#rna-seq

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Muscle Map

Every move you make is possible due to your skeletal muscles. These muscles differ in their mechanical and biological properties but how these are controlled at a molecular and cellular level is poorly understood. Researchers now construct an atlas of active genes and cell components in six different leg muscles using tissue samples collected from healthy men. Large-scale RNA analysis and cell highlighting by immunohistochemistry (pictured) revealed different levels of active genes, cell types and proteins, such as myosin heavy chain proteins (red, green, blue) present in muscle fibres. This revealed that muscles clustered into three main groups based on cell types present, with varying levels of active genes across groups. For example, muscles with higher levels of active slow-twitch muscle fibre genes contained more endothelial cells and blood capillaries. The results are now available as an open-source, interactive leg muscle atlas – a novel resource to help study the molecular features of muscles.

Written by Lux Fatimathas

Image from work by Tooba Abbassi-Daloii and colleagues

Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in eLife, February 2023

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Monocyte, Microlith, Mineral?

Every breath can be a struggle if you have the rare lung disease pulmonary alveolar microlithiasis. It’s caused by a faulty protein in the membranes of cells that line alveoli, tiny air sacs that fill your lungs. This causes mineral structures called hydroxyapatite microliths to form inside alveoli. Researchers now investigate this process by analysing RNA – a marker for gene activity – in lung tissue from human patients and a mouse model of the disease. Looking at immune cells called monocytes, which can develop into bone-degrading cells called osteoclasts, the team found that osteoclast-related genes were activated in alveolar monocytes. They also found that microliths, pictured using scanning electron microscopy at different resolutions in human (top) and mouse (bottom) lung tissue, contained osteoclast enzymes. This suggests that in response to microliths forming, osteoclast-like cells kickstart into action. This may present a new research avenue for pulmonary alveolar microlithiasis therapies.

Written by Lux Fatimathas

Image adapted from work by Yasuaki Uehara and colleagues

Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Nature Communications, March 2023

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Barrier Breakthrough

Your body contains hundreds of nerves but they can't all regenerate. After injury, peripheral nerves replace damaged sections but nerves in your central nervous system (CNS) can’t. Instead, brain cells called astrocytes cordon off damaged tissue (lesions) to help preserve healthy nerve tissue. These lesions form a barrier, preventing regeneration. Transplants of neural progenitor cells (NPCs), made from stem cells, may help. Researchers investigate by tagging NPCs and transplanting them, via a hydrogel, into uninjured or injured mouse CNS. RNA analysis revealed NPCs in uninjured mice matured into cells resembling healthy astrocytes, while NPCs in injured mice matured into cells resembling ‘reactive’ astrocytes, which arise after injury to partition off lesions. Fluorescence microscopy of injured CNS (pictured) revealed that adding NPCs (right) reduced lesion size (magenta) and helped bridge lesions via astrocytes (green) compared with injured CNS without NPCs (left) or only hydrogel (middle). The injury microenvironment, therefore, directs NPCs towards wound repair.

Written by Lux Fatimathas

Image from work by T. M. O’Shea and colleagues

Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Nature Communications, September 2022

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All Knees

It’s tough, it’s rubbery, it’s your cartilage. Your joints contain two types: articular cartilage, which covers the ends of your bones, and growth-plate cartilage, which forms the ends of long bones. Both comprise cells called chondrocytes. How these types of cartilage develop isn’t clear. Researchers now investigate in mice genetically engineered with fluorescently-tagged NFATc1 – a protein which when inactivated interferes with cartilage development. Using fluorescent microscopy of developing mouse knee joints, they found NFATc1-containing cells (progenitors) matured into articular chondrocytes but not growth-plate chondrocytes. NFATc1-containing progenitors also matured into cells that contribute to the joint lining, ligaments and developing kneecap (pictured, green). Analysing the RNA of articular chondrocytes revealed that NFATc1 levels dropped as the cells matured. Reducing NFATc1 levels in progenitors triggered articular cartilage formation while increasing levels blocked this from happening. NFATc1 is therefore vital for articular — but not growth-plate — cartilage development.

Written by Lux Fatimathas

Image from work by Fan Zhang and Yuanyuan Wang, and colleagues

Xuanwu Hospital, Capital Medical University, China; National Clinical Research Center for Geriatric Diseases, China

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in eLife, February 2023

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Transcriptome analysis reveals PER2 and CRY2 as biomarkers in bladder cancer and circadian rhythm pathways

Bladder cancer RNA-Seq data analysis highlighted significant insights into the molecular mechanisms and biological processes underlying tumorigenesis. Utilizing R packages such as edgeR, limma, and DESeq2, we preprocessed and normalized the data to identify differentially expressed genes (DEGs). Unsupervised learning methods, including PCA, facilitated the classification of these DEGs, revealing critical pathways associated with DNA replication, DNA repair, and cell cycle control. Gene ontology enrichment analysis further delineated the affected biological processes, cellular components, and molecular functions. Notably, our findings identified potential biomarkers, particularly the genes PER2 and CRY2, which are integral to the circadian rhythm pathway. Additionally, we observed that protein complexes CLOCK/BMAL1, CLOCK/BMAL2, and BMAL1/NPAS2 regulate the expression of several clock genes via E-boxes. The study also constructed a differential gene network in bladder cancer, providing insights into the regulatory functions of key genes involved in tumor progression. These findings not only enhance our understanding of bladder cancer biology but also pave the way for potential therapeutic targets and biomarkers.

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Unraveling Critical Insights: Designing New Gene Signature from RNA-seq Data

Creating a new gene signature from RNA sequencing (RNA-seq) data is a pivotal process in genomics. It has the potential to unlock critical insights into gene expression patterns. These patterns can further reveal biomarkers for various diseases, identify therapeutic targets, and elucidate underlying biological mechanisms governing health and diseases. Analyzing RNA-seq data enables researchers to discover genes that are differentially expressed in specific conditions, and provides a molecular signature that can be used to diagnose diseases, predict patient outcomes, and tailor treatments for individual patients.

Gene signatures are particularly valuable in personalized medicine as they can facilitate the development of targeted therapies based on the specific genetic makeup of a patient’s disease. In oncology, for instance, gene signatures can distinguish between different cancer subtypes, allowing for precise and effective treatment strategies. Gene signature applications extend to a wide range of diseases including cancer, cardiovascular disorders, neurological conditions, and infectious diseases.

This blog delineates the detailed process of developing a gene signature from RNA-seq data. It traces fundamental concepts like understanding RNA-seq technology and the significance of gene signatures. Further, it explores the preparation of data, including sample collection, data acquisition, and quality control followed by the processing and normalization of RNA-seq data, identification of differentially expressed genes (DEGs), and designing a robust gene signature. Eventually, it studies ways to apply and interpret these gene signatures in real-world scenarios, ensuring that the insights gained are actionable and relevant to ongoing research or clinical applications. Original Publish

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Gene network analysis reveals candidate genes related with the hair follicle development in sheep. He, et al., BMC genomics doi: 10.1186/s12864-022-08552-2.

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Workflow of circRNA microarray

With the widespread application of RNA sequencing (RNA-seq) technology and bioinformatics prediction, large numbers of circRNAs have been identified. However, the low abundance expression level of circRNA significantly limits the detection efficiency of RNA-seq. Therefore, a high-throughput microarray may act as a more sensitive, efficient, and cost-effective technology for circRNA expression profiling.