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相关概念视频

Mining Spatial Transcriptomics Datasets using DeepSpaceDB10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene...
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Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing08:58

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Here, we present a step-by-step, visual workflow for analyzing a single-cell time-course transcriptomics dataset of mouse skin wound healing using R. The protocol includes a standard pipeline for dataset download, quality control, visualizations, and cell type annotations using Seurat, and cell-cell interaction analysis using...
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Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

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Here, we present a method for aligning and cryosectioning multiple Zebrafish (Danio rerio) larvae samples and collecting them on a single slide for spatial transcriptomic analysis.
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Methods to Enable Spatial Transcriptomics of Bone Tissues07:43

Methods to Enable Spatial Transcriptomics of Bone Tissues

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Here, we describe a method that allows for the decalcification of freshly obtained bone tissues and the preservation of high-quality RNA. A method is also illustrated for sectioning Formalin Fixed Paraffin Embedded (FFPE) samples of non-demineralized bones to obtain good quality results if fresh tissues are not available or cannot be collected.
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Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics13:51

Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics

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We present a method for generating cDNA from environmental mRNA. In general, total RNA is first collected from the environment, rRNA is selectively removed, mRNA is selectively amplified, and cDNA synthesized from the enriched mRNA pool is sequenced. Recovered sequences can be annotated using standard bioinformatics techniques to identify the expressed...
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

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This procedure describes a quick and easy workflow to introduce siRNA into difficult to transfect cell lines and follow gene expression by real-time PCR. Use of an automated cell counter, multi-well electroporation plate, and automated electrophoresis station provide quick and reliable results without the need for expensive robotic...
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相关实验视频

Updated: Jan 20, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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STimage-1K4M:用于空间转录组学的一个基因病理学图像基因表达数据集.

Jiawen Chen1, Muqing Zhou1, Wenrong Wu1

  • 1University of North Carolina at Chapel Hill.

Advances in neural information processing systems
|January 19, 2026
PubMed
概括

一个新的数据集,STimage-1K4M,为病理图像的子区域提供了详细的基因表达数据. 这使得在计算病理学研究中能够进行更深入的多模式分析.

科学领域:

  • 计算病理学计算病理学
  • 生物信息学是一种生物信息学.
  • 多模式数据分析 多模式数据分析

背景情况:

  • 现有的医疗图像-文本数据集对于子区域缺乏细节细节.
  • 当前数据集中的高级文本摘要限制了对病理图像的深入分析.
  • 需要数据集,将详细的图像特征与基因组信息联系起来.

研究的目的:

  • 介绍STimage-1K4M,这是一个新型数据集,旨在为亚病理图像提供基因组特征.
  • 通过提供高分辨率的空间转录组学数据来弥合当前数据集的差距.
  • 促进计算机病理学的先进的多模式研究.

主要方法:

  • 利用空间转录学来捕捉基因表达在个别空间点的水平.
  • 开发了STimage-1K4M,包括来自空间转录组学数据的1,149张图像.
  • 将每个子图像与15,000 - 30,000个维度的基因表达数据配对,创建了4,293,195对.

主要成果:

  • STimage-1K4M包含大量的子图像和基因表达对.
  • 该数据集为分析病理图像提供了前所未有的细节性.
  • 成功地将详细的空间信息与全面的基因表达特征联系起来.

更多相关视频

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

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Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

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相关实验视频

Last Updated: Jan 20, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

665
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

2.9K
Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

911

结论:

  • STimage-1K4M显著提高了计算病理学多模数据分析的潜力.
  • 该数据集为创新的应用程序铺平了道路,这些应用程序需要精细的图像-基因组相关性.
  • 这一资源将加速在需要详细的细分层层面见解的领域的研究.