mRNA sequencing enables panoramic quantification of coding transcripts in cells, tissues, or organs under specific physiological conditions. It is an industry standard method for identification of differentially expressed genes, discovery of new transcripts, analysis of splice isomers and screening of fusion genes.
1. Poly-A selects library creation: Targeting high-quality eukaryotic total RNA, specifically enriching coding transcripts with poly-A tails to achieve extremely high sequencing depth ratio.
2. Ribo-Depletion: Special library construction for severely degraded FFPE tissues or the need to retain non-polyadenylated transcripts such as lncRNA.
3. Strand-Specific: Retains transcription direction information and avoids erroneous interference from the expression of complementary strands of overlapping genes for high-precision antisense RNA identification.
Differential expression transcriptome analysis delivers standard expression normalization tables (FPKM/TPM), differential analysis volcano plots, and functional pathway enrichment results.
| Analysis type | Delivery model/diagram | Main technical uses |
|---|---|---|
| Expression normalization | FPKM, TPM quantitative expression profiling | Assessing gene transcription activity abundance distribution among samples |
| Differential expression screening | Volcano Plot (volcano plot), Heatmap (cluster heat map) | Quickly locate significantly different genes in different experimental groups |
| Functional pathway mining | GO (Gene Ontology) pie chart / KEGG signaling pathway scatter plot | Identifying macro-metabolic and defense networks influenced by differential genes |
| Basic disembarkation parameters | Daily data output > 6 Gb/sample, Q30 > 85% | Ensure data depth is sufficient to support mutation detection of low-abundance genes |
RNA is easily degraded. Please ensure that the following extraction and purity parameters are met when sending samples:
| Sample subtype | Total quantity requirement | Concentration requirements | RIN value/quality score | Transportation form |
|---|---|---|---|---|
| Eukaryotic total RNA (plant/animal extraction) | ≥ 1.0 µg | ≥ 20 ng/µL | RIN ≥ 7.0 (no protein/residual organic solvent contamination) | Ultra-low temperature dry ice sealed transportation (with quality inspection gel chart) |
| Fresh animal and plant tissues | ≥ 100 mg | - | - | Quick freeze in liquid nitrogen, or place in RNAstable preservation solution, and transport on dry ice |
| Cell suspension (pellet) | ≥ 1 x 10^6 cells | - | - | After centrifugation in PBS, remove the supernatant and quickly freeze in liquid nitrogen for transportation. |
Long noncoding RNAs (lncRNAs) are transcripts that are greater than 200 bp in length and have no protein-coding potential. It plays a crucial "dark matter" role in gene epigenetic modification, transcriptional interference, and post-transcriptional translational regulation. We use high-performance sequencing hardware and self-developed lncRNA annotation algorithms to help the research team uncover the complex regulatory network of non-coding regions.
HST GENOMICS combines advanced computing platforms to predict lncRNA target genes:
1. Cis cis-acting prediction: Based on the spatial proximity of the genome, predict its transcriptional interference on proximal neighboring genes (e.g. within 100kb).
2. Trans trans-action prediction: Co-expression Network based on large-scale expression profiles to calculate the regulatory relationship between lncRNA and distant or cross-chromosomal genes.
3. CeRNA network linkage: Optional combined with small RNA (miRNA) sequencing to build a competitive endogenous RNA interaction network (lncRNA-miRNA-mRNA).
Provides multiple regeneration software to identify new lncRNAs and visualize expression networks of three-dimensional interaction networks.
| Database construction streamline | New lncRNA prediction tool | Co-expression network evaluation |
|---|---|---|
| Ribo-Zero Strand-Specific Library Construction | CNCI, CPC2, Pfam triple filter model | Cytoscape topological network visualization delivery (Cis/Trans) |
Non-coding RNA is relatively less abundant and therefore requires a higher starting sample size:
| Sample category | total demand | Concentration and Purity Requirements | RIN quality score |
|---|---|---|---|
| Total RNA extraction from cells/tissues | ≥ 2.0 µg | Concentration ≥ 50 ng/µL, OD 260/280: 1.8 - 2.0 | RIN ≥ 7.5 (electrophoresis quality inspection required) |
| FFPE paraffin embedded sample | ≥ 10 pieces (thickness 10 µm) | DV200 (proportion of nucleic acid fragments larger than 200nt) > 50% | - |
Single-cell transcriptome sequencing (scRNA-Seq) enables the interpretation of gene expression at the level of individual cells. The HST GENOMICS single-cell experimental platform is equipped with self-developed Helix-SC Microfluidic Controller, distribute dispersed cells into microdroplets at high speed, capture and establish sequencing libraries through high-fidelity barcodes, helping researchers discover rare new cells and differentiation transition states in heterogeneous tumor cell populations, multi-level differentiated immune subpopulations and developmental microenvironments.
1. High-precision clustering: Apply Seurat and Scanpy algorithms for quality control, dimensionality reduction, and render UMAP and t-SNE spatial projection maps.
2. Quasi-temporal differentiation trajectory: Use algorithms such as Monocle to perform quasi-sequential modeling of gene expression changes in cell subgroups, and outline the trajectory map of cell differentiation and development.
3. interactive client interaction: Data packaged and delivered directly HST-Loupe desktop visual analysis system, you can screen marker genes and explore cell subpopulations of interest with one click without coding.
Provides a complete single-cell quality control report (number of cells, average number of reads, median number of genes/cell) and dimensionality reduction rendering cluster diagram.
| Sequencing quality control indicators | HST delivery standards | Analytical Advantages |
|---|---|---|
| Single channel capture cell number | 500 - 10,000 cells/run lane | Suitable for high-depth, multi-tissue, batch and large-scale screening |
| Doublet Rate | ≤ 0.8% / 1000 cells | Extremely low interference from multi-cell aggregation, ensuring single-cell authenticity |
| Median number of genes/cell | ≥ 2,000 genes (based on human cell line standards) | Excellent sensitivity to prevent missed detection of low-abundance transcription factors |
Single-cell sequencing must maintain a high degree of activity and purity. Please ensure that the samples submitted meet the following extremely strict requirements:
| Project parameters | Cell suspension quality commitment | Specific operation suggestions |
|---|---|---|
| Cell Viability | ≥ 85% (FDA/AO two-color fluorometer re-inspection) | The time from tissue dissociation to machine use should be within 2 hours, and mechanical shearing and chemical enzymatic sterilization should be avoided. |
| cell suspension concentration | 700 - 1200 cells/µL | Use serum-free and phenol red-free suspension medium to prevent clumping. |
| Debris/impurity rate | No debris, dead cell nuclei and red blood cell residues | A 40µm pore size mesh must be used for secondary filtration and red blood cell lysis if necessary |