OSCR

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution.

Code ↔ Paper

22 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 22 matches
  1. [1] § RESULTS › Decoder-FFPE-seq decodes spatial cellular dynamics in OSCC before and after chemoimmunotherapy ↔ Analysis/Cell-cell_communication/3rd_run_ccc_enrich.R, lines 90 to 149 · score 0.99 · CCL19 CCR7, CCL5 CCR4, CCL5 CCR5, SELPLG SELL, FN1 CD44, SEMA4D · Report a mismatch
  2. [2] § RESULTS › Workflow of Decoder-FFPE-seq ↔ oligo_designer_toolsuite/pipelines/_cycle_hcr_probe_designer.py, lines 70 to 156 · score 0.90 · chain reaction, nt targeting sequence, designed split, right probe, DNA probe, RNA detection · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Spatial profiling of nonsensory GPCRs in the mouse brain ↔ Validation/rare_GPCR_genes.R, lines 1 to 45 · score 0.88 · nonsensory GPCR genes, opsin receptors, vomeronasal receptors, Mouse GPCRs, taste, Human · Report a mismatch
  4. [4] § MATERIALS AND METHODS › Prediction of ligand-receptor interaction ↔ Analysis/Cell-cell_communication/1st_run_ccc_commot.py, lines 35 to 53 · score 0.86 · ECM receptors, secreted signals, Ligand receptor, cell contact, CellChat, COMMOT · Report a mismatch
  5. [5] § RESULTS › Workflow of Decoder-FFPE-seq ↔ oligo_designer_toolsuite/pipelines/_seqfish_plus_probe_designer.py, lines 67 to 157 · score 0.78 · nt targeting sequence, mRNAs, DNA probe, RNA detection, probe design, PCR · Report a mismatch
  6. [6] § RESULTS › Decoder-FFPE-seq characterizes TME heterogeneity associated with clinical outcomes in archived clinical tissues ↔ Analysis/Cell-cell_communication/3rd_run_ccc_enrich.R, lines 90 to 149 · score 0.77 · COL1A1 CD44, CD47, CD74, MIF, SDC1, Tem · Report a mismatch
  7. [7] § MATERIALS AND METHODS › Cell niche identification of OSCC ↔ Analysis/cluster_niches.R, lines 70 to 154 · score 0.76 · buildSNNGraph, Leiden, scran, transformed, v1, niche · Report a mismatch
  8. [8] § RESULTS › Decoder-FFPE-seq profiles the mouse brain with high sensitivity and accuracy at single-cell resolution ↔ oligo_designer_toolsuite/pipelines/_scrinshot_probe_designer.py, lines 71 to 128 · score 0.73 · single cell resolution, high sensitivity, RNA sequencing, spatial mapping, brain, abundance · Report a mismatch
  9. [9] § RESULTS › Extending ST to non–poly(A)-tailed and nonhost RNA detection for spatial biology study in archival OSCC samples ↔ Analysis/Spagene_coloc.R, lines 66 to 90 · score 0.71 · G0S2, IL1B, mRNA, CXCL8, MALAT1, NEAT1 · Report a mismatch
  10. [10] § RESULTS › Decoder-FFPE-seq profiles the mouse brain with high sensitivity and accuracy at single-cell resolution ↔ oligo_designer_toolsuite/pipelines/_oligo_seq_probe_designer.py, lines 74 to 110 · score 0.70 · fine grained, RNA sequencing, single cell, gene expression, reproducibility, resolution · Report a mismatch
