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Microbial Single-Cell RNA-Seq


More and more studies have demonstrated that both the pathogen and the invaded host cell can exhibit a significant phenotypic heterogeneity during the infection process. We provide a robust microbial single-cell RNA-seq service employing the most advanced methods to reveal gene-expression heterogeneity in a population.

Our Advantages:
  • Standardized, validated laboratory methods and strict quality control after each procedure.
  • Streamlined single-cell sorting and lysis, reverse transcription, cDNA library construction, and multiplexing sequencing.
  • The most advanced microbial single-cell RNA-seq technologies and bioinformatics tools.
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We are dedicated to providing outstanding customer service, listening to customer requests and being reachable at all times.

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Introduction to our microbial single-cell RNA-seq

Microbial transcriptomes are commonly small, with only a few copies of each transcript, which is due to their lack of alternative splicing mechanism. The mRNAs of prokaryotes usually lack polyadenylation (polyA), therefore, traditional eukaryotic single-cell RNA-seq methods relying on polyA tailing protocols are not suitable for prokaryotic transcriptome analysis. Additionally, microbes commonly have tough cell walls composed of beta-glucan, chitin, and manno-protein. These characteristics of microorganisms make microbial single-cell RNA-seq a challenging task. To solve this problem, we developed a robust and validated microbial single-cell RNA sequencing platform.

We use fluorescence-activated cell sorting (FACS), micromanipulation or microfluidic platform to harvest single cells. After RNA isolation and quality control, extracted RNAs are reversely transcribed and amplified, and used to construct libraries using improved Smart-seq method or commercial kits. Validated libraries are then subjected to Illumina sequencing (PE100/SE50). Sequenced reads are preprocessed and analyzed by our experienced bioinformatics expert team. We help you investigate gene expression heterogeneity among individual cells, which can be a major determinant of the success of antimicrobial treatment and disease outcome.

Microbial single-cell RNA-seq workflow

Bioinformatics Analysis

Our bioinformatics analyses generally include read QC, transcriptomic mapping, read count, differential gene expression (DGE) analysis and other analyses. Our bioinformatics analysis content is flexible to your needs. Please feel free to contact us.

Bioinformatics Analysis Details
Read QC Read quality assessment, sequence trimming and filtering using tools like FastQC and FASTX Toolkit
Transcriptomic Mapping Read alignment using BWA and STAR; read annotation
Read Count Quantification of expression using tools such as RPKM, FPKM and TPM; between-sample normalization using DESeq
DGE Analysis Hierarchical clustering, principal component analysis (PCA), Spearman‘s correlation analysis, functional annotation, etc.
Other Analysis GO analysis, heatmaps, gene set enrichment analysis, etc.

Sample requirement

Sampling kits: we provide a range of microbial sampling kits for clients, including MicroCollect™ oral sample microbial collection products and MicroCollect™ stool sample collection products.

Deliverables: raw sequencing data, assembled and annotated sequences, quality-control dashboard, and the customized bioinformatics report.

References

  1. Hwang B, Lee J H, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & molecular medicine, 2018, 50(8): 1-14.
  2. Wang J, Chen L, Chen Z, et al. RNA-seq based transcriptomic analysis of single bacterial cells. Integrative Biology, 2015, 7(11): 1466-1476.
  3. Saliba A E, Li L, Westermann A J, et al. Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nature microbiology, 2017, 2(2): 16206.
  4. Picelli S, Björklund Å K, Faridani O R, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature methods, 2013, 10(11): 1096.

Areas of Interest

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