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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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The nucleolus is the most prominent substructure of the nucleus. When it was first discovered, it was considered to be an isolated organelle that forms fibrils and granules. In 1931, the relationship between the nucleolus and chromosomes was first described by Heitz. He observed that the appearance and size of nucleolus varies depending on the stage of the cell cycle. He also noticed constricted regions on different chromosomes clustered together at definite cell cycle stages. These regions,...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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Redefining normal breast cell populations using long noncoding RNAs.

Mainá Bitar1,2, Isela Sarahi Rivera1,3, Isabela Almeida1,2

  • 1Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia.

Nucleic Acids Research
|May 5, 2023
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Summary
This summary is machine-generated.

Researchers discovered thousands of long noncoding RNAs (lncRNAs) in breast cells. These lncRNAs can identify cell types and subtypes, offering new potential for breast cancer biomarkers and therapies.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Cell Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular gene expression.
  • Current scRNA-seq analyses often discard unannotated transcripts, potentially missing valuable biological information.
  • Long noncoding RNAs (lncRNAs) are a class of transcripts with regulatory roles, but their contribution to cell-type specificity in normal tissues is underexplored.

Purpose of the Study:

  • To discover and characterize novel long noncoding RNAs (lncRNAs) in human mammary epithelial cells.
  • To investigate the utility of lncRNA expression profiles for distinguishing cell types and subpopulations within the normal breast.
  • To evaluate the potential of breast-specific lncRNAs as biomarkers for breast cancer subtypes.

Main Methods:

  • Utilized single-cell RNA sequencing (scRNA-seq) on human mammary epithelial cells.
  • Developed methods to identify and analyze the expression of previously unannotated long noncoding RNAs (lncRNAs).
  • Applied computational clustering and classification algorithms based on lncRNA expression data.

Main Results:

  • Discovered thousands of novel long noncoding RNAs (lncRNAs) expressed in human breast cells.
  • Demonstrated that lncRNA expression profiles alone can effectively distinguish between luminal and basal cell types and their subpopulations.
  • Showed that lncRNA-based clustering identified finer basal cell subpopulations than gene-based clustering.
  • Identified a panel of 100 breast-specific lncRNAs capable of differentiating breast cancer subtypes more effectively than protein-coding markers.
  • Confirmed that these breast-specific lncRNAs have limited utility in distinguishing brain cell populations, emphasizing tissue specificity.

Conclusions:

  • Long noncoding RNAs (lncRNAs) represent a significant, largely untapped resource for understanding normal breast cell heterogeneity.
  • lncRNA expression patterns offer a powerful tool for high-resolution cell subpopulation identification.
  • Breast-specific lncRNAs hold considerable promise as novel biomarkers for diagnosing and classifying breast cancer subtypes.
  • Further annotation and analysis of tissue-specific lncRNAs are crucial for maximizing the potential of transcriptomic studies.