Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation
Open accessAbstract
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.
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Written from the paper’s full text.
TL;DR
Combining single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, this study maps enhancer-driven gene regulatory networks in mouse liver and shows that hepatocyte zonation is encoded by core hepatocyte transcription factors plus the repressors TBX3 and TCF7L1. DeepLiver decodes enhancer grammar at base-pair resolution and predicts activity and zonation effects of sequence variants.
Key findings
- The mouse liver atlas combined 12,898 high-quality single-cell multiome cells with 16,900 snRNA-seq transcriptomes and 9,702 snATAC-seq profiles, identifying 14 cell types; snATAC-seq yielded 486,888 accessible regions grouped into 82 regulatory topics.
- Zonation affected 2,697 genes and 8,805 regions in hepatocytes along the portocentral axis (adjusted P < 0.01), plus 220 genes/281 regions in LSECs and 275 genes/475 regions in HSCs; smFISH with 100 genes identified Glul+, Cyp2e1+ and Cps1+ hepatocyte zones.
- Core hepatocyte eRegulons included HNF4A, HNF1A, CEBPA, FOXA1, ONECUT1 and NFIB, while TBX3 and TCF7L1 were identified as pericentral and periportal repressors, respectively; 94.8% of TBX3 and 89.6% of TCF7L1 target regions overlapped target regions of at least one general core TF, and TBX3 ChIP–seq signal was stronger in periportal hepatocyte regions.
- MPRA tested 10,845 accessible regions and classified 2,913 as active in at least one system (806 only in vivo, 921 only in HepG2 cells, 1,186 in both; 4,285 inactive), corresponding to 40.5% of ATAC peaks active; DeepLiver predictions correlated with a published synthetic-sequence MPRA (R = 0.68), and in silico mutagenesis correlated with experimental saturation mutagenesis at R = 0.36–0.75.
Methods
- Mouse liver data generation used two 10x single-cell multiomics (ATAC + RNA) experiments, four 10x snRNA-seq and two 10x snATAC–seq experiments, plus 100-plex smFISH, TBX3 ChIP–seq, FACS-sorted hepatocyte MPRA by CD73/ECAD, luciferase assays and public human liver snATAC–seq.
- Computational analysis used pycisTopic/pycisTarget and SCENIC+ for eGRN inference, GAM fitting over pseudotime for zonation, AUCell, PCA/ANOVA, and DeepLiver hierarchical CNNs with transfer learning from a topic-CNN to MPRA-CNN and zonation-CNN, interpreted with DeepExplainer and TF-MoDISco.
- Functional validation used MPRA libraries (12K and 455 sequences) in mouse liver and HepG2 cells, FACS-zoned in vivo MPRA, luciferase reporter assays, and SCENIC+-based in silico Tbx3/Tcf7l1 knockdown and overexpression simulations.
Limitations
- The paper states that single-cell data are sparse, reducing sensitivity to detect negative correlations and potentially leading to false-negative predictions; it also states that CNN models require large training datasets and that small datasets may overfit, motivating transfer learning.
- The paper states that only male mice were used, although analyses indicated the core regulatory networks were not affected by sex; it also notes that bulk MPRA pools periportal and pericentral hepatocytes, making zonation TF variant effects difficult to assess, which they addressed with FACS-MPRA.
- Evident from the Methods: experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment; the paper also reports a hepatocyte batch effect related to mouse physiological state.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
47 of 59 cited sources checked · 24 supported · 0 flagged · accuracy not yet measured
- Study design and conductcitation
SCENIC+ eGRNs were validated using publicly available Hi-C data and TF ChIP-seq.
“Furthermore, we validated the SCENIC+ eGRNs using publicly available Hi-C data28 and TF chromatin immunoprecipitation followed by sequencing (ChIP–seq)29 (Extended Data Fig. 4a–e).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [28] Vietri Rudan, M. et al. Comparative Hi-C reveals that CTCF underlies evolution of chromosomal domain architecture. Cell Rep. 10, 1297–1309 (2015). source
- cites [29] Ballester, B. et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 3, e02626 (2014). source
- Findingcitation
These three hepatocyte zones are in agreement with earlier spatial transcriptomics studies.
“These zones are in agreement with earlier spatial transcriptomics studies6,8.”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [6] Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017). source
The main text and Extended Data Figure 7 report that the paper’s periportal and pericentral expression patterns agree with previous studies. But the paper reconstructs a gradient across nine lobule layers and identifies additional patterns in intermediate layers; it does not establish agreement on three discrete hepatocyte zones. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [8] Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396 (2022). source
- Findingcitation
69% of the 220 LSEC zonated genes overlap with zonated liver endothelial markers described previously.
“Out of the 220 LSEC zonated genes, 69% overlap with the zonated liver endothelial markers described previously11.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [11] Inverso, D. et al. A spatial vascular transcriptomic, proteomic, and phosphoproteomic atlas unveils an angiocrine Tie–Wnt signaling axis in the liver. Dev. Cell 56, 1677–1693 (2021). source
- Premisecitation
Chromatin accessibility is not necessarily always associated with enhancer activity.
“However, chromatin accessibility is not necessarily always associated with enhancer activity35.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [35] Jacobs, J. et al. The transcription factor Grainyhead primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes. Nat. Genet. 50, 1011–1020 (2018). source
- Study design and conductcitation
DeepLiver predictions correlate well with experimental measurements from a previously published MPRA dataset on synthetic sequences in vivo (R = 0.68).
“To validate DeepLiver predictions, we used a previously published MPRA dataset performed on synthetic sequences in vivo38, finding that DeepLiver predictions correlate well with the experimental measurements (R = 0.68) (Extended Data Fig. 7e).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [38] Smith, R. P. et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat. Genet. 45, 1021–1028 (2013). source
- Premisecitation
Transfer learning from sequence models trained with large datasets is a robust alternative.
“transfer learning from sequence models trained with large datasets has recently been shown to be a robust alternative59.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [59] Salvatore, M., Horlacher, M., Winther, O. & Andersson, R. Transfer learning reveals sequence determinants of regulatory element accessibility. NAR Genom Bioinform. 5, 2 (2023). source
- Rests on an assumption: CNN models require large input datasets for training.
- Study design and conductown data
DeepLiver predictions of the effect of enhancer mutations correlate with experimental saturation mutagenesis data on six enhancers from earlier studies (correlation ranging between 0.36 and 0.75).
“DeepLiver predictions of the effect of enhancer mutations correlate with experimental results (with a correlation ranging between 0.36 and 0.75; Extended Data Fig. 7f).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [41] Patwardhan, R. P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012). source
The Abstract and Results section report experimental saturation-mutagenesis measurements of mutation effects for three mammalian liver enhancers, so this paper supports the identification of an earlier experimental dataset. It does not report six enhancers or a comparison with DeepLiver predictions; the correlations of 0.36–0.75 are not established by this source. Page numbers are not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites [42] Kircher, M. et al. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10, 3583 (2019). source
- Study design and conductown data
Predicted binding sites for HNF4A, CEBPA, FOXA1 and ONECUT1 show specific ChIP-seq signals when centring the regions on the predicted binding sites.
“To validate the predicted binding sites, we compared our predictions with previously published ChIP–seq data for HNF4A, CEBPA, FOXA1 and ONECUT129, finding specific signals for the corresponding TFs when centring the regions on the predicted binding sites (Extended Data Fig. 8d).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [29] Ballester, B. et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 3, e02626 (2014). source
- Study design and conductown data
eRegulons were validated using publicly available Hi-C and ChIP-seq data.
“To validate these eRegulons, we used publicly available Hi-C and ChIP–seq data28,29.”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [28] Vietri Rudan, M. et al. Comparative Hi-C reveals that CTCF underlies evolution of chromosomal domain architecture. Cell Rep. 10, 1297–1309 (2015). source
- cites [29] Ballester, B. et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 3, e02626 (2014). source
- Study design and conductown data
DeepLiver in silico mutagenesis was validated by calculating the correlation between the effect of mutations predicted by DeepLiver and experimental saturation mutagenesis data on six enhancers from earlier studies (three in vivo, three HepG2).
“To validate DeepLiver in silico mutagenesis, we calculated the correlation between the effect of the mutations predicted by DeepLiver and experimental saturation mutagenesis data on six enhancers from earlier studies (three enhancers from in vivo studies and three enhancers from HepG2 cells)41,42.”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [41] Patwardhan, R. P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012). source
- cites [42] Kircher, M. et al. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10, 3583 (2019). source
- Study design and conductcitation
DeepLiver predictions were validated by calculating the correlation between DeepLiver predictions and a previously published MPRA dataset performed on synthetic sequences in vivo.
“To validate DeepLiver predictions, we calculated the correlation between the predictions of DeepLiver and a previously published MPRA dataset performed on synthetic sequences in vivo38.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [38] Smith, R. P. et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat. Genet. 45, 1021–1028 (2013). source
- Findingown data
There is a significant correlation between average chromatin accessibility around genes and gene expression (P < 2.2 × 10−16).
“Moreover, we found a significant correlation between the average chromatin accessibility around the genes and gene expression (P < 2.2 × 10−16; Extended Data Fig. 2a–e).”
Quote in the paperNot a substantial claim - Findingown data
The study mapped enhancer-gene regulatory networks across mouse liver cell types using single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning.
“Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types.”
Quote in the paperSubstantial claim- Rests on an assumption: The regulatory programs and enhancer logic encoding hepatocyte zonation states, and how the zonation state interacts with the core hepatocyte GRNs, are largely unclear.
- Findingown data
Regions that are accessible in hepatocytes show significantly higher enhancer activity compared with shuffled sequences.
“Regions that are accessible in hepatocytes show significantly higher enhancer activity compared with shuffled sequences (Fig. 3b,c), and replicate MPRA analyses are strongly correlated (with a correlation ranging between 0.82 and 1; Extended Data Fig. 6a).”
Quote in the paperSubstantial claim - Findingown data
220 and 275 genes and 281 and 475 regions vary along the portocentral axis in LSECs and HSCs, respectively (adjusted P < 0.01).
“We identified 220 and 275 genes and 281 and 475 regions that vary along the portocentral axis in LSECs and HSCs, respectively (adjusted P < 0.01; Methods).”
Quote in the paperNot a substantial claim - Findingown data
Zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types.
“We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types.”
Quote in the paperSubstantial claim - Findingown data
PCA of eGRN enrichment scores classified hepatocyte eRegulons based on their zonation state and mouse specificity.
“As we previously observed transcriptomic and epigenomic differences between the mice depending on their physiological state (Supplementary Note), we performed principal component analysis (PCA) of the eGRN enrichment scores to classify hepatocyte eRegulons on the basis of their zonation state and mouse specificity (Fig. 2c).”
Quote in the paperSubstantial claim - Findingown data
Some eRegulons were affected both by physiological status and zonation, including ESRRA and FOXQ1 (periportal) and PPARA (pericentral).
“Some eRegulons were affected both by the physiological status of the mice and zonation, including ESRRA and FOXQ1 (periportal) and PPARA (pericentral).”
Quote in the paperSubstantial claim - Findingown data
The HNF4A eRegulon, with 3,442 target genes and 26,127 target regions, contributes to both the core and the physiological-state-dependent programs.
“For example, the HNF4A eRegulon, with 3,442 target genes and 26,127 target regions, contributes to both the core and the physiological-state-dependent programs (Extended Data Fig. 4f–i).”
Quote in the paperSubstantial claim - Findingown data
LSEC and HSC clusters are also represented as a gradient, reflected by distinct chromatin accessibility topics and gene expression.
“Furthermore, the LSEC and HSC clusters were also represented as a gradient, reflected by distinct chromatin accessibility topics and gene expression (Extended Data Fig. 3).”
Quote in the paperNot a substantial claim - Findingown data
TBX3 LOF in inactive enhancers did not rescue the enhancers Hsd17b13 and Ass1.
“As predicted by DeepLiver, TBX3 LOF in inactive enhancers did not rescue the enhancers (Hsd17b13 and Ass1).”
Quote in the paperSubstantial claim - Findingown data
TBX3-binding sites predicted by SCENIC+ were less active in HepG2 cells compared with in vivo.
“The TBX3-binding sites predicted by SCENIC+ were less active in HepG2 cells compared with in vivo (Fig. 3e)”
Quote in the paperNot a substantial claim - Findingown data
HNF4A, FOXA1 and ONECUT1 ChIP-seq signals are observed in this Cyp2e1 enhancer region.
“In agreement, we observed HNF4A, FOXA1 and ONECUT1 ChIP–seq signals in this region (Fig. 6e).”
Quote in the paperNot a substantial claim - Findingown data
Zonation-CNN added TBX3 and TCF7L1 motifs as crucial sequence features to the hepatocyte enhancers.
“The zonation-CNN added TBX3 and TCF7L1 motifs as crucial sequence features to the hepatocyte enhancers”
Quote in the paperSubstantial claim - Findingreasoning
SCENIC+ could identify candidate repressors because their motif is significantly enriched in regulatory regions that are accessible in hepatocytes in which the TF is not expressed, while they are inaccessible in hepatocytes in which the TF is expressed.
