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Dual RNA-seq Data Analysis

Dual RNA-seq Data Analysis

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As one of the providers of dual RNA-seq data analysis, CD Genomics uses bioinformatics to help you quickly and accurately explore the interaction mechanism between two species. Our high-quality data analysis platform will be used to generate high-quality analysis results in a fast analysis cycle.

What is Dual RNA-seq Data Analysis?

Dual RNA-seq is a potent high-throughput sequencing technique employed to concurrently analyze the transcriptomes of two interacting organisms. This method offers comprehensive insights into host-pathogen, host-symbiont, or other biotic interactions by capturing dynamic changes in gene expression within both organisms during their interaction. Dual RNA-seq enables researchers to probe molecular mechanisms underlying these interactions, delineate key regulatory pathways, and comprehend co-evolutionary processes at the transcriptomic level. The revelations from Dual RNA-seq analyses are pivotal in deciphering intricate organismal interplays. For instance, in host-pathogen studies, researchers can pinpoint pathogen virulence factors and host immune responses, which may inform novel therapeutic approaches. In symbiotic relationships, the technique unveils mutualistic interactions and regulatory networks that facilitate mutual coexistence. Overall, Dual RNA-seq emerges as a robust and insightful approach for exploring complex molecular dialogues between interacting organisms.

Research Advances in Dual RNA-seq Analysis

Organisms regulate their gene expression to establish many interactions. A myriad of eukaryotic-prokaryotic interaction systems are being studied, focusing mainly on pathogen and host gene expression responses, and pathogen-associated molecular patterns. Changes in gene expression or transcriptome were first studied by microarray experiments focusing on only one interacting organism. The RNA sequencing approach (RNA-Seq) is a promising method to study two interacting organisms. At the beginning, this technology presented some limitations related to cost and large amounts of data management, which are being surpassed by the emergence of new sequencing methods and bioinformatics tools. RNA-seq is a simultaneous transcriptomic analysis of interacting symbionts (pathogens include bacteria, fungi, protozoa, etc., and hosts can be mammals or plants.).

Fig. 1. Flow chart of a dual RNA-seq experiment with example software programs.Fig. 1. Flow chart of a dual RNA-seq experiment with example software programs. (Naidoo S, et al, 2018)

Sample Submission Guidelines of Sequencing

Application of Dual RNA-seq Data Analysis

CD Genomics offers cutting-edge dual RNA-seq data analysis services designed to study dynamic host-pathogen interactions and analyze the transcriptomes of both hosts and pathogens to help clients understand the molecular mechanisms of infection, host immune response, and pathogen virulence.

The results of bioinformatic analysis of Dual RNA-seq data can be used in the following research fields:

Infectious disease research: help study infectious diseases and enables researchers to unravel the intricate interactions between hosts and pathogens. The ultimate goal is to develop new therapeutic interventions and discover biomarkers for disease diagnosis and prognosis.

Host-pathogen co-evolution studies: provide a snapshot of simultaneous changes in the host and pathogen transcriptomes during infection. Researchers can identify conserved and distinct molecular pathways that reveal evolutionary competition between hosts and pathogens.

Microbiome analysis: can be used to study host-microbiome interactions, thereby providing insight into the functional dynamics of microbial communities in the host environment.

Drug discovery and vaccine development: help identify host factors critical to pathogen survival and replication, as well as host immune responses that can be used for vaccine design.

Comparative genomics and pathogenesis: facilitate the study of comparative genomics and pathogenesis, enabling researchers to study genetic diversity, evolution and functional variation among different pathogens. This ultimately informs the development of infectious disease surveillance, diagnosis and control strategies.

CD Genomics Data Analysis Pipeline:

Quality control and pre-processing

Read alignment and mapping

Quantification and differential expression analysis

Functional analysis and interpretation

Dual RNA-seq Analysis Content:

Sequential Analysis Sequential analysis of reference genomic libraries, one after the other.
Combinatorial Analysis Libraries are aligned to chimeric reference genomes by linking reference genomes.

What Are the Advantages of Our Services?

Comprehensive End-to-End Solutions

At CD Genomics, we provide comprehensive, end-to-end solutions for dual RNA-seq projects. From sample collection and RNA extraction to data analysis and interpretation, our team of experts meticulously executes each step with precision and accuracy. This holistic approach ensures the generation of high-quality data and reliable results.

Advanced Technological Infrastructure

Our state-of-the-art technological infrastructure is equipped with the latest high-throughput sequencing platforms and bioinformatics tools. This allows us to handle complex dual RNA-seq projects efficiently and effectively, delivering high-resolution data that can unravel the intricate details of host-pathogen interactions or other biological partnerships.

