DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells.

Code ↔ Paper

34 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 34 matches

[1] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/01_rna_processing.R, lines 92–133 · score 0.98 · blacklist ratio, ribosomal content, nFeature_ATAC, nCount_RNA, nucleosome signal, nCount_ATAC
[2] § Methods › Mouse Brain Data Analysis ↔ 04_testis/base/01_rna_processing.R, lines 92–133 · score 0.94 · nucleosome signal, percent.mt, nCount_ATAC, nFeature_RNA, mitochondrial content, TSS enrichment
[3] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 03_brain/01_Platform_Comparison_Plots.R, lines 41–82 · score 0.90 · nFeature_ATAC, nCount_RNA, nCount_ATAC, nFeature_RNA, TSS enrichment, quality control
[4] § Methods › Mouse Brain Data Analysis ↔ 03_brain/01_Platform_Comparison_Plots.R, lines 41–82 · score 0.86 · percent.mt, nCount_ATAC, nFeature_RNA, TSS enrichment, QC metrics, FRiP
[5] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/01_rna_processing.R, lines 135–215 · score 0.85 · FindClusters, FindNeighbors, SCTransform, Dimensionality reduction, PCA, regressing
[6] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/03_link_peaks.R, lines 45–129 · score 0.82 · pct.1, pct.2, LinkPeaks, Rik, Gm, kb
[7] § Results › Benchmarking DUET‐seq Against Existing Multi‐omic Platforms ↔ 02_cellline/technical_qc_comparisons.ipynb, lines 902–987 · score 0.80 · SNARE seq2, sci CAR, SUM seq, SHARE seq, S4, HEK293T
[8] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/03_link_peaks.R, lines 45–129 · score 0.76 · min.pct, logfc.threshold, FindAllMarkers, expressed genes, linkage, SCT
[9] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/05_coverage_plot.R, lines 41–114 · score 0.75 · Leydig cells, Sertoli cells, elongating spermatids, Sycp2, Uchl1, Clu
[10] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 217–263 · score 0.74 · ES_Late, ES_Early, RS_Early, elongating spermatids, global, round
[11] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 136–202 · score 0.69 · Leydig cells, Sertoli cells, elongating spermatids, chromatin accessibility, macrophages, SPC
[12] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 217–263 · score 0.69 · ES_Early, RS_Early, round spermatids, elongating spermatids, TFs
[13] § Results › Design and Workflow of DUET‐seq ↔ 02_cellline/technical_qc_comparisons.ipynb, lines 902–987 · score 0.68 · asked, Mg2, S3, arms, mis, HEK293T
[14] § Methods › Trajectory Inference and Dynamic Regulatory Analysis ↔ 04_testis/spg/03_rna_velocity_analysis_scvelo.py, lines 25–46 · score 0.67 · AnnData, RNA velocities, unspliced, scVelo, embedding, matrices
[15] § Results › DUET‐seq Resolves Cellular Heterogeneity in Adult Mouse Brain and Enables Peak‐to‐gene Linking ↔ 03_brain/02_Peak_Gene_Regulation.R, lines 208–260 · score 0.66 · Slc17a7, gene activity, Gad2, Pdgfra, Plp1, chromatin accessibility
[16] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/04_celloracle_grn_perturbation_analysis.py, lines 131–178 · score 0.65 · core TFs, regulating genes, FigR, module, perturbation, SSCs
[17] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 03_brain/04_FigR_Core_Pipeline.R, lines 89–145 · score 0.65 · TF DORC, DORC genes, figR, regulatory networks, peak gene, score
[18] § Methods › Trajectory Inference and Dynamic Regulatory Analysis ↔ 03_brain/02_Peak_Gene_Regulation.R, lines 208–260 · score 0.64 · LinkPeaks, high confidence, peak gene, intersected, regulated, clustering
[19] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/04_link_peaks_visualization.R, lines 53–117 · score 0.64 · min.pct, logfc.threshold, FindAllMarkers, SCT, peak, gene
[20] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 136–202 · score 0.64 · peritubular myoid cells, Sertoli cells, linked peaks, chromatin accessibility, Sox9, gene expression
[21] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/06_correlation_plot.R, lines 1–85 · score 0.63 · peritubular myoid cells, Sertoli cells, linked peaks, chromatin accessibility, inferred, gene expression
[22] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/02_spg_visualization.R, lines 134–215 · score 0.62 · Pseudotime trajectory, Gfra1, Ret, Zbtb16, Foxo1, Kit
[23] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/02_atac_processing.R, lines 94–150 · score 0.62 · FindClusters, FindNeighbors, graph, Dimensionality, clustering, testis
[24] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 1–41 · score 0.60 · gene regulatory networks, regulation score, FigR, GRNs, TF, spermatogonial
[25] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 53–117 · score 0.59 · Positively correlated, elongating spermatids, stromal, log2, max, Sertoli
[26] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/base/03_link_peaks.R, lines 1–43 · score 0.58 · peak gene linkage, cis regulatory, regulatory elements, gene expression, clustering, ATAC
[27] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/base/03_link_peaks.R, lines 1–43 · score 0.57 · peak gene linkage, cis regulatory, regulatory elements, gene expression, clustering, ATAC
[28] § Methods › Species Mixing Experiment ↔ 02_cellline/human_mouse_mix_split.py, lines 103–133 · score 0.57 · species mixing, doublet rate, barnyard, RNA
[29] § Results › Benchmarking DUET‐seq Against Existing Multi‐omic Platforms ↔ 02_cellline/cellline_trackplot.R, lines 87–149 · score 0.57 · 65600000–66000000, 68900000–69000000, chr11, tracks, DUET seq, ISSAAC
[30] § Methods › Identification of DORCs and Regulatory Network Construction ↔ 04_testis/re/03_figr_dorc_identification.R, lines 1–39 · score 0.57 · Regulatory Chromatin, FigR, Domains, DORCs, workflow, gene expression
[31] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/02_spg_visualization.R, lines 134–215 · score 0.52 · annotation bars, pseudotime trajectory, Ridge, density, ratio, SPG
[32] § Results › Design and Workflow of DUET‐seq ↔ 02_cellline/human_mouse_mix_split.py, lines 103–133 · score 0.52 · species mixing, doublet rate, human, mouse, RNA, seq
[33] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 43–103 · score 0.51 · core TFs, FigR, refine, Crem, network, spermatogenic
[34] § Results › DUET‐seq Resolves Cellular Heterogeneity in Adult Mouse Brain and Enables Peak‐to‐gene Linking ↔ 04_testis/re/03_figr_dorc_identification.R, lines 1–39 · score 0.51 · Regulatory Chromatin, FigR, Domain, DORC, DUET seq, gene expression