“SCENIC+ could identify these candidate repressors because their motif is significantly enriched in regulatory regions that are accessible in hepatocytes in which the TF is not expressed, while they are inaccessible in hepatocytes in which the TF is expressed.”
Quote in the paperSubstantial claim - Findingown data
TF ChIP-seq showed that TBX3 binds to periportal regions that have low accessibility or are not accessible in pericentral hepatocytes, where Tbx3 is expressed.
“Accordingly, TF ChIP–seq showed that TBX3 binds to periportal regions that have low accessibility or are not accessible in pericentral hepatocytes, where Tbx3 is expressed.”
Quote in the paperSubstantial claim - Findingown data
TBX3 target regions are more active in vivo (20% and 5% of the regions are active in vivo and in HepG2 cells, respectively).
“TBX3 target regions are more active in vivo (20% and 5% of the regions are active in vivo and in HepG2 cells, respectively).”
Quote in the paperNot a substantial claim - Findingown data
TCF7L1 LOF resulted in an increase in enhancer activity in the periportal population.
“TCF7L1 LOF resulted in an increase of activity in the periportal population”
Quote in the paperSubstantial claim - Findingreasoning
TCF7L1 and HNF4A motifs often overlap, probably due to the similarity between the motifs (GATCAAAG and CAAAGTCA).
“This showed that TCF7L1 and HNF4A often overlap, which is probably due to the similarity between the motifs (GATCAAAG and CAAAGTCA, respectively; with the common bases between the motifs highlighted in bold).”
Quote in the paperNot a substantial claim - Findingown data
The mechanisms underlying hepatocyte zonation are controlled by the repressor TFs TCF7L1 and TBX3.
“we could disentangle mechanisms underlying hepatocyte zonation, controlled by the repressor TFs TCF7L1 and TBX3”
Quote in the paperSubstantial claim - Findingown data
Predicted active enhancers Aspg, Cdh1 and Dlgap1 exhibited increased activity when the TBX3-binding site was mutated.
“However, the predicted active enhancers (Aspg, Cdh1 and Dlgap1) exhibited increased activity when the TBX3-binding site was mutated.”
Quote in the paperSubstantial claim - Findingown data
The core regulatory networks identified are not affected by sex.
“Importantly, although we exclusively used male mice, our analyses show that these core regulatory networks are not affected by sex.”
Quote in the paperNot a substantial claim - Findingown data
FOXA1, HNF1A, CEBPA, NFIB and TBX3 motifs are often located close to HNF4A motifs.
“On the other hand, FOXA1, HNF1A, CEBPA, NFIB and TBX3 are often located close to HNF4A motifs (Extended Data Fig. 8e,f).”
Quote in the paperNot a substantial claim - Findingown data
Destruction of HNF1A, HNF4A, CEBPA and FOXA1 motifs reduced enhancer activity compared with wild-type counterparts.
“The destruction of these motifs reduced enhancer activity compared with their wild-type counterparts”
Quote in the paperSubstantial claim - Findingown data
TCF7L1 expression is anticorrelated with region accessibility and gene expression (−0.32 and −0.40).
“TCF7L1 expression is anticorrelated with region accessibility and gene expression (−0.32 and −0.40; Fig. 6b–e).”
Quote in the paperSubstantial claim - Findingown data
GOF variants of HNF4A, HNF1A, CEBPA and FOXA1 resulted in higher enhancer activity, as predicted by DeepLiver.
“GOF variants of HNF4A, HNF1A, CEBPA and FOXA1 indeed resulted in higher activity, as predicted by DeepLiver”
Quote in the paperSubstantial claim - Findingown data
SCENIC+ identified a core hepatocyte GRN controlled by HNF4A, HNF1A, CEBPA, FOXA1, NFIB and ONECUT1.
“First, SCENIC+7 identified a core hepatocyte GRN controlled by HNF4A, HNF1A, CEBPA, FOXA1, NFIB and ONECUT1”
Quote in the paperSubstantial claim - Findingown data
DeepLiver predicts that hepatocyte enhancers consist of a core hepatocyte code mixed with binding sites of the zonated repressor TFs TBX3 and TCF7L1, which bias enhancer activity to pericentral and periportal zones, respectively.
“The DeepLiver model provides meaningful interpretations of hepatocyte enhancers and predicts that these enhancers consist of a core hepatocyte code, mixed with binding sites of the zonated repressor TFs, TBX3 and TCF7L1, which bias enhancer activity to either pericentral or periportal zones, respectively.”
Quote in the paperSubstantial claim - Findingown data
Of the regions active in human HepG2 cells, 54% were promoters.
“By contrast, of the regions active in human HepG2 cells, 54% were promoters (Fig. 3d and Extended Data Fig. 6c).”
Quote in the paperNot a substantial claim - Findingown data
The paper shows direct implication of TCF7L1 and TBX3 in liver zonation regulation by enhancer-GRN mapping.
“here we show their direct implication in liver zonation regulation by enhancer-GRN mapping.”
Quote in the paperSubstantial claim - Findingown data
Motif enrichment analysis of active versus inactive regions followed by classifier-based feature selection using random-forest models identified HNF1A, HNF4A, FOXA1, CREB and AP-1 motifs as determining features in active enhancers.
“In agreement with this, motif enrichment analysis of active versus inactive regions followed by classifier-based feature selection using random-forest models identified HNF1A, HNF4A, FOXA1, CREB and AP-1 motifs as determining features in active enhancers (Fig. 3g, Methods and Extended Data Fig. 6d,e).”
Quote in the paperSubstantial claim - Findingown data
The SCENIC+ eGRN predicts that TBX3 and TCF7L1 directly repress each other.
“The SCENIC+ eGRN predicts that TBX3 and TCF7L1 directly repress each other.”
Quote in the paperSubstantial claim - Findingown data
TBX3 LOF variants showed increased activity in HepG2 cells as determined using MPRA.
“TBX3 LOF variants showed increased activity in HepG2 cells as determined using MPRA”
Quote in the paperSubstantial claim - Findingown data
Simulation of Tbx3 overexpression and Tcf7l1 knockdown can switch periportal hepatocytes to a pericentral state.
“Simulation of Tbx3 or Tcf7l1 knockdown and overexpression in hepatocytes (Methods) suggests that Tbx3 overexpression and Tcf7l1 knockdown can switch periportal hepatocytes to a pericentral state”
Quote in the paperSubstantial claim - Findingown data
The paper provides an extensive resource of the adult mouse liver, including a spatial and single-cell multi-omics atlas, eGRNs and enhancer activity.
“We provide an extensive resource of the adult mouse liver, including a spatial and single-cell multi-omics atlas, eGRNs and enhancer activity”
Quote in the paperSubstantial claim - Premisecitation
Blood flow from the portal vein to the central vein in each liver lobule creates a gradient of nutrients, oxygen, hormones and morphogens, resulting in a highly variable environmental axis.
“In each liver lobule, blood flows from the portal vein to the central vein, creating a gradient of nutrients, oxygen, hormones and morphogens that results in a highly variable environmental axis5 (Fig. 1a).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. 16, 395–410 (2019). source
- Premisecitation
Hepatocyte function and metabolism varies along the portocentral axis of the liver lobule, a phenomenon known as zonation.
“In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule.”
- Also stated in: § Main · plain
“Hepatocyte function and metabolism varies depending on the position along this portocentral axis, as they are exposed to different microenvironments, a phenomenon known as zonation5.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. 16, 395–410 (2019). source
- Also stated in: § Main · plain
- Study design and conductown data
Replicate MPRA analyses are strongly correlated (correlation ranging between 0.82 and 1).
“and replicate MPRA analyses are strongly correlated (with a correlation ranging between 0.82 and 1; Extended Data Fig. 6a).”
Quote in the paperNot a substantial claim - Interpretationown data
These cell states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors such as HNF4A, CEBPA, FOXA1 and ONECUT1.
“These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1.”
Quote in the paperSubstantial claim - Interpretationown data
Physiological states are not independent of the hepatocyte core eGRN.
“These physiological states are not independent of the hepatocyte core eGRN.”
Quote in the paperSubstantial claim - Interpretationown data
The spatial single-cell multiome atlas reveals that both cell type identity and cell states are congruently reflected at both the transcriptome and chromatin-accessibility level.
“In summary, our spatial single-cell multiome atlas of the mouse liver reveals that both cell type identity and cell states are congruently reflected at both the transcriptome and chromatin-accessibility level.”
Quote in the paperSubstantial claim - Interpretationown data
The hepatocyte zonation eGRN is a subset (or a layer) of the general hepatocyte eGRN.
“This suggests that the hepatocyte zonation eGRN is a subset (or a layer) of the general hepatocyte eGRN.”
Quote in the paperSubstantial claim - Interpretationown data
The study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state.
“Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.”
Quote in the paperSubstantial claim - Study design and conductown data
Using publicly available scRNA-seq data from male and female mice, the hepatocyte core eGRN is not affected by sexual dimorphism.
“Using publicly available scRNA-seq data from male and female mice, we confirmed that the hepatocyte core eGRN is not affected by sexual dimorphism (Supplementary Note and Supplementary Fig. 6).”
Quote in the paperNot a substantial claim - Study design and conductreasoning
The transfer-learning strategy overcomes the limited number of regions available for activity (4,215) and zonation (4,181 pericentral, 1,372 periportal, 12,122 general).
“This transfer-learning strategy overcomes the limited number of regions that we have available for activity (4,215) and zonation (4,181 pericentral, 1,372 periportal, 12,122 general) (Methods).”
Quote in the paperSubstantial claim - Interpretationown data
Aspg, Cdh1 and Dlgap1 enhancers are directly repressed by TBX3 through these sites.
“This indicates that these enhancers are directly repressed by TBX3 through these sites.”
Quote in the paperSubstantial claim - Study design and conductown data
The sorted cell fractions represented pericentral and periportal hepatocytes, as shown by bulk ATAC-seq profiles on the separate fractions agreeing with snATAC-seq zonated profiles.
“The sorted cell fractions indeed represented pericentral and periportal hepatocytes, as shown by bulk ATAC–seq profiles on the separate fractions, which agreed with the snATAC–seq zonated profiles”
Quote in the paperSubstantial claim - Interpretationown data
The paper unravelled the regulatory grammar underlying hepatocyte identity.
“In conclusion, we unravelled the regulatory grammar underlying hepatocyte identity.”
Quote in the paperSubstantial claim- Rests on an assumption: The mouse liver is a suitable model system for studying hepatocyte identity and zonation.
- Interpretationown data
The first and second principal components largely explained variance due to zonation and sample biases, respectively, based on the distribution of ANOVA P values over the PCs.
“We found that the first and second principal components largely explained the variance due to zonation and sample biases, respectively, based on the distribution of the ANOVA P value over the PCs.”
Quote in the paperNot a substantial claim- Rests on an assumption: The pseudotime order represents the distance along the portocentral axis.
- May combine two claims. 70
- Classified as probably not a substantial claim. 50
- The quote only partly states this claim. 3
May combine two claims.
InterpretationcitationTCF7L1 and TCF7L2 may bind to the same motif-TCF7L2 pericentrally for activation and TCF7L1 periportally for repression.
“this may suggest that TCF7L1 and TCF7L2 bind to the same motif—TCF7L2 pericentrally for activation and TCF7L1 periportally for repression14.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [14] Shy, B. R. et al. Regulation of Tcf7l1 DNA binding and protein stability as principal mechanisms of Wnt/β-catenin signaling. Cell Rep. 4, 1–9 (2013). source
The Introduction and Results report Tcf7l1 acting as a transcriptional repressor, including in DNA-binding-dependent reporter experiments. They do not establish that TCF7L1 and TCF7L2 bind the same motif or assign them periportal and pericentral roles; the reported experiments concern embryonic stem cells and breast cancer cells, not liver zonation. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationBEC clusters are located in the bile ducts; pericentral and periportal VECs surround the corresponding vessels, together with fibroblasts; and Kupffer cells are preferentially located in periportal and mid zones without a strong zonation pattern.
“Moreover, BEC clusters could be clearly located in the bile ducts; pericentral and periportal VECs surround the corresponding vessels, together with fibroblasts; and Kupffer cells are preferentially located in periportal and mid zones without a strong zonation pattern, in agreement with recent studies8.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [8] Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396 (2022). source
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
Findingown data40.5% of ATAC peaks are active by MPRA, consistent with other studies.
“In other words, 40.5% of ATAC peaks are active by MPRA, consistent with other studies37.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [37] Kwasnieski, J. C., Fiore, C., Chaudhari, H. G. & Cohen, B. A. High-throughput functional testing of ENCODE segmentation predictions. Genome Res. 24, 1595–1602 (2014). source
The Discussion (page unavailable) reports that only about 26% of predicted enhancer sequences were active in its reporter assay, which supports the broad comparison that not all tested candidate regions are active. The paper does not report ATAC peaks or a 40.5% activity rate, so it cannot substantiate that specific figure or population. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataGeneral hepatocyte-specific eRegulons were found for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB, and zonation-associated eRegulons such as ESR1 and SOX9 periportally and PPARG and AR pericentrally.
