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Space-time logic of liver gene expression at sub-lobular scale

Nature Metabolism volume 3, pages 43–58 (2021)Cite this article

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Abstract

The mammalian liver is a central hub for systemic metabolic homeostasis. Liver tissue is spatially structured, with hepatocytes operating in repeating lobules, and sub-lobule zones performing distinct functions. The liver is also subject to extensive temporal regulation, orchestrated by the interplay of the circadian clock, systemic signals and feeding rhythms. However, liver zonation has previously been analysed as a static phenomenon, and liver chronobiology has been analysed at tissue-level resolution. Here, we use single-cell RNA-seq to investigate the interplay between gene regulation in space and time. Using mixed-effect models of messenger RNA expression and smFISH validations, we find that many genes in the liver are both zonated and rhythmic, and most of them show multiplicative space-time effects. Such dually regulated genes cover not only key hepatic functions such as lipid, carbohydrate and amino acid metabolism, but also previously unassociated processes involving protein chaperones. Our data also suggest that rhythmic and localized expression of Wnt targets could be explained by rhythmically expressed Wnt ligands from non-parenchymal cells near the central vein. Core circadian clock genes are expressed in a non-zonated manner, indicating that the liver clock is robust to zonation. Together, our scRNA-seq analysis reveals how liver function is compartmentalized spatio-temporally at the sub-lobular scale.

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Fig. 1: An scRNA-seq approach to space-time gene expression in mouse liver.
Fig. 2: Space-time mRNA expression profiles categorized with mixed-effect models.
Fig. 3: Properties of dually zonated and rhythmic mRNA profiles.
Fig. 4: smFISH analysis of rhythmic and zonated transcripts.
Fig. 5: Space-time logic of compartmentalized hepatic functions for Z + R genes.
Fig. 6: Rhythmic activity of Wnt signalling.
Fig. 7: Wnt targets could be explained by rhythmically expressed Wnt ligands from NPCs.

Data availability

All scRNA-seq data has been deposited in GEO with accession code GSE145197. Reconstructed spatio-temporal gene profiles are available as Matlab files at https://github.com/naef-lab/Circadian-zonation The whole dataset of gene profiles along with the analysis is available online as a web-application at the URL https://czviz.epfl.ch/. The application was built in Python using the library Dash by Plotly (version 1.0).

Code availability

The code for fitting the mixed-effects models and generating the main figures is available at https://github.com/naef-lab/Circadian-zonation

Details regarding the statistics, software and data are provided in the Reporting Summary.

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Acknowledgements

We thank D. Mauvoisin for assistance with the animal work, C. Gobet for bioinformatics advice and the EPFL BIOP facility for advice with microscopy. This work was supported by the Rothschild Caesarea Foundation’ fund managed by Weizmann Institute and EPFL, a Swiss National Science Foundation Grant 310030_173079 (to F.N.), and the EPFL. S.I. is supported by the Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, The Leir Charitable Foundations, Richard Jakubskind Laboratory of Systems Biology, Cymerman-Jakubskind Prize, The Lord Sieff of Brimpton Memorial Fund, the Wolfson Foundation SCG, the Wolfson Family Charitable Trust, Edmond de Rothschild Foundations, the I-CORE programme of the Planning and Budgeting Committee and the Israel Science Foundation (grants 1902/12 and 1796/12, the Israel Science Foundation grant no. 1486/16, the Chan Zuckerberg Initiative grant no. CZF2019-002434, the Broad Institute-Israel Science Foundation grant no. 2615/18, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 768956), the Bert L. and N. Kuggie Vallee Foundation and the Howard Hughes Medical Institute (HHMI) international research scholar award.

Author information

Author notes

  1. These authors contributed equally: Colas Droin, Jakob El Kholtei, Keren Bahar Halpern.

Authors and Affiliations

  1. Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Colas Droin

  2. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Jakob El Kholtei

  3. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Keren Bahar Halpern

  4. Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Clémence Hurni

  5. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Milena Rozenberg

  6. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Sapir Muvkadi

  7. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Shalev Itzkovitz

  8. Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Felix Naef

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Contributions

F.N. and S.I. conceived of the study. K.B.H., J.E.K. and C.H. prepared all the samples and performed the experiments. C.D. and F.N designed the modelling. C.D., J.E.K., K.B.H. and C.H. analyed the data. M.R. and S.M. assisted with the scRNA-seq and smFISH experiments. F.N and S.I. supervised the study. C.D., J.E.K. and F.N. wrote the manuscript. All authors reviewed the manuscript and provided input.

