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Spatial epigenome–transcriptome co-profiling of mammalian tissues
Open accessPublished version of a preprint
Abstract
Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context 1–5 . However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.
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TL;DR
The paper presents spatial ATAC–RNA-seq and spatial CUT&Tag–RNA-seq for genome-wide joint profiling of chromatin accessibility or histone modifications and mRNA on the same tissue section at near-single-cell resolution. These methods were applied to embryonic and juvenile mouse brain and adult human hippocampus, revealing spatial epigenetic–transcriptional relationships including epigenetic priming and region-specific gene regulation.
Key findings
- In P22 mouse brain with 100 × 100 barcodes, spatial ATAC–RNA-seq yielded a median of 14,284 unique fragments per pixel, with 19% enriched at transcription start sites and 26% in peaks; RNA detected 22,914 genes, averaging 1,073 genes and 2,358 UMIs per pixel.
- Spatial CUT&Tag–RNA-seq on P22 mouse brain gave median unique fragments per pixel of 10,644 for H3K27me3, 10,002 for H3K27ac and 2,507 for H3K4me3; paired RNA averaged 2,011, 1,513 and 1,329 genes per pixel, respectively.
- Joint clustering of E13 mouse embryo ATAC and RNA identified a new neuronal cluster J10 not readily resolved by single modalities; in P22 mouse brain, 21,417 significant peak-to-gene linkages were detected by ATAC–RNA-seq and 19,468 from H3K27ac CUT&Tag–RNA-seq.
- Region-specific regulation was observed: H3K27me3 anticorrelated with RNA in corpus callosum (for example, Mal, Mag and Car2 low CSS/high RNA; Grin2b and Syt1 high CSS/low RNA), while H3K27ac and H3K4me3 correlated with RNA; potential bivalency/poising was noted for genes such as Ptprz1 and Fnbp1. In human hippocampus, PROX1 was highly expressed in the granule cell layer but showed modest chromatin accessibility.
Methods
- Frozen tissue sections were fixed, subjected to Tn5 transposition for ATAC or antibody-guided pA-Tn5 CUT&Tag, then in-tissue reverse transcription; sequential microfluidic barcode A and B ligation generated 50 × 50 or 100 × 100 pixel grids (2,500 or 10,000 pixels), with separate gDNA and cDNA libraries sequenced on Illumina NovaSeq 6000 (paired-end, 150 bp).
- Samples included E13 mouse embryo, P21/22 mouse brain and adult human hippocampus from a 31-year-old male; pixel sizes were 50 μm for embryo and human tissue and 20 μm for juvenile mouse brain. Analyses used Signac, Seurat and ArchR, with label transfer to scRNA-seq, scATAC-seq and scCUT&Tag atlases, gene activity score (GAS), chromatin silencing score (CSS), peak-to-gene links, pseudotime and GO enrichment.
- Reproducibility was high: Pearson correlations were r = 0.98 for ATAC and r = 0.98 for RNA in spatial ATAC–RNA-seq, and r = 0.96 for CUT&Tag (H3K27ac) and r = 0.89 for RNA in spatial CUT&Tag–RNA-seq.
Limitations
- The paper states that low RNA expression despite open chromatin may be due in part to sequencing depth and RNA detection sensitivity, and interprets some such cases as lineage priming.
- The method is near-single-cell, not single-cell: 50-μm pixels contained an average of 25 cells for E10 mouse embryo and 1–9 cells for human hippocampus, while 20-μm pixels contained 1–3 cells in juvenile mouse brain; when more than one cell type was present in a pixel, the major cell type was assigned.
- Evident from the text: the human analysis used one 31-year-old male hippocampus sample, limiting generalizability, and the epigenome–transcriptome relationships are largely correlative rather than causally tested.
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70 Own result · 25 Not checked yet · 22 Own interpretation · 13 Own method · 4 No basis given · 1 Partly supported · accuracy not yet measured
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In the paper
“which may indicate lineage priming10,27 of these genes in embryonic brain development.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
What the sources say
- Partly supportedcites [10] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020). sourceExperimental — accuracy not yet measured
The Results section, “Lineage Priming at Enhancers Precedes Gene Expression in DORCs” (page unavailable), reports that chromatin accessibility can precede RNA expression and indicate lineage priming. Its examples concern hair follicle differentiation, not Pax6, Sox2, or Myt1l in embryonic brain development; it supports the proposed interpretation but not its application to those specific genes and setting. (LLM, conf=0.87)
- Partly supportedcites [27] Meijer, M. et al. Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility. Neuron 110, 1193–1210 (2022). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) describes chromatin priming as accessible immune-gene promoters with low or no expression, supporting the proposed interpretation of the accessibility–expression pattern. The paper studies immune genes in oligodendroglia, however, and does not establish lineage priming of Pax6, Sox2, or Myt1l during embryonic brain development. (LLM, conf=0.87)
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In the paper
“Most 20-μm pixels contained one to three cells per pixel in juvenile mouse brain (Supplementary Fig. 1b).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
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In the paper
“We detected 21,417 significant peak-to-gene linkages between regulatory elements and target genes”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
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In the paper
“The loss of chromatin accessibility at the Pax6 and radial glia marker Fabp7 loci preceded downregulation of the corresponding RNAs”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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In the paper
“These clusters agreed with the anatomical annotation by Nissl staining and showed good concordance between CUT&Tag and RNA in spatial patterns (Fig. 3a–c), which was further confirmed by Belayer34 analysis (Supplementary Fig. 7c).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
What the sources say
- Not checked yetcites [34] Ma, C., Chitra, U., Zhang, S. & Raphael, B. J. Belayer: modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics. Cell Syst. 13, 786–797 (2022). source
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“These clusters agreed with the anatomical annotation by Nissl staining and showed good concordance between CUT&Tag and RNA in spatial patterns (Fig. 3a–c), which was further confirmed by Belayer34 analysis (Supplementary Fig. 7c).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
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In the paper
“Some marker genes (Pax6, Sox2 and Myt1l) were highly accessible in some regions of the embryonic brain but showed modest or low RNA expression”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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In the paper
“Chromatin accessibility GAS and gene expression along this trajectory showed dynamic changes in selected marker genes”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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In the paper
“we found a higher number of ATAC clusters (14) compared with RNA clusters (11)”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
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In the paper
“We observed a high degree of concordance in spatial cell type distribution as determined by ATAC versus RNA”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 13
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In the paper
“Using a 100 × 100 barcode scheme, the mapping area covers nearly the entire hemisphere of a P22 mouse brain coronal section.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
plainSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“we obtained a median of 10,644 (H3K27me3), 10,002 (H3K27ac) and 2,507 (H3K4me3) unique fragments per pixel, of which 12% (H3K27me3), 17% (H3K27ac) and 67% (H3K4me3) overlapped with transcription start site regions and 12% (H3K27me3), 21% (H3K27ac) and 54% (H3K4me3) were located in peaks”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
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In the paper