Paper

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The authors' code

R · 129 lines · 4.5 KB · no license · 4 matches

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It can be read at the source: 04_testis/base/03_link_peaks.R.

Overview

Authors: Dong Cheng^1,2, Zijun Meng³, Lan Wei⁴, Pingjing Yang¹, Fanfan Zhang¹, Zhiyi Luo¹, Ting Lai¹, Chunli Li⁵, Mengyao Zhao¹, Mengqin Xu¹, Jiaqi Wang¹, Linjun Li⁶, Huarong Chen⁷, Jin Li⁸, Ailong Huang^1,9, Youquan Bu^2,9, Liuyang Zhao^1,10,11

ORCID iDs: Huarong Chen, Jin Li, Ailong Huang, Liuyang Zhao

Institutions: Dalian Medical University (China); Second Affiliated Hospital of Chongqing Medical University (China); The Affiliated Yongchuan Hospital of Chongqing Medical University (China); Chongqing Medical University (China); Children's Hospital of Chongqing Medical University (China); Chinese University of Hong Kong (Hong Kong SAR China); Chongqing Dazu District People's Hospital (China)

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), article e77986

Dates: received 15 April 2026; accepted 18 September 2026; published online 27 September 2026; in print September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/advs.77986 · PMID 42801560 · PMCID PMC13616282 · OpenAlex W7214524852

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning

Keywords: biology, chromatin, computational biology, gene expression, rna, transcriptome