Rigorous Quality Control Protocols

Quality control is a cornerstone of our dual RNA-seq services. We employ rigorous protocols at every stage of the process to ensure the accuracy and reliability of our data. From the initial assessment of RNA integrity to the final steps of data normalization, our meticulous approach minimizes errors and maximizes data quality.

Customizable Data Analysis and Reporting

Understanding that each project is distinct, we provide customizable data analysis and reporting services. Our bioinformatics team collaborates with clients to grasp their specific research questions and goals, delivering personalized analyses that offer actionable insights. Our detailed reports are crafted to be clear and comprehensive, aiding in the interpretation of complex data into meaningful conclusions.

Proficient experience in multi-omics joint analysis

We specialize in offering comprehensive sequencing services, including but not limited to: integrated Genomics and Metabolomics Analysis, Genomics and Transcriptomics Analysis, Microbiomics and Metabolomics Analysis, Proteomics and Transcriptomics Analysis, and Transcriptomics and Metabolomics Analysis.. Our professional expertise is dedicated to addressing and resolving your research challenges.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

How It Works

CD Genomics is committed to advancing the field of host-pathogen interactions through cutting-edge dual RNA-seq data analysis. Our tailored solutions, bioinformatics expertise and dedication to quality ensure that researchers gain accurate, reliable and meaningful insight into the complex world of host-pathogen interactions. If you are interested in our services, please contact us for more detailed information.

Reference

  1. Naidoo S, Visser E A, Zwart L, et al. Dual RNA-seq to elucidate the plant–pathogen duel[J]. Current issues in molecular biology, 2018, 27(1): 127-142.

What Does Analysis of Dual RNA-seq Data Show?

Basic Data Analysis

Distribution of Sequencing Quality

Fig 2. the distribution of sequencing quality.Fig 2. Sequencing quality distribution.

Distribution of A/T/G/C Base

Fig 3. the distribution of A/T/G/C.Fig 3. A/T/G/C Distribution.

Evaluation of the Transcriptomic Library

Insert Length Test

Fig 4. Distribution of insert size for a sample.Fig 4. Distribution of insert size for a sample. The distance between the start and end sites of the paired-end Reads on the reference genome is on the horizontal axis and the the number of paired-end Reads or inserts at different distances between the start and end sites is on the vertical axis.

mRNA Analysis

Gene Expression Distribution

Fig 5. the distribution of FPKM density.Fig 5. FPKM density distribution.

Fig 6. Boxplot of FPKM.Fig 6. Boxplot of FPKM for each sample.

Differential Expression Analysis

Correlation between Samples

Fig 7. Correlation analysisFig 7. Correlation analysis between samples.

Screening Differentially Expressed Genes

Fig 8. MA plotFig 8. MA plot of differentially expressed genes

Title: Dual RNA-seq identifies genes and pathways modulated during Clostridioides difficile colonization

Publication: mSystems

Main Methods: RNA sequencing, RNA-seq analysis

Abstract: This study employs dual RNA-sequencing to investigate C. difficile infection dynamics in an in vitro human gut model. It identifies temporal changes in bacterial and host gene expression over 3–24 hours, highlighting the induction of colonic toxin receptors and immune response downregulation in host cells. Bacterial gene analysis reveals downregulation of cell wall proteins like slpA and modulation of purine/pyrimidine pathways. Notably, a mutant lacking proline-proline endopeptidase shows enhanced epithelial cell adhesion, suggesting its role in infection. These findings enhance understanding of initial infection events and potential therapeutic targets against C. difficile.

Main Research Results:

Dual RNA-seq of C. difficile-infected gut epithelial cells in an in vitro gut model:

Employing an in vitro gut infection model with Caco-2 and HT29-MTX cells, we infected with C. difficile R20291 in a vertical diffusion chamber (VDC) system. After confirming adherence and toxin production over 3–24 hours, we extracted RNA from infected and control samples. Sequencing revealed high alignment to both human and bacterial genomes, with a notable presence of human rRNA reads despite efficient rRNA removal, underscoring methodological considerations in dual RNA-sequencing studies.

Fig 9. The schematic illustrates a dual RNA-seq setupFig 9. The schematic illustrates a dual RNA-seq setup using a human gut model with C. difficile infection over various timepoints.

Principal component analysis (PCA) of human and bacterial RNA-seq data from an in vitro gut infection model revealed distinct clustering of human cell samples by treatment groups across timepoints, highlighting significant transcriptional changes upon infection. Gene expression profiles in both infected and uninfected human cells showed temporal alterations, with 24-hour controls resembling late-stage infected groups, indicating cumulative stress responses in the static system. Bacterial sample analysis, after outlier removal, exhibited consistent transcriptional shifts from culture controls at all timepoints.

