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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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The GeneCards suite: from gene data mining to disease genome sequence analyses
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The Global Assessment Scale: A Procedure for Measuring Overall Severity of Psychiatric Disturbance
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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 101–200 of 482
Cancer therapy resistance from a spatial‐omics perspective
Zhang, Y., Yang, C., Chen, X., Wu, L. L., Yuan, Z., Zhang, F., & Qian, B. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70396 · Cited by 19
Environmental epigenetics: new horizons in redefining biological and health outcomes
Faraji, J., & Metz, G. A. S. (2026) · Environment International · doi:10.1016/j.envint.2026.110072 · Cited by 22
Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia
Wang, M., Yan, H., Tian, X., Yue, W., Liu, Y., Fan, L., Hu, K., Sun, Y., Zhao, Y., Lou, J., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00110-3 · Cited by 20
Self-citationRevealing spatial multimodal heterogeneity in tissues with SpaTrio
Yang, P., Jin, L., Liao, J., Jin, K., Shao, X., Li, C., Qian, J., Cheng, J., Yu, D., Guo, R., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100446 · Cited by 19
Ma, Y., Shi, W., Dong, Y., Sun, Y., & Jin, Q. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb46050298 · Cited by 22
“…a variant of this method, involves targeting specific histone modifications with antibodies and a protein A-tethered Tn5-DNA complex for CUT&Tag, mirroring the steps of ATAC–RNA-seq and culminating in the spatial co-profiling of histone modification occupancy and the transcriptome [30].”
MethodSingle-cell and spatial multiomic inference of gene regulatory networks using SCRIPro
Chang, Z., Xu, Y., Dong, X., Gao, Y., & Wang, C. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae466 · Cited by 18
“The spatial ATAC-RNA-seq multiomics dataset of the P22 mouse brain was retrieved from the cited source (GSE205055).”
BackgroundInfluentialSpatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone
Yan, J.-Y., Jiang, Z.-F., Zhang, S., Yu, Q.-C., Lu, Y.-J., Miao, R.-Z., Tang, Z.-Y., Fan, J., Wu, L., Dan, D.-D. G., et al. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70224 · Cited by 18
SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
Dong, M., Su, D. G., Kluger, H. M., Fan, R., & Kluger, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-58089-7 · Cited by 25
Self-citationZuo, C., Xia, J., Xu, Y., Xu, Y., Gao, P., Zhang, J., Wang, Y., & Chen, L. (2025) · Nature Communications · doi:10.1038/s41467-025-60575-x · Cited by 21
“We used MultiVI 34 to map both RNA-seq and ATAC-seq from the P22 mouse brain coronal section 67 into latent features (Supplementary Table 1).”
Li, L., Dong, L., Zhang, H., Xu, D. H., & Li, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf304 · Cited by 23
“…[5]) and advanced multi-omics platforms that profile the same tissue section (such as DBiT-seq [6], 10 × Genomics Xenium [7], SPOTS [8], Stereo-CITE-seq [9], spatial ATAC–RNA-seq and CUT&Tag-RNA-seq [10],Spatial-CITE-seq [11], MISAR-seq [12], SM-Omics [13], MiP-seq [14], and tri-omics [15]).”
Microglia networks within the tapestry of alzheimer’s disease through spatial transcriptomics
Zhou, Y. F., & Glass, C. K. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00897-y · Cited by 21
Kim, D. Y., Lee, J., Choi, J., Shin, H., Lee, J. S., & Kim, E. J. (2025) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2025.12.009 · Cited by 20
A technical comparison of spatial transcriptomics platforms across six cancer types
Cervilla, S., Mendoza, D. G., Perez, E., Real, F. X., Musulén, E., Aprea, J., Esteller, M., & Porta‐Pardo, E. (2026) · Genome biology · doi:10.1186/s13059-026-03937-y · Cited by 51
“Advances in spatial profiling now allow measurement of DNA alterations [14], epigenetic modifications [15], and, most commonly, RNA [16–23] and protein [24–26] expression, directly within intact tissue sections.”
Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics
Sun, Y., Yu, N., Zhang, J., & Yang, B. (2025) · Micromachines · doi:10.3390/mi16040426 · Cited by 19
“As a universal and compatible strategy, microchannel-based SRT methods represented by DBiT-seq extend [93,94] or integrate [95,96] multi-omics data such as proteomes, epigenomes, and so on, thus”
Zhang, D., Yu, N., Yuan, Z., Li, W., Sun, X. G., Zou, Q., Li, X., Liu, Z., Zhang, W., & Gao, R. (2024) · GigaScience · doi:10.1093/gigascience/giae089 · Cited by 22
Self-citationAdvances in Spatial Omics Technologies
Hui, T., Zhou, J., Yao, M., Xie, Y., & Zeng, H. (2025) · Small Methods · doi:10.1002/smtd.202401171 · Cited by 18
Farbehi, N., Alinejad-Rokny, H., Chtanova, T., & Rnjak‐Kovacina, J. (2025) · Cell Biomaterials · doi:10.1016/j.celbio.2025.100099 · Cited by 19
Ng, C. X., Lee, S. Y., Yap, X. Y., Wong, Y. H., Loh, J. S., Ang, K. P., Lee, W. H., Xu, W., Yong, P. V. C., & Lee, S. H. (2025) · Discover Oncology · doi:10.1007/s12672-025-04085-8 · Cited by 20
“The ability to simultaneously map epigenomic and transcriptomic features at near single-cell resolution opens new avenues to investigate how regulatory networks are spatially organized within the TME that leads to tumor heterogeneity [301].”
Engineering next-generation microfluidic technologies for single-cell phenomics
Lambert, C. L. G., van Mierlo, G., Bues, J., Guillaume-Gentil, O. J., & Deplancke, B. (2025) · Nature Genetics · doi:10.1038/s41588-025-02198-y · Cited by 17
“DBiT-seq was the first reported spatial technology to successfully co-map tens of proteins alongside the transcriptome and was later extended to measure chromatin accessibility (spatial ATAC-seq 95 ), the epigenomic state 95,96 or protein abundance (spatial cellular indexing of transcriptomes and epitopes (CITE-seq) 9…”
InfluentialDeng, T., Jiang, X., Zhao, Z., Zhao, X., Bi, A., Ouyang, S., Sun, S., Qiu, S., Fan, M., Xue, Q., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.06.011 · Cited by 17
Cis-regulatory elements: systematic identification and horticultural applications
Li, T., Zeng, W., Zhu, F., & Lü, P. (2025) · aBIOTECH · doi:10.1007/s42994-025-00237-0 · Cited by 17
Luo, M., Liu, J., Su, Y., Jiang, T., Wu, X., Gao, P., Qin, T., Wu, M., Li, Q., Zhang, S., et al. (2025) · Advanced Science · doi:10.1002/advs.202506313 · Cited by 16
Integrative deep learning of spatial multi-omics with SWITCH
Li, Z., Qu, S., Liang, H., Tang, R., Zhang, X., Fan, L., Yang, J., Gan, Z., Gao, S., Zhang, Y., et al. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00891-w · Cited by 20
“We next benchmarked SWITCH on a P22 mouse brain dataset from spatial CUT&TAG–RNA-seq, profiling RNA and H3K27ac (histone H3 lysine 27 acetylation) within the same section 11 .”
InfluentialDecoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights
Ashouri, A., Zhang, C., & Gaiti, F. (2023) · Genes · doi:10.3390/genes14101856 · Cited by 17
Spatial omics: An innovative frontier in aging research
Chen, Y., Yang, S., Yu, K., Zhang, J., Wu, M., Zheng, Y., Zhu, Y., Dai, J., Wang, C., Zhu, X., et al. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102158 · Cited by 17
Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry
Shi, W., Zhang, J., Huang, S., Fan, Q., Cao, J., Zeng, J., Wu, L., & Yang, C. J. (2024) · JACS Au · doi:10.1021/jacsau.4c00118 · Cited by 17
Roetzer-Pejrimovsky, T., Nenning, K., Kiesel, B., Klughammer, J., Rajchl, M., Baumann, B., Langs, G., & Wöehrer, A. (2024) · GigaScience · doi:10.1093/gigascience/giae057 · Cited by 16
Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomicsPreprint
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.01.535228 · Cited by 16
Metabolic regulation in adult and aging skeletal muscle stem cellsPreprint
Sartorelli, V., & Ciuffoli, V. (2024) · Genes & Development · doi:10.1101/gad.352277.124 · Cited by 19
Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics
Huan, C., Li, J., Li, Y., Zhao, S., Yang, Q., Zhang, Z., Li, C., Li, S., Guo, Z., Yao, J., et al. (2024) · BME Frontiers · doi:10.34133/bmef.0084 · Cited by 17
“The Spatial-CUT&Tag-RNA-seq technique involves incubation with primary antibodies against epigenetic markers to assess histone modifications [ 32 ].”
Gao, X., Ding, J.-E., Xie, J., & Xu, H. (2025) · Acta Pharmacologica Sinica · doi:10.1038/s41401-025-01499-6 · Cited by 16
“Recently developed spatial epigenomic, transcriptomic and proteomic methods, such as spatial, spatial CUT&Tag – RNA-seq, have been used to dissect epigenetic and transcriptional states in tissues, dynamically [285].”
The epigenetic landscape of mesenchymal stem cell and extracellular vesicle therapy
Zhang, S., Zhang, Z., Sui, B., Zheng, C., & Fu, Y. (2025) · Trends in Cell Biology · doi:10.1016/j.tcb.2025.03.008 · Cited by 19
Harnessing the potential of spatial multiomics: a timely opportunity
Li, X. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01507-3 · Cited by 15
Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling
Sandoval, L., Ismail, W. M., Mazzone, A., Dumbrava, M. G., Fernandez, J. A., Munankarmy, A., Lasho, T. L., Binder, M., Simon, V. A., Kim, K., et al. (2023) · Genes · doi:10.3390/genes14061245 · Cited by 14
“Furthermore, some of the most recent advances with technologies enabling the integration of spatial information with single-cell resolution are revolutionizing the way we understand tissue organization and interactions [6].”
BackgroundBioengineering translational models of lymphoid tissues
Michaels, Y. S., Buchanan, C. F., Gjorevski, N., & Moisan, A. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00101-0 · Cited by 16
“Although the low T cell output of these bioengineered thymus models might preclude further analysis of TCR repertoire and function, when this was possible, a broad coverage and similar representation of TCR gene segments were observed when compared to native thymic tissue or blood 97–99 , 102 .”
Differential usage of DNA modifications in neurons, astrocytes, and microglia
Tooley, K. B., Chucair‐Elliott, A. J., Ocañas, S. R., Machalinski, A. H., Pham, K. D., Hoolehan, W., Kulpa, A. M., Stanford, D. R., & Freeman, W. M. (2023) · Epigenetics & Chromatin · doi:10.1186/s13072-023-00522-6 · Cited by 13
“Tissue-level analysis of the brain have provided important insights into epig-enomic mechanisms regulating the genome [89–91], but the presence of mixed cell populations in whole tissue samples obscures the cell type-specificity of these mechanisms.”