Corresponding authors

Correspondence to Shalev Itzkovitz or Felix Naef.

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The authors declare no competing interests.

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Peer review information Primary Handling Editors: George Caputa; Pooja Jha. Nature Metabolism thanks Dominic Grun, Ueli Schibler and Jan S. Tchorz for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 scRNA-seq pre-processing.

a, Histogram of number of UMIs per cell barcode for each mouse. Red patches mark the cells used for background estimation (100-300 UMI/cell barcode), gray patches mark the cells used for downstream analysis (1000-10000 UMI/cell barcode). b, Histogram of fraction of all UMIs mapping to mitochondrial genes. Filter used for downstream analysis in grey (0.09–0.35). c, smFISH staining of a liver lobule with probes against Cyp2e1 (red) and Cyp2f2 (green). CV = central vein, PN = portal node. Overall, the data combine 10 images from from two mice. d, Expression of Cyp2e1 and Cyp2f2 in cells with different fraction of mitochondrial expression. Three different filters for the fraction of UMIs mapping to mitochondrial genes (0–0.09, 0.09–0.35, 0.35–1) were applied, the data of all mice merged and the resulting datasets visualized as t-SNE plots. e, Violin plots for the correlations between Cyp2e1 and Cyp2f2 expression in single hepatocyte populations with different filters for fractions of mitochondrial expression. Each dot represents one mouse (n = 10 mice for each distribution) and the shape of the violin represents the density of points. f, Comparison of the zonation profiles of Z and Z + R genes obtained in our current study and the previous reconstruction from Halpern et al. 8. Profiles were interpolated to fit 15 layers, where 1 is pericentral and 8 is periportal. Dots indicate the center of mass (expression-weighted lobule layer) of the Z and Z+R genes computed in both datasets, for gene having an average expression of at least 10−5 in Halpern et al. r is the correlation coefficient, p the corresponding p-value from a standard linear regression.

Extended Data Fig. 2 Log-transformed reconstructed profiles, pre-filtering of the genes and comparison with external datasets.

a-c, Expression levels of the reconstructed profiles for the genes from Fig. 1f–h after log-transformation (Methods). Dots in represent data points from the individual mice. Lines represent mean expression per time point. Shaded areas represent one standard deviation (SD) across the mice (n=2 or n=3 depending on the time point, Methods). d, Biological variability of gene profiles across independent replicate liver samples, quantified in terms of the average relative replicate variance. 0 shows perfectly reproducible profiles while 1 the most variable genes (Methods). Genes inside the bottom-right box (x-cutoff at 10−5; y-cutoff at 0.5) are selected and contain all but one of the reference genes. Colored dots show reference zonated genes (blue) and reference rhythmic genes (orange). e, Comparison of the peak times for rhythmic genes in R and Z+R, with the dataset from Atger et al. PNAS 25. Circular correlation coefficient is 0.746 (Methods). f, Boxplot of the mRNA half-lives (data from Wang, J. et al. 35) shows that R genes as a group (median, orange line) are the shortest-lived. Box limits are lower and upper quartiles, whiskers extend up to the first datum greater/lower than the upper/lower quartile plus 1.5 times the interquartile range. Remaining points are marked. MW stands for the two-sided Mann-Whitney test, and KW stands for Kruskal-Wallis test.

Extended Data Fig. 3 Z+R and ZxR transcripts with corresponding rhythmic protein accumulation in bulk mass spectrometry data.

a-b, Rhythmic proteins corresponding to Z+R (a) and ZxR (b) transcripts were selected from Robles et al., 27 (from original Supplementary Table 2), and fitted with harmonic regression (p-value of rhythmicity from F-tests are indicated above the plot). Only proteins having a p-value<0.01 are shown. c, Scatter plot of the phase of the fits from the transcripts (x-axis) against the phase of the fits from the proteins (y-axis). The diagonal is indicated with a dashed grey line, the theoretical upper bound (6 h) for the delay between mRNA and protein is indicated with a dashed red line. All rhythmic proteins (q<0.2 in the original analysis) are represented.