“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB21–23, and zonation-associated eRegulons such as ESR1 and SOX9 periportally24,25 and PPARG and AR pericentrally26,27.”
Quote in the paperSubstantial claim2 of 7 sources checked- cites [21] Kyrmizi, I. et al. Plasticity and expanding complexity of the hepatic transcription factor network during liver development. Genes Dev. 20, 2293–2305 (2006). source
The passage labeled Abstract identifies HNF-1α, HNF-4, C/EBPα, FoxA1 and HNF-6 (ONECUT1) as regulators of liver-specific gene expression, supporting the background for much of the cited list. It does not identify NFIB or report the citing authors’ eRegulon findings. The zonation claims carry separate citations and are not attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [22] Lau, H. H., Ng, N. H. J., Loo, L. S. W., Jasmen, J. B. & Teo, A. K. K. The molecular functions of hepatocyte nuclear factors—in and beyond the liver. J. Hepatol. 68, 1033–1048 (2018). source
- cites [23] Roy, S. et al. miR-1224 inhibits cell proliferation in acute liver failure by targeting the antiapoptotic gene Nfib. J. Hepatol. 67, 966–978 (2017). source
- cites [24] Uebi, T., Umeda, M. & Imai, T. Estrogen induces estrogen receptor alpha expression and hepatocyte proliferation in the livers of male mice. Genes Cells 20, 217–223 (2015). source
- cites [25] Alison, M. R. & Lin, W.-R. Periportal SRY (sex determining region Y)-box 9-positive hepatocytes: progenitors with a biliary leaning. Hepatology 70, 1470–1473 (2019). source
- cites [26] Berthier, A., Johanns, M., Zummo, F. P., Lefebvre, P. & Staels, B. PPARs in liver physiology. Biochim. Biophys. Acta 1867, 166097 (2021). source
Section 3.2, Liver zonation (page not supplied), reports striking centrilobular Pparg expression in mouse hepatocytes, supporting the pericentral PPARG association. It does not report a PPARG eRegulon or establish pericentral AR eRegulon activity. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [27] Chen, K.-W., Chen, Y.-S., Chen, P.-J. & Yeh, S.-H. Androgen receptor functions in pericentral hepatocytes to decrease gluconeogenesis and avoid hyperglycemia and obesity in male mice. Metabolism 135, 155269 (2022). source
May combine two claims.
Findingown dataNtn4+ LSECs and Ngfr+ HSCs are located periportally, whereas Kit+ LSECs and Spon2+ HSCs are located pericentrally.
“This integrated spatial and single-cell analysis also confirmed that Ntn4+ LSECs and Ngfr+ HSCs were located periportally, whereas Kit+ LSECs and Spon2+ HSCs were located pericentrally, as previously reported9,10 (Extended Data Fig. 3).”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [9] Su, T. et al. Single-cell transcriptomics reveals zone-specific alterations of liver sinusoidal endothelial cells in cirrhosis. Cell. Mol. Gastroenterol. Hepatol. 11, 1139–1161 (2021). source
In Results, “Spatial Lobular Locations of Heterogeneous Liver EC Populations Were Determined” (page unavailable), Ntn4 is identified as a periportal landmark and Kit as a marker of pericentral LSECs. The paper does not establish the claimed periportal location of Ngfr+ HSCs or pericentral location of Spon2+ HSCs. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [10] Dobie, R. et al. Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis. Cell Rep. 29, 1832–1847 (2019). source
In Results, “Uncovering HSC Zonation across the Healthy Liver Lobule” (page unavailable), the paper places NGFR-high HSCs near the portal vein, supporting the Ngfr+ HSC portion. It identifies Adamtsl2-high, rather than Spon2+ HSCs as central-vein-associated, and our copy does not establish the claimed locations of Ntn4+ or Kit+ LSECs. (LLM, conf=0.89)
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataSCENIC+ analysis revealed 180 eRegulons, including SPI1, JDP2, RUNX1 in Kupffer cells; EBF1 and PAX5 in B cells; GATA3 and LEF1 in T cells; GATA4, MEIS1 and MAF in LSECs; LHX2 and TEAD1 in HSCs; WT1 in fibroblasts; and TEAD4, DOX4 and HNF1B in BECs, among others, in agreement with literature.
“This analysis revealed 180 eRegulons, including SPI1, JDP2, RUNX1 in Kupffer cells; EBF1 and PAX5 in B cells; GATA3 and LEF1 in T cells; GATA4, MEIS1 and MAF in LSECs; LHX2 and TEAD1 in HSCs; WT1 in fibroblasts; and TEAD4, DOX4 and HNF1B in BECs, among others, and in agreement with literature15–20.”
Quote in the paperNot a substantial claim4 of 6 sources checked- cites [15] Bennett, H., Troutman, T. D., Sakai, M. & Glass, C. K. Epigenetic regulation of kupffer cell function in health and disease. Front. Immunol. 11, 609618 (2021). source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [16] Bullerwell, C. E. et al. EBF1 drives hallmark B cell gene expression by enabling the interaction of PAX5 with the MLL H3K4 methyltransferase complex. Sci. Rep. 11, 1537 (2021). source
- cites [17] Stolarczyk, E., Lord, G. M. & Howard, J. K. The immune cell transcription factor T-bet. Adipocyte 3, 58–62 (2014). source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [18] de Haan, W. et al. Unraveling the transcriptional determinants of liver sinusoidal endothelial cell specialization. Am. J. Physiol. Gastrointest. Liver Physiol. 318, G803–G815 (2020). source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [19] Mann, J. & Mann, D. A. Transcriptional regulation of hepatic stellate cells. Adv. Drug Deliv. Rev. 61, 497–512 (2009). source
- cites [20] Poncy, A. et al. Transcription factors SOX4 and SOX9 cooperatively control development of bile ducts. Dev. Biol. 404, 136–148 (2015). source
Classified as probably not a substantial claim.
PremisecitationPrevious single-cell and spatial transcriptomics studies have shown that the hepatocyte transcriptome varies along the portocentral axis.
“Previous single-cell and spatial transcriptomics studies have shown that not only hepatocyte function, but also their transcriptome, varies along this axis6.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [6] Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017). source
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationRecent studies define a cell type as a continuous set of cell states aligned with the range of cellular phenotypes resulting from interaction between the cell type and its microenvironment.
“Recent studies define a cell type as a (continuous) set of cell states, which can be aligned with the range of cellular phenotypes resulting from the interaction between a cell type and its microenvironment4.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [4] Zeng, H. What is a cell type and how to define it? Cell 185, 2739–2755 (2022). source
In “Cell types versus cell states” (page unavailable), the paper says a cell type can exist in different states, that states can respond to context, and that state transitions tend to be continuous. It does not define a cell type as a continuous set of states or equate that set with the range of phenotypes produced by interaction with its microenvironment. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataeRegulons depend on nutritional status (AGMAT, MLXIPL), hormone levels (NR1I2, NR1I3, RXRA), and circadian rhythm (CLOCK).
“This enabled us to identify eRegulons that depend on nutritional status, such as AGMAT30 and MLXIPL31; on hormone levels, such as NR1I2, NR1I3 and RXRA32; and on circadian rhythm, such as CLOCK33.”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [30] Iyer, R. K., Kim, H. K., Tsoa, R. W., Grody, W. W. & Cederbaum, S. D. Cloning and characterization of human agmatinase. Mol. Genet. Metab. 75, 209–218 (2002). source
- cites [31] Hunt, L. C. et al. The glucose-sensing transcription factor MLX promotes myogenesis via myokine signaling. Genes Dev. 29, 2475–2489 (2015). source
- cites [32] Sever, R. & Glass, C. K. Signaling by nuclear receptors. Cold Spring Harb. Perspect. Biol. 5, a016709 (2013). source
The Abstract (page not provided) describes hormone-regulated nuclear-receptor transcription, and Table 1 identifies RXR as a receptor with a ligand. But the copy does not identify NR1I2 or NR1I3 or establish that eRegulons associated with these three genes depend on hormone levels. The nutritional-status and circadian-rhythm examples have separate citations and are not assessed here. (LLM, conf=0.83)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationTF footprinting suggests that direct repressor binding may occur within nucleosome-occupied regions, while activator binding is strongly associated with nucleosome depletion.
“TF footprinting suggests that direct repressor binding may occur within nucleosome-occupied regions, while activator binding is strongly associated with nucleosome depletion54.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [54] Berest, I. et al. Quantification of differential transcription factor activity and multiomics-based classification into activators and repressors: diffTF. Cell Rep. 29, 3147–3159 (2019). source
In “Determination of the molecular function of TFs: transcriptional repressors and activators” (page unavailable), the REST footprint shows accessibility at the binding motif but compact surrounding chromatin, whereas the STAT2 footprint shows increased accessibility around the binding site. This supports the repressor–activator contrast, but the paper does not establish that repressors bind within nucleosome-occupied regions or directly measure nucleosome depletion in this comparison. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTcf7l1 and Tbx3 expression is zonated in the adult mouse liver.
“while it has been previously reported that Tcf7l1 and Tbx3 expression is zonated in the adult mouse liver43,53”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [43] Ben-Moshe, S. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat. Metab. 1, 899–911 (2019). source
The Results section (“Regulation of Wnt signaling components by miRNA”) reports that Tcf7l1 is periportally zonated in hepatocytes, supporting that part of the claim. Our copy does not report zonated Tbx3 expression; a search for “Tbx3” found no passage addressing it. No page number was available in the indexed copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [53] Brosch, M. et al. Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control. Nat. Commun. 9, 4150 (2018). source
The 'Signatures of transcriptional and epigenetic zonation' section reports strong zonation of TBX3 expression, but the finding is from human liver. Our copy does not report zonated Tcf7l1 expression in adult mouse liver; its discussion of a similarly named factor concerns TCF7L2, not TCF7L1. No page number is available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationHuman HepG2 cells express Tbx3 and can be used as a model to test the effect of mutating TBX3 sites in hepatocyte enhancer sequences.
“Human HepG2 cells express Tbx3 and can therefore be used as a model to test the effect of mutating TBX3 sites in hepatocyte enhancer sequences45.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [45] Feng, X. et al. T-box transcription factor Tbx3 contributes to human hepatocellular carcinoma cell migration and invasion by repressing E-cadherin expression. Oncol. Res. 26, 959–966 (2018). source
The Results section, “Expression of Tbx3 in HCC Tissues and Cell Lines” (page not supplied), reports Tbx3 protein expression in HepG2 cells. The paper does not establish HepG2 as a model for testing mutations of TBX3 sites in hepatocyte enhancer sequences; its experiments address HCC cell migration and invasion. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTCF7L1 and TBX3 are well-known repressors in development.
“TCF7L1 and TBX3 are indeed well-known repressors in development51,52”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [51] Athanasouli, P. et al. The Wnt/TCF7L1 transcriptional repressor axis drives primitive endoderm formation by antagonizing naive and formative pluripotency. Nat. Commun. 14, 1210 (2023). source
- cites [52] Khan, S. F. et al. The roles and regulation of TBX3 in development and disease. Gene 726, 144223 (2020). source
May combine two claims.
FindingcitationActivity-CNN added ETS and AP-1 sites underlying higher enhancer activity, in agreement with previous MPRA studies in the liver.
“whereas the activity-CNN added ETS and AP-1 sites underlying higher enhancer activity, in agreement with previous MPRA studies in the liver60.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [60] Klein, J. C. et al. A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nat. Methods 17, 1083–1091 (2020). source
In Results (‘Different MPRA designs yield different results’; page unavailable), ETS-related features are more predictive of episomal activity. In Results (‘Appending sequence context leads to differences in the results of MPRAs’; page unavailable), predicted binding of JUN and FOS, components of AP-1, helps explain activity differences between enhancer lengths in HepG2 assays. The paper does not establish that adding both ETS and AP-1 sites raises enhancer activity; it cautions that correlated features limit causal interpretation. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
Study design and conductcitationPredicted binding hits were validated using ChIP-seq data for HNF4A, ONECUT1, CEBPA and FOXA1.
“To validate the predicted binding hits, we used the ChIP–seq data for HNF4A, ONECUT1, CEBPA and FOXA129, as described for the SCENIC+ eRegulons.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [29] Ballester, B. et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 3, e02626 (2014). source
Classified as probably not a substantial claim.
Study design and conductcitationBatch effects between samples were corrected using harmonypy on the scaled topic distributions.
“Batch effects between samples were corrected using harmonypy70 (v.0.0.6) on the scaled topic distributions”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [70] Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). source
Classified as probably not a substantial claim.
Study design and conductcitationPseudotime order was calculated using DPT per cell type using harmony-corrected PCs and topics from snRNA-seq and snATAC-seq.
