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Densitoquant

Manufactured by 3DHISTECH
Sourced in Hungary

DensitoQuant is a digital densitometry analysis software developed by 3DHISTECH. It provides automated quantitative analysis of optical density measurements from digital images.

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2 protocols using densitoquant

1

Quantitative Analysis of Erdr1 Expression

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To quantitatively measure the level of Erdr1 expression, a Panoramic Scan™ slide scanner (3D HISTECH, Budapest, Hungary) was used. The degree of expression was assessed with the DensitoQuant application component of Pannoramic Viewer software™ (3D HISTECH). Before analysis, sample RGB values for cells were manually set. DensitoQuant™ (3DHISTECH Ltd., Budapest, Hungary) was then used to automatically assess the intensity of immunostained-positive cells, and presented them in different coloured images based on the expression intensity, as follows: weak positive (yellow), moderate positive (orange), strong positive (red), negative nuclei (blue), and negative pixels (white). Based on different colour intensities, the H (‘histological’) scores were then calculated using the following equation: H score = 1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+); 0 = negative staining, 1+ = weak staining, 2+ = moderate staining, 3+ = strong staining [28 (link)].
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2

Quantitative Immunohistochemical Analysis of Immune Markers

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After IHC staining, all TMA slides were scanned and evaluated through Pannoramic Viewer (3DHISTECH, Budapest, HUNGARY). CD73 expression was evaluated by IHC score. As previously described, the IHC score was generated by multiplying the percentage of immunoreactive cells by their corresponding staining intensity [18 (link)]. The mean optic density (MOD) was used to quantify the staining density of HHLA2 assisted by the Densitoquant and HistoQunant module from 3DHISTECH [31 (link)]. CD8 + TILs, Foxp3 + Tregs, CD68 + , and CD163 + TAMs were calculated manually. For each patient, five independent microscopic fields (400 ×) of the immune cell-infiltrating area were selected and counted manually by 2 investigators blinded to the patient information. The discrepancy between investigators was resolved together. The cutoff values were determined by R for optimal survival separation.
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