Month: Juni 2023

INTRODUCTION

CD3 typically refers to Cluster of Differentiation 3. T cells have a combination of proteins on their cell surface called CD3.  a type of immune cell involved in adaptive immune responses. It is essential for the growth, activation, and signaling of T cells. In both research and diagnostics, CD3 is frequently employed as a marker to distinguish and describe T cells.

Function CD3

When the T cell receptor (TCR) complex is activated, CD3 plays a crucial role in signal transmission. It is crucial for the activation of T cells, which triggers immunological reactions. To ensure optimal antigen recognition abilities, CD3 is crucial in T cell growth and maturation. Also known as a co-receptor, CD3 facilitates TCR binding to antigen-presenting cells for effective T cell activation.

Structure CD3

The CD3 complex consists of CD3γ, CD3δ, CD3ε, and CD3ζ subunits. CD3γ, CD3δ, and CD3ε form a heterodimeric complex with extracellular, transmembrane, and cytoplasmic domains. CD3ζ exists as a homodimer and contains extracellular, transmembrane, and cytoplasmic regions. The CD3 complex associates with the T cell receptor (TCR) α and β chains to form the functional TCR-CD3 complex on the surface of T cells. The cytoplasmic tails of CD3ε and CD3ζ contain immunoreceptor tyrosine-based activation motifs (ITAMs), which play a role in intracellular signaling upon TCR engagement. 

CD3 protein (epsilon /delta ectodomain dimer). CD3 is present on the surface of T-lymphocytes and is required for T-cell activation.

• The γ subunit of the CD3 complex is commonly known as CD3 gamma.
• The δ subunit of the CD3 complex is commonly known as the CD3 delta.
• The ε subunit of the CD3 complex is commonly referred to as CD3 epsilon.
• The ζ subunit of the CD3 complex is commonly referred to as CD3 zeta.

Function CD3 in the immune response 

CD3 is responsible for transmitting intracellular signals upon T cell receptor (TCR) engagement, leading to T cell activation and initiation of immune responses. It plays a crucial role in T cell development, ensuring proper maturation and selection of functional T cells. CD3 acts as a co-receptor, enhancing TCR binding to antigen-presenting cells for efficient T-cell activation. CD3 is essential for T cell-mediated immune responses and immune system functionality.

Schematic representation of the T-cell receptor-CD3 complex. The heterocomplex is formed by variable TCR-α and TCR-β chains coupled to three dimeric signaling transduction modules CD3δ/ε, CD3γ/ε, and CD3ζ/ζ or CD247. CD3, Cluster of differentiation 3; CD247, a cluster of differentiation 247 or CD3ζ/ζ; ITAM, immunoreceptor tyrosine-based activation motif; TCR, T-cell receptor.

CD3 uses in annotations.

CD3 is used for annotations because it is a reliable marker specifically expressed on T cells. It enables accurate identification and annotation of T cells within tissue samples or cellular populations. CD3 staining aids in quantifying T cell numbers, assessing their distribution, and studying their role in diseases. Additionally, CD3 annotations offer important diagnostic and prognostic data as well as a way to track the success of targeted or immunotherapy treatments.

Detection and analysis

The following steps were taken to detect, assess, and annotate CD3 expression:

1. Sample preparation: HeLa cells were treated with paraformaldehyde to preserve their structure and antigenicity. To allow antibody penetration into the cells, Triton X-100 was used to permeabilize the cells.
2. Blocking: The cells were treated with a blocking solution containing 10% serum to reduce non-specific binding. This process reduces background staining and improves antibody binding selectivity.
3. Incubation with antibodies: The cells were treated with a combination of two primary antibodies: mouse anti-beta tubulin and a polyclonal antibody against CD3/CD8 (Product # PA5-102404). A 1:200 dilution of the CD3/CD8 antibody was utilized. This primary antibody identifies just CD3 or CD8 proteins in the sample.
4. After incubating with primary antibodies, the cells were tagged with secondary antibodies conjugated with fluorescent dyes. The CD3/CD8 antibody was detected using a goat anti-rabbit IgG Alexa Fluor 594 (red) antibody, while the beta-tubulin antibody was detected using a goat anti-mouse IgG Alexa Fluor 488 (green).
5. Imaging and analysis: Images of the stained cells were captured using a fluorescence microscope. The presence of CD3/CD8 is indicated by the red fluorescence, while beta tubulin is represented by the green fluorescence. The expression and localization of CD3/CD8 can be assessed by comparing and evaluating fluorescence signals.
6. Annotation: Annotations can be established based on observed fluorescence patterns to identify CD3/CD8-positive cells or specific cellular areas where CD3/CD8 is located. This data can be utilized to further analyze and interpret the biological significance of CD3/CD8 in the experimental system.

HSA KIT SOFTWARE

By automating cell detection, enabling quantitative analysis of CD3 expression, providing annotation tools for marking CD3-positive cells, facilitating visualization of staining patterns, assisting in data management, providing advanced image processing algorithms, and improving the efficiency and accuracy of CD3 annotation tasks, HSA KIT software plays a critical role in making CD3 annotations.

The HS Analysis software is quite useful in assisting medical practitioners in detecting diseases. It allows them to diagnose and assess numerous medical disorders by evaluating high-resolution images of tissues and organs.

The HS Analysis touch

HS-analysis provides quality control and analysis services for raw materials and final products in a variety of industries, including pharmaceuticals, cosmetics, food, and agriculture. Chemical and physical testing, microbiological analysis, and stability testing are among the analytical services provided by the organization.