“Pseudotime order was calculated using the DPT() function of destiny (v.3.2.0)80 per cell type using as input the harmony corrected PCs and topics from the snRNA-seq and snATAC–seq analyses, respectively.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [80] Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016). source
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataEnriched motifs and TF cistromes were identified using pycisTarget with a custom database on DARs, binarized topics, curated DARs and topics, and MACS2 bdgdiff regions, with and without promoters, and using pycisTarget and DEM7.
“pycisTarget (v.1.0.1.dev42+gb6707ee) was run using a custom database with the consensus regions on DARs, binarized topics (with Otsu thresholding), curated DARs and topics and MACS2 (v.2.2.7.1) bdgdiff regions, with and without promoters, and using pycisTarget and DEM7.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [7] Bravo González-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat. Methods. https://doi.org/10.1038/s41592-023-01938-4 (2023). source
The Methods (PBMC analysis; Spatial GRN mapping, page unavailable) describe using pycisTarget with a custom consensus-region database on DARs and Otsu-binarized topics, with and without promoters, using cisTarget and DEM. Our copy does not describe the claimed curated DARs and topics or MACS2 bdgdiff regions as inputs to that analysis; it also reports a different pycisTarget version. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationsnRNA data from male and female livers was obtained from a previous study.
“snRNA data from male and female livers was obtained from a previous study88.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [88] Goldfarb, C. N., Karri, K., Pyatkov, M. & Waxman, D. J. Interplay between GH-regulated, sex-biased liver transcriptome and hepatic zonation revealed by single-nucleus RNA sequencing. Endocrinology 163, bqac059 (2022). source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe 12K library included 795 shuffled regions as negative control and 360 positive controls that previously showed activity in HepG2 and have mouse orthologous regions accessible in mouse liver.
“Moreover, we included 795 shuffled regions as a negative control, and 360 positive controls that previously showed activity in HepG2, and for which a mouse orthologous region can be found that is also accessible in the mouse liver.”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [60] Klein, J. C. et al. A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nat. Methods 17, 1083–1091 (2020). source
The Results section (“Implementation and testing of nine MPRA strategies”; page unavailable) describes positive controls previously shown to have enhancer activity in HepG2, supporting that part of the claim. But it describes a 2,440-sequence library with 204 controls, not a 12K library with 795 shuffled negative controls and 360 positive controls. The cited passage does not establish the claimed mouse-ortholog or mouse-liver accessibility criteria. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [91] Ernst, J. et al. Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nat. Biotechnol. 34, 1180–1190 (2016). source
The Online Methods describe a 250-region pilot design and a 15,720-region scale-up design, but do not describe the claimed 12K library. Searches for 795 shuffled regions, 360 positive controls, and mouse-liver-accessible orthologs found no passage addressing those details. (LLM, conf=0.75)
Experimental — accuracy not yet measured - cites [92] The ENCODE Project Consortium et al.Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020). source
The ‘Transposase accessible regions’ section describes accessible regions in mouse tissues, and ‘Test cCREs with transgenic mouse assays’ describes a different set of 151 tested regions. Neither identifies a 12K library or the claimed controls. Searches for 795, 360, HepG2, and the 12K library found no passage establishing their numbers or selection criteria. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.
Study design and conductown dataSCENIC+ was run with default parameters on the complete dataset and on hepatocytes only, using only multiome cells for eGRN inference.
“SCENIC+ was run with the default parameters on the complete dataset and using only hepatocytes, using http://nov2020.archive.ensembl.org/ as Biomart host.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [7] Bravo González-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat. Methods. https://doi.org/10.1038/s41592-023-01938-4 (2023). source
May combine two claims.
Findingown dataAmong general core eRegulons, ONECUT1, CEBPA, HNF1A, FOXA1 and NFIB were identified, while TBX3 and TCF7L1 are core pericentral and periportal (repressive) eRegulons, respectively.
“Among the general core (shared across all mice) eRegulons, we identified ONECUT1, CEBPA, HNF1A, FOXA1 and NFIB, while TBX3 and TCF7L1 are core pericentral and periportal (repressive) eRegulons, respectively (Fig. 2d).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn all animals, there is a unidirectional gradient within hepatocytes, corroborated by both gene expression and region accessibility.
“In all animals, we found a unidirectional gradient within hepatocytes, corroborated both by gene expression and region accessibility (Fig. 1c–f).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOther hepatocyte motifs (HNF4A, CEBPA, FOXA1, ONECUT1) are enriched in TBX3 ChIP-seq regions, and there is strong overlap with ChIP-seq regions for these TFs, supporting interaction between the general and zonated hepatocyte programs.
“Together with the TBX3 motif, we also found other hepatocyte motifs enriched in the TBX3 ChIP–seq regions, such as HNF4A, CEBPA, FOXA1 and ONECUT1, and a strong overlap with the ChIP–seq regions for these TFs, which further supports the interaction between the general and the zonated hepatocyte programs (Extended Data Fig. 5c–f).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data94.8% and 89.6% of the target regions of TBX3 and TCF7L1, respectively, overlap with the target regions of at least one general core TF (HNF4A, HNF1A, CEBPA, ONECUT1, FOXA1, NFIB).
“This cooperativity, or blending, of the hepatocyte GRN with TFs controlling cell state is even stronger for zonation: 94.8% and 89.6% of the target regions of TBX3 and TCF7L1, respectively, overlap with the target regions of at least one of the general core TFs (HNF4A, HNF1A, CEBPA, ONECUT1, FOXA1, NFIB).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTBX3 ChIP-seq signal was stronger in periportal hepatocyte regions, and TBX3 ChIP-seq peaks were more accessible in periportal hepatocytes compared with pericentral hepatocytes.
“In agreement with SCENIC+ predictions, the TBX3 ChIP–seq signal was stronger in periportal hepatocyte regions, and TBX3 ChIP–seq peaks were more accessible in periportal hepatocytes compared with in pericentral hepatocytes (Fig. 2g,h).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTcf7l1 is expressed periportally, and its candidate target regions are accessible pericentrally, whereas Tcf7l2 is expressed in all hepatocytes.
“Vice versa, Tcf7l1 is expressed periportally, and its candidate target regions are accessible pericentrally, whereas Tcf7l2 is expressed in all hepatocytes (Fig. 2a).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere is a mid-lobular area where Cyp2e1 and Cps1 are co-expressed, reflected by overlap of topic 60 and topic 66 cell contributions.
“We also identified a mid-lobular area where Cyp2e1 and Cps1 are co-expressed, also reflected by the overlap of topic 60 (pericentrally intermediate) and topic 66 (periportal) cell contributions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThere is a batch effect in hepatocytes related to differences in physiological state between the mice.
“We also identified an interesting batch effect in hepatocytes, related to differences in physiological state between the mice (Supplementary Note and Supplementary Figs. 1–5).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn hepatocytes, three major zones agree with three regulatory topics across the portocentral axis: a Glul+ zone around the central vein (topic 58), a Cyp2e1+ zone including pericentral and mid-lobular hepatocytes (topic 60), and a Cps1+ zone containing mid-lobular to periportal hepatocytes (topic 66).
“In hepatocytes, we identified three major zones that agree with the three regulatory topics found across the portocentral axis in hepatocytes; a Glul+ zone that comprises the hepatocytes surrounding the central vein (topic 58), a Cyp2e1+ zone that includes pericentral and mid-lobular hepatocytes (topic 60) and a Cps1+ zone that contains mid-lobular to periportal hepatocytes (topic 66).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTbx3 is expressed only in pericentral hepatocytes, whereas its candidate target regions are accessible only periportally.
“Notably, Tbx3 is expressed only in pericentral hepatocytes, whereas its candidate target regions are accessible only periportally.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataUsing shuffled sequences as background, an optimal activity cut-off classified 2,913 enhancers as active in at least one of the two systems (806 only in vivo, 921 only in HepG2 cells, 1,186 in both, adjusted P < 0.1) and 4,285 regions as inactive.
“We used the shuffled sequences as a background to derive an optimal activity cut-off (Methods), which classified 2,913 enhancers as active in at least one of the two systems (806 only in vivo, 921 only in HepG2 cells, 1,186 in both, adjusted P < 0.1; Methods) and 4,285 regions as inactive.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOther immune cell types (such as B cells and T cells) are located across all zones.
“Other immune cell types (such as B cells and T cells) are located across all zones (Extended Data Fig. 2k).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong candidate enhancers near the pericentral gene Axin2, six TCF7L1 target regions are predicted; among candidate enhancers near the periportal gene Cdh1, two TBX3 target regions are predicted; in both cases, these regions are bound by additional core general TFs.
“For example, among the candidate enhancers near the pericentral hepatocyte gene Axin2, we predict six TCF7L1 target regions. Among the candidate enhancers near the periportal gene Cdh1 we predict two TBX3 target regions. In both cases, these regions are bound by additional core general TFs (Fig. 2e,f).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMotif enrichment analysis identified HNF1A, PPARA, NFIA, NFIB, HNF4A, CEBPA, FOXA1 and ONECUT1 motifs in regions accessible across all hepatocytes, and TBX3 and TCF7L1/2 motifs in regions accessible periportally and pericentrally, respectively.
“This resulted in the identification of HNF1A, PPARA, NFIA, NFIB, HNF4A, CEBPA, FOXA1 and ONECUT1 motifs in regions accessible across all hepatocytes, and TBX3 and TCF7L1/2 motifs in regions accessible periportally and pericentrally, respectively (Fig. 2a).”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningChromatin regions in which accessibility is negatively associated with Tbx3 expression are located nearby genes that are anti-expressed with Tbx3 (same for Tcf7l1).
“In other words, the chromatin regions in which accessibility is negatively associated with Tbx3 expression are located nearby genes that are anti-expressed with Tbx3 (same for Tcf7l1).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSCENIC+ identifies TBX3 and TCF7L1 as transcriptional repressors in pericentral and periportal hepatocytes, targeting 193 and 520 regions and 77 and 119 genes, respectively.
“Importantly, SCENIC+ identifies TBX3 and TCF7L1 as transcriptional repressors in pericentral and periportal hepatocytes, targeting 193 and 520 regions and 77 and 119 genes, respectively (Fig. 2b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSimulation of Tbx3 knockdown or Tcf7l1 overexpression can switch pericentral hepatocytes to a periportal state.
“whereas Tbx3 knockdown or Tcf7l1 overexpression can switch pericentral hepatocytes to a periportal state (Fig. 7a–c).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTBX3- and TCF7L1-binding sites are located predominantly within hepatocyte enhancers, in close proximity to binding sites of hepatocyte core TFs.
“Importantly, TBX3- and TCF7L1-binding sites are located predominantly within hepatocyte enhancers, in close proximity to binding sites of the hepatocyte core TFs.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataHNF1A and HNF4A GOF variants resulted in increased activity in both sorted fractions, with milder effects for CEBPA and FOXA1 variants.
“HNF1A and HNF4A GOF variants resulted in increased activity in both fractions, with milder effects for CEBPA and FOXA1 variants.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSCENIC+-predicted target regions of HNF1A, HNF4A, FOXA1, CEBPA, ONECUT1, NFIB and TCF7L1 are all significantly more active compared with shuffled regions, with 45%, 39%, 43%, 39%, 35%, 32% and 26% of their predicted target regions active, respectively.
“The SCENIC+-predicted target regions of HNF1A, HNF4A, FOXA1, CEBPA, ONECUT1, NFIB and TCF7L1 are all significantly more active compared with the shuffled regions, with 45%, 39%, 43%, 39%, 35%, 32% and 26% of their predicted target regions active, respectively (Fig. 3e,f).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAccessibility of this Cdh1 enhancer and Cdh1 gene expression are anticorrelated with TBX3 expression (−0.44 and −0.17, respectively).
“Both accessibility of this enhancer, and Cdh1 gene expression, are anticorrelated with TBX3 expression (−0.44 and −0.17, respectively; Fig. 5b–e).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataHNF4A and FOXA1 ChIP-seq signals are present in this Cdh1 enhancer region, but no CEBPA nor ONECUT1 ChIP-seq signals.
“In agreement, we find HNF4A and FOXA1 ChIP–seq signals in this region, but no CEBPA nor ONECUT1 ChIP–seq signals (Fig. 5e).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVariants of the predicted binding sites of the zonation TFs TBX3 and TCF7L1 showed changes in bulk MPRA, but these are more difficult to assess because periportal and pericentral hepatocytes are pooled.
“Variants of the predicted binding sites of the zonation TFs TBX3 and TCF7L1 also showed changes, but these are more difficult to assess from these bulk experiments in which periportal and pericentral hepatocytes are pooled”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOn the Cyp2e1 enhancer, a NFIB site contributes to accessibility (but not activity), and a TCF7L1/2 site is uniquely found in the zonation model, contributing to make the enhancer pericentral.
