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InForm software: a semi-automated research tool to identify presumptive human hepatic progenitor cells, and other histological features of pathological significance
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InForm software: a semi-automated research tool to identify presumptive human hepatic progenitor cells, and other histological features of pathological significance
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InForm software: a semi-automated research tool to identify presumptive human hepatic progenitor cells, and other histological features of pathological significance
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Journal Article
InForm software: a semi-automated research tool to identify presumptive human hepatic progenitor cells, and other histological features of pathological significance
2018
Overview
Hepatic progenitor cells (HPCs) play an important regenerative role in acute and chronic liver pathologies. Liver disease research often necessitates the grading of disease severity, and pathologists’ reports are the current gold-standard for assessment. However, it is often impractical to recruit pathologists in large cohort studies. In this study we utilise PerkinElmer’s “InForm” software package to semi-automate the scoring of patient liver biopsies, and compare outputs to a pathologist’s assessment. We examined a cohort of eleven acute hepatitis samples and three non-alcoholic fatty liver disease (NAFLD) samples, stained with HPC markers (GCTM-5 and Pan Cytokeratin), an inflammatory marker (CD45), Sirius Red to detect collagen and haematoxylin/eosin for general histology. InForm was configured to identify presumptive HPCs, CD45
+ve
inflammatory cells, areas of necrosis, fat and collagen deposition (p < 0.0001). Hepatitis samples were then evaluated both by a pathologist using the Ishak-Knodell scoring system, and by InForm through customised algorithms. Necroinflammation as evaluated by a pathologist, correlated with InForm outputs (r
2
= 0.8192, p < 0.05). This study demonstrates that the InForm software package provides a useful tool for liver disease research, allowing rapid, and objective quantification of the presumptive HPCs and identifies histological features that assist with assessing liver disease severity, and potentially can facilitate diagnosis.
