Nanopore RNA Sequencing Revealed Long Non-Coding and LTR Retrotransposon-Related RNAs Expressed at Early Stages of Triticale SEED Development is a 3rd generation NGS
The application can be compared to scFAST-seq and a fake long read 2nd genetation NGS
The Combinatin tumour researchon of Single cell Mutation Expression Analysis
Projects like TCGA and ICGC had depicted mutation patterns in various cancer tissues, but the tumour
microenvironment remains unclear. Although the human tumour atlas network (HTAN), which utilizes 3'
single-cell transcriptome-seq technology, has provided valuable information on the cellular composition, it
cannot detect mutations, leaving many questions and assumptions in tumour research, for example:
1. Do driver mutations really occur in just tumour cells?
2. If some mutations make cancer cells sensitive to certain treatments, why do some patients still have no response to those treatments?
3.
3. Tumours are the result of accumulated gene mutations. How many mutations do normal cells need to accumulate to become
tumour cells, and what functional impact do these mutations have on tumour cells?
4. When multiple mutations or co-mutations occur, are they in the same group of cancer cells or different ones, and how can we
distinguish when considering therapeutic methods for patients?
5. What is the underlying mechanism of tumour metastasis, which tumour clone contributes to the metastasis, what are their
characteristics, and how can we control it?
6. Is acquired resistance caused by new mutations or transcriptional reprogramming during therapy?
7.
7. In the era of precision medicine, how can we effectively select appropriate drug targets and tailor effective treatments based
on the specific characteristics of individual patients?
Tumours exhibit dual heterogeneity in mutation and expression, where mutations are the
"cause" and phenotype is the "effect". Detecting both mutations and expression within single
cells is essential for a comprehensive understanding of challenging questions. The innovative
scFAST-seq technology captures full-length RNAs with random primers to detect both
mutation and expression, providing novel insights for scientific research on tumour
development and metastasis.
1. Mutation-induced functional changes and indications for combination therapy
Figure 1. Expression of CD47 in cells with KRAS mutants and KRAS .
Compared to KRAS , CD47 is significantly upregulated in KRAS , which
releases "Don't eat me" signals to macrophages and achieves immune
escape. This implies combined CD47-targeted drugs may lead to better
therapeutic effects.
Application:
Mutation-induced
Mutation-induced changes in cell expression and function, screening drug
target databases, exploring combination therapy targets, and achieving
personalized precisi
on treatment.
2. Locating co-mutations
3.Tracing mutated single cells
Figure 2. Differences in mutations between primary and metastatic tumours.
Metastatic tumour shows a higher number of tumour cells with KRAS and
TP53 co-mutations compared to primary tumour, suggesting that cells with
KRAS and TP53 co-mutations may have a high metastatic potential (yellow
dots represent a cell with both KRAS and TP53 mutations).
Application:
Investigate the impact of co-mutation on cell function and provide new
Investigate the impact of co-mutation on cell function and provide new
perspectives for cancer therapi
es.
l subpopulations
4. Research on the process
Figure 3. A drug-related mutation at the Y position of gene X is specifically
present in "non-tumour cells".
A. Cell clusters and annotation;
B. Expression of gene X in different cell groups;
C. Coverage of the Y position of gene X in single cells: each dot represents a
cell,with red dots indicati
Application
umour occurrence and devel
ng detection of the Y position sequence of gene X
in the cell, and gray dots indicati
ng no coverage of the Y position of gene X in
the cell.
D. The Y position mutation of gene X is specifically present in non-epithelial
cells (the gene and site information is not shown because the data has not
been published).
Application: This gives us a hint that precision medicine guidance should
not solely rely on the detection of mutations, but also on the identification
of the cells carrying these mutations.
Figure 4. Mutation accumulation along the trajectory from normal
epitheli
al cells to tumour cells. Meanwhile, the trend of changes in gene
expression along the trajectory can be analyzed.
Applications-Describe the dynamic evolution process of mutation accumulation; search
for biomarkers related to early screening, diagnosis, and prognosis of
tumours.-Use PDX/organoid models to perform scFAST-seq detection before and
after drug treatment; explore drug sensitivity-related biomarkers; study
drug-resistant mutations or transcriptional reprogramming that occur
during the drug resistance progress.
Comparison
scFAST-seq, self-developed by Beijing SeekGene BioSciences Co., Ltd