“On the other hand, a NFIB site contributes to accessibility (but not activity), and a TCF7L1/2 site is uniquely found in the zonation model, contributing to make the enhancer pericentral.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPericentral enhancers showed higher activity in the pericentral fraction, and periportal enhancers showed higher activity in the periportal fraction.
“As expected, pericentral enhancers showed higher activity in the pericentral fraction, and vice-versa”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTopic-CNN could recapitulate the core hepatocyte code, with sequence features associated with the same TFs as identified by SCENIC+.
“The topic-CNN could recapitulate the core hepatocyte code, with sequence features associated with the same TFs as identified by SCENIC+.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataDeepLiver is a hierarchical deep learning model trained to classify DNA sequences to liver regulatory topics (topic-CNN) using 219,823 annotated regions.
“We first trained a convolutional neural network (CNN) to classify DNA sequences to the liver regulatory topics (called topic-CNN) using 219,823 annotated regions as input.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor the MPRA-CNN, patterns promoting enhancer activity correspond to HNF4A, CEBPA, HNF1A, FOXA1 and AP-1 motifs, and several promoter-related motifs such as ETS, NRF1 and THAP.
“For the MPRA-CNN, we identified patterns promoting enhancer activity corresponding to HNF4A, CEBPA, HNF1A, FOXA1 and AP-1 motifs, and several promoter-related motifs such as ETS, NRF1 and THAP.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWe identified between 1,235 and 6,991 target regions for TBX3, TCF7L1, FOXA1, HNF1A, HNF4A, NFIB, ONECUT1 and CEBPA, with good overlap with SCENIC+-predicted target regions (17-70%).
“We identified between 1,235 and 6,991 target regions for TBX3, TCF7L1, FOXA1, HNF1A, HNF4A, NFIB, ONECUT1 and CEBPA, with a good overlap with SCENIC+-predicted target regions (17–70%; Extended Data Fig. 8a–c).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe samples were merged, obtaining 29,798 high-quality cells.
“The samples were merged, obtaining 29,798 high-quality cells”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFrom the zonation model, motifs for HNF4A, ONECUT1, CEBPA, HNF1A, FOXA1, CREB and NFIB were identified as regulators of accessibility across all hepatocytes; TCF7L1/2 and TBX3 motifs are associated with pericentral and periportal accessibility, respectively, in agreement with GRN-level analyses.
“From the zonation model, motifs for HNF4A, ONECUT1, CEBPA, HNF1A, FOXA1, CREB and NFIB were identified as regulators of accessibility across all hepatocytes; whereas TCF7L1/2 and TBX3 motifs are associated with pericentral and periportal accessibility, respectively, in agreement with our GRN-level analyses.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOn a Cdh1 enhancer, DeepLiver finds that FOXA1, HNF4A and HNF1A sites are drivers of enhancer accessibility and activity, whereas TBX3 sites (one dimer motif and two monomers) are predicted to make the enhancer periportal.
“For example, on a Cdh1 enhancer, DeepLiver finds that FOXA1, HNF4A and HNF1A sites are drivers of enhancer accessibility and activity, whereas TBX3 sites (one dimer motif, and two monomers) are predicted to make the enhancer periportal (Fig. 5a).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe three models resulted in higher AUROC and AUPR compared to a random control classifier, and topics associated with cell types had higher performance than low-contributing topics.
“The three models resulted in a higher AUROC and AUPR compared to a random control classifier, and topics associated with cell types had higher performance than low-contributing topics (Extended Data Fig. 7b–d).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPredicted TBX3 and TCF7L1 repressive sites are conserved between mouse and human, with similar accessibility patterns along the portocentral axis.
“revealing that the predicted TBX3 and TCF7L1 repressive sites are conserved between species, with similar accessibility patterns along the portocentral axis”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe motif-based classifier predicts enhancer activity with an AUROC of 0.71 (random AUROC, 0.54) and an AUPR of 0.44 (random AUPR, 0.27).
“This motif-based classifier predicts enhancer activity with an area under the receiver operating characteristic curve (AUROC) of 0.71 (random AUROC, 0.54) and an area under the precision-recall curve (AUPR) of 0.44 (random AUPR, 0.27; Extended Data Fig. 6e).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTBX3 LOF and TCF7L1 GOF resulted in an increase in enhancer activity in the pericentral fraction.
“TBX3 LOF and TCF7L1 GOF resulted in an increase in activity in the pericentral fraction”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOn a pericentral Cyp2e1 enhancer, DeepLiver identifies HNF1A, FOXA1 and ONECUT1 sites that contribute to enhancer accessibility and activity, and an ETS site that contributes to activity but not accessibility.
“On a pericentral Cyp2e1 enhancer, DeepLiver identifies HNF1A, FOXA1 and ONECUT1 sites that contribute to enhancer accessibility and activity, and an ETS site that contributes to activity but not accessibility, as observed in other enhancers too (Fig. 6a; 10.6084/m9.figshare.24115986).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataWeights learned by the topic-CNN were used to initialize two additional CNNs: MPRA-CNN to predict MPRA activity in vivo and zonation-CNN to predict zonation using zonated accessibility classes.
“The weights learned by the topic-CNN model were then used to initialize two additional CNNs, one to predict MPRA activity in vivo (MPRA-CNN) and another to predict zonation (zonation-CNN, using zonated accessibility classes as the output variable).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong mouse regions active in vivo in the mouse liver, 64% are distal enhancers and 27% are promoters.
“Among the mouse regions that are active in vivo in the mouse liver, 64% are distal enhancers and 27% are promoters.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataLeiden clustering with resolution 0.6 resulted in 11 clusters corresponding to 14 cell types based on previous labelling.
“Leiden clustering with a resolution of 0.6 resulted in 11 clusters, corresponding to 14 cell types based on previous labelling.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataThe 5′ 12K library yielded 8,835,050 enhancer-barcode assignments, with 78.3% barcodes assigned to a unique enhancer.
“This resulted in 8,835,050 enhancer-barcode assignments for the 5′ 12K library, with 78.3% barcodes assigned to a unique enhancer.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFor the 455 library, 13 periportal enhancers, 21 pericentral enhancers, 2 positive controls (Aldob and LTV141) and 44 shuffled regions were selected.
“For the second library (455 library), we selected 13 periportal enhancers, 21 pericentral enhancers, 2 positive controls (Aldob and LTV141) and 44 shuffled regions.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataLeiden clustering using 14 PCs resulted in 19 clusters corresponding to 11 cell types annotated by marker gene expression.
“Using 14 PCs, we performed Leiden clustering, resulting in 19 clusters that corresponded to 11 cell types that were annotated on the basis of marker gene expression.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data275, 220 and 2,697 genes and 281, 475 and 8,805 regions vary along the portocentral axis in HSCs, LSECs and hepatocytes, respectively.
“We identified 275, 220 and 2,697 genes and 281, 475 and 8,805 regions that vary along the portocentral axis in HSCs, LSECs and hepatocytes, respectively.”
Quote in the paperSubstantial claim- Rests on an assumption: The pseudotime order represents the distance along the portocentral axis.
Classified as probably not a substantial claim.
Findingown dataHi-C data processing resulted in 890,488 region-gene links.
“resulting in 890,488 region–gene links”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.
FindingreasoningFor the 455 library, mutations affecting activity and/or zonation were manually introduced based on DeepLiver saturation mutagenesis, and 259 bp windows based on DeepExplainer information content were selected, resulting in 16 periportal and 25 pericentral windows with 370 sequence variants.
“For each enhancer, we manually introduced mutations affecting activity and/or zonation based on the saturation mutagenesis of DeepLiver.”
- Also stated in: § MPRAs · plain
“This resulted in 16 periportal and 25 pericentral windows, with 370 sequence variants in total.”
Quote in the paperSubstantial claim- Also stated in: § MPRAs · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataAfter filtering, the female sample contained 5,342 high-quality cells and the male sample contained 4,860 high-quality cells.
“After filtering, the female sample contained 5,342 high-quality cells and the male sample contained 4,860 high-quality cells.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe selected topic model has 82 topics.
“we selected a model with 82 topics”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe 3′BC libraries yielded 723,805 and 71,658 unique enhancer-barcode assignments, with 85.5% and 97.8% barcodes assigned to a unique enhancer, respectively.
“This resulted in libraries with 723,805 and 71,658 unique enhancer-barcode assignments, with 85.5% and 97.8% barcodes assigned to a unique enhancer, respectively.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataeRegulons with positive region-to-gene relationships, at least 20 target genes, and correlation between gene-based and region-based AUC scores above 0.4 were retained, yielding 180 high-quality eRegulons.
“eRegulons with positive region-to-gene relationships, at least 20 target genes and a correlation between gene-based and region-based AUC scores above 0.4 were retained, obtaining 180 high-quality eRegulons.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataFor the 12K library, 10,845 candidate regions were selected based only on accessibility in hepatocytes, including shared regions (4,163, of which 1,386 are accessible in hepatocytes and BECs), hepatocyte-specific regions (4,357), and periportal, intermediate and pericentral hepatocyte-specific regions (795, 527 and 656, respectively).
“For the first library (hereafter, the 12K library), we selected 10,845 candidate regions based only on accessibility in hepatocytes.”
- Also stated in: § MPRAs · plain
“This set also includes shared regions (accessible in hepatocytes and at least one other cell type, 4,163, out of which 1,386 are accessible in hepatocytes and BECs); regions specifically accessible across all hepatocytes (4,357); and regions that are only accessible in periportal, intermediate and pericentral hepatocytes (795, 527 and 656, respectively).”
Quote in the paperNot a substantial claim- Also stated in: § MPRAs · plain
May combine two claims.
Interpretationown dataSCENIC+ identified HNF4A, HNF1A, CEBPA, ONECUT1, FOXA1 and NFIB as core general hepatocyte TFs, and repressors TBX3 and TCF7L1 as repressors of the zonation programs, together with additional networks related to the animal's physiological state.
“In summary, SCENIC+ identified HNF4A, HNF1A, CEBPA, ONECUT1, FOXA1 and NFIB as core general hepatocyte TFs, and the repressors TBX3 and TCF7L1 as repressors of the zonation programs, together with additional networks related to the animal’s physiological state.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationTCF7L1 is a paralogue of TCF7L2, the WNT-effector TF that is active pericentrally.
“TCF7L1 is a paralogue of TCF7L2, the WNT-effector TF that is active pericentrally13;”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13] Ip, W., Shao, W., Chiang, Y. A. & Jin, T. The Wnt signaling pathway effector TCF7L2 is upregulated by insulin and represses hepatic gluconeogenesis. Am. J. Physiol. Endocrinol. Metab. 303, E1166–E1176 (2012). source
May combine two claims.
Interpretationown dataeGRN inference shows that expression of periportal and pericentral genes is directly regulated at the chromatin level, contrary to recently published studies that focused on the accessibility of their promoters.
“Together, eGRN inference shows that expression of periportal and pericentral genes is directly regulated at the chromatin level, contrary to recently published studies that focused on the accessibility of their promoters34 (Extended Data Fig. 5a,b).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] Sun, T. et al. ZNRF3 and RNF43 cooperate to safeguard metabolic liver zonation and hepatocyte proliferation. Cell Stem Cell 28, 1822–1837 (2021). source
May combine two claims.
InterpretationreasoningThese observations suggest that TBX3 and TCF7L1 may repress these regions.
“These observations suggest that TBX3 and TCF7L1 may repress these regions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenHepatocyte clusters with low gene expression and a high percentage of mitochondrial reads were removed, as well as doublets called with Scrublet.
“Hepatocyte clusters with low gene expression and a high percentage of mitochondrial reads were removed, as well as doublets called with Scrublet (v.0.2.3)67.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataDeepLiver assigns given DNA sequences to cell types (represented by topics) in the liver and predicts activity and zonation patterns of hepatocyte enhancers.
“Together, DeepLiver assigns given DNA sequences to cell types (represented by topics) in the liver and predicts activity and zonation patterns of hepatocyte enhancers (Fig. 4a).”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataDeepLiver decodes enhancer accessibility, activity and zonation at base-pair resolution, and can predict variants that modulate enhancer activity and zonation in hepatocytes.
“In summary, DeepLiver decodes enhancer accessibility, activity and zonation at the base-pair resolution, and can predict variants that modulate enhancer activity and zonation in hepatocytes.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe grammar of hepatocyte enhancers that encodes their zonation pattern includes TBX3- and TCF7L1/2-binding sites, while HNF1A and HNF4A are the most relevant binding sites regarding activity.
“In summary, our results suggest that the grammar of hepatocyte enhancers that encodes their zonation pattern includes TBX3- and TCF7L1/2-binding sites, while HNF1A and HNF4A are the most relevant binding sites regarding activity.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductnone givenSingle-cell data are sparse, which reduces the sensitivity to detect negative correlations and can lead to false-negative predictions.
“However, a key limitation is that single-cell data are sparse, which reduces the sensitivity to detect negative correlations, and can lead to false-negative predictions.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningKnockdown or overexpression of one of TBX3 or TCF7L1 provokes upregulation or downregulation of the other, respectively, and of the other's target genes.
