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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Sep 14, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

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Leveraging RNA-seq deconvolution to improve complex in vitro model characterization.

Brad C Hansen1, Christopher M Arian2, Yuting Zeng3

  • 1Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA; Institute for Stem Cells and Regenerative Medicine, University of Washington, Seattle, Washington, USA.

The Journal of Biological Chemistry
|July 23, 2025
PubMed
Summary
This summary is machine-generated.

RNA-seq deconvolution effectively characterizes complex in vitro models (CIVMs) by predicting cell proportions. Using imputed single-cell references enhances accuracy for toxicology and drug development applications.

Keywords:
cell culturecomputational biologyintestinetestistranscriptomics

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Area of Science:

  • Biotechnology
  • Genomics
  • Toxicology

Background:

  • Complex in vitro models (CIVMs) are crucial for drug development but require advanced characterization methods.
  • Traditional single-cell analysis tools are unsuitable for CIVMs due to cell stress, loss, and cost.
  • RNA-seq deconvolution offers a promising alternative for analyzing CIVM cellular composition.

Purpose of the Study:

  • To benchmark RNA-seq deconvolution methods for characterizing two distinct CIVMs: intestinal organoids and neonatal rodent testes.
  • To evaluate the impact of single-cell RNA sequencing (scRNA-seq) imputation methods on deconvolution accuracy.
  • To assess the utility of deconvolution in understanding cellular dynamics within CIVMs.

Main Methods:

  • Benchmarking multiple RNA-seq deconvolution algorithms using bulk RNA-seq data from CIVMs.
  • Utilizing publicly available scRNA-seq datasets as references for deconvolution.
  • Applying various imputation techniques to scRNA-seq data to restore gene distribution.
  • Analyzing deconvolution results to infer cell population dynamics in intestinal and testis CIVMs.

Main Results:

  • Deconvolution accuracy varied among methods but provided insights into cell population dynamics.
  • In intestinal organoids, deconvolution revealed enterocyte emergence from LGR5+ stem cells during differentiation.
  • In testis organoids, deconvolution identified retained germ cells, proliferating peritubular myoid cells, and stable Leydig cells under hormonal stimulation.
  • Imputed single-cell references significantly improved deconvolution accuracy.

Conclusions:

  • RNA-seq deconvolution is a valuable tool for characterizing complex in vitro models (CIVMs).
  • The method aids in understanding cellular composition and dynamics in engineered tissue models.
  • Improved deconvolution accuracy with imputed scRNA-seq references supports its application in toxicology and drug development.