  11. [11] § MATERIALS AND METHODS › Spatial profiling of nonsensory GPCRs in the mouse brain ↔ Validation/rare_GPCR_genes.R, lines 47 to 101 · score 0.69 · nonsensory GPCR genes, Visium HD, Spearman, correlation, Decoder FFPE, Stereo · Report a mismatch
  12. [12] § RESULTS › Extending ST to non–poly(A)-tailed and nonhost RNA detection for spatial biology study in archival OSCC samples ↔ oligo_designer_toolsuite/pipelines/_merfish_probe_designer.py, lines 74 to 167 · score 0.65 · single molecule FISH, smFISH, mRNA, detection, probe, genes · Report a mismatch
  13. [13] § MATERIALS AND METHODS › Comparison of Decoder-FFPE-seq with similar ST platforms ↔ Validation/rare_GPCR_genes.R, lines 47 to 101 · score 0.64 · cor.test, Visium HD, Spearman, Decoder FFPE, correlation, platforms · Report a mismatch
  14. [14] § RESULTS › Decoder-FFPE-seq profiles the mouse brain with high sensitivity and accuracy at single-cell resolution ↔ Validation/rare_GPCR_genes.R, lines 1 to 45 · score 0.63 · Nonsensory GPCRs, mouse brain, Decoder FFPE seq, MERFISH, platform, cells · Report a mismatch
  15. [15] § RESULTS › Workflow of Decoder-FFPE-seq ↔ oligo_designer_toolsuite/pipelines/_cycle_hcr_probe_designer.py, lines 70 to 156 · score 0.58 · library preparation, digestion, trigger, reactions, reconstruction, amplification · Report a mismatch
  16. [16] § RESULTS › Evaluation and benchmarking of Decoder-FFPE-seq ↔ oligo_designer_toolsuite/pipelines/_seqfish_plus_probe_designer.py, lines 67 to 157 · score 0.58 · spatial mapping, single cell, nature, rounded, Fluorescence, spacing · Report a mismatch
  17. [17] § RESULTS › Evaluation and benchmarking of Decoder-FFPE-seq ↔ oligo_designer_toolsuite/pipelines/_scrinshot_probe_designer.py, lines 71 to 128 · score 0.56 · single cell resolution, spatial mapping, optimized, spacing, sensitivity, abundance · Report a mismatch
  18. [18] § MATERIALS AND METHODS › Design of probe libraries ↔ oligo_designer_toolsuite/sequence_generator/_genomic_region_generator.py, lines 1332 to 1424 · score 0.54 · Oligo Designer Toolsuite, downloaded · Report a mismatch
  19. [19] § MATERIALS AND METHODS › Prediction of ligand-receptor interaction ↔ Analysis/Cell-cell_communication/1st_run_ccc_commot.py, lines 35 to 53 · score 0.54 · ligand receptor, cell communication, COMMOT · Report a mismatch
  20. [20] § MATERIALS AND METHODS › Prediction of ligand-receptor interaction ↔ Analysis/Cell-cell_communication/2nd_run_commot_cluster.py, lines 97 to 128 · score 0.52 · CellChat, COMMOT, ligand, receptor · Report a mismatch
  21. [21] § RESULTS › Decoder-FFPE-seq decodes spatial cellular dynamics in OSCC before and after chemoimmunotherapy ↔ Analysis/Cell-cell_communication/3rd_run_ccc_enrich.R, lines 1 to 15 · score 0.52 · post MPR, Tem, Tn, responders, Tex, CD8 · Report a mismatch
  22. [22] § MATERIALS AND METHODS › Spatial profiling of nonsensory GPCRs in the mouse brain ↔ Analysis/WGCNA_region.r, lines 176 to 235 · score 0.51 · GO biological process, enrichment, enriched, database, genes · Report a mismatch