“Consequently, the knockdown or overexpression of one of the TFs provokes the upregulation or downregulation of the other, respectively (and downregulation and upregulation of the target genes of the other as well; Fig. 7c).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAnimal experiments were conducted according to KU Leuven ethical guidelines and approved by the KU Leuven Ethical Committee for Animal Experimentation (ECD P007/2021).
“All of the animal experiments were conducted according to the KU Leuven ethical guidelines and approved by the KU Leuven Ethical Committee for Animal Experimentation (ECD P007/2021).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThese repressor sites form an integral, and probably evolutionary selected, part of the state-specific hepatocyte enhancer logic, rather than repressing genes through distinct regulatory regions.
“This shows that, rather than repressing genes through distinct regulatory regions, these repressor sites form an integral, and probably evolutionary selected, part of the state-specific hepatocyte enhancer logic.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenCells with at least 350 genes expressed and a percentage of mitochondrial reads below 10% were retained.
“cells with at least 350 genes expressed and a percentage of mitochondrial reads below 10% were retained.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor each model, the best epoch was selected based on accuracy and loss on the test data (10% of the input data).
“For each model, the best epoch was selected on the basis of its accuracy and loss on the test data (10% of the input data; Extended Data Fig. 7a).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataA GAM was fitted on accessibility and expression over pseudotime to assess regions affected by zonation, after filtering for genes fitted with adjusted P < 0.01.
“To assess the number of regions and regions affected by zonation, we took the shared regions in hepatocytes, LSECs and HSCs (based on topics) and marker genes, respectively, and a GAM was fitted on the basis of their accessibility and expression over pseudotime (representing zonation). After filtering for genes fitted with adjusted P < 0.01.”
Quote in the paperSubstantial claim- Rests on an assumption: The pseudotime order represents the distance along the portocentral axis.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown data100 genes were selected based on gene expression patterns and literature for molecular cartography.
“In total, 100 genes were selected on the basis of their gene expression patterns (marker genes for a cell type or group of cell types) on our in-house mouse liver dataset and literature (Supplementary Table 1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor the luciferase experiments, four replicates were performed per condition.
“For the luciferase experiments, we performed four replicates per condition.”
- Also stated in: § Luciferase reporter assay · plain
“Four biological replicates were performed per condition.”
Quote in the paperNot a substantial claim- Also stated in: § Luciferase reporter assay · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenFor the single-cell experiments, at least two experiments were performed per technique (snRNA-seq, snATAC-seq or single-cell multiomics).
“For the single-cell experiments we performed at least two experiments per technique (snRNA-seq, snATAC–seq or single-cell multiomics).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataANOVA over AUC values along pseudotime per sample with Bonferroni adjusted P values was used to rank eRegulons or signatures by zonation and/or sample effects.
“To rank eRegulons (or signatures) based on how affected they are by zonation and/or sample, we performed ANOVA over the AUC values along the pseudotime per sample, and calculated Bonferroni adjusted P values.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenDimensionality reduction using only the 100 selected genes ensured that all cell types could be distinguished with this gene panel.
“Moreover, we performed dimensionality reduction using only these 100 genes to ensure that all cell types could be distinguished with this gene panel.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe ratio of signals matching to codes used and not used in the experiment was used to estimate specificity (false positives).
“The ratio of signals matching to codes used in the experiment and signals matching to codes not used in the experiment were used as estimation for specificity (false positives).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductcitationCluster annotation was performed based on motif enrichment, gene activity and label transfer from the annotated transcriptome with Seurat.
“Cluster annotation was performed based on motif enrichment, gene activity and label transfer from the annotated transcriptome with Seurat71”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataTwo additional multiome datasets on unstarved mice were generated to validate circadian rhythm effects.
“To further validate the circadian rhythm effects, we generated two additional multiome datasets on unstarved mice (using the 10x protocol).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor the smFISH experiment, three replicates were performed.
“For the smFISH experiment we performed three replicates.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenDrop-out imputation was performed by multiplying the region-topic and topic-cell probabilities.
“Drop-out imputation was performed by multiplying the region-topic and topic-cell probabilities.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenDifferentially expressed genes were calculated using Seurat's FindMarkers, retaining genes with log[FC] > 0.75 and adjusted P < 0.05.
“Differentially expressed genes were calculated using Seurat’s FindMarkers function (v.4.0.3), retaining genes with log[FC] > 0.75 and adjusted P < 0.05.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataHarmony was used to correct for batch effects on the selected principal components.
“To correct for batch effects, we used Harmony on the selected principal components (34)”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenHepatocyte DARs and shared hepatocyte topics were curated by hierarchical clustering on pseudobulk probabilities, removing lowly accessible and generally accessible regions, and defining non-overlapping groups between gradient groups.
“Hepatocyte DARs and shared hepatocyte topics were curated by performing hierarchical clustering on the pseudobulk probabilities, removing a small fraction of lowly accessible and generally accessible regions, and defining non-overlapping groups between the different gradient groups.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe investigators were not blinded to allocation during experiments and outcome assessment.
“The investigators were not blinded to allocation during experiments and outcome assessment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor the MPRA experiments, at least two replicates were performed per condition (system (HepG2, AML12, mouse) and library (3′, 5′)).
“For the MPRA experiments we performed at least two replicates per condition (system (HepG2, AML12, mouse) and library (3′, 5′)).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenNo statistical methods were used to predetermine sample size.
“No statistical methods were used to predetermine sample size.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataModel performance was assessed using ninefold cross validation.
“To assess the performance of the models, we performed ninefold cross validation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe experiments were not randomized.
“The experiments were not randomized.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataThe snATAC-seq dataset was filtered retaining cells with at least 1,000 fragments, FRiP > 0.4 and TSS enrichment > 7, resulting in 22,600 high-quality cells.
“retaining cells with at least 1,000 fragments, FRiP > 0.4 and TSS enrichment > 7, resulting in 22,600 high-quality cells.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataAfter correcting sample effects with Harmony, Leiden clustering was performed.
“After correcting sample effects with Harmony70 (v.1.0, applied on the scaled topic distributions), we performed Leiden clustering with resolution 0.6”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The regulatory programs and enhancer logic encoding hepatocyte zonation states, and how the zonation state interacts with the core hepatocyte GRNs, are largely unclear.
“Yet, whether and how these variable states are encoded by genomic regulatory programs and enhancer logic, and how the zonation state interacts with the core hepatocyte GRNs are largely unclear.”
The paper relies on this without support.
The mouse liver is a suitable model system for studying hepatocyte identity and zonation.
“Using the mouse liver as a model system, we aimed to depict the core identity, as well as the various cell states, of hepatocytes, alongside their gene regulatory programs.”
The paper relies on this without support.
CNN models require large input datasets for training.
“A key limitation of CNN models is that they require large input datasets for training.”
The paper relies on this without support.
Discretization of dynamic populations is strongly affected by parameter selection.
“Yet, the discretization of dynamic populations is not a trivial task and is strongly affected by parameter selection.”
The paper relies on this without support.
The pseudotime order represents the distance along the portocentral axis.
“The pseudotime order represents the distance along the portocentral axis.”
The paper relies on this without support.
High nucleotide importance on DeepExplainer plots represents potential binding sites for TFs.
“High nucleotide importance on DeepExplainer plots represents potential binding sites for TFs.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SDS was enriched in zone 1 (periportal), while CYP2E1 expression increased in zone 3 (pericentral), as previously described in mouse.
“SDS was enriched in zone 1 (periportal), while CYP2E1 expression increased in zone 3 (pericentral), as previously described in mouse24–27 (Fig. 1c, e, f; Supplementary Fig. 5a).”
From Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution (2025) · cites this paper as [27]
In “Zonation drives cell state heterogeneity in hepatocytes” (page unavailable), the paper identifies a Cyp2e1-positive zone encompassing pericentral and mid-lobular mouse hepatocytes, supporting the pericentral part of the claim. The Fig. 1 caption names Sds among genes imaged by smFISH but does not state that it is enriched in periportal zone 1. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSpatial gene expression in human hepatocytes is better described by a continuous variable determined by the relative distance between the portal and central areas of the lobule, similar to observations in mouse.
“Collectively, these observations suggest that spatial gene expression in human hepatocytes is better described by a continuous variable determined by the relative distance between the portal and central areas of the lobule, similar to observations in mouse7,26,27.”
From Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution (2025) · cites this paper as [27]
The 'Zonation drives cell state heterogeneity in hepatocytes' section reports a gene-expression gradient along the portocentral axis, and Fig. 1 identifies the atlas as mouse liver (page not provided). This supports the mouse comparison, but the cited paper does not establish that the same continuous description applies to human hepatocytes. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn healthy human liver, hepatocyte zonation reflects a continuous gradient from periportal to pericentral regions rather than distinct zones.
“While MERFISH allows to us to define these zones, it is also evident through pseudotime analysis, measurement of gene expression across individual lobules, and transcriptional analysis performed across lobules, that zonation reflects a gradient from periportal to pericentral regions rather than distinct zones.”
From Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution (2025) · cites this paper as [27]
The section “Zonation drives cell state heterogeneity in hepatocytes” reports a periportal-to-pericentral hepatocyte gradient in animals, supported by gene expression and chromatin accessibility. But it also identifies three major, overlapping zones. The human-liver analysis reports similar chromatin-accessibility patterns along the axis, not that healthy human hepatocytes form a continuous gradient rather than distinct zones. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
LSEC and HSC zonated-region counts are reversed in Methods
MinorThe main text assigns 281 zonated regions to LSECs and 475 to HSCs. Methods assigns 475 to LSECs and 281 to HSCs.
“We identified 220 and 275 genes and 281 and 475 regions that vary along the portocentral axis in LSECs and HSCs, respectively”
“We identified 275, 220 and 2,697 genes and 281, 475 and 8,805 regions that vary along the portocentral axis in HSCs, LSECs and hepatocytes, respectively.”
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
Cites a retracted work: 10.3727/096504017x15145624664031
InfoThe cited work was retracted on 2026-09-14 (notice 10.32604/or.2026.092122), not before this paper was published (2024-01).
Cited work 10.3727/096504017x15145624664031 · retracted 2026-09-14 · notice 10.32604/or.2026.092122 · this paper published 2024-01
“Human HepG2 cells express Tbx3 and can therefore be used as a model to test the effect of mutating TBX3 sites in hepatocyte enhancer sequences 45 .”
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Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · cited 2× · see citing works
“CNN-based enhancer modelling has recently gained traction, due to the ability of these models to interpret enhancer grammar 55–58 .”
InfluentialSelf-citationDecoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works
Self-citationGenome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162 · cited 1× · see citing works
“For this latter subset, we included both the human and the corresponding mouse sequences 60,91,92 .”
Jacobs, J., Atkins, M., Davie, K., Imrichova, H., Romanelli, L., Christiaens, V., Hulselmans, G., Potier, D., Wouters, J., Taskiran, I. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0140-x · Cited by 155 · cited 1× · see citing works
“However, chromatin accessibility is not necessarily always associated with enhancer activity 35 .”
Self-citationSpace-time logic of liver gene expression at sub-lobular scale
Droin, C., Kholtei, J. E., Bahar Halpern, K., Hurni, C., Rozenberg, M., Muvkadi, S., Itzkovitz, S., & Naef, F. (2021) · Nature Metabolism · doi:10.1038/s42255-020-00323-1 · Cited by 148 · cited 1× · see citing works
“To obtain the circadian rhythm signatures, we used the scRNA-seq data from the mouse liver at different timepoints of the circadian rhythm from a previous study 81 , performing differential expression analysis between the different timepoints with Seurat (v.4.0.03) 71 .”
InfluentialAPPRIS 2017: principal isoforms for multiple gene sets
Rodrı́guez, J. M., Rodriguez-Rivas, J., Domenico, T. D., Vázquez, J., Valencia, A., & Tress, M. L. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx997 · Cited by 139 · see citing works
Berthier, A., Johanns, M., Zummo, F. P., Lefebvre, P., & Staels, B. (2021) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2021.166097 · Cited by 125 · cited 1× · see citing works
“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB 21–23 , and zonation-associated eRegulons such as ESR1 and SOX9 peripor-tally 24,25 and PPARG and AR pericentrally 26,27 .”
Transcription factors SOX4 and SOX9 cooperatively control development of bile ducts
Poncy, A., Antoniou, A., Cordi, S., Pierreux, C. E., Jacquemin, P., & Lemaigre, F. P. (2015) · Developmental Biology · doi:10.1016/j.ydbio.2015.05.012 · Cited by 125 · cited 1× · see citing works
“…analysis revealed 180 eRegulons, including SPI1, JDP2, RUNX1 in Kupffer cells; EBF1 and PAX5 in B cells; GATA3 and LEF1 in T cells; GATA4, MEIS1 and MAF in LSECs; LHX2 and TEAD1 in HSCs; WT1 in fibroblasts; and TEAD4, DOX4 and HNF1B in BECs, among others, and in agreement with literature 15–20 .”
