Long non-coding RNA Sequencing (lncRNA-seq)

CD Genomics provides highly sensitive strand-specific lncRNA-seq service for lncRNA profiling and quantification, novel lncRNA identification and annotation, revealing lncRNA functions and biology and providing vital insights into disease mechanisms and cell biology. Please contact our expert team to discuss how our lncRNA-seq service can refine your NGS projects.

Overview

Long non-coding RNAs (lncRNAs) are a large class of RNA molecules exceeding 200 nucleotides in length that do not encode proteins. lncRNAs encompass nearly 30,000 different transcripts in humans, hence lncRNA transcripts account for the major part of the non-coding transcriptome. lncRNAs take part in a variety of biological processes such as organization of nuclear domains and regulation of transcription, and have been implicated in several diseases. However, compared with mRNAs, most lncRNA are less well studied and their functions are largely unknown. With the development of next-generation sequencing (NGS) technologies, RNA-seq is emerging as the major transcriptome profiling method that has many advantages over traditional methods such as microarray in many aspects such as novel transcript and splice junction identification, as well as allele-specific expression analysis. lncRNA sequencing (lncRNA-seq) provides a genome-wide picture of lncRNAs in a given sample under specific conditions, revealing changes in the expression of lncRNAs, as well as lncRNA functions and biology. Strand-specific RNA-seq protocol that retains strand of origin information provides a greater resolution for sense/antisense profiling, which is necessary for the accurate identification of antisense lncRNAs.

Features

Project Workflow

Bioinformatics Analysis Pipeline

In-depth data analysis:

• Remove unqualified transcripts
• Mapping to genome
• Transcript assembly
• Transcript expression profiling and differential expression
• Novel lncRNAs prediction
• Functional annotation
• Target gene prediction

Sample Requirements

Total RNA sample (quantity ≥ 2 μg, concentration ≥ 50 ng/μL)

Exosomal RNA (quantity ≥ 5 ng), for more information about Exosome lncRNA-seq

Cells sample (quantity ≥ 2×10⁶)

Tissue sample (quantity ≥ 500 mg)

RIN ≥ 7.0, with smooth baseline; OD260/280: 1.8-2.2; OD260/230: ≥ 1.8.

Sample storage: RNA can be dissolved in ethanol or RNA-free ultra-pure water and stored at -80°C. RNA should avoid repeated freezing and thawing.

Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.

Deliverable: raw data as BAM files, coverage summary, QC report, custom bioinformatics analyses.

Case Studies

• • Impact of FOXA2 Knockdown on LncRNAs in iPSC-Derived Pancreatic Cells

    • Background

      Decreased FOXA2 expression has been shown to have adverse effects on α- and β-cell mass during the differentiation of induced pluripotent stem cells (iPSCs) into pancreatic islets. However, the intricate relationship between FOXA2 and long non-coding RNAs (lncRNAs) in this context remains largely unexplored.

      Methodology

      Long non-coding RNA sequencing and correlation analysis between lncRNA and differentially expressed genes (DEGs).

      Differentially expressed lncRNAs. (Elsayed et al., 2023)

      Results and Findings

      Differential Expression of lncRNAs:

      • In pancreatic progenitor cells (PPs) lacking FOXA2, 442 lncRNAs were down-regulated, while 114 lncRNAs were up-regulated compared to control cells.
      • Similarly, in pancreatic islet cells lacking FOXA2, 177 lncRNAs exhibited down-regulation, and 59 lncRNAs were up-regulated compared to control cells.

      Correlation with Key Pancreatic Genes and TFs:

      Notably, at both PP and islet stages, strong correlations were observed between the expression of lncRNAs and several critical pancreatic genes and transcription factors (TFs) during pancreatic differentiation.

      Identification of DE-lncRNAs:

      Correlation analysis pinpointed 12 differentially expressed (DE) lncRNAs that were closely associated with key down-regulated pancreatic genes at both the PP and islet cell stages.