Paper

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The authors' code

R · 170 lines · 6.5 KB · no license · 4 matches

  1. library(dplyr)
  2. library(Seurat)
  3. library(ggplot2)
  4. library(biomaRt)
  5. library(data.table)
  6. library(readxl)
  7. ##########mouse GPCR gene
  8. mus38g<-read_xlsx('MouseHumanRatGPCRs.xlsx')
  9. colnames(mus38g)<-mus38g[2,]
  10. mus38g<-mus38g[-c(1:2),]
  11. mus38g<-filter(mus38g,!Subgroup %in% c('Opsin receptors','Taste 1 receptors','Taste 2 receptors','Vomeronasal receptors')) %>%
  12. filter(!grepl('Mrgpr|Taar', `Gene Symbol (Mouse)`))
  14. ##########load and subset MERFISH
  15. merfish<-read.csv('../datasets_mouse_brain_map_BrainReceptorShowcase_Slice1_Replicate1_cell_by_gene_S2R1.csv',header = T,row.names = 1,check.names = F)
  16. position<-read.csv('datasets_mouse_brain_map_BrainReceptorShowcase_Slice2_Replicate1_cell_metadata_S2R1.csv',header = T,row.names = 1,check.names = F)
  17. merfish<-CreateSeuratObject(counts = t(merfish),assay = 'Spatial')
  18. position<-position[,c('center_x','center_y')]
  19. colnames(position) = paste0("Spatial_",1:ncol(position))
  20. merfish[["spatial"]] <- CreateDimReducObject(embeddings = as.matrix(position),
  21. key = "Spatial",assay = "Spatial")
  22. coord<-data.frame(merfish@reductions$[email hidden])
  23. coord<-coord[coord$Spatial_2>=3500 & coord$Spatial_1>=4200 & coord$Spatial_1<=7000,]
  24. merfish<-subset(merfish,cells = rownames(coord))
  27. mfgene<-Features(mf)[!grepl('Blank',Features(merfish))]
  28. merfish<-subset(merfish,features = mfgene)
  30. mfexpr<-GetAssayData(merfish,assay = 'Spatial',layer = 'counts')
  31. mfexpr<-data.frame(mfexpr)
  33. mfexpr<-data.frame(X=rownames(mfexpr),total_expression=rowSums(mfexpr),mean_expression=rowMeans(mfexpr))
  34. mfexpr['ADGRF3','X']='Adgrf3'
  35. mfexpr<-filter(mfexpr,X %in% mus38g$`Gene Symbol (Mouse)`)
  37. ##########load SRT data
  38. platform<-'Decoder-FFPE-Seq'
  39. data<-Load10X_Spatial('Decoder-FFPE-seq_8um_brain')
  40. STexpr<-GetAssayData(data,assay = 'Spatial',layer = 'counts')
  42. expr<-data.frame(total=rowSums(STexpr),mean=rowMeans(STexpr))
  43. expr$X=rownames(expr)
  44. expr<-filter(expr,X %in% c(mus38g$`Gene Symbol (Mouse)`))
  45. write.csv(expr,paste0(platform,'_nonsensory_GPCR.csv'),row.names = F)
  47. ##########calculate correlation
  48. expr<-left_join(mfexpr,expr,by = 'X')
  49. length(which(is.na(expr$total)))
  50. expr[which(is.na(expr$total)),'total']=0
  51. expr[which(is.na(expr$mean)),'mean']=0
  52. expr[which(is.na(expr$total_expression)),'total_expression']=0
  53. expr[which(is.na(expr$mean_expression)),'mean_expression']=0
  55. expr.st<-expr$mean
  56. expr.sc<-expr$mean_expression
  57. res<-cor.test(expr.st,expr.sc,method = 'spearman')
  58. corr <-round(res$estimate,4)
  59. pval <-max(res$p.value,2.2e-16)
  60. fplot<-data.frame(ST=expr.st,sc=expr.sc)
  62. ggplot(fplot,aes(x=log(ST+1e-6),y=log(sc+1e-6)))+
  63. geom_point(alpha=1,size=1) +
  64. scale_x_continuous(n.breaks = 7)+
  65. scale_y_continuous(n.breaks = 6)+
  66. geom_smooth(method = lm,linetype=1,color='red',se=F)+
  67. guides(alpha='none')+
  68. labs(x=paste0('Log(mean counts)\n',platform),
  69. y='Log(mean counts)\nMERFISH')+
  70. theme_bw()+
  71. theme(legend.position = 'right',
  72. axis.text.x = element_text(hjust = 1,colour = 'black',size=14),
  73. axis.text.y = element_text(colour = 'black',size=14,face = 'plain'),
  74. axis.title.x = element_text(colour = 'black',size=18,face = 'plain'),