Shy, B. R., Wu, C.-I., Khramtsova, G. F., Zhang, J. Y., Olopade, O. I., Goss, K. H., & Merrill, B. J. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.06.001 · Cited by 124 · cited 1× · see citing works
“TCF7L1 is a paralogue of TCF7L2, the WNT-effector TF that is active pericentrally 13 ; this may suggest that TCF7L1 and TCF7L2 bind to the same motif—TCF7L2 pericentrally for activation and TCF7L1 periportally for repression 14 .”
Transcriptional regulation of hepatic stellate cells
Mann, J., & Mann, D. A. (2009) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2009.03.011 · Cited by 119 · see citing works
Berest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K. D., Giles, H., Bruch, P.-M., Huber, W., Dietrich, S., Helin, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.106 · Cited by 119 · see citing works
Cross-species analysis of enhancer logic using deep learning
Minnoye, L., Taskiran, I. I., Mauduit, D., Fazio, M., Van Aerschot, L., Hulselmans, G., Christiaens, V., Makhzami, S., Seltenhammer, M., Karras, P., et al. (2020) · Genome Research · doi:10.1101/gr.260844.120 · Cited by 116 · cited 1× · see citing works
“The model is a hybrid CNN–recurrent-neural-network multiclass classifier and its architecture was adopted from earlier studies 55,56,58 , trained with Tensorflow (v.1.15) with minor adaptations.”
InfluentialSelf-citationInverso, D., Shi, J., Lee, K. H., Jakab, M., Ben-Moshe, S., Kulkarni, S. R., Schneider, M., Wang, G., Komeili, M., Vélez, P. A., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.05.001 · Cited by 114 · cited 1× · see citing works
“Out of the 220 LSEC zonated genes, 69% overlap with the zonated liver endothelial markers described previously 11 .”
The roles and regulation of TBX3 in development and disease
Khan, S. F., Damerell, V., Omar, R., Du Toit, M., Khan, M., Maranyane, H. M., Mlaza, M., Bleloch, J., Bellis, C., Sahm, B. D. B., et al. (2020) · Gene · doi:10.1016/j.gene.2019.144223 · Cited by 99 · cited 1× · see citing works
“TCF7L1 and TBX3 are indeed well-known repressors in development 51,52 and, while it has been previously reported that Tcf7l1 and Tbx3 expression is zonated in the adult mouse liver 43,53 , here we show their direct implication in liver zonation regulation by enhancer-GRN mapping.”
Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control
Brosch, M., Kattler, K., Herrmann, A., von Schönfels, W., Nordström, K., Seehofer, D., Damm, G., Becker, T., Zeissig, S., Nehring, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06611-5 · Cited by 98 · cited 1× · see citing works
“TCF7L1 and TBX3 are indeed well-known repressors in development 51,52 and, while it has been previously reported that Tcf7l1 and Tbx3 expression is zonated in the adult mouse liver 43,53 , here we show their direct implication in liver zonation regulation by enhancer-GRN mapping.”
Cloning and Characterization of Human Agmatinase
Iyer, R. K., Kim, H. K., Tsoa, R. W., Grody, W. W., & Cederbaum, S. D. (2002) · Molecular Genetics and Metabolism · doi:10.1006/mgme.2001.3277 · Cited by 97 · cited 1× · see citing works
“This enabled us to identify eRegulons that depend on nutritional status, such as AGMAT 30 and MLXIPL 31 ; on hormone levels, such as NR1I2, NR1I3 and RXRA 32 ; and on circadian rhythm, such as CLOCK 33 .”
Ballester, B., Medina-Rivera, A., Schmidt, D., Gonzàlez-Porta, M., Carlucci, M., Chen, X., Chessman, K., Faure, A. J., Funnell, A. P., Goncalves, A., et al. (2014) · eLife · doi:10.7554/elife.02626 · Cited by 96 · cited 7× · see citing works
“This resulted in the identification of HNF1A, PPARA, NFIA, NFIB, HNF4A, CEBPA, FOXA1 and ONECUT1 motifs in regions accessible across all hepatocytes, and TBX3 and TCF7L1/2 motifs in regions accessible periportally and peri-centrally, respectively (Fig.”
InfluentialBravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · cited 2× · see citing works
“Multiome cells (with gene expression, chromatin accessibility, regulatory topics and eGRNs) were mapped into a template of the liver lobule and the smFISH spatial map using ScoMAP (v.0.1.0) 12 .”
InfluentialSelf-citationZNRF3 and RNF43 cooperate to safeguard metabolic liver zonation and hepatocyte proliferation
Sun, T., Annunziato, S., Bergling, S., Sheng, C., Orsini, V., Forcella, P., Pikiolek, M., Kancherla, V., Holwerda, S., Imanci, D., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.05.013 · Cited by 84 · cited 2× · see citing works
“Together, eGRN inference shows that expression of periportal and pericentral genes is directly regulated at the chromatin level, contrary to recently published studies that focused on the accessibility of their promoters 34 (Extended Data Fig.”
InfluentialMultiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic
Verfaillie, A., Svetlichnyy, D., Imrichova, H., Davie, K., Fiers, M., Kalender Atak, Z., Hulselmans, G., Christiaens, V., & Aerts, S. (2016) · Genome Research · doi:10.1101/gr.204149.116 · Cited by 82 · cited 2× · see citing works
“To assess whether the predicted enhancers are active, we performed a massively parallel reporter assay (MPRA) using a previously published enhancer-barcoding strategy 36 (Fig.”
Self-citationIp, W., Shao, W., Chiang, Y.-T. A., & Jin, T. (2012) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00249.2012 · Cited by 77 · cited 1× · see citing works
“TCF7L1 is a paralogue of TCF7L2, the WNT-effector TF that is active pericentrally 13 ; this may suggest that TCF7L1 and TCF7L2 bind to the same motif—TCF7L2 pericentrally for activation and TCF7L1 periportally for repression 14 .”
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
Hopfensitz, M., Müssel, C., Wawra, C., Maucher, M., Kühl, M., Heiko Neumann, B., & Kestler, H. A. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.62 · Cited by 70 · cited 1× · see citing works
“…were binarized based on gradient boosting machine regression importance scores using the 85th, 90th and 95th quantile; the top 5, 10 and 15 regions per gene and using the BASC method for binarization 76 . eRegulons between the two runs (with all cells and only hepatocytes) were merged.”
Epigenetic Regulation of Kupffer Cell Function in Health and Disease
Bennett, H., Troutman, T. D., Sakai, M., & Glass, C. K. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.609618 · Cited by 66 · cited 1× · see citing works
“…analysis revealed 180 eRegulons, including SPI1, JDP2, RUNX1 in Kupffer cells; EBF1 and PAX5 in B cells; GATA3 and LEF1 in T cells; GATA4, MEIS1 and MAF in LSECs; LHX2 and TEAD1 in HSCs; WT1 in fibroblasts; and TEAD4, DOX4 and HNF1B in BECs, among others, and in agreement with literature 15–20 .”
A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumorsPreprint
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/761429 · Cited by 66 · see citing works
The glucose-sensing transcription factor MLX promotes myogenesis via myokine signaling
Hunt, L. C., Xu, B., Finkelstein, D., Fan, Y., Carroll, P. A., Cheng, P.-F., Eisenman, R. N., & Demontis, F. (2015) · Genes & Development · doi:10.1101/gad.267419.115 · Cited by 64 · cited 1× · see citing works
“This enabled us to identify eRegulons that depend on nutritional status, such as AGMAT 30 and MLXIPL 31 ; on hormone levels, such as NR1I2, NR1I3 and RXRA 32 ; and on circadian rhythm, such as CLOCK 33 .”
GRaNIE and GRaNPA: inference and evaluation of enhancer‐mediated gene regulatory networks
Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M. M., Daga, N., Nogina, D., Müller‐Dott, S., Reyes‐Palomares, A., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311627 · Cited by 63 · see citing works
Improved assay-dependent searching of nucleic acid sequence databases
Gans, J. D., & Wolinsky, M. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn301 · Cited by 57 · cited 1× · see citing works
“After these fast screens, every kept probe candidate was mapped to the background transcriptome using ThermonucleotideBLAST 85 and probes with stable off-target hits were discarded.”
Unraveling the transcriptional determinants of liver sinusoidal endothelial cell specialization
de Haan, W., Øie, C., Benkheil, M., Dheedene, W., Vinckier, S., Coppiello, G., Aranguren, X. L., Beerens, M., Jaekers, J., Topal, B., et al. (2020) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00215.2019 · Cited by 57 · see citing works
miR-1224 inhibits cell proliferation in acute liver failure by targeting the antiapoptotic gene Nfib
Roy, S., Bantel, H., Wandrer, F., Schneider, A. T., Gautheron, J., Vucur, M., Tacke, F., Trautwein, C., Luedde, T., & Roderburg, C. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2017.06.007 · Cited by 56 · cited 1× · see citing works
“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB 21–23 , and zonation-associated eRegulons such as ESR1 and SOX9 peripor-tally 24,25 and PPARG and AR pericentrally 26,27 .”
Uebi, T., Umeda, M., & Imai, T. (2015) · Genes to Cells · doi:10.1111/gtc.12214 · Cited by 52 · cited 1× · see citing works
“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB 21–23 , and zonation-associated eRegulons such as ESR1 and SOX9 peripor-tally 24,25 and PPARG and AR pericentrally 26,27 .”
Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
Atak, Z. K., Taskiran, I. I., Demeulemeester, J., Flerin, C., Mauduit, D., Minnoye, L., Hulselmans, G., Christiaens, V., Ghanem, G.-E., Wouters, J., et al. (2021) · Genome Research · doi:10.1101/gr.260851.120 · Cited by 48 · cited 2× · see citing works
“CNN-based enhancer modelling has recently gained traction, due to the ability of these models to interpret enhancer grammar 55–58 .”
InfluentialSelf-citationTBX3 acts as tissue-specific component of the Wnt/β-catenin transcriptional complex
Zimmerli, D., Borrelli, C., Jauregi-Miguel, A., Söderholm, S., Brütsch, S., Doumpas, N., Reichmuth, J., Murphy-Seiler, F., Aguet, M., Basler, K., et al. (2020) · eLife · doi:10.7554/elife.58123 · Cited by 47 · see citing works
Goldfarb, C. N., Karri, K., Pyatkov, M., & Waxman, D. J. (2022) · Endocrinology · doi:10.1210/endocr/bqac059 · Cited by 46 · cited 1× · see citing works
“Analysis of snRNA-seq data from male and female livers snRNA data from male and female livers was obtained from a previous study 88 .”
The immune cell transcription factor T-bet: A novel metabolic regulator
Stolarczyk, E., Lord, G. M., & Howard, J. K. (2014) · Adipocyte · doi:10.4161/adip.26220 · Cited by 32 · see citing works
Athanasouli, P., Balli, M., De Jaime-Soguero, A., Boel, A., Papanikolaou, S., van der Veer, B. K., Janiszewski, A., Vanhessche, T., Francis, A., El Laithy, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36914-1 · Cited by 31 · cited 1× · see citing works
“TCF7L1 and TBX3 are indeed well-known repressors in development 51,52 and, while it has been previously reported that Tcf7l1 and Tbx3 expression is zonated in the adult mouse liver 43,53 , here we show their direct implication in liver zonation regulation by enhancer-GRN mapping.”
Bullerwell, C. E., Robichaud, P. P., Deprez, P. M. L., Joy, A. P., Wajnberg, G., D’Souza, D., Chacko, S., Fournier, S., Crapoulet, N., Barnett, D. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-81000-5 · Cited by 30 · see citing works
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet AllocationPreprint
Chen, J., Li, K., Zhu, J., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08628 · Cited by 20 · see citing works
Chen, K.-W., Chen, Y.-S., Chen, P.-J., & Yeh, S.-H. (2022) · Metabolism · doi:10.1016/j.metabol.2022.155269 · Cited by 16 · cited 1× · see citing works
“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB 21–23 , and zonation-associated eRegulons such as ESR1 and SOX9 peripor-tally 24,25 and PPARG and AR pericentrally 26,27 .”
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad026 · Cited by 13 · see citing works
vib-singlecell-nf/vsn-pipelines: v0.14.0
Flerin, C. C., Davie, K., Hulselmans, G., & Waegeneer, M. D. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3703108 · Cited by 13 · see citing works
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“We found general hepatocyte-specific eRegulons for CEBPA, HNF1A, HNF4A, ONECUT1, FOXA1 and NFIB 21–23 , and zonation-associated eRegulons such as ESR1 and SOX9 peripor-tally 24,25 and PPARG and AR pericentrally 26,27 .”
vib-singlecell-nf/vsn-pipelines: v0.26.1
Waegeneer, M. D., Flerin, C. C., Davie, K., et al. (2021) · Zenodo · doi:10.5281/zenodo.5070473
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An organotypic atlas of human vascular cells
Barnett, S. N., Cujba, A.-M., Yang, L., Maceiras, A. R., Li, S., Kedlian, V. R., Pett, J. P., Polański, K., Miranda, A. M. A., Xu, C., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03376-x · Cited by 132 · see citing works
Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution
Watson, B. R., Paul, B., Rahman, R. U., Amir-Zilberstein, L., Segerstolpe, Å., Epstein, E. T., Murphy, S., Geistlinger, L., Lee, T., Shih, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55325-4 · Cited by 123 · see citing works
“Collectively, these observations suggest that spatial gene expression in human hepatocytes is better describedbyacontinuousvariabledeterminedbytherelativedistance between the portal and central areas of the lobule, similar to observations in mouse 7,26,27 .”