Topic: Genomics and Chromatin Dynamics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Prevention and Control of Emerging and Major Infectious Diseases‐National Science and Technology Major Project (2025ZD01905600); National Natural Science Foundation of China (82372356, 82372630); Chongqing Talents Program‐Youth Top‐notch Talents (CQYC20220512141, 20880); CQMU Program for Youth Innovation in Future Medicine (W0161, W0143)

Citations: not cited yet (Europe PMC); 94 references in the paper

Abstract

Joint profiling of chromatin accessibility and gene expression in the same cell enables direct linkage of regulatory elements to transcriptional output, but existing approaches remain limited by low co‐capture sensitivity, proprietary platforms, and high costs. Here we present DUET‐seq, an open‐source droplet microfluidic platform for joint profiling of chromatin accessibility and gene expression from the same nucleus. DUET‐seq combines programmable dissolvable dual‐linker hydrogel beads with one‐step intra‐droplet RT‐PCR to physically co‐index RNA and transposed chromatin fragments, completing library preparation within 12 h. Optimization of joint reaction conditions, including suppression of residual Tn5 activity, achieves ∼3,200 genes per HEK293T cell while maintaining high‐quality chromatin accessibility profiles. By providing a fully disclosed reagent‐and‐device ecosystem, DUET‐seq enables researchers to optimize lysis and reaction chemistry for non‐standard tissues without proprietary constraints. We demonstrate the platform's versatility by mapping over 14,000 cis‐regulatory element‐to‐gene linkages in adult mouse brain and by profiling ∼29,000 nuclei across six stages of postnatal spermatogenesis, where paired measurements reveal two modes of temporal regulatory decoupling—epigenetic priming and chromatin inertia—that are inaccessible to unimodal assays. DUET‐seq thus provides an accessible, cost‐effective framework for joint single‐nucleus multi‐omic profiling, with broad applicability across developmental biology, disease epigenomics, and functional genomics.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 34 matches between paragraphs and lines of code.

biochengd/DUET-seq

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: bd130dc6669158a5c0734db73fd829187b4ba5e2, 24 August 2026

Languages: R (21), Python (4), Jupyter (2)

Size: 60 files, 27 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

Holds: README, 2 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: ggplot2 (19 files), Seurat (19 files), tidyverse (17 files), patchwork (12 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), ComplexHeatmap (2 files), Harmony (2 files), pheatmap (2 files), seaborn (2 files), circlize (1 file), clusterProfiler (1 file), cowplot (1 file), h5py (1 file), igraph (1 file), SciPy (1 file), scVelo (1 file), SingleCellExperiment (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

28 files, not copied: shown from their source

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01_preprocessing/duet-seq/extract_bc_right_rush.py — Python, 80 lines, shown from its source
02_cellline/cellline_trackplot.R — R, 149 lines, 1 match, shown from its source
02_cellline/correlation_analysis.ipynb — Jupyter, 1 line, shown from its source
02_cellline/genomic_annotation.R — R, 135 lines, shown from its source
02_cellline/human_mouse_mix_split.py — Python, 145 lines, 2 matches, shown from its source
02_cellline/technical_qc_comparisons.ipynb — Jupyter, 987 lines, 2 matches, shown from its source
03_brain/01_Platform_Comparison_Plots.R — R, 136 lines, 2 matches, shown from its source
03_brain/02_Peak_Gene_Regulation.R — R, 312 lines, 2 matches, shown from its source
03_brain/03_ChromVAR_Motif_Analysis.R — R, 422 lines, shown from its source
03_brain/04_FigR_Core_Pipeline.R — R, 145 lines, 1 match, shown from its source
03_brain/05_FigR_Visualization.R — R, 137 lines, shown from its source
03_brain/06_Genomic_TrackPlots.R — R, 103 lines, shown from its source
04_testis/base/01_rna_processing.R — R, 215 lines, 3 matches, shown from its source
04_testis/base/02_atac_processing.R — R, 150 lines, 1 match, shown from its source
04_testis/base/03_link_peaks.R — R, 129 lines, 4 matches, shown from its source
04_testis/base/04_link_peaks_visualization.R — R, 202 lines, 4 matches, shown from its source
04_testis/base/05_coverage_plot.R — R, 114 lines, 1 match, shown from its source
04_testis/base/06_correlation_plot.R — R, 177 lines, 1 match, shown from its source
04_testis/base/07_chromvar_visualization.R — R, 194 lines, shown from its source
04_testis/re/01_pseudobulk_analysis.R — R, 177 lines, shown from its source
04_testis/re/02_feature_plots.R — R, 89 lines, shown from its source
04_testis/re/03_figr_dorc_identification.R — R, 185 lines, 2 matches, shown from its source
04_testis/re/04_construct_stage_specific_grn.R — R, 263 lines, 4 matches, shown from its source
04_testis/spg/01_spg_analysis.R — R, 166 lines, shown from its source
04_testis/spg/02_spg_visualization.R — R, 216 lines, 2 matches, shown from its source
04_testis/spg/03_rna_velocity_analysis_scvelo.py — Python, 143 lines, 1 match, shown from its source
04_testis/spg/04_celloracle_grn_perturbation_analysis.py — Python, 227 lines, 1 match, shown from its source
README.md — Text, 52 lines, shown from its source