“For the RNA data, 25,881 (H3K27me3), 23,415 (H3K27ac) and 22,731 (H3K4me3) genes were detected with an average of 2,011 (H3K27me3), 1,513 (H3K27ac) and 1,329 (H3K4me3) genes per pixel (4,734 (H3K27me3), 3,580 (H3K27ac) and 2,885 (H3K4me3) UMIs per pixel, respectively”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“From this sample we obtained a median of 14,284 unique fragments per pixel, of which 19% were enriched in the transcription start site regions and 26% located in peaks”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“For the 50-μm pixel size we obtained an average of 25 cells for E10 mouse embryo1 and between one and nine cells for human hippocampus3.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
What the sources say
- Not checked yetcites [1] Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in yissue. Cell 183, 1665–1681 (2020). source
- Not checked yetcites [3] Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022). source
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In the paper
“Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Abstract
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In the paper
“For the RNA portion a total of 22,914 genes were detected, with an average of 1,073 genes and 2,358 unique molecular identifiers (UMIs) per pixel (spatial ATAC–RNA-seq)”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
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In the paper
“we observed immature oligodendrocytes and oligodendrocytes in cluster A3 corresponding to corpus callosum. A thin layer of radial glia-like cells was found in the lateral ventricle, medium spiny neurons (D1MSN and D2MSN) in striatum and inhibitory neurons in the lateral septal nucleus30”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
What the sources say
- Not checked yetcites [30] Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). source
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In the paper
“We identified 13 and 15 specific clusters for H3K27me3 and RNA, 12 and 13 clusters for H3K27ac and RNA and 11 and 12 clusters for H3K4me3 and RNA, respectively (Fig. 3a–c).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
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“Monocle2 pseudotime analysis showed a bifurcation in chromatin accessibility but not in RNA expression; one path led to regions close to the ventricular zone (green pixels, Extended Data Fig. 3d,e) and the other terminated in regions distal to the ventricular zone (blue pixels).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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“Chromatin accessibility of marker genes identified major regions such as striatum (Pde10a and Adcy5, markers of medium spiny neurons, cluster A1), corpus callosum (Sox10, Mbp and Tspan2, markers of oligodendroglia, cluster A3), cortex (Mef2c, Neurod6 and Nrn1, clusters A0 and A4, markers of excitatory neurons; Cux2, cluster A4) and lateral ventricle (Dlx1, Pax6, Notch1 and Sox2, markers of ependym”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
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In the paper
“Spatial ATAC–RNA-seq on E13 mouse embryo identified eight major ATAC clusters and 14 RNA clusters”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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“genes involved in neuronal identity, axonogenesis and synapse organization (GO Myt1l to Dnm3 in Extended Data Fig. 3a), such as Dcx and Tubb3, showed increased expression in spatial pseudotime but chromatin accessibility at their loci was already elevated at earlier stages”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
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“radial glia (neural stem/progenitor cells) were observed predominantly in the ventricular layer, and differentiated cell types such as postmitotic premature neurons and inhibitory interneurons were enriched in those regions distant from the ventricular layer”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Spatial RNA clusters also showed region-specific gene expression such as striatum (Pde10a, cluster R2, medium spiny neurons), corpus callosum (Mbp and Tspan2, cluster R5, oligodendroglia) and cortex (Mef2c, clusters R0 and R1, excitatory neurons)”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
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In the paper
“Satb2 showed the highest activity in clusters C8 and C9 (H3K27ac) and C6 and C7 (H3K4me3), but the lowest CSS in clusters C1 and C3 (H3K27me3) in the cortical layer.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
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“For instance, in the superficial cortical layer, genes for GABAergic regulation of synaptic transmission were enriched in H3K27me3 whereas glutamatergic synapse transmission genes had high expression and low H3K27me3 (Supplementary Figs. 9b, 10b and 11b; GOs for striatum, superficial and deeper cortical layers).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
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“TF motifs (NEUROD1 and SNAI1) and their spatial patterns were visualized in different tissue regions, and positive TF regulators were identified (Extended Data Fig. 10b,c).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“These techniques were applied to comapping of embryonic and juvenile mouse brain, as well as adult human brain.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Discussion
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In the paper
“We identified seven and eight major clusters for ATAC and RNA, respectively, and the spatial pattern agreed with major anatomical landmarks (Fig. 5a)3, including specific marker genes (Fig. 5d).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, PROX1, a signature gene defining granule neuron identity during pyramidal neuron fate selection44, is indeed highly expressed in GCL but showed modest chromatin accessibility (Fig. 5d).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The ATAC cluster A4 represents the granule cell layer (GCL) (THY1, BCL11B) and cluster A6 corresponds to the choroid plexus (Fig. 5a,d).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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“Tspan2 was enriched in cluster C4 (H3K27ac) and cluster C1 (H3K4me3) but depleted in cluster C5 (H3K27me3) in corpus callosum, associated with oligodendrocyte lineage development (Supplementary Fig. 6).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These genes are involved in oligodendrocyte differentiation and myelination and other processes such as protein catabolism and localization (Supplementary Figs. 13 and 14).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Gpr88, a marker gene of MSN, was enriched in striatum for ATAC, H3K27ac and H3K4me3 but with low CSS in striatum for H3K27me3 (Extended Data Fig. 9f).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, we observed an enrichment of MOL within the corpus callosum, a thin layer of ependymal cells in the lateral ventricle, excitatory neurons in cerebral cortex and MSN in the striatum.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We have demonstrated either ATAC or CUT&Tag in conjunction with RNA for spatial multiomics mapping.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Discussion
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general H3K4me3 and H3K27ac showed high correlations, with their signals anticorrelated with H3K27me3 (Fig. 4e).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Granule cells (GC) were detected in the GCL, cornu ammonis (CA) neurons were enriched in CA3–4 regions and VLMC were strongly distinguished in choroid plexus as opposed to other regions.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Spatial epigenome–transcriptome cosequencing yielded two layers of spatial omics information directly in tissue, with data quality similar to that previously obtained by single modalities.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Discussion
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Clustering of CSS or GAS based on different modifications resolved all major tissue regions (Fig. 3a–c and Supplementary Fig. 6) and identified region-specific marker gene modifications.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast to H3K27me3, we observed a robust correlation between RNA and activating marks H3K4me3 or H3K27ac in the corpus callosum, with genes highly expressed and with high deposition in these two modalities regulating processes in oligodendroglia (Fig. 4b,c and Supplementary Fig. 12; GO for H3K27ac quadrant I and H3K4me3 quadrant I).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The serpentine channel design allows simultaneous processing of five tissue samples, each mapped for 10,000 pixels.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Discussion
plaindirectn = 5- Asserted: passes — The authors put it forward as true.
