Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

iPS Cell Differentiation

iPS Cell Differentiation

The ability of induced pluripotent stem cells or iPSCs to differentiate into most body cell types has stimulated repair and regenerative medicine research over the past few decades. iPSC-derived blood cells, hepatocytes, beta islet cells, cardiomyocytes, neurons, and other cell types can repair injuries or regenerate damaged tissue in diseases such as diabetes and neurodegenerative disorders.

EPS and iPS Cells in Disease Research

EPS and iPS Cells in Disease Research

Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

Blood·2026

Same author

The Medulloblastoma Meta-Analysis Portal Enables Integrative Exploration of Clinical and Genomic Data from Patients.

Cancer research·2025

Same author

MODE: high-resolution digital dissociation with deep multimodal autoencoder.

bioRxiv : the preprint server for biology·2025

Same author

<i>iModMix</i>: Integrative Module Analysis for Multi-omics Data.

bioRxiv : the preprint server for biology·2024

Same author

cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.

Bioinformatics (Oxford, England)·2024

Same author

imply: improving cell-type deconvolution accuracy using personalized reference profiles.

Genome medicine·2024

Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026

Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026

Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026

Same journal

Genomic sequence evolution underlying human neocortical interareal diversification.

Genome biology·2026

Same journal

Regulatory mechanisms driven by functional 3'-UTR variants in alcohol use disorder and related traits.

Genome biology·2026

Same journal

A longitudinal single-nucleus transcriptomic atlas of bovine placentation reveals dynamic cellular hierarchies and regulatory programs.

Genome biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Pancreatic Islet Embedding for Paraffin Sections

Pancreatic Islet Embedding for Paraffin Sections

Published on: June 29, 2018

ISLET: individual-specific reference panel recovery improves cell-type-specific inference.

Hao Feng¹, Guanqun Meng², Tong Lin³

¹Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. hxf155@case.edu.

|July 26, 2023

Summary

This summary is machine-generated.

ISLET is a new statistical method for creating personalized gene expression references from repeated samples. It improves accuracy in identifying cell-type-specific gene signatures, particularly for conditions like pancreatic islet autoantibodies.

Keywords:

Cell-type-specific differential expression Deconvolution Individual-specific reference panel Temporal measures

More Related Videos

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Published on: March 9, 2022

Related Experiment Videos

Last Updated: Jun 17, 2026

Pancreatic Islet Embedding for Paraffin Sections

Pancreatic Islet Embedding for Paraffin Sections

Published on: June 29, 2018

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Published on: March 9, 2022

Area of Science:

Computational Biology
Genomics
Statistical Genetics

Background:

Accurate transcriptome reference panels are crucial for cell-type-specific gene expression analysis.
Existing methods often struggle with longitudinal data and individual-specific variations.

Purpose of the Study:

To introduce ISLET, a novel statistical framework for inferring individual-specific and cell-type-specific transcriptome reference panels.
To evaluate ISLET's performance in reference estimation and downstream analyses using simulation studies and real-world data.

Main Methods:

ISLET models repeatedly measured bulk gene expression data to leverage shared information within subjects.
The framework optimizes the usage of longitudinal data for enhanced reference panel construction.

Main Results:

ISLET demonstrates outstanding performance in reference estimation compared to existing methods.
The method accurately identifies cell-type-specific differentially expressed genes in downstream analyses.
Application to longitudinal childhood blood transcriptomes confirmed cell-type-specific gene signatures for pancreatic islet autoantibody.

Conclusions:

ISLET provides a robust and novel approach for generating individual-specific transcriptome references from repeated measurements.
The framework enhances the accuracy of cell-type-specific gene expression analysis, with implications for understanding complex diseases.