Uncovering the Links Between Circadian Rhythm Disruption and Liver Disease with Single Cell Sequencing

Circadian rhythm disruption (CRD) has become increasingly common in modern society due to shift work, nighttime light exposure, and irregular sleep schedules. This disruption has been linked to a variety of health conditions, including metabolic and liver diseases, yet the cellular mechanisms driving these associations remain poorly understood.

To shed light on this critical question, we sat down with Nick Ciccone, Research Fellow at the University of Oxford, to learn how his lab is using Parse Biosciences’ Evercode™ technology and Trailmaker data analysis software to investigate how circadian misalignment affects liver function at the single cell level.

Metabolic liver disease is an increasing global health concern particularly as this can develop into more serious conditions such as liver cirrhosis and liver cancer. Using large human datasets we are beginning to appreciate the links between fatty liver disease with sleep and circadian rhythm disruption (SCRD), but the mechanisms that underlie this remain unknown. A better understanding of these mechanisms will allow us to develop effective therapeutic strategies to alleviate the threat of circadian-dependent metabolic liver disease. Modern lifestyles frequently involve SCRD due to shift work, light at night (including visual displays i.e. tablets and smartphones), and social jetlag, with shift work alone affecting approximately 20% of the global workforce. Concurrently, dietary challenges and rising obesity rates contribute to increasing prevalence of metabolic liver diseases that are co-morbid for cardiovascular diseases, metabolic syndrome, and type II diabetes.

An ideal model that will give us mechanistic insights would require sampling of liver tissue, a difficult task in humans so we turned to a mouse model. This is an attractive model as chronic jet lag in mice has been documented to cause disruption in liver metabolism and hepatocellular carcinoma even on normal mouse chow. To investigate the interaction between SCRD and dietary stress, we compared mice maintained under standard 12:12 light-dark cycles with those subjected to weekly Chronic Jet lag (CJ). All mice were given a fatty liver disease inducing diet to mimic dietary stress.

Given the liver is a highly heterogenous organ with numerous cell types, we performed single nuclei (sn)RNA-seq to gain a deeper perspective of the molecular mechanisms responding to circadian misalignment. This strategy would potentially pinpoint a particular liver cell type(s) that could be driving the pathology of circadian misalignment. Something that would not be possible using a bulk transcriptomic analysis approach. 

The ability to freeze tissue that allows pooling so all samples can be processed together is particularly attractive to scientists studying circadian biology. This has given me the opportunity to process samples taken at different time points, all at once, minimizing variations between samples due to downstream processing as well as saving time! Also you can prepare a Parse sequencing library without specialized equipment which gave me more control of my entire experimental workflow, without having to surrender my samples to a single cell facility for processing (which I would have done using 10x) or waiting for a time slot on specialized equipment which, if in demand, can be a long wait!

Trailmaker has been a lifesaver. Without Trailmaker, I would have had to rely on collaborators for aspects of data analysis, which can be annoyingly slow. Being able to explore the data myself meant I could spot patterns and relationships that might have been overlooked if someone else had done the analysis. Because I know the biology, and have a deep understanding of the system I’m studying, I was able to connect those findings back to the bigger picture of circadian disruption and liver disease. That biological context is critical; it meant I wasn’t just looking at numbers or plots, but interpreting them in a way that aligned with the underlying science. Without knowledge of R you can access your data to make immediate discoveries.

Analysis from our initial bulk RNAseq experiments and additional protein analysis suggested that the circadian misaligned liver was more sensitive to the stress hormones, glucocorticoids. This is particularly interesting as these hormones are known to follow a circadian profile of release and action. An increase in glucocorticoid action fitted in well with the observed phenotypes we found too, namely increased lipid deposition in the liver and markers of immuno-suppression. The embedded Enrichr analysis in Trailmaker revealed that differentially expressed genes (DEGs) in the major cell clusters of our snRNA-seq dataset were targets of nuclear receptors including glucocorticoid receptor (GR), supporting the idea that sensitivity to stress hormones was increased in the circadian misaligned liver. We also compared publicly available mouse liver transcriptomic dataset where mice in these studies were treated with the stress drug agonist, dexamethasone. We found that the hepatocyte cell cluster in our snRNA-seq dataset had very similar patterns of gene expression to this experiment. Volcano plots of different cell clusters conclusively revealed that hepatocytes were the cell type that showed the most differentially expressed genes, suggesting that this cell type is a major contributor of the pathology associated with circadian misalignment. This hepatocyte centric change in gene expression could be mediated by GR signalling. To further explore this idea we analyzed GR binding sites within genes that were differentially expressed and found that adjacent genomic sequences were enriched for recognition sequences for transcription factors predominantly expressed in our hepatocyte cluster, HNF4a and STAT5B.

And I illustrated this in our paper with a UMAP that I drew in Trailmaker!

One particular feature that I loved was the trajectory cell analysis that allowed me to track progression of a cell through a dynamic biological process like cell differentiation or development, even though the cells were captured at a single time point. This analysis helped me formulate the idea that the cell cluster I named “transitory cells” were a mixture of cell types from different origins including differentiating monocytes and de-differentiating hepatocytes. This cell cluster amounted to about 35% of all cells and were initially difficult to ascribe to a particular cell type based on the gene expression profiles. So Trailmaker really helped me to work out that this cell cluster was a mixture of immature cell types.

Taken together we identify circadian misalignment as a mechanistic driver of fat accumulation in the liver. We present several lines of evidence that phase shifts, such as shiftwork, drive a gain in GR action in the liver, and that this is responsible for fat accumulation, such as is seen in human shiftworkers, and in animal models. We propose that this represents a therapeutic opportunity to mitigate the risks of shiftwork, and phase misalignment by targeting the gain the liver GR transactivation, possibly targeting the receptor with antagonists, or hepatic glucocorticoid re-activation by targeting 11bHSD1.

The customer and technical support is excellent. Troubleshooting is available on request for all facets of the process. Ideal for researchers only starting out on a single cell transcriptomic adventure! Trailmaker allows a researcher without any knowledge of R or coding to analyze their own data. Things are getting better all the time too. I prepared my library using the V2 kit, I hear V3 is quicker and even better. This illustrates that Parse are continually developing their products to be the best they can.

Nick Ciccone is a Research Fellow at the University of Oxford. His work focuses on the molecular mechanisms underlying metabolic liver disease and its intersection with circadian rhythm disruption. By combining innovative mouse models with single cell technologies, his research aims to uncover how lifestyle factors such as shift work and dietary stress contribute to liver disease progression — and to identify therapeutic opportunities to mitigate these risks.