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In the paper
“However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Abstract
What the sources say
- Not checked yetcites [1] Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in yissue. Cell 183, 1665–1681 (2020). source
- Not checked yetcites [2] Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022). source
- Not checked yetcites [3] Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022). source
- Not checked yetcites [4] Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022). source
- Not checked yetcites [5] Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Main
What the sources say
- Not checked yetcites [6] Allaway, K. C. et al. Genetic and epigenetic coordination of cortical interneuron development. Nature 597, 693–697 (2021). source
- Not checked yetcites [7] Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019). source
- Not checked yetcites [8] Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018). source
- Not checked yetcites [9] Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069 (2021). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“we developed spatially resolved, genome-wide comapping of the epigenome and transcriptome by simultaneous profiling of chromatin accessibility and messenger RNA expression (spatial assay for transposase-accessible chromatin and RNA using sequencing (spatial ATAC–RNA-seq)), or histone modifications and mRNA expression”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Main
What the sources say
- Not checked yetcites [3] Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022). source
- Not checked yetcites [2] Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Although computational methods can integrate data from multiple omics10, they cannot readily uncover the mechanistic links between different omics layers.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Main
What the sources say
- Not checked yetcites [10] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Previously we developed deterministic barcoding in tissue for spatial omics sequencing for spatially resolved comeasurement of the transcriptome and a panel of proteins1, and recently it was also realized in the 10X Visium platform11.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Main
What the sources say
- Not checked yetcites [1] Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in yissue. Cell 183, 1665–1681 (2020). source
- Not checked yetcites [11] Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01536-3 (2023). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Spatial epigenomics, transcriptomics and proteomics emerged recently1–5 but most of these can capture only one layer of the omics information.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Main
What the sources say
- Not checked yetcites [1] Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in yissue. Cell 183, 1665–1681 (2020). source
- Not checked yetcites [2] Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022). source
- Not checked yetcites [3] Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022). source
- Not checked yetcites [4] Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022). source
- Not checked yetcites [5] Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464 (2022). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Integration of single-cell ATAC-seq mouse brain atlas data30 with spatial ATAC-seq data facilitated identification of all major cell types, and label transfer was then used to assign cell types to spatial locations where the epigenetic state may control specific cell type formation”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
What the sources say
- Not checked yetcites [30] Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We developed a microchip with 100 serpentine microchannels to barcode 100 × 100 or, in total, 10,000 pixels per tissue section and up to five samples per chip”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial ATAC–RNA-seq of mouse brain
plainn = 5- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, our spatial ATAC–RNA-seq can be used to decipher the gene regulation mechanism and spatiotemporal dynamics during tissue development.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of mouse embryo
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Correlation analysis between replicates showed high reproducibility (r = 0.98 for ATAC, r = 0.98 for RNA in spatial ATAC–RNA-seq, r = 0.96 for CUT&Tag (H3K27ac), r = 0.89 for RNA in spatial CUT&Tag–RNA-seq”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Technology workflow and data quality
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As mentioned above, this potential H3K4me3/H3K27me3 bivalency may reflect transcriptional poising.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, both ATAC and RNA can readily resolve all major tissue features in this region, but spatial cosequencing of epigenome and transcriptome may provide new insights into the dynamic gene regulation mechanism that cannot be realized by single modalities.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This could also be due in part to the presence in proximity of both oligodendrocyte precursor cells (expressing Ptprz1) and mature oligodendrocytes (where Ptprz1 is repressed) in the corpus callosum.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These technologies allow us to examine spatiotemporal dynamics and genome-wide gene regulation mechanisms in the tissue context.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“PROX1, a signature gene defining granule neuron identity during pyramidal neuron fate selection44”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
What the sources say
- Not checked yetcites [44] La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These results are in accordance with ATAC/H3K27me3/H3K27ac Nano-CUT&Tag analysis37 of oligodendroglial differentiation in the juvenile brain, which showed two waves of H3K27me3 repression during this process37.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
What the sources say
- Not checked yetcites [37] Bartosovic, M. & Castelo-Branco, G. Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01535-4 (2022). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“For H3K27ac and H3K4me3, active genes should correspond to high GAS2,13 (Methods).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
What the sources say
- Not checked yetcites [2] Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022). source
- Not checked yetcites [13] Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A biological replicate was also performed on P21 mouse brain (Extended Data Fig. 8; 20-μm pixel size, 50 × 50 barcodes).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial CUT&Tag–RNA-seq of mouse brain
plainSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“such as Ptprz1, a marker of oligodendrocyte precursor cells38,39”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Region-specific gene expression regulation
What the sources say
- Not checked yetcites [38] Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016). source
- Not checked yetcites [39] Marques, S. et al. Transcriptional convergence of oligodendrocyte lineage progenitors during development. Dev. Cell 46, 504–517 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This might be attributed to a minimal demand for synthesis of new PROX1 transcripts in postmitotic mature granule cells, and is thus not required to maintain an active open chromatin state.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Spatial comapping of human brain
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All procedures described were approved by the local committee for ethical experiments on laboratory animals in Sweden (Stockholms Norra Djurförsöksetiska nämnd, nos. 1995/2019 and 7029/2020).”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Methods > Preparation of tissue slides
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experimental procedures were conducted following European directive no. 2010/63/EU, local Swedish directive no. L150/SJVFS/2019:9, Saknr no. L150 and Karolinska Institutet complementary guidelines for procurement and use of laboratory animals, no. Dnr. 1937/03-640.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Methods > Preparation of tissue slides
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Brain tissue collection was conducted with New York State Psychiatric Institute Institutional Review Board approval, and informed consent obtained from next of kin who agreed to donate the brains and participate in psychological autopsy interviews.”
