lncRNA Sequencing Detection: Comprehensive Long Non-Coding RNA Profiling Services

lncRNA sequencing detection is an advanced next-generation sequencing (NGS) technology designed to comprehensively profile long non-coding RNAs (lncRNAs), revealing their expression patterns, structural features, and regulatory functions across diverse biological samples. This service enables researchers to discover novel lncRNAs, identify differentially expressed transcripts, and explore lncRNA-mediated regulatory networks involved in disease and development. With high-throughput Illumina sequencing platforms and a standardized bioinformatics pipeline, lncRNA sequencing delivers accurate, reproducible, and publication-ready data for life science research.

Technical Overview

Long non-coding RNAs (lncRNAs) are a class of non-protein-coding transcripts longer than 200 nucleotides that play critical regulatory roles in gene expression, chromatin remodeling, transcriptional interference, post-transcriptional processing, and epigenetic modification. Although lncRNAs were once considered transcriptional "noise," accumulating evidence has demonstrated that they participate in nearly every aspect of cellular physiology and are closely associated with the onset and progression of cancers, neurological disorders, cardiovascular diseases, and developmental abnormalities.

lncRNA sequencing detection is a whole-transcriptome approach based on next-generation sequencing technology. By removing ribosomal RNA (rRNA) through depletion strategies or enriching polyadenylated transcripts, the method captures both poly(A)+ and poly(A)- lncRNAs, enabling genome-wide identification and quantification of known and novel lncRNAs. Compared with microarray-based approaches, lncRNA sequencing offers unbiased discovery capability, a wider dynamic range, higher sensitivity for low-abundance transcripts, and the ability to detect novel transcripts without prior sequence knowledge.

The service typically includes total RNA quality control, strand-specific library construction, deep sequencing on Illumina platforms, and a complete bioinformatics analysis pipeline covering lncRNA identification, expression quantification, differential expression analysis, target gene prediction, and functional enrichment interpretation.

Testing Samples

Animal Tissue Samples

Fresh or frozen animal tissues such as tumor tissue, liver, brain, heart, kidney, spleen, and lung are accepted. Samples should be rapidly dissected, rinsed to remove blood contamination, cut into small pieces (approximately 50-100 mg), immediately snap-frozen in liquid nitrogen, and stored at -80°C. Repeated freeze-thaw cycles must be avoided to prevent RNA degradation.

Blood Samples

Peripheral blood samples can be submitted as whole blood collected with EDTA anticoagulant tubes, or as isolated peripheral blood mononuclear cells (PBMCs). For plasma or serum preparation, blood should be centrifuged within 2 hours of collection to remove cellular components. Hemolysis must be minimized, as free hemoglobin and RNases can significantly compromise RNA integrity and downstream library quality.

Cell Samples

Cultured cell pellets (mammalian, primary, or stem cells) should be harvested at the logarithmic growth phase, washed with pre-cooled PBS, centrifuged, and the supernatant thoroughly removed. Cell pellets of 1 x 10^6 to 5 x 10^6 cells are recommended. Pellets may be stored in TRIzol reagent or frozen at -80°C after snap-freezing in liquid nitrogen.

Plant Tissue Samples

Plant materials such as leaves, roots, stems, flowers, fruits, and seeds are acceptable. Because plant tissues contain abundant polysaccharides, polyphenols, and secondary metabolites that interfere with RNA extraction, samples should be flash-frozen immediately after harvest and ground thoroughly in liquid nitrogen. A minimum of 100-500 mg of tissue is generally recommended depending on the species.

Total RNA Samples

Pre-extracted total RNA can be submitted directly. RNA concentration should be measured by Qubit or equivalent fluorometric methods, purity assessed by NanoDrop (A260/280 ratio of 1.8-2.1, A260/230 ratio greater than 1.8), and integrity evaluated by Agilent 2100 Bioanalyzer. An RNA Integrity Number (RIN) of 7.0 or above is strongly recommended for reliable library construction.

Testing Items

lncRNA sequencing detection services generally encompass the following analytical components:

• Quality control and data preprocessing: Raw read filtering, adapter trimming, and removal of low-quality reads to obtain clean data.
• Alignment and transcript assembly: Mapping clean reads to the reference genome using HISAT2 or STAR, followed by transcript reconstruction with StringTie for novel transcript discovery.
• Novel lncRNA identification: Filtering candidate transcripts by length (greater than 200 bp), exon number (typically two or more exons), and expression threshold, followed by coding potential assessment using CPC2, CNCI, CPAT, and Pfam protein domain search.
• lncRNA classification and annotation: Categorization of lncRNAs into lincRNA, antisense lncRNA, intronic lncRNA, and sense-overlapping lncRNA classes, with comparison against public databases such as GENCODE, Ensembl, RefSeq, and NONCODE.
• Expression quantification: Calculation of lncRNA and mRNA expression levels using FPKM/TPM metrics for both known and novel transcripts.
• Differential expression analysis: Identification of significantly differentially expressed lncRNAs and mRNAs between groups using DESeq2 or edgeR with statistical thresholds such as fold change greater than 2 and adjusted p-value less than 0.05.
• Target gene prediction: Prediction of cis-regulated targets through co-location analysis of lncRNAs with neighboring protein-coding genes (typically within 100 kb upstream and downstream) and trans-regulated targets through expression correlation analysis.
• Functional enrichment analysis: GO and KEGG pathway enrichment of lncRNA target genes to interpret potential biological functions and signaling pathways.
• Advanced analyses: lncRNA-mRNA co-expression network construction, competing endogenous RNA (ceRNA) network analysis involving miRNA interactions, and lncRNA family or conservation analysis upon request.