The differential gene expression in infected human samples across multiple timepoints is investigated. Significant upregulation (205 genes) and downregulation (196 genes) were identified, with distinct responses observed at each timepoint, particularly prominent at 3 hours post-infection. Notably, 70% of differentially expressed genes were uniquely altered at this early stage, suggesting dynamic and time-dependent transcriptional changes in host cells during infection, highlighting the importance of early-stage responses in understanding pathogenic interactions.

Fig 10. DESeq2 (v1.36.0) identified significant gene expressionFig 10. DESeq2 (v1.36.0) identified significant gene expression differences (adjusted P < 0.05, |log2(FC)| > 1) in bacterial and human cells.

Comparative analysis of planktonically grown vs. cell-infected C. difficile revealed significant upregulation (222 genes) and downregulation (229 genes), with subsets persistently altered across multiple timepoints. Among bacterial DEGs, 137 genes were upregulated and 149 downregulated at 3 hours, with notable persistence up to 24 hours. Heatmaps illustrated top human and bacterial genes, while volcano plots highlighted dynamic gene expression changes over time.

Fig 11. expressed host and bacterial genesFig 11. Differentially expressed host and bacterial genes

Host responses to C. difficile attachment and multiplication:

During C. difficile infection, notable changes in mammalian gene expression were observed, including upregulation of apolipoprotein A-IV (APOA4) at 12 and 24 hours, mucin 13 (MUC13) at 24 hours, and low-density lipoprotein receptor-related protein 4 (LRP4) at 3 and 12 hours. Frizzled-4 protein (FZD4) showed significant upregulation at 3 hours only. Conversely, tumor necrosis factor receptor superfamily members 11B (TNFRSF11B) and 12A (TNFRSF12A) were downregulated at multiple timepoints, along with IL-8 (CXCL8) and fibrinogen genes (FGA, FGB, FGG) at early timepoints. These findings highlight diverse cellular responses early in infection.

Pathway enrichment analysis during C. difficile infection highlighted the dynamic modulation of host cellular processes. At 3 hours, pyrimidine ribonucleotide salvage pathways showed activation, despite gene downregulation. Enriched pathways included CD27 signaling and IL-17A signaling. By 6 hours, acute phase response signaling was potentially inhibited, contrasting pathways like IL-17A signaling and Rho GTPase signaling. At 12 and 24 hours, pathways such as protein kinase C signaling and FXR/RXR activation were prominent, indicating sustained host responses. Commonly activated pathways across timepoints included IL-17 and cell junction signaling pathways, revealing consistent host adaptation during infection.

Fig 12. Host pathways and single-gene expression profilesFig 12. Host pathways and single-gene expression profiles during infection.

Conclusions:

This study explores initial transcriptional changes in human epithelial cells and C. difficile during contact in an in vitro gut infection model. While the model partially mimics gut conditions, lacking a complex mucus layer, insights into common anaerobic bacterial responses are noted. Comparisons with other gut pathogens and commensals are warranted to distinguish C. difficile-specific responses. Nonetheless, the study illuminates key genes and pathways involved in early C. difficile-host interactions, offering insights into potential interaction mechanisms.

1. What samples are suitable for Dual RNA-seq Data Analysis?

Suitable samples for Dual RNA-seq analysis include infected tissues, cultured cells, or organoids where both host and pathogen are present. These can be from in vitro infection models, clinical samples from infected patients, or animal models, capturing the interaction dynamics between the host and pathogen.

2. How long does it usually take to complete Dual RNA-seq Data Analysis?

Completing Dual RNA-seq data analysis typically spans several weeks, contingent upon factors such as sequencing depth, computational resources, and analysis complexity. Initial stages such as RNA extraction and library preparation may require a few days, whereas subsequent data processing, mapping, and differential expression analysis can extend over additional weeks.

3. What is the difference between Dual RNA-seq and miRNA sequencing?

Dual RNA-seq encompasses the simultaneous sequencing of entire transcriptomes from both a host organism and a pathogen, elucidating gene expression changes and interactions during infection or interaction studies. In contrast, miRNA sequencing is tailored to target and sequence microRNAs (miRNAs), short non-coding RNAs pivotal in post-transcriptional gene regulation. Dual RNA-seq affords a comprehensive view of transcriptomic responses in both host and pathogen contexts, while miRNA sequencing centers on discerning the regulatory roles of miRNAs within a single organism.

4. What other data can be jointly analyzed with Dual RNA-seq data?

Dual RNA-seq data can be jointly analyzed with corresponding metadata such as host-pathogen interactions, immune response profiles, and environmental conditions. Integrating these datasets enhances understanding of how pathogens influence host gene expression, immune evasion strategies, and the impact of environmental factors on infection dynamics.

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