§ Spatial epigenome–transcriptome co-profiling of mammalian tissues > Methods > Preparation of tissue slides
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
The selected tissues (E13 mouse embryo, P21/22 mouse brain, adult human hippocampus) are representative models for studying spatial epigenome–transcriptome relationships in mammalian tissues.
“We applied these techniques to embryonic and juvenile mouse brain, as well as adult human brain hippocampus, to dissect epigenetic and transcriptional states and their role in the regulation of cell types dynamics in tissue.”
The paper relies on this without support.
As of the time of the study, current technologies had not been able to perform unbiased genome-wide comapping of epigenome and transcriptome on the same tissue section at the cellular level.
“As of today, current technologies have not been able to perform unbiased genome-wide comapping of epigenome and transcriptome on the same tissue section at the cellular level.”
The paper relies on this without support.
To comprehensively understand the mechanism of gene regulation, different layers of omics information need to be profiled simultaneously.
“However, to comprehensively understand the mechanism of gene regulation, different layers of omics information need to be profiled simultaneously.”
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?
The cited source says what the claim attributes to it.
In the citing paper
“the group extended this approach to spatial ATAC-RNA-seq and spatial assay of cleavage under targets and tagmentation and RNA sequencing (spatial CUT&Tag-RNA-seq) [86], which achieve co-profile of epigenome and transcriptome”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [86]
In the citing paper
“Recently, DBiT-seq has further demonstrated its versatility by spatial co-profiling of mRNA transcriptome and hundreds of cellular proteins as part of spatial CITE-seq126 and of transcriptome and epigenome as part of spatial ATAC–RNA-seq127, spatial CUT&Tag–RNA-seq127 and (reported in a preprint) SPARC-seq128.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [127]
Citations
Cites
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
clusterProfiler 4.0: A universal enrichment tool for interpreting omics data
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Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
The GeneCards suite: from gene data mining to disease genome sequence analyses
Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., et al. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.5 · Cited by 5,550
The Global Assessment Scale: A Procedure for Measuring Overall Severity of Psychiatric Disturbance
Endicott, J. (1976) · Archives of General Psychiatry · doi:10.1001/archpsyc.1976.01770060086012 · Cited by 4,982
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
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Self-citationMolecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
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Sorted by citations
Items 301–400 of 482
Effect of SNPs on Litter Size in Swine
Guo, Z., Lv, L., Liu, D., Ma, H., & Radović, Č. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb46070378 · Cited by 1
“Diseases can alter immune genes in swine populations [60].”
BackgroundEpigenomic Sequencing Technologies
Chen, J., Keshavarzian, T., & Lupien, M. (2024) · Royal Society of Chemistry eBooks · doi:10.1039/9781837674916-00109 · Cited by 1
Unlocking cross-modal interplay of single-cell joint profiling with CellMATE
Wang, Q., Zhang, B., Guo, Y., Gong, L., Li, E., & Yang, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae582 · Cited by 2
“We further applied CellMATE to spatial paired data of mouse brain [41].”
A Novel Targeted Long-read Sequencing Approach Boosts Transcriptomic Profiling
Tian, X., & Fan, R. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae090 · Cited by 1
“…the high multiplexing capacity demonstrated by introducing different barcodes points to the great potential for further integration in single-cell and spatial multi-omics sequencing [2–6] in order to map transcriptomic complexities at single-cell level and in the native tissue microenvironment.”
Self-citationTranscriptional and epigenetic regulation of memory storage
Abel, T. G., Basu, B., & Chatterjee, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00076-6 · Cited by 1
The next generation of in situ multi-omics
Salla, M., Penkert, K., & Ludwig, L. S. (2025) · Nature Methods · doi:10.1038/s41592-024-02571-5 · Cited by 1
Zhou, W., Fan, X., Li, L., Zheng, J., Liu, X., Jin, W., & Tian, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag432 · Cited by 2
“[ 72].”
Ayabe, R. I., Seo, Y. D., Melendez, B., Fields, B. C., Diggs, L. P., Lazcano, R., Singh, B. B., Wani, K., Ingram, D. R., Johnson, S., et al. (2025) · JCO Precision Oncology · doi:10.1200/po-25-00107 · Cited by 1
Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft
Zhu, S., Tao, H.-X., Cornell, R. A., & Liu, H. (2025) · Human Mutation · doi:10.1155/humu/6824122 · Cited by 1
Epigenome-wide association study (EWAS) methods and applications
Tanić, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26759-8.00028-x · Cited by 1
Dissecting Normal and Abnormal Human Kidney Development Using Multiomics
Klomp, L. S., Mavrommatis, L., Arcolino, F. O., Lantermans, H. C., Levtchenko, E. N., Kuppe, C., & Westland, R. (2025) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000951 · Cited by 1
A Single-Cell Omics Technical Guide for Advancing Neuropsychiatric Research
Casmey, K., Zimmermann, M., Xie, Y., Codeluppi-Arrowsmith, S. A., & Turecki, G. (2025) · Genes · doi:10.3390/genes16121394 · Cited by 3
“These technologies can also profile spatial transcriptomics with multiple epigenomic mechanisms, such as Spatial ATAC-RNA-seq [326,327], Spatial CUT&Tag-RNA-seq [326,327], Spatial-DMT [328], Patho-DBiT [329], and SPACE-seq [330].”
