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10X Genomics v2 0 10x genomics
Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using <t>10X</t> Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17
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Raytest GmbH advanced image data analyser aida software version 2.0
Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using <t>10X</t> Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17
Advanced Image Data Analyser Aida Software Version 2.0, supplied by Raytest GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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PrimerDesign Inc pyromark assay design software
Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using <t>10X</t> Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17
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Image Search Results


Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using 10X Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17

Journal: Cell reports. Medicine

Article Title: Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma.

doi: 10.1016/j.xcrm.2025.102188

Figure Lengend Snippet: Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using 10X Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17

Article Snippet: *_ga_B3E4QL2TPR* MTcxMDc3MDI1My4yMS4wLjE3MTA3Nz AzNTcuMC4wLjAhttps://tcga-pancanatlas-hub.s3.us-east-1.amazonaws.com/ download/Survival_SupplementalTable_ S1_20171025_xena_sp Fibroblast atlas single-cell data, Luo et al. https://www.nature.com/ articles/s41467-022-34395-2 GEO: GSE210347 Software and algorithms 10X Cell Ranger ARC pipeline, v2.0 10X Genomics https://www.10xgenomics.com/support/ software/cell-ranger-arc/latest 10X Space Ranger pipeline, v2.1.1 10X Genomics https://www.10xgenomics.com/support/ software/space-ranger/latest CellBender, python package, v0.2.0 https://www.nature.com/articles/ s41592-023-01943-7 https://github.com/broadinstitute/ CellBender Scrublet, python package, v0.2.3 https://www.sciencedirect.com/ science/article/pii/S2405471218304745 https://github.com/AllonKleinLab/scrublet Scanpy, python package, v1.9.5 https://genomebiology.biomedcentral. com/articles/10.1186/s13059-017-1382-0 https://github.com/scverse/scanpy Infercnvpy, python package, v0.4.5 N/A https://github.com/icbi-lab/infercnvpy Harmony, python package, v0.0.9 https://www.nature.com/articles/ s41592-019-0619-0 https://github.com/slowkow/harmonypy Signac, R package, v1.14.0 https://www.nature.com/articles/ s41592-021-01282-5 https://github.com/stuart-lab/signac MACS2, python package, v2.2.9.1 https://genomebiology.biomedcentral. com/articles/10.1186/gb-2008-9-9-r137 https://pypi.org/project/MACS2/ Seurat, R package, v5.1.0 https://www.nature.com/articles/ s41587-023-01767-y https://satijalab.org/seurat/ MILOpy, python package, v0.1.1 https://www.nature.com/articles/ s41587-021-01033-z https://github.com/emdann/milopy cNMF, python package, v1.3.4 https://elifesciences.org/articles/43803 https://github.com/dylkot/cNMF GSEA, python package, v0.10.8 https://www.pnas.org/doi/10.1073/ pnas.0506580102 https://github.com/zqfang/GSEApy (Continued on next page) Cell Reports Medicine 6, 102188, June 17, 2025 e2 OPEN ACCESS

Techniques: Sequencing, Biomarker Discovery