BackgroundDNA microarray chips: Fabrication and cutting-edge applications
Xu, J., Chun, H., Wang, L., Mei, H., Chen, S., & Huang, X. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.155937 · Cited by 15
Self-citationInvestigation of human aging at the single-cell level
Li, Y., Wang, Q., Xuan, Y., Zhao, J., Li, J., Tian, Y., Chen, G., & Tan, F. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102530 · Cited by 16
Rubenstein, A. B., Smith, G. R., Zhang, Z., Chen, X., Chambers, T. L., Ruf-Zamojski, F. M., Mendelev, N., Cheng, W. S., Zamojski, M., Amper, M. A. S., et al. (2025) · Genome Research · doi:10.1101/gr.280051.124 · Cited by 14
Zhang, T., Zhang, H., Zhao, Z., Shao, S., Jiang, Y., Zhang, X., & Wang, G. (2025) · Genome biology · doi:10.1186/s13059-025-03637-z · Cited by 14
“Additionally, spatial ATAC–RNA-seq and CUT&Tag-RNA-seq [14] can capture gene expression profiles, chromatin accessibility, and spatial location information.”
Proteoformics: Current status and future perspectives
Fang, Z., Zhang, Y., Feng, X., Li, N., Chen, L., & Zhan, X. (2025) · Journal of Proteomics · doi:10.1016/j.jprot.2025.105524 · Cited by 18
Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases
Lin, S., Wang, Z., Cui, Y., Zou, Q., Han, C., Yan, R., Yang, Z., Zhang, W., Gao, R., Song, J., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02969-9 · Cited by 19
Strategies to control cellular spatial organization in microphysiological systems
Truong, H. D., Ge, Z., Chng, E., Tran, Y.-V., Zhang, Y., & Lim, C. T. (2026) · Microsystems & Nanoengineering · doi:10.1038/s41378-025-01141-9 · Cited by 18
“DBiT-seq, for example, utilizes orthogonal micro fl uidic channel fl ows to deposit spatial barcodes across tissue grids, producing high-resolution omics maps that simultaneously measure not only RNA but proteins and epigenetic features 338,339 .”
Epigenomic Characterization of Lymphoid Neoplasms
Duran‐Ferrer, M., & Martín-Subero, J. I. (2023) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-051122-100856 · Cited by 16
Multi-omics analysis in developmental bone biology
Matsushita, Y., Noguchi, A., Ono, W., & Ono, N. (2023) · Japanese Dental Science Review · doi:10.1016/j.jdsr.2023.10.006 · Cited by 16
“…co-sequencing chromatin accessibility and gene expression (spatial ATAC – RNA-seq), or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution (spatial CUT & Tag – RNA-seq) are of great interest in biomedical research [81].”
BackgroundFactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis
Wang, L., Zhang, H., Yi, B., Xie, W., Yu, K., Li, W., Li, K., & Zhao, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf157 · Cited by 13
“The original independent analysis of the two omics layers by Zhang et al. [44] is presented in Fig.”
Self-citationLu, Y., Ma, S., Chan, Y.-T., Wu, Y., Feng, Y., & Wang, N. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.08.057 · Cited by 15
“For example, spatial-ATAC-seq identified closed chromatin regions at CXCL13 loci in hypoxic tumor cores, correlating with reduced cytotoxic T cell infiltration, while spatial CUT&Tag detects H3K27me3-rich zones in stromal compartments that suppress dendritic cell activation [99].”
Self-citationSingle‐Cell and Spatial Omics: Methods and Applications
Cen, X., Huang, X., Deng, E., Gong, X., Tan, N., Ye, J. T., Wang, Y., Eils, R., Luo, Q., Li, Y., et al. (2026) · MedComm · doi:10.1002/mco2.70713 · Cited by 14
“Technologies such as Spatial-ATAC-RNA-seq and Spatial-CUT&Tag-RNA-seq [ 129, 134 ] were developed to profile chromatin accessibility and HMs alongside RNA, which leveraged microfluidic barcoding platforms as exemplified by DBiT-seq [ 130 ].”