From slices to deep dishes: spatial transcriptomics and translatomics of thick tissue blocks
Pleška, M., & Vicković, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02815-y · Cited by 1
Modern understanding of epigenetic inheritance mechanisms and their role in the evolutionary process
Toshchakova, V. A., Noskov, S. A., Khazieva, K. R., Erofeeva, T. V., & Toshchakov, S. V. (2025) · Molecular Genetics Microbiology and Virology (Russian) · doi:10.17116/molgen2025430426 · Cited by 1
Thoughts on the scientific research of the connotation of traditional Chinese medicine patterns
Xie, K., Ouyang, Y., Wang, Y., & Li, C. (2025) · Journal of Traditional Chinese Medical Sciences · doi:10.1016/j.jtcms.2025.12.004 · Cited by 1
Wu, Z., Liu, W., Chu, J., Yao, J., Yubo, F., & Wang, Y. (2026) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2026.100427 · Cited by 1
Advances in spatial omics for the analysis of prostate cancer
Xu, J., Wang, D., Di, X., & Liu, Y. (2026) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-026-06396-3 · Cited by 1
“…such as spatial ATAC–RNA and spatial CUT&Tag–RNA can directly link accessibility or histone modification states to transcriptional activity within the same coordinates, providing new insight into androgen-receptor (AR) signaling dynamics, lineage plasticity, and therapy-induced reprogramming [137].”
Self-citationMacrophage plasticity in thyroid eye disease: dual effects of inflammation and fibrosis
Li, X., Ren, Z., Gao, L., Xiong, C., & Liao, H. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1782204 · Cited by 1
Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics
Wang, H., Yuan, Z., Su, Y., Zheng, C., & Sun, X. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2517283123 · Cited by 3
Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding
Öztürk, K., Klie, A., & Carter, H. K. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092724-043507 · Cited by 1
scKAN: Integration of multi-modal single-cell data via adaptive Kolmogorov–Arnold networks
Zheng, L., Zhang, Y., Tian, S., Ding, B., Wang, S., Wang, X., Gu, A., Yang, L., Wang, M., & Zhang, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113807 · Cited by 1
Zheng, X., Huang, X., Zhou, X., Meng, D., Qiao, Z., Yuan, Z., & Cheng, L. (2026) · Advanced Science · doi:10.1002/advs.75478 · Cited by 1
“…[ 8 ], SPOTS [ 9 ], SM-omics [ 10 ], stereo-CITE-seq [ 11 ]), in situ joint transcriptome-epigenome profiles (e.g., spatial-RNA-ATAC-seq [ 12 ], MISAR-seq [ 13 ]), and in situ joint transcriptome-metabolome profiles (e.g., combining spatial transcriptomics with desorption electrospray…”
Spatial multi-omics imputation and embedding with SpaMIE
Liu, W., Xiang, D.-W., Jiang, X., Bai, Y.-H., Li, S., Li, W., Lv, S.-X., Li, Q., Jiang, J.-K., & Liu, J. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101456
“23,24 As the P21 section lacked RNA measurements, SpaMIE was first used to impute RNA before integration.”
Mao, S., Yang, C., Zhen, C., He, Z., Luo, Y., & Zhang, L. (2026) · Communications Biology · doi:10.1038/s42003-026-10462-y
Challenges and emerging strategies for genome-wide evaluation of loss of imprinting in cancer
Amin, M. T., Coussement, L., & Meyer, T. D. (2026) · British Journal of Biomedical Science · doi:10.3389/bjbs.2026.16709 · Cited by 1
He, Y., Wei, Y., Zhi, J., Huang, C. J., Han, L., Liu, S., & Wang, L. (2026) · GigaScience · doi:10.1093/gigascience/giag075 · Cited by 1
“…Spatial CITE-seq, SM-Omics) go a step further, allowing researchers to concurrently obtain transcriptomic and epigenomic information from the same tissue section, providing possibilities for understanding the epigenetic regulation of gene expression within the native microenvironment [ 57–61 ].”
Self-citationARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration
Wang, X., Su, Y., Hao, G., Wang, M., Wang, Y., & Li, X. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag465 · Cited by 1
“…dataset (Huang et al. 2024), the spatial epigenome–transcriptome mouse brain dataset (Huang et al. 2024), the mouse thymus stereoCITE-seq dataset (Long et al. 2024), Mouse Embryo E13 spatial transcriptomics dataset (Zhang et al. 2023), and the mouse embryo Spatial-Mux-seq dataset (Guo et al. 2025).”
Dissecting human organ development using spatial technologies
Zhang, X., Vandana, J. J., Greenspun, B. C., & Chen, S. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.05.004 · Cited by 1
Next-generation kidney tissue analysis — spatial omics and digital pathology
Yoshikawa, T., Bülow, R. D., Boor, P., & Yanagita, M. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01102-z · Cited by 3
“These approaches have been successfully applied to mouse and human tissues, including embryo 99 , brain 99,100 , thymus 101 and tonsil 99 .”
ZDHHC18-YTHDF2 axis suppresses USP13 to drive cardiac hypertrophy
Ba, L., Wang, R., Zhao, Z., Du, Y., Wang, R., Wu, N., Zhang, Q., Sun, H., & Qi, H. (2026) · Pharmacological Research · doi:10.1016/j.phrs.2026.108343 · Cited by 1
Spatial biology in cancer epigenetics
Crespo-García, E., & Esteller, M. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70310 · Cited by 2
Ji, X., Fang, P., Wan, Y., Zhao, X., Xiong, C., Guo, F., Yang, Q., Yan‐Do, R., Qi, L., Xu, C., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01745-0 · Cited by 2
Li, W., Jiang, Y.-X., Zhao, Q., Xu, Y., Dai, D.-L., Rong, Y., Zhao, X., & Zhang, H. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-02067-8
“Using a spatial ATAC–RNA mouse brain dataset (postnatal day 22 coronal section) 14 , we introduced random masking to simulate degraded data and applied DePass for enhancement.”
