngineTM offers an execution platform to expand processing capability and speed of image analysis. Viewer gives 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 AuthorTM module the APP can be customized to fit other purposes. AuthorTM offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.


REFERENCES

USERS

This APP was developed for, and validated by, Dr. Karl Rudolphi and Rolf Keiffer, Sanofi Deutschland GmbH, R&D Aging, Quality of Life. 

LITERATURE

1. Gerwin, N. et al. Osteoarthritis & Cartilage 18 (S3): 24-34, 2010

2. Hayami, T. et al. Bone 38:234-243, 2006

3. Pritzker KP. et al. Osteoarthritis Cartilage 14:13-29, 2006

4. Rudolphi K. Et al. Osteoarthritits Cartilage in press for OARSI, 2012

INTRODUCTION
Surgically-induced knee joint instability in rats by transection of the Anterior Cruciate Ligament (ACLT), partial medial Meniscectomy (pMx) or a combination of both (ACLTpMx), is one of the most commonly used experimental animal models of osteoarthritis (OA) [See REFERENCES: 1,2]. It shows very similar anatomical location and histopathological features as the human disease. The model is suitable for studying basic pathophysiological aspects of OA and for the pharmaceutical development of disease-modifying drugs. 

The severity of joint destruction can best be demonstrated in coronal histological sections of the whole knee joint; stained with Safranin-O-Fast Green (or Hematoxilin-Eosin, see related APP no. 10026). The most important histopathological hallmarks are surface fibrillation and erosion of the articular cartilage, decrease in chondrocyte number, loss of proteoglycan staining and a sclerosis (thickening) of the subchondral bone plate.

Traditionally, the evaluation of the severity of joint damage is done by subjective semi-quantitative grading of the different histopathological features using defined scoring systems [See REFERENCES: 3]. However, even with 'blinding' of the observers and sample randomization, this methodology is prone to bias, suffers from low sensitivity to change and requires considerable experience in histopathology. On the other hand, quantitative digital histomorphometry of cartilage destruction and subchondral bone sclerosis can offer an objective, less time consuming and more sensitive assessment of OA histopathology [See REFERENCES: 4].

This APP can be used for quantifying a number of parameters relating to joint damage: Cartilage volume/area, surface fibrillation and proteoglycan loss (intensity measure). The APP has been verified in both age-dependency studies and treatment studies. 

KEYWORDS
Osteoarthritis, Joint, knee, Safranin-O-Fast Green, Rat, ACLT, pMx, ACLTpMx, Cartilage, Chondrocytes, Fibrillation, Bone sclerosis, quantitative, digital pathology, image analysis.

METHODS
This APP can be used on virtual slides with several sections on each slide. Initially an Analysis Box with a predefined size is placed manually at the medial tibia cartilage, including potential lesion area and excluding osteophytes.

The APP will delineate the residual cartilage, the proteoglycan-stained cartilage, bone, and joint space within the Analysis Box.

On the cartilage surface towards the femur, the surface length and Euclidean length between measurement points can be obtained for calculation of the Fibrillation Index.

Due to the often weak differences in staining on bone and cartilage, and the lack of Proteoglycan stained cartilage on some sections, it is necessary to perform a review step. In the review step all images are reviewed manually, if necessary corrections are made to the tissue classification, and a calculation protocol is applied to update the results.

QUANTITATIVE OUTPUT VARIABLES
The output variables obtained from this APP include:

- Cartilage Area - Proteoglycan: Area of Safranin-O stained cartilage within the Analysis Box
- Cartilage Area: Total area of cartilage within the Analysis Box
- Cartilage Area Fraction – Proteoglycan: Fraction of cartilage stained with Safranin-O
- Cartilage Fibrillation: Euclidean distance between ends of cartilage surface towards femur divided with distance between ends of cartilage along cartilage surface


AUXILIARY APPS (included)
Auxiliary APPs are used for additional process steps, e.g. finding Region of Interest (ROI).

RECALCULATE
The auxiliary APP 'Aux 01 RECALCULATE' can be used for recalculation of output values, after manual editing of tissue delineation. 


STAINING PROTOCOL
Execution of this APP is dependent on the standardized staining protocol '10027 - Rat knee joint, Saf-O-Fast Green, Joint Damage - Staining Protocol', developed by, Dr. Karl Rudolphi and Rolf Keiffer, Sanofi Deutschland GmbH, R&D Aging, Quality of Life.

The protocol is made available electronically upon purchase of the APP.


ADDITIONAL INFORMATION
The APP utilizes the EngineTM and Viewer software modules, where EngineTM offers an execution platform to expand processing capability and speed of image analysis. Viewer gives 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 AuthorTM module the APP can be customized to fit other purposes. AuthorTM offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.


REFERENCES

USERS

This APP was developed for, and validated by, Dr. Karl Rudolphi and Rolf Keiffer, Sanofi Deutschland GmbH, R&D Aging, Quality of Life. 

LITERATURE

1. Gerwin, N. et al. (2010) Osteoarthritis & Cartilage 18 (S3): 24-34

2. Hayami, T. et al. (2006) Bone 38:234-243

3. Pritzker KP. et al. (2006) Osteoarthritis Cartilage 14:13-29

4. Rudolphi K. Et al. (2012) Osteoarthritits Cartilage in press for OARSI 2012