  75. axis.title.y = element_text(colour = 'black',size=18,face = 'plain'),
  76. panel.border = element_blank(),
  77. axis.line = element_line(colour = 'black',size=1),
  78. legend.text = element_text(colour = 'black',size=14,face = 'plain'),
  79. legend.title = element_text(colour = 'black',size=18,face = 'plain'),
  80. panel.grid.major = element_blank(),
  81. panel.grid.minor = element_blank())+
  82. annotate("text",x=-10,y=5,label=paste0('R = ',corr,"\np < ",pval),color='black',size=6,hjust=0)
  84. ##########compare with other platforms
  85. expr<-read.csv('Decoder-FFPE-seq_nonsensory_GPCR.csv',header = T)
  86. colnames(expr)<-c('Decoder-FFPE-seq_total','Decoder-FFPE-seq_mean','gene')
  87. for(i in c('Visium HD','Stereo-N-FFPE')){
  88. tmp<-read.csv(paste0(i,'_nonsensory_GPCR.csv'),header = T)
  89. colnames(tmp)<-c(paste0(i,'_total'),paste0(i,'_mean'),'gene')
  90. expr<-full_join(expr,tmp,by='gene')
  91. }
  93. library(tidyr)
  95. fplot<-na.omit(expr)
  97. expr[is.na(expr)] =0
  98. fplot <- expr
  100. rownames(fplot)<-fplot$gene
  101. fplot$gene<-NULL
  103. ##non_zero
  104. fplot <- fplot %>%
  105. filter(across(all_of(colnames(fplot)), ~ .x > 0))
  107. fplot<-data.frame(t(colSums(fplot)),check.names = F)
  110. fplot <- fplot %>%
  111. pivot_longer(
  112. cols = colnames(fplot),
  113. names_to = c("technology", "statistic"),
  114. names_sep = "_",
  115. values_to = "Total UMI"
  116. )
  117. write.csv(fplot,'nonsensory_GPCRs_total_UMI.csv',row.names = F)
  120. fplot$UMI_um<-ifelse(fplot$technology=='Stereo-N-FFPE',fplot$`Total UMI`/(10^2), fplot$`Total UMI`/(8^2))
  121. fplot$technology<-factor(fplot$technology,levels = c("Decoder-FFPE-seq","Visium HD", "Stereo-N-FFPE"))
  123. fplot2<-filter(fplot,statistic == 'mean')
  124. p=ggplot(fplot2, aes(x = technology, y = `UMI_um`,fill = technology)) +
  125. geom_bar(stat = "identity", width = 0.9) +
  126. labs(
  127. title = "Sum of average expression of GPCR genes in MERFISH panel",
  128. x = "Platform",
  129. y = "Total UMIs per um"
  130. #scale_y_log10(n.breaks = 6)+
  131. scale_fill_manual(values = c(#'MERFISH'="#FED9A6",
  132. "Decoder-FFPE-seq"="#FBB4AE","Visium HD" = "#B3CDE3", "Stereo-N-FFPE"= "#DECBE4"))+
  133. theme_minimal() +
  134. theme(
  135. plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
  136. panel.grid.major = element_blank(),
  137. panel.grid.minor = element_blank(),
  138. axis.text = element_text(size = 10),
  139. axis.text.x = element_text(angle = 45,hjust=1)
  140. )
  141. p
  142. pdf('Sum of average expr_GPCR_um.pdf',7,5)
  143. print(p)
  144. dev.off()
  145. fplot2$`Total UMI`<-NULL
  146. write.csv(fplot2,'nonsensory_GPCRs.4polt.csv',row.names = F)
  149. fplot1<-filter(fplot,statistic == 'total')
  150. p=ggplot(fplot1, aes(x = technology, y = `Total UMI`,fill = technology)) +
  151. geom_bar(stat = "identity", width = 0.9) +
  152. labs(
  153. title = "Total UMIs of GPCR genes in MERFISH panel",
  154. x = "Platform",
  155. y = "Total UMIs"
  156. ) +
  157. scale_y_log10(n.breaks=6)+
  158. scale_fill_manual(values = c('MERFISH'="#FED9A6","Decoder-FFPE-seq"="#FBB4AE","Visium HD" = "#B3CDE3", "Stereo-N-FFPE"= "#DECBE4"))+
  159. theme_minimal() +
  160. theme(
  161. plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
  162. panel.grid.major = element_blank(),
  163. panel.grid.minor = element_blank(),
  164. axis.text = element_text(size = 10),
  165. axis.text.x = element_text(angle = 45,hjust=1)
  166. )
  167. p
  168. pdf('Total UMI_359g.pdf',7,5)
  169. print(p)
  170. dev.off()