Spatial transcriptomics in plants: From cellular maps to mechanistic insight
Wang, Y., Tan, Z., Freeman, N. A., Phukan, U. J., Greer, S. A., & Mukhtar, M. S. (2026) · Plant Communications · doi:10.1016/j.xplc.2026.102070 · Cited by 1
Trauernicht, M., Franceschini-Santos, V. H., Yücel, H., Filipovska, T., & van Steensel, B. (2026) · Molecular Systems Biology · doi:10.1038/s44320-026-00240-7 · Cited by 1
Tuning in to epigenetic cross-talk
Marx, V. (2023) · Nature Methods · doi:10.1038/s41592-023-01870-7 · Cited by 1
Singh, S., Sutkus, L. T., Li, Z., Baker, S., Bear, J., Dilger, R., & Miller, D. J. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110139 · Cited by 2
Understanding developing kidneys and Wilms tumors one cell at a time
Pop, N. S., Dolt, K. S., & Hohenstein, P. (2025) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2024.11.005 · Cited by 1
Liu, Q., Wang, C., Zhong, Q., Zhu, F., Gao, B., Zheng, K., Guo, M., Li, W., Zhang, Y., Zhang, S., et al. (2025) · The Innovation Life · doi:10.59717/j.xinn-life.2025.100153 · Cited by 1
The histone lysine methyltransferase NSD3 drives osteosarcomagenesis by inactivating ARID3A
Lu, J., Shao, Y. W., Saw, S., Fang, H., Song, G., Chen, Y., Tang, Q., Wood, G. A., Wang, J., Waterhouse, P. D., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.218045 · Cited by 1
Liu, T., Lin, Y., Luo, X., Sun, Y., & Zhao, H. (2026) · Communications Biology · doi:10.1038/s42003-025-09479-6 · Cited by 3
“Compared to spot-based spatial technologies 4,13 , these methods facilitate the capture of gene expression at a single-molecule/subcellular resolution, which can then be aggregated to deducegeneexpressionlevelsandcellstructureinsingle-cellresolution.”
Bazan, N. G., Bhattacharjee, S., Kala-Bhattacharjee, S., Ledet, A. J., & Mukherjee, P. K. (2023) · Research Square · doi:10.21203/rs.3.rs-3185942/v1 · Cited by 1
Epigenetic Modifications and Their Relevance in the Normal and Pathological BrainPreprint
Lossi, L., & Merighi, A. (2023) · Preprints.org · doi:10.20944/preprints202311.1530.v1 · Cited by 1
Broad H3K4me3 Domain Is Associated with Spatial Coherence during Mammalian Embryonic DevelopmentPreprint
Cao, X., Ma, T., Fan, R., & Yuan, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.11.570452 · Cited by 1
Self-citationMendoza-Ferri, M. G., Lozachmeur, G., Duvina, M., Perret, L., Merciris, D., Gigout, A., & Mendoza-Parra, M. A. (2025) · Genome Research · doi:10.1101/gr.280305.124 · Cited by 1
EYKTHYR reveals transcriptional regulators of spatial gene programsPreprint
Krieger, S., Haber, E., & Ma, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.19.654884 · Cited by 1
“We next investigated a spatial ATAC–RNA-seq sample of the mouse embryonic hindbrain [8] ( Fig.”
Yan, C., He, Z., Ye, L., Zheng, S., Lin, N., Li, G., & Li, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.27.678942 · Cited by 1
“To evaluate cross-modality learning at subcellular resolution, we applied our framework to a co-assay dataset of mouse brain spatial transcriptomics and epigenomics 20 .”
InfluentialSingle-cell spatial multi-omics molecular pathology enabled by SuperFocusPreprint
Lu, Y., Tian, X., Vicari, M., Enninful, A., Bao, S., Bai, Z., Liu, C., Zhang, X., Andrén, P. E., Lundeberg, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.26.696575 · Cited by 1
“In a human hippocampus spatial epigenome–transcriptome dataset (19), SuperFocus inferred cell-level chromatin accessibility profiles that supported accurate cell-type annotation, spatial motif analysis, and the reconstruction of gene regulatory programs together with SuperFocus-resolved gene…”
Self-citationSPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologiesPreprint
Wang, H., Yuan, J., Li, K., Chen, X., Yan, X., Lin, P., Tang, Z., Wu, B., Nan, H., Lai, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703207 · Cited by 1
“Next, we used a real spatial transcriptome-epigenome data of mouse brain to benchmark SPCoral and other methods 31 .”