The latest artificial intelligence is a vital technology for the automatic interpretation of tissue samples in the HS Analysis program.

 By evaluating high-resolution photographs of tissues and organs, the HS Analysis program can assist medical practitioners in diagnosing illnesses. And, when the program generates data characterizing the patient’s current health, it will aid doctors in picking the appropriate therapy.

The images below show the CD3 of HS Analysis KIT

Follow these steps to create CD3 annotations in HSA KIT software:

1. Open the image with the T-cell imaging or immunofluorescence staining that you want to annotate with CD3.

2. Using the software’s drawing tools, manually outline or trace the CD3-positive portions on the image. Annotations are often created using forms such as polygons, or circles.

3. Within the software, locate the color channel settings or channels panel. You can adjust the visibility of specific color channels using this panel.

Find the blue color channel in the channels panel and turn it off. This will disable the blue color, rendering it invisible in the image.

Similarly, under the Channels panel, locate the red color channel and disable its appearance. This will disable the red color.

4. Check that the image now only shows the green color channel, which reflects the CD3 staining.

5. Using the appropriate annotation tools, create the desired annotations on the CD3-positive locations.

Show this video to clarify further.

Future Directions

For a better understanding of T-cell biology and disease-related insights, the future of image analysis for CD3 holds the potential for automation, AI-driven algorithms, high-throughput analysis, multiplexing and spatial analysis, deep learning-based segmentation, integration with omics data, and interactive visualization.

Analyzing psoriasis skin disease in HSA KIT deep learning software is able to detect a skin that caused by psoriasis, Psoriasis is a chronic autoimmune disease that primarily affects the skin, causing it to develop red, scaly patches, Specialists at HS Analysis are able to use advanced deep learning AI software to diagnose this disease to use it in clinics, institutions and health care facilities.

Knowing your psoriasis type can help your healthcare provider create a treatment plan. Most people experience one type at one time, but it is possible to have more than one type of psoriasis.

Artificial intelligence

HSA kit works on the development of AI machine and deep learning methods which refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. AI systems are designed to perceive their environment, reason about it, and take appropriate actions to achieve specific goals.  Deep learning can be utilized to define or identify skin diseases by leveraging its ability to learn intricate patterns and features from large datasets.

The HS Analysis‘ touch:

 One key technology of automatic interpretation of tissue samples in the HS Analysis software is the latest artificial intelligence. We are developing a DL in the cloud to analyze smartphone images in 2D, but also surface features (heatmaps and thus 3D) with CNNs. we have the ability to create ground truth data and train models for both detection of skin and detection of plaques.

How we develop our AI: Firstly, we collect real data images and annotate them based on colors for the skin we use yellow and we annotate all the shown skin areas in the picture to get the best possible AI model with minimum mistakes.

Examples on skin AI annotations:

The next step Is we annotate inflamed areas on the skin using red color and then we train the model too be able to automatically detect the plaques.

Examples on plaques AI annotations:

Model training :

We train each model individually Then we tests and optimize the models, we use our testing dataset to evaluate how well our AI model performs on the task of distinguishing psoriasis from other skin conditions and normal skin. The datasets consist of various images, each image is detected 100 times ,we select segmentation model type which detect object and draw exact border around objects the structures depends on the AI that we want which are skin or inflamed , and we use (horizontal-flip, vertical-flip, and rotation) in order to not change the meaning of the image, and we train the newest model version based on the existing versions. 

The Future Of HSA KIT AI’S

A very important aspect of this project is looking to improve the AI model  in the future and there are a lot of ideas that we can integrate and develop to improve the quality and versatility of the AI model to include and be able to detect the skin and plaques for many different photos and different types of noise and artefacts and get a better and more accurate result despite these artefacts. We take a look at the challenges and artefacts and find there are different types of them and the task is for the AI to be able to detect the correct targets despite these artefacts being present in the image.

Here are some of these Types of noise or artefacts that we want to improve on:

1-Image blurring: blurring is one of the most common things that will hinder the AI from correctly detecting and solving this problem will be very helpful to detect skin and plaques from other background objects.

2-Lighting and Shadows: Another very common artefact is the light and shadow presence and the different contrast that happens on the image that detours the detection.

3-Out of focus images: Almost all of the images that were used to train the Al model are from smartphones and sometimes the patient sends images that are out of focus.

4-Edges and boarders accuracy: To have an excellent and highly accurate model annotating the edges and boarders accurately is very important to not include any other unwanted objects or pixels that will affect the model accuracy.

5-Unwanted images: Having the ability to automatically exclude images that are not useable to annotate be it by not including any skin or having the identity of the patient visible and having inappropriate or private images being sent.

Being able to beat these challenges in the future will certainly make the AI model extremely accurate and having state of the art annotations.

Explainable Artificial Intelligence (xAI)

Explainable Artificial Intelligence (xAI) is a field focused on developing AI systems that can provide understandable explanations for their decisions and actions. Unlike traditional AI models that often operate as „black boxes,“ xAI aims to enhance transparency and interpretability in AI systems. By incorporating techniques such as rule-based systems, decision trees, attention mechanisms, or generating textual or visual explanations, xAI enables users to comprehend the reasoning behind AI outputs. This has significant implications for various sectors, including healthcare, finance, and law, where the ability to understand AI decisions is crucial. With explainability, experts can validate and verify AI models, identify biases or errors, and ensure fairness and ethical standards are maintained. Moreover, xAI empowers individuals to make informed decisions based on AI outputs, fostering trust, accountability, and the responsible use of AI technologies in our daily lives.

This video shows how we train the skin and plaques models and test it.