rare_GPCR_genes.R at commit c6afe4f, no license · at the source

Overview

Authors: Jiao Cao1, Jun Zeng2, Yiwei Meng1, Shanqing Huang1,3, Jing Zhang1,3, Xing Fang4, Yang Guo4, Weixiong Shi1,3, Yutong Liu2, Zhong Zheng1, Jianchen Fang1, Xuepeng Xiong2, Gang Chen2, Lingling Wu1, Chaoyong Yang1,3
ORCID iDs: Jiao Cao, Jun Zeng, Yiwei Meng, Jing Zhang, Yang Guo, Weixiong Shi, Zhong Zheng, Jianchen Fang, Xuepeng Xiong, Gang Chen, Lingling Wu, Chaoyong Yang
  1. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Department of pathology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
  2. State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, TaiKang Center for Life and Medical Sciences, Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan 430079, China
  3. The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Physical Chemical of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
  4. Suzhou Dynamic Biosystems Co. Ltd., Suzhou 215200, China
Institutions: Renji Hospital (China); Wuhan University (China); Xiamen University (China)
Journal: Science advances, volume 12, issue 44, article eady5085
Dates: received 5 December 2025; accepted 29 September 2026; published online 30 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.ady5085 · PMID 42962014 · PMCID PMC13721974 · OpenAlex W7224911460
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
MeSH: Single-Cell Analysis*, Tissue Fixation*, Transcriptome*, Animals, Carcinoma, Squamous Cell, Formaldehyde, Gene Expression Profiling, Humans, Mice, Mouth Neoplasms, Paraffin Embedding, Sequence Analysis, RNA, Single-Cell Gene Expression Analysis, Spatial Transcriptomics (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Formalin-fixed paraffin-embedded (FFPE) tissue–compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution, and researcher accessibility remains challenging. To address these challenges, we here present a dendrimeric DNA coordinate barcoding design for FFPE tissue–adapted spatial RNA sequencing (Decoder-FFPE-seq) by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a >2.6-fold sensitivity increase in whole-transcriptome mouse brain and archived human sample profiling over existing platforms, resolving potential cellular identity of low-abundance genes, finer tissue architecture, and capturing an epithelial-mesenchymal transition–associated signature for prognostic value. With expanded probe libraries, Decoder-FFPE-seq captures long noncoding and viral RNAs, revealing their spatial coregulatory networks with messenger RNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, two CD8+ T cell subpopulations were respectively identified in pre- and postchemoimmunotherapy samples, potentially linked to treatment response. Decoder-FFPE-seq establishes a robust, high-resolution, and accessible framework for sensitive ST profiling of archival tissues in fundamental and translational studies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

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HelmholtzAI-Consultants-Munich/oligo-designer-toolsuite

License: MIT
State: the link answers, verified on 1 November 2026
Evidence: files inventoried
Commit: 1f26e321abd44a1ff94cfce0a4a560f4269bf955, 18 September 2026
Languages: Python (78), Jupyter (11)
Size: 251 files, 89 scripts
Software Heritage: not archived
Found in: the text, “Design of probe libraries”
Holds: README, license file, environment (Dockerfile, environment.yaml, pyproject.toml, docs/requirements.txt), tests, continuous integration, documentation, 11 notebooks
Not found: CITATION.cff
Tools: pandas (24 files), Biopython (21 files), NumPy (7 files), SciPy (5 files), NetworkX (3 files), BCFtools (2 files), Matplotlib (2 files), BEDTools (1 file), SAMtools (1 file), seaborn (1 file)
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At the source: github.com/HelmholtzAI-Consultants-Munich/oligo-designer-toolsuite

Zenodo 19872452

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At the source: doi.org/10.5281/zenodo.19872452

Zenodo 19873057

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DynamicBiosystems/DynamicST

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At the source: github.com/DynamicBiosystems/DynamicST

DynamicBiosystems/DynamicST-Assist

License: none: the authors keep all their rights
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Evidence: files inventoried
Commit: a2bf3fd49dfc60862ce30a12927a3e85375c3353, 23 October 2024
Size: 20 files, 0 scripts
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At the source: github.com/DynamicBiosystems/DynamicST-Assist

Zenodo 19872995

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At the source: doi.org/10.5281/zenodo.19872995

MIW-91/Decoder-FFPE-seq

License: none: the authors keep all their rights
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Evidence: files inventoried
Commit: c6afe4f69febd557b5a0ac1e2938df46e55fcf97, 15 October 2025
Languages: R (9), Python (2)
Size: 12 files, 11 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README
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Tools: tidyverse (6 files), ggplot2 (4 files), Seurat (4 files), anndata (2 files), data.table (2 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files), patchwork (2 files), Scanpy (2 files), SciPy (2 files), seaborn (2 files), broom (1 file), cowplot (1 file), igraph (1 file), UMAP (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 1 November 2026: the link answers
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At the source: github.com/MIW-91/Decoder-FFPE-seq

miw-91/dynamicst-assist

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Commit: a2bf3fd49dfc60862ce30a12927a3e85375c3353, 23 October 2024
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At the source: github.com/MIW-91/DynamicST-Assist/tree/V1.0

The paper's code and data availability statement is in the Data section.