At the source: github.com/biochengd/DUET-seq

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

Tracing map

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
27 scripts, each with its path and the digest of its content;
34 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

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Data

No dataset and no data link were found in the paper.

Data Availability Statement

The raw sequencing data and processed datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE345817 and are publicly available. The following publicly available datasets were utilized for benchmarking and comparative analysis: 10x Multiome and ISSAAC‐seq data from ArrayExpress (E‐MTAB‐11264); sci‐CAR‐seq (GSE117089), SHARE‐seq (GSE140203), SNARE‐seq2 (GSE157660), Paired‐seq (GSE130399), and SUM‐seq (GSE253165) from the Gene Expression Omnibus (GEO); and HT‐scCAT‐seq from GSA (CRA025996). The 10x Multiome mouse brain dataset was downloaded from the 10x Genomics repository. All original code and analysis scripts (including the barcode whitelist and complete oligonucleotide sequence list) used to support the findings of this study have been deposited in a GitHub repository (https://github.com/biochengd/DUET‐seq (https://github.com/biochengd/DUET-seq)).

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, pages, dates, 17 authors, 6 keywords, 4 funders, 64 references.

Cite

This paper

Cheng, D., Meng, Z., Wei, L., Yang, P., Zhang, F., Luo, Z., Lai, T., Li, C., Zhao, M., Xu, M., Wang, J., Li, L., Chen, H., Li, J., Huang, A., Bu, Y., & Zhao, L. (2026). DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells. Advanced science (Weinheim, Baden-Wurttemberg, Germany), e77986. https://doi.org/10.1002/advs.77986

BibTeX

@article{cheng2026duet, author = {Cheng, Dong and Meng, Zijun and Wei, Lan and Yang, Pingjing and Zhang, Fanfan and Luo, Zhiyi and Lai, Ting and Li, Chunli and Zhao, Mengyao and Xu, Mengqin and Wang, Jiaqi and Li, Linjun and Chen, Huarong and Li, Jin and Huang, Ailong and Bu, Youquan and Zhao, Liuyang}, title = {{DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells}}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, year = {2026}, month = sep, pages = {e77986}, publisher = {Wiley}, issn = {2198-3844}, doi = {10.1002/advs.77986}, url = {https://doi.org/10.1002/advs.77986}, pmid = {42801560}, pmcid = {PMC13616282} }

RIS

TY - JOUR AU - Cheng, Dong AU - Meng, Zijun AU - Wei, Lan AU - Yang, Pingjing AU - Zhang, Fanfan AU - Luo, Zhiyi AU - Lai, Ting AU - Li, Chunli AU - Zhao, Mengyao AU - Xu, Mengqin AU - Wang, Jiaqi AU - Li, Linjun AU - Chen, Huarong AU - Li, Jin AU - Huang, Ailong AU - Bu, Youquan AU - Zhao, Liuyang TI - DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany) J2 - Adv Sci (Weinh) PY - 2026 DA - 2026/09/27 SP - e77986 SN - 2198-3844 PB - Wiley DO - 10.1002/advs.77986 UR - https://doi.org/10.1002/advs.77986 LA - en ER -

CSL-JSON

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