Microbial Single-Cell RNA-Seq

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Microbial Single-Cell RNA-Seq

Microbial Single-Cell RNA-Seq

Increasing numbers of studies have demonstrated that pathogen and invaded host cell can both 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 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 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, cell walls of microbes are usually tough, 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 and validated a robust microbial single-cell RNA sequencing platform.

Fluorescence-activated cell sorting (FACS), micromanipulation or microfluidic platform are used 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 experts. We help to investigate the gene expression heterogeneity among individual cells, which can be a major determinant of the success in 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

1. Cells: single cells or a small number of cells stored in a PCR tube, containing 4 uL 1xPBS, along with a negative control.
2. RNA: 28S/18S ≥ 1, RIN ≥ 7.
3. No contamination. Please transport samples along with enough dry ice.

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

Hwang B, Lee J H, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & molecular medicine, 2018, 50(8): 1-14.
Wang J, Chen L, Chen Z, et al. RNA-seq based transcriptomic analysis of single bacterial cells. Integrative Biology, 2015, 7(11): 1466-1476.
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.
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.