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Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials and/or archived in an independent repository. Specifically, all data generated in this study, including image files and raw and processed sequencing data, are available in Zenodo (https://doi.org/10.5281/zenodo.19872452) or the National Omics Data Encyclopedia (NODE: www.biosino.org/node/home) under accession ID: OEP00006593. Toolkits for processing the Decoder-FFPE-seq raw data are open for download from Zenodo (https://doi.org/10.5281/zenodo.19873057) or GitHub (https://github.com/DynamicBiosystems/DynamicST and https://github.com/DynamicBiosystems/DynamicST-Assist). Scripts in this study are available in Zenodo (https://doi.org/10.5281/zenodo.19872995) or the GitHub (https://github.com/MIW-91/Decoder-FFPE-seq). Public Visium CytAssist_FFPE, Visium, and Visium HD datasets were obtained from the 10x Genomic website: FFPE mouse brain (www.10xgenomics.com/datasets/mouse-brain-coronal-section-2-ffpe-2-standard); FFPE human breast cancer (www.10xgenomics.com/datasets/human-breast-cancer-ductal-carcinoma-in-situ-invasive-carcinoma-ffpe-1-standard-1-3-0); FF human breast cancer (www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-2-1-standard-1-0-0); and FF human lymph node (www.10xgenomics.com/datasets/human-lymph-node-1-standard-1-1-0.). Visium HD data of a FFPE mouse brain section were deposited in www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he. Patho-DBiT public datasets of the FFPE mouse brain (mRNA sequencing only) are available in the NCBI Gene Expression Omnibus database (GEO: www.ncbi.nlm.nih.gov/geo/) with accession number GSE274641 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE274641). The public Stereo-seq V2 data of the mouse brain were obtained from the STOmics website: www.stomics.tech/col1217. Decoder-seq mouse MOB data were deposited in the GEO database under the accession number GSE235896 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE235896). Slide-seq V2 mouse hippocampus data (Puck_200115_08) were from the Single Cell Protal database: https://singlecell.broadinstitute.org/single_cell/study/SCP815/highly-sensitive-spatial-transcriptomics-at-near-cellular-resolution-with-slide-seqv2#study-summary. The reference snRNA-seq data of the mouse brain and the reference scRNA-seq of OSCC were respectively obtained from EMBL’s European Bioinformatics Institute (EMBL-EBI: www.ebi.ac.uk/) with accession number E-MTAB-11115 and the GEO database with accession number GSE234933 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE234933). MERFISH data were obtained from https://info.vizgen.com/mouse-brain-map. The CRC datasets are available in the Genome Sequence Archive (GSA: https://ngdc.cncb.ac.cn/gsa/) database with accession number HRA006753. The bulk RNA-seq data of the TCGA-HNSC, TCGA-CESC, and TCGA-ESCA cohorts are available through the Genomic Data Commons (GDC; GDC Data Protal: https://portal.gdc.cancer.gov). All materials generated in this study, including whole-transcriptome mouse and human probe panels, the OSCC-related lncRNA probe panel, and the HPV probe panel, are described in the “Design of probe libraries” section in Materials and Methods. The probe sequences are provided in the Supplementary tables. Beyond the probe panels, no additional materials were generated.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 1 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 14 MeSH terms, 78 references.

Cite

This paper

Cao, J., Zeng, J., Meng, Y., Huang, S., Zhang, J., Fang, X., Guo, Y., Shi, W., Liu, Y., Zheng, Z., Fang, J., Xiong, X., Chen, G., Wu, L., & Yang, C. (2026). Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution. Science advances, 12(44), eady5085. https://doi.org/10.1126/sciadv.ady5085

BibTeX

@article{cao2026decoder,
author = {Cao, Jiao and Zeng, Jun and Meng, Yiwei and Huang, Shanqing and Zhang, Jing and Fang, Xing and Guo, Yang and Shi, Weixiong and Liu, Yutong and Zheng, Zhong and Fang, Jianchen and Xiong, Xuepeng and Chen, Gang and Wu, Lingling and Yang, Chaoyong},
title = {{Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution}},
journal = {Science advances},
year = {2026},
month = oct,
volume = {12},
number = {44},
pages = {eady5085},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.ady5085},
url = {https://doi.org/10.1126/sciadv.ady5085},
pmid = {42962014},
pmcid = {PMC13721974}
}

RIS

TY - JOUR
AU - Cao, Jiao
AU - Zeng, Jun
AU - Meng, Yiwei
AU - Huang, Shanqing
AU - Zhang, Jing
AU - Fang, Xing
AU - Guo, Yang
AU - Shi, Weixiong
AU - Liu, Yutong
AU - Zheng, Zhong
AU - Fang, Jianchen
AU - Xiong, Xuepeng
AU - Chen, Gang
AU - Wu, Lingling
AU - Yang, Chaoyong
TI - Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/10/30
VL - 12
IS - 44
SP - eady5085
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.ady5085
UR - https://doi.org/10.1126/sciadv.ady5085
LA - en
ER -

CSL-JSON

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2026,
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30
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}
}

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