Testing Methods

The experimental workflow of lncRNA sequencing detection is designed to maximize lncRNA capture while ensuring data quality and reproducibility:

• RNA extraction and quality assessment: Total RNA is extracted using column-based or TRIzol-based protocols optimized for the sample type. Concentration, purity, and integrity are verified using Qubit, NanoDrop, and Agilent 2100 Bioanalyzer or equivalent instrumentation.
• rRNA depletion or poly(A) enrichment: Because many lncRNAs lack a poly(A) tail, rRNA depletion (for example, Ribo-Zero or RNase H-based methods) is the recommended strategy for comprehensive lncRNA coverage. Poly(A) enrichment is an alternative for studies focused exclusively on polyadenylated lncRNAs.
• Strand-specific library construction: Directional libraries are prepared using dUTP-based or ligase-based strand-specific protocols, preserving transcript orientation information that is essential for accurate lncRNA identification and antisense transcript annotation. Library size distribution is validated by Bioanalyzer and quantified by qPCR.
• High-throughput sequencing: Libraries are sequenced on Illumina NovaSeq, HiSeq, or MGISEQ platforms, typically generating 150 bp paired-end reads with a recommended depth of 6-12 GB of clean data per sample for standard lncRNA profiling.
• Bioinformatics analysis: A standardized computational pipeline performs read alignment, transcript assembly, coding potential filtering, expression quantification, differential expression screening, target gene prediction, and functional annotation. All results are delivered with detailed graphic and tabular reports.
• Optional validation: Candidate lncRNAs can be experimentally validated by quantitative real-time PCR (qRT-PCR) to confirm sequencing-based expression profiles.

Applications

lncRNA sequencing detection has become an indispensable tool across multiple research and translational domains:

• Cancer research: Discovery of oncogenic and tumor-suppressive lncRNAs, identification of diagnostic and prognostic biomarkers, and elucidation of lncRNA regulatory networks in tumor initiation, metastasis, and drug resistance.
• Developmental biology: Characterization of lncRNA dynamics during embryogenesis, cell differentiation, and organogenesis, including stage-specific and tissue-specific expression profiling.
• Neuroscience: Investigation of lncRNA involvement in neurodevelopmental disorders, neurodegenerative diseases such as Alzheimer's and Parkinson's disease, and synaptic plasticity.
• Cardiovascular research: Exploration of lncRNA roles in cardiac hypertrophy, heart failure, atherosclerosis, and myocardial infarction, supporting biomarker and therapeutic target discovery.
• Immunology and inflammation: Analysis of lncRNA regulation of immune cell differentiation, activation, and inflammatory signaling pathways.
• Plant biology and agricultural science: Study of lncRNAs associated with stress responses, drought and salt tolerance, flowering time regulation, and crop quality traits.
• Animal breeding and veterinary science: Identification of lncRNAs related to growth traits, muscle development, lactation, and disease resistance in livestock species.
• Biomarker and drug development: Screening of circulating lncRNAs as liquid biopsy markers and evaluation of lncRNA-targeted therapeutic strategies.

FAQ

What is the difference between lncRNA sequencing and mRNA sequencing?

lncRNA sequencing employs rRNA depletion rather than poly(A) enrichment, which allows detection of both polyadenylated and non-polyadenylated transcripts, including the majority of lncRNAs and other non-coding RNAs. Standard mRNA-seq typically captures only poly(A)+ transcripts. lncRNA sequencing analysis additionally includes novel lncRNA identification, coding potential evaluation, lncRNA classification, and target gene prediction, whereas mRNA-seq focuses on protein-coding gene expression.

What sample quality is required for lncRNA sequencing?

Total RNA with a RIN value of 7.0 or above is recommended, though samples with RIN between 6.0 and 7.0 may be accepted after technical evaluation. RNA should be free of genomic DNA contamination, protein contamination, and inhibitors. Samples with severe degradation are not recommended, as degraded RNA compromises library yield and data reliability.

How much sample material is needed?

Typical requirements are 50-100 mg of animal tissue, 100-500 mg of plant tissue, 1-5 million cells, or 1-2 micrograms of total RNA. For precious or limited samples, low-input library preparation kits can be applied; in such cases, please consult the technical team before submission.

Can lncRNA sequencing also detect circular RNAs and miRNAs?

Standard lncRNA sequencing primarily captures linear lncRNAs and mRNAs. Circular RNAs require specialized circRNA-seq protocols with RNase R treatment, and miRNAs require small RNA sequencing due to their short length. Integrated multi-omics packages combining lncRNA-seq, circRNA-seq, miRNA-seq, and whole transcriptome sequencing are available for comprehensive non-coding RNA studies.

What sequencing depth is recommended?

A data volume of 6 GB of clean data per sample is generally sufficient for standard lncRNA profiling. For low-abundance lncRNA detection, complex transcriptome reconstruction, or ceRNA network studies, 10-12 GB or more per sample is recommended to improve statistical power and coverage.

What is the typical project turnaround time?

For standard projects, the total turnaround time from sample quality control through sequencing and bioinformatics analysis is typically 45-60 working days, depending on sample number, sequencing depth, and the complexity of customized analysis requirements.

How are differentially expressed lncRNAs defined?

Differential expression is determined using DESeq2 or edgeR with thresholds commonly set at absolute fold change greater than 2 and adjusted p-value (FDR) less than 0.05. Thresholds can be adjusted according to project requirements and data distribution characteristics.

Is qRT-PCR validation necessary?

While not mandatory, qRT-PCR validation of selected differentially expressed lncRNAs is strongly recommended and frequently requested by peer-reviewed journals, as it independently confirms sequencing-based expression measurements and strengthens the credibility of the research findings.