Time-course liver microarray data from 0 to 14 days in a 70% partial hepatectomy mouse model.

Area of Science:

• * Hepatology and regenerative medicine.
• * Molecular biology and genomics.

Background:

• * The liver possesses remarkable regenerative capacity, capable of restoring mass after significant resection through compensatory hypertrophy.
• * The precise molecular mechanisms governing liver regeneration following major surgical removal remain incompletely understood.
• * Understanding these mechanisms is crucial for developing therapeutic strategies for liver disease and injury.

Purpose of the Study:

• * To investigate the temporal gene expression profiles following 70% partial hepatectomy (PH) in a mouse model.
• * To correlate gene expression data with physiological measures of liver regeneration (body and liver weight).
• * To analyze the influence of individual variability on regenerative capacity.

Main Methods:

• * Procurement of microarray data at multiple time points (0-14 days) post-70% PH in mice.
• * Measurement of body weight and liver weight to assess compensatory hypertrophy.
• * Generation of three biological replicates per time point to capture individual differences.
• * Quality control assessment using RNA integrity number and QC statistics.
• * Hierarchical clustering and analysis of cell cycle-related gene expression for data validation.

Main Results:

• * High-quality microarray data were obtained, confirmed by RNA integrity and QC metrics.
• * Hierarchical clustering supported the reliability of the dataset.
• * Expression patterns of cell cycle-related genes provided insights into the regenerative process.

Conclusions:

• * The generated microarray dataset provides a valuable resource for studying liver regeneration.
• * This data facilitates the elucidation of molecular pathways involved in compensatory hypertrophy after 70% PH.
• * Further analysis of this dataset can uncover novel targets for promoting liver repair.