      Validation using RT-qPCR

      To validate our findings, a subset of these DE-lncRNAs was subjected to RT-qPCR analysis, providing experimental confirmation of their differential expression patterns.

FAQ

• • Is there a gap between transcriptome-mined lncRNA data and direct lncRNA sequencing, and how does it affect analysis?

    • Transcriptome mining targets polyA(+) lncRNAs with high RNA quality, while direct lncRNA sequencing captures polyA(+) and polyA(-) lncRNAs, even in partially degraded samples. The key impact is that direct sequencing provides a more comprehensive lncRNA dataset for analysis.

• • Can lncRNA sequencing be done without transcriptome data?

    • Yes, lncRNA sequencing can be performed without prior transcriptome data. Two methods are commonly used:

      • Genome-Guided Method: Align sequencing reads to a reference genome and then assemble transcripts, including lncRNAs.
      • De Novo Method: Assemble transcripts directly from reads, suitable for species without a reference genome.

      Both methods yield insights into the transcriptome, including lncRNAs. Alignment to a reference genome can be done after sequencing for validation and annotation.

• • How to choose a suitable lncRNA sequencing program?

    • To choose the right lncRNA sequencing program, define your research goals, ensuring they align with your budget and resources. Look for programs offering strand-specific library construction to minimize errors, and seek those that provide diverse data, including mRNA and potentially circRNA information, for comprehensive RNA analysis. Review user feedback and verify compatibility with your sample type and research platform, while also assessing the availability of robust data analysis tools. Consider seeking recommendations from peers or experts in the field to make an informed decision that best suits your research needs, setting the stage for a successful exploration of long non-coding RNAs.

• • Should I choose lncRNA sequencing or transcriptome sequencing?

    • Choosing between lncRNA sequencing and transcriptome sequencing depends on your research objectives and the level of specificity you require.

      If your primary focus is on studying lncRNA expression and its functional role:

      LncRNA Sequencing: This option is tailored for your needs. LncRNA sequencing specifically targets long non-coding RNAs (lncRNAs). It provides a deep and comprehensive analysis of lncRNA expression patterns and their potential functional significance.

      However, if you have a broader interest in the transcriptome, including protein-coding genes and various RNA species:

      Transcriptome Sequencing: This approach offers a more encompassing view of the entire transcriptome, including lncRNAs, mRNAs, and other RNA species. It can be useful if your research questions involve understanding the interplay between different RNA types or exploring global gene expression dynamics.

• • What is the recommended data size for lncRNA sequencing?

    • When considering lncRNA sequencing, it's essential to optimize your data size for accurate and comprehensive results. Due to the relatively low expression levels of lncRNAs, we recommend a minimum data size of 10 gigabytes (10G) for your sequencing experiments. This data size allows for a thorough exploration of both lncRNA and mRNA information within your samples.

• • How to distinguish between mRNA and lncRNA sequences in lncRNA-seq data?

    • You can differentiate between mRNA and lncRNA sequences by the length of the ORF. While most mRNAs typically have longer ORFs, lncRNAs may lack ORFs altogether or have significantly shorter ones (typically around 100 nucleotides or less).

• • Will the sequence and location information of the lncRNA be provided in the final lncRNA results?

    • Absolutely! Our final lncRNA results come complete with comprehensive sequence and location information. This valuable data is meticulously determined through a thorough process of mapping to the reference genome. You can count on us to furnish you with all the essential details you need about your lncRNA of interest.

References:

1. Liu X, Ma Y, Yin K, et al. Long non-coding and coding RNA profiling using strand-specific RNA-seq in human hypertrophic cardiomyopathy. Scientific data, 2019, 6(1): 1-7.
2. Zhu W, Tian L, Yue X, et al. LncRNA expression profiling of ischemic stroke during the transition from the acute to subacute stage. Frontiers in neurology, 2019, 10: 36.
3. Elsayed, Ahmed K., Nehad M. Alajez, and Essam M. Abdelalim. "Genome-wide differential expression profiling of long non-coding RNAs in FOXA2 knockout iPSC-derived pancreatic cells." (2023).