SpaMOAL: A spatial multi-omics graph contrastive learning method for spatial domains identificationPreprint
Wang, J.-X., Huo, Y.-Y., Zhao, R., Pan, Y., Wang, H., & Li, X.-Y. (2026) · bioRxiv · doi:10.64898/2026.02.23.707433 · Cited by 1
Toward Computationally Complete Spatial OmicsPreprint
Li, W., Mao, L., Liu, Y., Peng, F., Sachs, N., Wu, W., Yiu, S. P. T., Yan, H., Schroeder, A. R., Yu, X., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.03.709303 · Cited by 1
Self-citationBouwers, E. G. (2011) · PUB – Publications at Bielefeld University (Bielefeld University)
Las grandes reformas al derecho de familia en el proyecto de Código Civil y Comercial argentino 2012
Medina, G. (2013) · Anuario Uruguayo Crítico de Derecho de Familia y Sucesiones
Lin, L., Cai, Q., & Chen, J. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.04.032
CUT&tag: A powerful epigenetic tool for chromatin profilingPreprint
Li, P., fu, Z., Jiang, S., Sun, Y., zheng, S., & Zong, L. (2023) · doi:10.22541/au.169203623.39699591/v1
Tackling the complexity of CKD-associated cardiovascular disease
Saar-Kovrov, V. (2024) · doi:10.26481/dis.20240516vs
A machine learning tool for spatial multi-omics
(2024) · Nature Methods · doi:10.1038/s41592-024-02358-8
Unlocking cross-modal interplay of single-cell and spatial joint profiling with CellMATEPreprint
Wang, Q., Zhang, B., Gong, L., Guo, Y., Li, E., & Yang, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.06.610031
Emerging Technologies for Epigenetic Drug Discovery
(2024) · Royal Society of Chemistry eBooks · doi:10.1039/9781837674916-part2
Liu, B., Zhang, Y., Zhang, C., & Tian, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14064-8.00014-x
Leveraging spatial multiomics to unravel tissue architecture in embryo development
Jiang, F., Wang, H., Li, Z., Cui, G., & Peng, G. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.11.007 · Cited by 1
Interpretable Multi-Omics Representation Learning Via Covariate-Augumented Generalized Factor ModelPreprint
Liu, W., Xiang, D., Zhou, X., & Jiang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5143837
hiPSC-Driven Organoid Construction and Application Prospects
Liu, B., Mu, Y., & Wang, D. (2025) · Regenerative medicine and dentistry. · doi:10.53941/rmd.2025.100005
“Current methodologies enable high spatial resolution analysis of gene expression mechanisms, including techniques such as CuRVE [115], DBiT-seq, Spatial-ATAC-seq, Spatial-CUT&Tag, Spatial-CITE-seq, Spatial ATAC–RNA-seq, and Spatial CUT&Tag–RNA-seq [116].”
Gene expression and chromatin state mapped in space
Llorens-Bobadilla, E. (2025) · Nature Protocols · doi:10.1038/s41596-025-01151-x
Functional enhancers: concept, screening, and validation
Lin, H., Chen, F., & Huang, J. (2025) · Scientia Sinica Vitae · doi:10.1360/ssv-2025-0113
Sun, Y., Jiang, W., Cai, K.-W., Wei, N.-N., Lai, F.-T., Gao, R.-X., Kuang, Z.-Y., Zhou, J.-L., Liu, A., Zhu, H.-W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.14.654019
Spatial Insights in Cardiovascular Aging
Dai, Z., Ding, H., Zhang, Q., Fu, L., & Tai, S. (2025) · Aging and Disease · doi:10.14336/ad.2025.0272
“Building on this foundation, the team further developed two spatial epigenetic (STE) techniques: Spatial-ATAC-RNA-seq and Spatial-CUT&Tag-RNA-seq [73].”
InfluentialInformatics at the Frontier of Cancer Research
Noller, K., Botsis, T., Cámara, P. G., Ciotti, L., Cooper, L., Goecks, J., Griffith, M., Haas, B. J., Ideker, T., Karchin, R., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-2829 · Cited by 2
“…can be gained through computational integration of multiplexed IHC data with related single-cell proteotranscriptomic (e.g., CITE-seq) datasets (171) or by using new spatial multiomics or epigenetics technologies that enable concurrent measurements of ST and other omics data modalities (172).”
Self-citationSpatial joint profiling of DNA methylome and transcriptome in mammalian tissuesPreprint
Lee, C. N., Fu, H., Cardilla, A., Zhou, W., & Deng, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.01.662607
Self-citationCNA detection from spatial transcriptomics using SpaCNAPreprint
Zhang, Z., Wang, X., Xuan, H., Jin, Z., & Xi, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.08.663688
Li, Z. (2025) · Cotton Genomics and Genetics · doi:10.5376/cgg.2025.16.0011
Library Size in Spatial ATAC-seq: Technical Confounder or Biology?Preprint
Ji, K. X., & Ji, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.15.676443
SpatialFuser: a unified framework for integrative analysis of unpaired spatial multi-omics dataPreprint
Cai, W., & Li, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.14.676067
The histone methyltransferase SETD2 regulates adult brain structure, connectivity and neurogenesis
Atterton, C., Naumann, H., Mitchell, B., Meibusch, B., Currey, L., Kurniawan, N. D., Thor, S., & Piper, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-18780-7
“…as single nucleus multiome sequencing methods (snRNA/snATACseq) 54 , combined with methods such as CUT&RUN for profiling of histone modifications 55 , or even spatial sequencing methodologies 56,57 , would allow for an in-depth characterisation of how these aNSCs differ in the absence of Setd2 .”
Special Issue “Molecular Research in Breast Cancer: Pathophysiology and Treatment”
Frangou, C. G. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199732
“…approaches move beyond bulk averages to track minority populations, such as stem-like, therapy-tolerant, and immune-evasive states that often determine patient outcomes [7], while showing how biochemical, mechanical, and microenvironmental cues modulate oncogenic signaling and cell fate outcomes.”
Epigenomic alterations in psychiatric disorders and glioblastoma
Šmon, J., Jovčevska, I., Paska, A. V., & Kouter, K. (2025) · Epigenomics · doi:10.1080/17501911.2025.2572960
How to get the most out of your cancer spatial transcriptomics data
Kaplan, D. I., Guo, X. Y., Hirimuthugoda, S. D., Cain, L., Weng, S., Le, D., Comben, J., & Trigos, A. (2025) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2025.103657
Li, Y., Stanojevic, S., He, B., Jing, Z., Huang, Q., Kang, J., & Garmire, L. X. (2025) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf285
“Spatial omics technologies are one of the breakthroughs in science in the last several years (Liu et al. 2020, Rao et al. 2021, Deng et al. 2022, Zhang et al. 2023).”
Parmar, A., Nigám, S. K., Cai, K., Falah, K., Ermakov, V. S., Wang, K., & Ferguson, C. (2025) · Epigenetics & Chromatin · doi:10.1186/s13072-025-00648-9
“Accordingly, H3K27ac is one of the key epigenetic modifications capable of reprogramming gene expression states during tissue development [53].”
