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Macro-transcriptome sequencing

Product Description
Case Interpretation

Macro-transcriptomics studies the genome-wide transcription of organisms and the regulation of transcription at a holistic level for a specific environment and a specific period of time in a community. Macro-transcriptome sequencing can study in situ the composition of active strains and the expression of active genes in a microbial community of the specific habitat at a specific time and space. In combination with the detection of physicochemical factors, macro-transcriptome can study the differences in the composition of active components between microbial communities in space and time due to the differences in physicochemical and other indicators among the samples.

 

technology roadmap:

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  Reference technology:

 

Sample requirements

Sequencing strategy

Lead time

Sample type: Environmental microbial RNA samples

Sample concentration: ≥100 ng/μl

Total sample  mass: ≥4 μg

Sample purity: OD260/280=1.8~2.2, OD260/230=1.8~2.2; 28S: 18S≥1.

Integrity:RIN≥7

Sequencing mode: Illumina/MGI PE150

Sequencing depth: 6-12G clean data

45 days

 

Product advantages:
A. High coverage Through different sequencing schemes, almost all the transcriptome expression and structure information of samples can be obtained, and the transcriptome information of low abundance can be obtained at the same time;
B. Multi-platform solutions: provide Illumina and Pacbio multi-platform solutions;
C. No species restriction: the detection scope covers all microorganisms, animals and plants and human samples.

Case 1: RNA-seq identification of endosperm differentiation related regulatory modules by laser capture microdissection of maize components
Research background:
Endosperm is a nutritive component in angiosperms for embryo development or seed germination. In cereals, endosperm is the main source of food, feed and industrial raw materials in the world. However, the gene network that regulates the differentiation of endosperm cells is largely unknown.
Research content:
In order to study the gene network during endosperm development, we first studied the mRNA expression of 5 cell types of endosperm: 1 embryo and 4 maternal components.
Research results:
Co-expression modules related to cell types were identified through gene co-expression network analysis, and some imprinted genes and sequence-activated genes that have been studied were enriched in the modules. Through in-depth analysis of the modules related to the endosperm transport layer, we identified the key regulatory gene MRP-1 of the module, which regulates the differentiation and function of the endosperm transport layer. Through comparison, it was found that there were different expression patterns between the components of grain and maternal line, and there was a higher correlation between the aleurone layer of embryo and endosperm.
These studies provide a high-resolution network set for the gene activity of different components of maize kernel, and contribute to the study of the regulation module of the main endosperm cell type differentiation.

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Figure 1. PCA analysis of gene expression of different cell components                   Figure 2 Regulation network of endosperm transport layer related modules

 

Case 2 Improved genetic diagnosis of Mendelian disease by transcriptome sequencing
Research background:
The emergence of WES and WGS has greatly accelerated our ability to identify mutated genes. The mutations of these known and new pathogenic genes can explain many Mendelian diseases. Although these technologies are the mainstream methods for analyzing Mendelian diseases, their success rate in detecting these pathogenic mutations is still low, and the main challenge is that the ability of WES and WGS to detect gene mutations greatly exceeds our ability to explain the functional and clinical impact of these mutations.
Research content:
One way to further improve our understanding of these genetic variations is to integrate functional genomic information, such as RNA-seq. The cDNA analysis of single case and single gene of Mendelian disease patients has been proved to be effective, and RNA-seq has been used to detect some pathogenic mutations, which have been identified by DNA sequencing before. In this study, researchers extracted RNA from damaged muscle tissue in patients with primary muscular diseases (including myopathy and muscular dystrophy) and performed RNA-seq. Among these cases, 13 have been previously diagnosed to carry mutations that affect transcription, such as functional deletion mutations or splice site mutations, to verify the accuracy of RNA-seq in detecting abnormal transcription events. The remaining 50 undiagnosed patient cohorts include cases with mutations or strong candidate genes predicted by DNA sequencing that are most likely to affect splicing, and cases with candidate genes not screened in genetic diagnosis.
Research results:
Gene expression and homologous isomers vary greatly among different tissues. This shows that for many diseases, sequencing disease-related tissues is very important to correctly explain genetic variation. The expression of the most common pathogenic genes in most muscle diseases is not high in blood and fibroblasts, which indicates that RNA-seq using tissues easily obtained from blood or fibroblasts may not be sufficient to detect the transcription abnormalities in some genes. Therefore, the researchers chose to extract RNA from the original muscle tissue biopsy samples. Muscle biopsy is a routine analysis process for diagnosis and evaluation of patients with muscle disease.

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Figure 1: Some abnormal splicing events identified in patients                  Figure 2: Recurrent splicing site mutations in four patients with collagen dystrophy

 

 

 reference

  1. Zhan J, Thakare D, Ma C, et al. RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation[J]. Plant Cell, 2015, 27(3):513-531.

  2.Beryl B,Jamie L,Taru T,et al.Improving genetic diagnosis in Mendelian disease with transcriptome sequencing[J]. ence translational medicine, 2017, 9(386):eaal5209.

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