Predicting gene expression from DNA sequence using deep learning models
Barbadilla-Martínez, L., Klaassen, N. H. M., van Steensel, B., & de Ridder, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00841-2 · Cited by 83 · see citing works
“DeepLiver, a transfer-learning CNN incorporating MPRA data, was used to dissect the regulatory grammar of enhancers in multiple mouse liver cell types 59 .”
Liver macrophages: development, dynamics, and functions
Nusse, Y., & Kubes, P. (2025) · Cellular & Molecular Immunology · doi:10.1038/s41423-025-01298-3 · Cited by 50 · see citing works
Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium
Hecker, N., Kempynck, N., Mauduit, D., Abaffyová, D., Vandepoel, R., Dieltiens, S., Borm, L. E., Sarropoulos, I., González‐Blas, C. B., Man, J. D., et al. (2025) · Science · doi:10.1126/science.adp3957 · Cited by 44 · see citing works
Self-citationLiu, F., Beck, S., Yang, L., Luo, H., & Zhang, K. C. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00922-2 · Cited by 52 · see citing works
Liu, Y., Zhu, K., Peng, W., Liu, Z., & Mao, X. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02631-6 · Cited by 35 · see citing works
Decoding biology with massively parallel reporter assays and machine learningPreprint
Fleur, A. L., Shi, Y., & Seelig, G. (2024) · Genes & Development · doi:10.1101/gad.351800.124 · Cited by 26 · see citing works
Jordan, A. M., Drake, R. J. G., & Hodivala‐Dilke, K. (2025) · Physiological Reviews · doi:10.1152/physrev.00046.2024 · Cited by 24 · see citing works
Modelling and design of transcriptional enhancers
Winter, S. D., Konstantakos, V., & Aerts, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00280-y · Cited by 20 · see citing works
“These models take 500-bp DNA sequences as input and perform binary multi-label classification of cell-type-specific chromatin accessibility peaks using either a combination of convolutional and LSTM layers 25,27,28,70 or only convolution (in the case of DeepBrain 77 ).”
InfluentialSelf-citationEvaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex
Johansen, N., Kempynck, N., Zemke, N. R., Somasundaram, S., Winter, S. D., Hooper, M. J., Dwivedi, D., Lohia, R., Wehbe, F., Li, B., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100879 · Cited by 19 · see citing works
Self-citationCREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species
Kempynck, N., Winter, S. D., Blaauw, C. H., Konstantakos, V., Ekşi, E. C., Dieltiens, S., Abaffyová, D., Bercier, V., Taskiran, I. I., Hulselmans, G. J., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03057-2 · Cited by 24 · see citing works
“…line accessibility and predictions from DeepCCL with topic accessibility, we identified a set of potential MES-like biopsy topics (topics, 8, 21, 25 and 20) and three candidate oligodendrocyte progenitor cell/neural progenitor cell (OPC/NPC)-like topics (topics 14, 18 and 19; Supplementary Fig.”
Self-citationZonation, Zonation, Zonation: The Real Estate of the Liver
Yasaka, T. M., Kim, C., Meadows, V., & Monga, S. P. (2025) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-042624-091820 · Cited by 21 · see citing works
Active learning of enhancers and silencers in the developing neural retina
Friedman, R. Z., Ramu, A., Lichtarge, S., Wu, Y., Tripp, L., Lyon, D., Myers, C. A., Granas, D. M., Gause, M., Corbo, J. C., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2024.12.004 · Cited by 18 · see citing works
Liver sinusoidal endothelial cells in hepatic fibrosis: opportunities for future strategies
Chen, T., Zhang, H., Shan, W., Zhou, J., & You, Y. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.151881 · Cited by 19 · see citing works
Nuclear receptors: pathophysiological mechanisms and drug targets in liver disease
Dubois, V., Lefèbvre, P., Staels, B., & Eeckhoute, J. (2024) · Gut · doi:10.1136/gutjnl-2023-331741 · Cited by 17 · see citing works
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Wang, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110408 · Cited by 15 · see citing works
Perspective on recent developments and challenges in regulatory and systems genomics
Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 14 · see citing works
Hepatocyte Period 1 dictates oxidative substrate selection independent of the core circadian clock
Sun, J., Zhang, Y., Adams, J. A., Higgins, C. B., Kelly, S. C., Zhang, H., Cho, K., Johnson, U. G., Swarts, B. M., Wada, S.-I., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114865 · Cited by 13 · see citing works
Ayala, I., Hebbale, S. K., Mononen, J., Brearley-Sholto, M. C., Shannon, C. E., Valdez, I., Fourcaudot, M. J., Bakewell, T. M., Zagorska, A., Romero, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58714-5 · Cited by 11 · see citing works
Bioinspired Spatially Ordered Multicellular Lobules for Liver Regeneration
Wang, J., Huang, D., Ren, H., & Zhao, Y. (2025) · Research · doi:10.34133/research.0634 · Cited by 10 · see citing works
“In addition, recent research has merely imitated each other, making geometric modifications without truly simulating the spatiotemporal distribution of liver lobule cells [ 26 , 27 ].”
Immune cell states: Critical building blocks of the plant immune system
Nobori, T. (2025) · Cell Host & Microbe · doi:10.1016/j.chom.2025.06.016 · Cited by 10 · see citing works
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Bisphenol S Exposure and MASLD: A Mechanistic Study in Mice
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Pathogenesis and research progress of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis
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Comparative Genomics and Epigenomics of Transcriptional Regulation
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MetaNet: a scalable and integrated tool for reproducible omics network analysis
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Advancing liver metabolic zonation with single-cell and spatial omics
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“Bravo González-Blas et al. mapped the gene regulatory networks underly‐ ing liver zonation and identified TCF7L1 and TBX3 as key transcription factors in maintaining zonation [46].”
Hypoxia in MASLD: a spatial determinant of the pathogenesis
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Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV
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BRAIN-MAGNET: A functional genomics atlas for interpretation of non-coding variants
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Organoid Models: Revolutionizing Disease Modeling and Personalized Therapeutics
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HDI-STARR-seq: Condition-specific enhancer discovery in mouse liver in vivo
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Overlapping binding sites underlie TF genomic occupancyPreprint
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Regulated necrosis at the crossroads of liver inflammation and cancer development
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“A canonical example is liver zonation, where hepatocytes along the portal-central vein axis showing gradients of oxygen, nutrients and morphogens, leading to gradual reprogramming of metabolism and gene regulation [162].”
Microbial reprogramming of the gut-liver nexus in MASLD: Lessons from Akkermansia muciniphila
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Enhancer Elements in Plants: Genomic Context and Biological Functions
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Diagnosing Autoimmune Hepatitis: Histological Correlations and Emerging Technologies
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“For example, Bravo González-Blas et al. used a spatial single-cell multiome approach to map enhancer–gene regulatory networks in mouse liver, identifying transcription factors such as TCF7L1 and TBX3 that drive zonation-specific gene programs [109].”
The diabetes gene Tcf7l2 organizes gene expression in the liver and regulates amino acid metabolism
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“4 C ), akin to hepatocyte differentiation in the liver (31).”
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Epigenetic regulation in MASLD – insight for therapeutic target discovery
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“Furthermore, the authors demonstrate that of the 6823 genes expressed within hepatocytes, 2697 are differentially expressed across zones, accompanied by differential chromatin accessibility within 8805 regions (out of 14,005 hepatocyte-specific regions) [72].”
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IGF2 mediates Hippo signaling to control liver sizePreprint
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Self-citationOptimisation of Xenium automated in situ sequencing for PAXgene-fixed tissue samples
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Advancing cancer detection and treatment using longitudinal routine clinical data
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Predictive design of tissue-specific mammalian enhancers that function in vivo in the mouse embryoPreprint
Chen, S., Loubière, V., Hollingsworth, E. W., Jacinto, S. H., Dizehchi, A., Schreiber, J. M., Kvon, E. Z., & Stark, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.22.695948 · Cited by 1 · see citing works
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Pathogenesis and research progress of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis
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Activation and Spatial Redistribution of RNA Splicing Factors Trigger Hepatic Regeneration
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Liang, X., Miao, Y., Han, D., Li, Y., Zhang, W., & Wang, Z. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag437
“…from different mouse tissues generated by different platforms, including mouse embryonic brain cells at day 18 (10x Genomics), mouse skin (SHARE-seq) [ 19 ], adult mouse cerebral cortex (SHARE-seq) [ 71 ], thymic epithelial cells (10x Genomics) [ 72 ], and mouse liver (10x Genomics) [ 73 ] (Fig.”
The origin and molecular evolution of the mammalian liver cell architecturePreprint
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Challenges in predicting chromatin accessibility differences between species
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“The differential motif enrichment between strong and weak candidate enhancers yielded some motifs, including motifs of TFs known to play important roles in the liver like HNF4A [ 63 , 64 ] and CEBPA [ 63 , 65 ].”
Single-cell activation screen identifies hepatic maturation regulators with zonal resolution
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“In summary, the dual activation of Nr1i3 and Nfix , in combination with microenvironmental signaling factors, cooperatively enhanced hepatic maturation and metabolic functionality by activating pericentral GRNs and suppressing embryonic and periportal GRNs.”
InfluentialTrauernicht, M., Franceschini-Santos, V. H., Yücel, H., Filipovska, T., & van Steensel, B. (2026) · bioRxiv · doi:10.64898/2026.01.05.697632 · Cited by 1 · see citing works
“Genome-wide accessibility profiles generated by ATAC-seq are now widely used to infer TF activity (36-40), dissect cis-regulatory logic (41), and reconstruct gene regulatory networks (42,43).”
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Deep Learning in Multiomics Sciences: Where We are, Emerging Topics, and Future Challenges
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Spatial protein gradients pattern liver metabolism
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Abnormalities of liver sinusoidal endothelial cells in primary biliary cholangitis
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Spatiotemporal Transcriptomics Characterizes Immune Microenvironment During Mouse Liver Aging
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Spatial immune niche remodeling of the neutrophil-macrophage axis inchronic liver disease
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Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo
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“Notably, models trained only on ATAC-seq data or only on validated enhancers (direct training without transfer learning) performed worse, with PPVs dropping between 20.9% and 52.1% depending on the tissue (Extended Data Fig.”
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“Single-cell and spatial transcriptomics, epigenomics, proteomics, and metabolomics have revealed that liver development proceeds through continuous, state-dependent trajectories rather than discrete lineage endpoints 8,20,58,126,127 .”
Zonated cholesterol sensing by SIRT2 drives MASLD-HCC
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Multi-organelle signatures map cell-state diversity and metabolic adaptation in tissues
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“All calculations performed by RcisTarget are based on motifs (40, 41).”
Hippo signaling drives mesenchymal cell fate and heterogeneity in liver cancer.
Souza, N. A., Kowalczyk, W., Soheily, S., Canoot, G., Ostyn, T., Corral-Ibáñez, M., Sarfi, M., Hillen, H., Sansores-García, L., Huffel, L. V., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.102064
Self-citation
- Authors
- Bravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., Demeulemeester, J., Psatha, N., Mauduit, D., Halder, G., & Aerts, S.
- Affiliations
- Vlaams Instituut voor Biotechnologie (BE)
- VIB-KU Leuven Center for Cancer Biology (BE)
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature Cell Biology
- Published
- 2024-01
- Volume
- 26
- Issue
- 1
- Pages
- 153-167
- ISSN
- 1465-7392 (ISSN-L)
- 1476-4679
- PMC
- PMC10791584
- OpenAlex
- W4390615703
- Semantic Scholar
- 6f8b3d0adbe28929c1ebe36cd85f994cb4946956
- Cited by
- 85
- References
- 100
- Citations by year
- 2026: 40 · 2025: 32 · 2024: 13
- FWCI
- 9.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Cell Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- Funders
- Fonds Wetenschappelijk Onderzoek (G0B5619N, 11F1519N)
- KU Leuven (C14/18/092)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- liver zonation, hepatocyte heterogeneity, chromatin accessibility, TCF7L1, TBX3, HNF4α, CEBPA, FOXA1, Onecut1, massively parallel reporter assay, spatial multi-omics, mouse liver
- MeSH terms
- Deep Learning, Multiomics, Animals, Liver, Mammals, Hepatocytes, Mice, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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