Identifying multimodal molecular programs with mTopicPreprint
Rutkowski, P., Ochocka, N., Panas, D., & Tabaka, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.21.695756
“P22 mouse brain: spatial ATAC/H3K4me3/H3K27me3/H3K27ac-RNA The bimodal P22 mouse brain datasets include ATAC or histone modification (H3K4me3, H3K27me3, and H3K27ac) and RNA modalities, co-profiled on 100 × 100 microfluidic chips with 20-µ m channels [4].”
InfluentialMulti-task spatial omics analytics for high-precision modeling of tissue architecture with STAXPreprint
Guo, Z.-H., HUANG, D.-S., & Zhang, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.30.697018
“S1b ): mouse brain dataset 1 (epigenomics) [14], human dorsolateral prefrontal cortex dataset (DLPFC, transcriptomics) [9], mouse brain dataset 2 (translatomics) [15], mouse spleen dataset (proteomics) [16], pig embryo dataset (metabolomics) [17].”
Trauernicht, M., Franceschini-Santos, V. H., Yücel, H., Filipovska, T., & van Steensel, B. (2026) · bioRxiv · doi:10.64898/2026.01.05.697632 · Cited by 1
“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).”
Enhancer Dynamics for Gene Regulation in the Cardiovascular System
Wang, Z., & Gan, P. (2026) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.125.323110
spaNetReg: A Dependency-Aware Graph Neural Network for Inferring TRNS from Spatial ATAC-Seq
Miao, J., Hou, P., & Tian, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356838
Spatial Multi-Omics Integration Via Information-Aware Multi-View Contrastive Learning
Zhang, C., Dai, L.-Y., Zhu, R., Wang, J., Shang, J., & Li, F. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356267
“Recently, spatial multi-omics technologies have developed rapidly, and some representative spatial sequencing technologies have emerged such as SPOTS, Stereo-CITE-seq, spatial-CITE-seq, spatial-ATAC-RNA-seq[1–4].”
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Mu, S., Wang, Z., Liao, Y., Liang, J., Zhang, D., Wang, C., Xie, J., Sheng, X., Zhang, T. Q., Huang, W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.03.703456
Self-citationZBED6–IGF2–PIK3C3 autophagy axis drives ccRCC progression: A multi-omics integration study
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“14 However, the observed spatial co-expression pattern indicated a significant negative correlation between their expression levels across two ccRCC samples.”
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“…the course of TERT-related PCOS illness Off-target effects, immunological clearance, and delivery efficiency Single-Cell Spatial Epigenom-ics [211] Combining single-cell epigenetic profiling (e.g., histone code, methylome) with spatial transcriptomics Demonstrates how TERT is regulated by…”
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“While there are recent successes in simultaneous spatial profiling of multiple modalities on the same tissue [35, 66], most of the widely available spatial technologies measure only one single modality.”
Self-citationL-type calcium channel-mediated lipid metabolic reprogramming in gastric adenocarcinoma progression
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“( 2) Histone modifications, such as H3K4me3 and H3K27ac, may promote chromatin remodeling and enhance the expression of the CACNA1D gene [20].”
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“In parallel, the emergence of multi-omics platforms, such as SPOTS [25], spatial-CITE-seq [26], spatial-epigenome-transcriptome [27], and Stereo-CITE-seq [28], enables the simultaneous acquisition of transcriptomics, antibody-derived tag (ADT), and chromatin accessibility (ATAC) data from the same…”
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Influential
Metadata
- Authors
- Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., Liu, Y., Xiao, Y., Rosoklija, G. B., Dwork, A. J., Mann, J. J., Leong, K. W., Boldrini, M., Wang, L., Haeussler, M., Raphael, B. J., Kluger, Y., Castelo-Branco, G., & Fan, R.
- Affiliations
- Yale University (US)
- Yale Cancer Center (US)
- Karolinska Institutet (SE)
- Princeton University (US)
- Broad Institute (US)
- Columbia University (US)
- Macedonian Academy of Sciences and Arts (MK)
- New York State Psychiatric Institute (US)
- Columbia University Irving Medical Center (US)
- University of California, Santa Cruz (US)
- Corresponding author
- Deng, Yanxiang
- Fan, Rong
- ORCID
- Venue
- Nature
- Published
- 2023-03-15
- Volume
- 616
- Issue
- 7955
- Pages
- 113-122
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC10076218
- OpenAlex
- W4324344255
- Semantic Scholar
- bde8da8ff6a896af73232add8cdb5f49a044ecc1
- Cited by
- 483
- References
- 58
- Citations by year
- 2026: 172 · 2025: 163 · 2024: 87 · 2023: 59
- FWCI
- 42.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Yale University
- Li Ka Shing Foundation
- Society for Immunotherapy of Cancer
- Göran Gustafssons Stiftelser
- European Commission (HORIZON2020, 794689)
- Cancerfonden (190394 Pj, 2019-)
- Svenska Sällskapet för Medicinsk Forskning (JUB2019)
- Karolinska Institutet
- Knut och Alice Wallenbergs Stiftelse (2019–0107)
- Vetenskapsrådet (2019-01360)
- National Institutes of Health (1u01ca294514-01, 1rf1mh128876-01)
- National Institute on Aging (U54AG076043, U54AG079759)
- National Institute of Mental Health (RF1 MH128876, RM1MH132648)
- National Cancer Institute (U01 CA294514, UH3CA257393, UG3CA257393, U24CA248453, R01CA245313, U54CA274509)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK106857)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- spatial epigenomics, spatial transcriptomics, chromatin accessibility, histone modifications, H3K27me3, H3K27ac, H3K4me3, single-cell resolution, mouse brain, human brain, cell dynamics, gene expression regulation, epigenetic priming, tissue structure
- MeSH terms
- Epigenome, Aging, Animals, Brain, Chromatin, Gene Expression Regulation, Histones, Humans, Mammals, Organ Specificity, Gene Expression Profiling, Epigenesis, Genetic, Mice, Epigenomics, Single-Cell Analysis, Transcriptome
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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