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Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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Updated: Jan 9, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

CellUntangler:通过深度生成模型,在单细胞数据中分离不同的生物信号.

Sarah Chen¹, Aviv Regev², Anne Condon¹

¹Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

|December 2, 2025

概括

一个新的深度学习模型CellUntangler在单细胞RNA测序数据中将复杂的生物信号 (如细胞周期) 与细胞类型分开. 这种方法通过解开多个同时发生的细胞过程来增强分析.

关键词:

细胞循环中的细胞循环.深度生成模型的模型.一个超标空间的超标空间.非欧几里得空间的空间.干扰是一种干扰.伪空间是一种伪空间.一个单细胞RNA测序.时间空间时间空间.变量自动编码器变量自动编码器

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Published on: February 11, 2022

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Last Updated: Jan 9, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
一个单细胞分析.

背景情况:

单细胞RNA测序 (scRNA-seq) 揭示了细胞复杂性,但与同时发生的生物过程作斗争.
现有的方法往往过于简化,专注于单个过程,可能会丢失关键的生物信息.
解开细胞类型和细胞周期等并发信号对于准确的生物解释至关重要.

研究的目的:

开发一种新的深度生成模型,CellUntangler,用于在单细胞数据中解开多个生物信号.
解决现有方法在处理同步细胞过程中的局限性.
为分析复杂的单细胞基因表达数据提供灵活的框架.

主要方法:

介绍了CellUntangler,这是一个深度生成模型,利用具有多个子空间的潜空间.
每个子空间都经过几何定制,以捕获不同的生物信号.
将模型应用于scRNA-seq数据集,包括具有和没有细胞周期活性的数据集.

主要成果:

细胞Untangler成功地将细胞循环与其他过程分开,例如细胞类型.
该框架在解脱空间信息,组织解离效应,干扰素反应和细胞类型认同等额外信号方面展示了概括性.
该模型允许在基因表达水平上选择性增强或过信号.

结论:

CellUntangler提供了一种强大而灵活的工具,用于在单细胞数据中剖析复杂的生物过程.
能够分离多个信号的能力提高了scRNA-seq数据分析的准确性和深度.
这种方法促进了对细胞异质性和功能的更细致的理解.