Background. Human liver hepatocytes are central to our bodies’ vast metabolic functions, the detoxification of xenobiotics & drugs as well as for protein synthesis. Therefore, efficacy and safety of newly developed compounds are routinely tested in primary human hepatocytes as gold standard. However, recent single-cell data surfaced a somewhat surprising ‚division of labor‘ in hepatocytes – a level of cellular heterogeneity potentially encoded in structural cues (such as liver zonation) or correlating with cellular features, like ploidy.
Challenge. The current study just published in Genome Biology, was aimed at uncovering - and the first molecular characterization – of potential functional heterogeneity in human liver hepatocytes. For this, primary human hepatocytes were exposed to metabolic stress or multi-drug treatments –mimicking chronic liver disease such as fatty liver disease, or patients in need of multi-drug medication.
Outcome. Single cell transcriptomic analyses of commercially available, human primary hepatocytes (donors covering a broad age-range) allowed the authors to annotate 4 distinct hepatocyte subgroups – with 3 of them presenting metabolic active states, but statistically robust differential transcriptional profiles indicating unique and diverse metabolic specialization.
Intriguingly, all three could be computationally mapped onto published in-vivo data sets from human livers, establishing a correlation between in vivo and in vitro annotations and subgroup specializations.
Crucially, the exposure to an industry-standard five-drug cocktail corroborated rather distinct metabolic profiles/capacity in the newly discovered hepatocyte subgroups – a conclusion derived from subgroup-specific gene expression profiles in line with the concept of hepatocyte (population) heterogeneity. Further corroboration comes indeed from treatments that mimic hepatic steatosis, a frequent condition in obese patients suffering from fat accumulation in the liver or during ageing. While the transcriptional variability was increased in subgroups I and II, subgroup III responded with a general though coordinated gene-transcription and reduced transcriptional variability.
Ultimately, and to this end not too surprising, subgroup specific drug/xenobiotic-metabolic gene expression profiles were impaired under steatosis-mimicking conditions.
Conclusion. Previous results proposed – in a general sense – heterogeneity within seemingly homogeneous hepatocyte populations. Such a notion may have tremendous impact on liver (cell) function – and hence crucial clinical decisions as far as the actual metabolic capacity, regenerative potential and treatment regimes, respective decisions on liver transplantation are concerned. However, detailed and single-cell resolved functional analyses substantiating the notion of hepatocyte heterogeneity – and hence informing future clinical decision making are sparse.
The here presented, robust, genome-scale datasets are therefore a first intriguing treasure-throve for scientists and clinicians to thoroughly drill into the molecular features of functionally distinct hepatocyte populations. They should thus enable the derivation of novel potential biomarker and diagnostic tools to distinctly stratify liver damage in clinical settings – hence paving the way for genuine, evidence-based personalized medicine.