Single-cell profiling reveals a potent role of quercetin in promoting hair regeneration

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Abstract

Hair loss affects millions of people at some time in their life, and safe and efficient treatments for hair loss are a significant unmet medical need. We report that topical delivery of quercetin (Que) stimulates resting hair follicles to grow with rapid follicular keratinocyte proliferation and replenishes perifollicular microvasculature in mice. We construct dynamic single-cell transcriptome landscape over the course of hair regrowth and find that Que treatment stimulates the differentiation trajectory in the hair follicles and induces an angiogenic signature in dermal endothelial cells by activating HIF-1α in endothelial cells. Skin administration of a HIF-1α agonist partially recapitulates the pro-angiogenesis and hair-growing effects of Que. Together, these findings provide a molecular understanding for the efficacy of Que in hair regrowth, which underscores the translational potential of targeting the hair follicle niche as a strategy for regenerative medicine, and suggest a route of pharmacological intervention that may promote hair regrowth.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Author and article information

Contributors

Qian Zhao

Yandong Zheng

Dongxin Zhao

Liyun Zhao

Lingling Geng

Shuai Ma

Yusheng Cai

Chengyu Liu

Yupeng Yan

Juan Carlos Izpisua Belmonte

Si Wang:

ORCID: https://orcid.org/0000-0003-2431-0612

Weiqi Zhang:

ORCID: https://orcid.org/0000-0002-8885-5104

Guang-Hui Liu:

ORCID: https://orcid.org/0000-0001-9289-8177

Jing Qu:

ORCID: https://orcid.org/0000-0002-3988-5067

Journal

Journal ID (nlm-ta): Protein Cell

Journal ID (iso-abbrev): Protein Cell

Journal ID (publisher-id): proteincell

Title: Protein & Cell

Publisher: Oxford University Press (US )

ISSN (Print): 1674-800X

ISSN (Electronic): 1674-8018

Publication date Collection: June 2023

Publication date (Electronic): 30 November 2022

Publication date PMC-release: 30 November 2022

Volume: 14

Issue: 6

Pages: 398-415

Affiliations

Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University , Beijing 100053, China

Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, Xuan Wu Hospital, Capital Medical University , Beijing 100053, China

State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China

University of Chinese Academy of Sciences , Beijing 100049, China

Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203, China

Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University , Beijing 100053, China

State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences , Beijing 100101, China

Institute for Stem Cell and Regeneration, Chinese Academy of Sciences , Beijing 100101, China

Beijing Institute for Stem Cell and Regenerative Medicine , Beijing 100101, China