Extended Data Fig. 4 Additional validations for the Z+R category.

a-b, smFISH (Stellaris, Methods) for Elovl3 (Z+R) and Arg1 (Z+R). smFISH quantifications were made for ZT0 and ZT12 (Methods). Left: representative images at ZT0, ZT12 for Elovl3 (a) or Arg1 (b). Pericentral veins (CV) and a periportal node (PN) are marked. Scale bar - 20 µm. Right: quantified profiles for each gene at the two time points from smFISH (top, line plot is the mean number of mRNAs, shaded area indicate SD across twelve images), and scRNA-seq data (bottom, line plot is mean expression, shaded area is SD across mice, n=2 or n=3 depending on the time point, Methods).

Extended Data Fig. 5 The core circadian-clock is not zonated.

a, Spatial and temporal profiles and fits for circadian core-clock genes. Peak times are indicated on the temporal representation. For the genes Cry1 and Clock, additional dashed lines represent fits for the R model, as the Schwartz BIC weights from the R and Z+R models were close (Supplementary Table 2). b, Amplitudes and peak times of the core-clock circadian genes in a polar coordinate representation (clock-wise ZT times are indicated, distance from the center corresponds to the amplitude) show the expected organization of core clock transcript in the liver.

Extended Data Fig. 6 Spatio-temporal mRNA expression profiles for heat-responsive genes. Represented genes correspond to the top 200 targets from the ChIP-Atlas list for mouse HSF1.

a, Polar plot representation of the transcripts that are R, Z+R, or ZxR among the HSF1 targets from the ChIP-Atlas (http://chip-atlas.org/). Genes involved in chaperone functions (chaperones, co-chaperones, or chaperone facilitators) are named. Color indicates zonation, while grey dots show purely rhythmic genes. b, Spatial representation of the transcripts corresponding to proteins involved in chaperone functions, separated in central (left, cytoplasmic function) and portal (right, endoplasmic reticulum function) zonation.

Extended Data Fig. 7 Rhythmicity of Wnt targets in bulk RNA-seq, and proteomics liver time series data.

a, Enrichment of rhythmic genes (R, Z+R and ZxR) among the targets of the Wnt pathway, computed on the bulk dataset (Atger et al., 25). Targets above a given percentile (x-axis) of Apc-KO fold change are considered. The percentage of rhythmic genes in the whole Atger et al. dataset is indicated by a dashed blue line. b, Bulk mRNA (coming from Atger et al. dataset) rhythmicity profiles of Wnt targets among the top-50 targets with highest Apc-KO fold change. Gene profiles are centered around their mean. An enrichment of the phases around ZT8-14 is observed, in agreement with Fig. 6a. c, Polar plot representation of the individual gene phases and amplitudes represented in panel b (bulk data). d, Temporal representation of selected genes profiles from the scRNA-seq (top, n=2 or n=3 animals depending on the time point, Methods) and bulk proteomics (bottom, data from Robles et al. 27, n=2 replicates per time point sampled every 3 h) data. Represented profiles are ones with (1) the highest Apc-KO fold change, (2) a significantly rhythmic protein (p < 0.05, standard harmonic regression, F-test), and (3) belonging to the Z+R or ZxR category. e, Enrichment/depletion at different times (window size: 3 h), of both positive and negative Ras (N=31 and N=33, respectively) and Hypoxia (N=73 and N=41, respectively) targets (background: all R and Z+R genes). Colormap shows p-values (two-tailed hypergeometric test): red (blue) indicates enrichment (depletion).

Supplementary information

Reporting Summary

Supplementary Table 1

Characteristics of fitted gene profiles.

Supplementary Table 2

Probability of each model.

Supplementary Table 3

KEGG analysis using EnrichR.

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Droin, C., Kholtei, J.E., Bahar Halpern, K. et al. Space-time logic of liver gene expression at sub-lobular scale. Nat Metab 3, 43–58 (2021). https://doi.org/10.1038/s42255-020-00323-1

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  • DOI: https://doi.org/10.1038/s42255-020-00323-1

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