Enhanced In Situ Spatial Proteomics by Effective Combination of MALDI Imaging and LC-MS/MS
Schäfer, F., Tomar, A., Sato, S., Teperino, R., Imhof, A., & Lahiri, S. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100811 · Cited by 15
Deciphering microglia phenotypes in health and disease
Balak, C. D., Han, C. Z., & Glass, C. K. (2024) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2023.102146 · Cited by 14
Manukyan, A., Bahry, E., Wyler, E., Becher, E., Reguant, A. P., Plumbom, I., Dikmen, H. O., Elezkurtaj, S., Conrad, T., Altmüller, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.15.571667 · Cited by 14
DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods
Greenstreet, L., Afanassiev, A., Kijima, Y., Heitz, M., Ishiguro, S., King, S. B., Yachie, N., & Schiebinger, G. R. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.005 · Cited by 15
“6,8,55 Positional indexing has scaled 2D spatial genomics to the level of whole organs and small organisms.”
BackgroundCarraro, C., Bonaguro, L., Srinivasa, R., van Uelft, M., Isakzai, V., Schulte-Schrepping, J., Gambhir, P., Elmzzahi, T., Montgomery, J. V., Hayer, H., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100598 · Cited by 14
Technical Advances and Applications of Spatial Transcriptomics
Liang, G., Yin, H. Z., & Ding, F. (2023) · GEN Biotechnology · doi:10.1089/genbio.2023.0032 · Cited by 15
“82 This improvement allows the co-mapping of spatial epigenomics and spatial transcriptomics.”
Systems immunology spanning tumors, lymph nodes, and periphery
Sidiropoulos, D. N., Ho, W. J., Jaffee, E. M., Kagohara, L. T., & Fertig, E. J. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100670 · Cited by 12
Cross-modality representation and multi-sample integration of spatially resolved omics data
Li, Z., Cui, X., Chen, X., Gao, Z., Liu, Y., Pan, Y., Chen, S., Lv, H., Zhai, L., & Jiang, R. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag214 · Cited by 12
Self-citationSpatial-transcriptomic profiling: a new lens for understanding myelofibrosis pathophysiology
Peroni, E., Calistri, E., Amato, R., Gottardi, M., & Rosato, A. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01877-3 · Cited by 13
“The 10x Genomics Visium Spatial Gene Expression integrates spatial barcoding with RNA sequencing, capturing both gene expression and morphological information [82].”
BackgroundAdvances in Epigenomic Sequencing and Their Applications in Cancer Diagnostics
Lin, L., & Liu, Y. (2025) · Precision Chemistry · doi:10.1021/prechem.5c00014 · Cited by 11
“152 Integrating epigenome and transcriptome data at single-cell resolution enables novel discoveries about spatial epigenetic priming, cellular differentiation, and gene regulatory networks in intact tissue architecture.”
Self-citationZhao, B., Wan, J., Zhou, H., Yang, J., & Wan, H. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1669796 · Cited by 14
Radosevic‐Robin, N., Kossaï, M., & Penault-Llorca, F. M. (2023) · Translational Breast Cancer Research · doi:10.21037/tbcr-23-38 · Cited by 12
Zhang, C., Wang, L., & Shi, Q. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.028 · Cited by 11
“…sequencing technology has expanded from spatial transcriptomics to spatial multi-omics, such as spatial-CITE-seq [53], which measures both the transcriptome and proteome in space, and spatial-ATAC-RNA-seq [54], which simultaneously measures the transcriptome and chromatin accessibility in space.”
