Pathological examination has always been known as the "gold standard" of disease diagnosis, which is the theoretical basis for understanding the nature of diseases. However, traditional pathological diagnosis faces subjective factors and lack of objective data. Inefficient and time-consuming; There are no accurate statistics, and the reproducibility is low. To this end, scientists have been seeking and creating new analytical technologies and methods: digital pathology, whole slide imaging system, machine learning, artificial intelligence...

 

 

The work of pathological diagnosis is complex, but the process of pathological image analysis is very simple: from standardized production to staining, scanning into digital images of whole slides, and finally to image analysis, management and sharing, the first three steps have been carried out efficiently with equipment upgrades and technological development. The last step of section observation is still very difficult, and at this stage, it is necessary to rely on the rich scientific research experience of pathologists to make judgments, such as encountering a large number of sections together, which is very time-consuming and manpower-consuming.

Multicolor teacher: I want to do data analysis after imaging, do you have any software recommendations?

Xiao Ai: At present, there are still many data analysis software on the market, such as free you can download imageJ and Qupath, but the disadvantage is that you need to explore the usage by yourself; Our company has installed two software, HALO and StrataQuest, the HALO pathological image analysis platform, StrataQuestIt is our TG scanner platform that comes with it (basically all the analysis needs in the pathological field can be met, and tissue flow scatter plots and histograms can be produced), which is definitely much better than open source software in terms of function, analysis accuracy and operability, and there is a software professional technical support team that can answer questions. You can choose the right analysis software according to your actual situation, if you really don't know where to start, you can also find Absin, we can provide data analysis services~

Then the HALO introduced by Xiaoai to you is an accurate and simple pathological image analysis platform born for the difficult pain points of pathological diagnosis. HALO is compatible with most scanner image file formats on the market (LEICA, Hamamatsu, 3D Histech, Zeiss, Akoya, KFBIO, JPG, TIFF) and can be imported directly into HALO software for editing and analysis without any format conversion. It is widely used in the quantitative study of pathological images in the fields of neuroscience, metabolomics, oncology, toxicological pathology, etc.

 

 

mIHC quantification content:

The number of cells/nuclei in the total analysis area, the number of each marker/nucleus, the number of each marker/the proportion of nuclei, the average fluorescence intensity of each marker-positive cells, the average number of cells/nuclei per square micron, the area count and the colocalization information of polychromatic cells, etc.

Spatial analysis content:

(a) Nearest neighbor analysis; (b) proximity analysis; (c) infiltration analysis; (d) Density heat map analysis

1. Nearest Neighbor Analysis: Determine the average distance between any two cells or groups of objects and the number of unique neighbors.

1) Population A Cells – the total number of detected groups A;

2) Average Distance (μm) to Population B (μm) – the average distance (μm) from group A to group B;

3) Number of Unique Population B – The total number of unique population B detected.

 

 

2. Proximity Analysis: Calculate the number of cells or objects within a certain distance from another object or cell.

1) Population A Count – the total number of detected group A;

2) Population B Count – the total number of detected group B;

3) Population A within input range μm of Population B – the total number of group B detected within the input range of group A;

4) % Population A within input range μm of Population B – the percentage of group B detected within the input range of group A;

5) Average distance (μm) to population B (μm) – the average distance (μm) from group A to group B;

6) Number of Unique Population B – the total number of unique population B detected;

7) Average number of Population A within input range μm of Population B— The average number of group B detected within the input range of group A.

Raw data and images of Immune Positive ≤ 500 μm and Immune Positive > 500 μm within the specified range can be obtained as shown below.

 

 

3. Infiltration Analysis: Determine the number of cells or objects within the set range of the annotation region of interest.

1) Population A Count – the total number of detected group A;

2) Population A within interface area – the total number of group A detected in the interface area;

3) Population A average distance to interface – the average distance from group A to the interface line (μm);

4) Total interface area (mm2) – the total area of the total interface area;

5) Population A average density per mm2 – the average density of group A per square millimeter.

Immunopositive cells outside the interface, immunopositive (internal) cells in the interface, and immunopositive (external) cells at the interface can be analyzed as shown below, and an infiltration histogram can be provided (negative values correspond to the internal region of the interface, positive values correspond to the outer region of the interface).

 

 

4. Density Heatmap: Calculate and visualize the density of cells or objects in the analysis area.

1) Area Analyzed (μm2) – The total area of the analysis area in μm2;

2) Cell Population Count – the number of detected cell populations;

3) Average Cell Population within Distance μm Radius – The average number of cell populations counted within the set radius around each pixel. Add the values and divide by the number of pixels within the ROI. This output value is equal to the average cell population density output multiplied by the area of the circle of a given radius;

4) Average cell population density (cell population/μm2) – This output scales the above metric to the average number of objects per region unit. The average density of objects counted in the area around each user-defined pixel divided by the area of the circle of a given radius;

 

 

5) the minimum number of cell populations within a set radius – the minimum number of objects to be counted in a user-defined area around each pixel;

6) Minimum cell population density (cell population/μm2) – the minimum density of objects to be counted in a user-defined area around each pixel;

 

 

7) Maximum number of cell populations within a set radius – the maximum number of objects to be counted within a user-defined area around each pixel;

8) Maximum cell population density (cell population/μm2) – The maximum density of objects counted in a user-defined area around each pixel.

 

 

 

 

Absin provides antibodies, proteins, ELISA kits, cell culture, detection kits, and other research reagents. If you have any product needs, please contact us.

Absin Bioscience Inc. Email: worldwide@absin.net

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