Searching journal content for articles similar to Zimmerman et al. 32 (10): 1892.

Displaying results 1-10 of 47
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  1. Method: High-resolution spatial transcriptomics in fixed tissue using a cost-effective PCL-seq workflow
    • Xue Dong,
    • Mengzhu Hu,
    • Xiaonan Cui,
    • Wenjian Zhou,
    • Jingtao Cai,
    • Guangyao Mao,
    • and Weiyang Shi
    Genome Res. September 2025 35: 2052-2063; Published in Advance August 5, 2025, doi:10.1101/gr.279906.124
    ...heterogeneous tissue structures. Its ability to adapt to irregularly shaped ROIs highlights its utility for exploring spatially resolved transcriptomics in complex tissue samples.View larger version: In this window In a new window Figure 3. Analysis of PCL-seq for ROI-specific profiling in the fresh...
  2. Method: Infernape uncovers cell type–specific and spatially resolved alternative polyadenylation in the brain
    • Bowei Kang,
    • Yalan Yang,
    • Kaining Hu,
    • Xiangbin Ruan,
    • Yi-Lin Liu,
    • Pinky Lee,
    • Jasper Lee,
    • Jingshu Wang,
    • and Xiaochang Zhang
    Genome Res. October 2023 33: 1774-1787; Published in Advance October 31, 2023, doi:10.1101/gr.277864.123
    ....Single-cell RNA-seq (scRNA-seq) and spatial transcriptomic methods based on oligo(dT) priming and barcoding have revolutionized our understanding of cellular heterogeneity in animal tissues (Klein and Macosko 2017). Recent studies have uncovered more than 100 neuronal subtypes that are transcriptionally specified...
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  3. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...within spatial transcriptomic data sets. This level of granularity is critical for resolving fine-scale tissue architecture and cellular dynamics.As sequencing technology matures and costs decrease, the throughput and resolution of single-cell and spatial data are increasing. Large-scale single...
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  4. Method: A model-based constrained deep learning clustering approach for spatially resolved single-cell data
    • Xiang Lin,
    • Le Gao,
    • Nathan Whitener,
    • Ashley Ahmed,
    • and Zhi Wei
    Genome Res. October 2022 32: 1906-1917; Published in Advance October 5, 2022, doi:10.1101/gr.276477.121
    ...opportunity to map RNA molecules in their tissue locations, allowing for comprehensive profiling of cell heterogeneity (Liao et al. 2021).The technologies used to profile spatially resolved single-cell transcriptomics (or targeted genes) can be divided into two types. The first type includes the hybridization...
  5. Method: A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis
    • Daijun Zhang,
    • Ren Qi,
    • Xun Lan,
    • and Bin Liu
    Genome Res. August 2025 35: 1794-1808; Published in Advance July 14, 2025, doi:10.1101/gr.280281.124
    ...MSU-BIT University, Shenzhen, Guangdong 518172, China; 4Zhongguancun Academy, Beijing 100094, China Corresponding author: bliu@bliulab.netAbstractThe development of spatial transcriptomics (ST) technologies has revolutionized the way we map the complex organization and functions of tissues...
  6. Method: Spatial Cellular Networks from omics data with SpaCeNet
    • Stefan Schrod,
    • Niklas Lück,
    • Robert Lohmayer,
    • Stefan Solbrig,
    • Dennis Völkl,
    • Tina Wipfler,
    • Katherine H. Shutta,
    • Marouen Ben Guebila,
    • Andreas Schäfer,
    • Tim Beißbarth,
    • Helena U. Zacharias,
    • Peter J. Oefner,
    • John Quackenbush,
    • and Michael Altenbuchinger
    Genome Res. September 2024 34: 1371-1383; Published in Advance September 4, 2024, doi:10.1101/gr.279125.124
    ...spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which...
  7. Method: Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ
    • Joseph W. Foley,
    • Chunfang Zhu,
    • Philippe Jolivet,
    • Shirley X. Zhu,
    • Peipei Lu,
    • Michael J. Meaney,
    • and Robert B. West
    Genome Res. November 2019 29: 1816-1825; Published in Advance September 13, 2019, doi:10.1101/gr.234807.118
    ...hybridization or sequencing methods (Femino et al. 1998; Lee et al. 2014; Lubeck et al. 2014; Chen et al. 2015), which completely preserve spatial information about the tissue sample but do not perform gene expression profiling for the entire transcriptome of every cell. However, it is more sensitive to small...
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  8. Resource: The time-resolved transcriptome of C. elegans
    • Max E. Boeck,
    • Chau Huynh,
    • Lou Gevirtzman,
    • Owen A. Thompson,
    • Guilin Wang,
    • Dionna M. Kasper,
    • Valerie Reinke,
    • LaDeana W. Hillier,
    • and Robert H. Waterston
    Genome Res. October 2016 26: 1441-1450; Published in Advance August 16, 2016, doi:10.1101/gr.202663.115
    ...The time-resolved transcriptome of C. elegans Max E. Boeck 1 , 2 , Chau Huynh 1 , Lou Gevirtzman 1 , Owen A. Thompson 1 , Guilin Wang 3 , Dionna M. Kasper 3 , Valerie Reinke 3 , LaDeana W. Hillier 1...
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  9. Method: A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples
    • Shuai Guo,
    • Xiaoqian Liu,
    • Xuesen Cheng,
    • Yujie Jiang,
    • Shuangxi Ji,
    • Qingnan Liang,
    • Andrew Koval,
    • Yumei Li,
    • Leah A. Owen,
    • Ivana K. Kim,
    • Ana Aparicio,
    • Sanghoon Lee,
    • Anil K. Sood,
    • Scott Kopetz,
    • John Paul Shen,
    • John N. Weinstein,
    • Margaret M. DeAngelis,
    • Rui Chen,
    • and Wenyi Wang
    Genome Res. January 2025 35: 147-161; Published in Advance November 25, 2024, doi:10.1101/gr.278822.123
    ...scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues...
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  10. Research: Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy
    • Lujia Chen,
    • Xiangduo Kong,
    • Kevin G. Johnston,
    • Ali Mortazavi,
    • Todd C. Holmes,
    • Zhiqun Tan,
    • Kyoko Yokomori,
    • and Xiangmin Xu
    Genome Res. May 2024 34: 665-679; Published in Advance May 22, 2024, doi:10.1101/gr.278717.123
    ...gene expression, only three genes were analyzed in these prior studies (Chau et al. 2021).The recently developed single-cell spatial transcriptomics technology multiplexed error-robust fluorescence in situ hybridization (MERFISH) can profile spatial locations of hundreds to thousands of RNA species...
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