BackgroundSelf-citationMapping Cell Atlases at the Single‐Cell Level
Ye, F., Wang, J., Li, J., Mei, Y., & Guo, G. (2023) · Advanced Science · doi:10.1002/advs.202305449 · Cited by 13
Spatial epigenomic niches underlie glioblastoma cell state plasticityPreprint
Kint, S., Younes, S. T., Bao, S., Long, G., Wouters, D., Stephenson, E. L., Lou, X., Zhong, M., Zhang, D., Su, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653178 · Cited by 11
Self-citationOmics medicine: what the clinicians should know
Zamperla, M. G., Barbi, V., Negri, S., Atlante, S., & Gaetano, C. (2026) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2026.106759 · Cited by 11
Brain and Blood Biomarkers of Major Depressive Disorder
Zallar, L. J., & Dupont, M. B. (2026) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2025.4613 · Cited by 14
The Future of Epigenetics: Emerging Technologies and Clinical Applications
Iyer, K. A., Tenchov, R., Sasso, J. M., Thite, T., Deng, Y., & Zhou, Q. A. (2026) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.5c00729 · Cited by 12
Self-citationRoles of histone post-translational modifications in meiosis
Nie, H., Kong, X., Song, X., Guo, X.-Y., Li, Z., Fan, C., Zhai, B., Yang, X., & Wang, Y. (2024) · Biology of Reproduction · doi:10.1093/biolre/ioae011 · Cited by 9
Omics Approaches to Investigate the Pathogenesis of Suicide
Boldrini, M., Xiao, Y., Singh, T., Zhu, C., Jabbi, M., Pantazopoulos, H., Gürsoy, G., Martinowich, K., Punzi, G., Vallender, E. J., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.05.017 · Cited by 13
“…to human brain studies can generate mechanistic insights into the pathogenesis of cell and neuronal loss previously reported in suicide (12,13) and help lead to the discovery of cell-speci fi c (14) and region-speci fi c (15,16) molecules that regulate cell fate, trajectory, state, and connectivity.”
BackgroundSelf-citationA hitchhiker's guide to single‐cell epigenomics: Methods and applications for cancer research
Moreno‐Gonzalez, M., Sierra, I., & Kind, J. (2024) · International Journal of Cancer · doi:10.1002/ijc.35307 · Cited by 11
“77 While this technology does not yet provide single-cell resolution, future developments might allow us to jointly obtain spatial, epigenome, and transcriptome information at a single-cell level.”
Microglial Responses to Alzheimer's Disease Pathology: Insights From “Omics” Studies
Reid, A. N., Jayadev, S., & Prater, K. E. (2025) · Glia · doi:10.1002/glia.24666 · Cited by 10
Chen, M., Cheng, R., He, J., Chen, J., & Zhang, J. (2025) · Genome biology · doi:10.1186/s13059-025-03576-9 · Cited by 10
“The spatial ATAC–RNA-seq mouse brain data [60] was downloaded from https:// zenodo. org/ recor ds/ 10362 607 Zhang et al. [60] presented two technologies (i.e., spatial ATAC– RNA-seq and spatial CUT&Tag-RNA-seq) for spatially resolved, genome-wide, joint profiling of the epigenome, and…”
Genetic landscape of cancer: mechanisms, key genes, and therapeutic implications
Karnwal, A., Dutta, J., Aqueel-Ur-Rehman, Al-Tawaha, A. R., & Nesterova, N. (2025) · Clinical & Translational Oncology · doi:10.1007/s12094-025-04019-4 · Cited by 14
“Mammalian tissues comprise 200–300 distinct cell types [129].”
HALO: hierarchical causal modeling for single cell multi-omics data
Mao, H. H., Jia, M., Di, M. E., Valenzi, E., Cai, X. T., Singh, H., Lafyatis, R., Zhang, K., & Benos, P. V. (2025) · Nature Communications · doi:10.1038/s41467-025-63921-1 · Cited by 14
“By accounting for these asynchronous changes, HALO effectively captures both coupled representations (shared across modalities) and decoupled representations (modality-specific).”
Decoding Human Biology and Disease Using Single-Cell Omics Technologies
Shi, Q., Chen, X., & Zhang, Z. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.06.003 · Cited by 11
“Recent years have witnessed the rapid development of sequencing-based spatially resolved omics technologies [315–330].”
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Self-citationChromatin accessibility provides a window into the genetic etiology of human brain disease
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Putting cancer immunotherapy into spatial context in the clinic
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“Next, we applied SpaMI to a spatial epigenome-transcriptome data obtained using spatial ATAC-RNA-seq [14] technology on coronal sections of 22-day-old mouse brains.”
Identification, Design, and Application of Noncoding Cis-Regulatory Elements
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Spatial Multiomics Toward Understanding Neurological Systems
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Advances in Spatial Multi-Omics in Gastric Cancer
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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
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