Single-cell sequencing high-scoring tumor articles ideas collection

Editor:MobiDrop Biotechnology (Zhejiang) Co. │ Release Time:2023-06-25 

As single-cell sequencing technology is gradually applied in scientific, clinical and pharmaceutical research, the average number of related articles published each month is up to 100, so in the field of oncology, where single-cell sequencing is most used, how do experts use this technology to explore deeply in the related fields? We have selected some parts of the article to learn more about them.


Article 1: Analyzing the mechanism of toxic side effects of oncology drugs

Article title: Intermittent PI3Kδ inhibition sustains anti-tumour immunity and curbs irAEs




Research idea:

The main objective of this study was to understand the deep mechanisms of immune system-related toxicities of PI3Kδ inhibitors.


To analyze the effects of PI3Kδ inhibitors on the tumor microenvironment, the authors first collected patient samples from PI3Kδ inhibitor clinical trials in solid tumors, performed whole-tumor RNA sequencing, immunohistochemistry, and RNA sequencing of tumor-infiltrating CD8+ T cells, and found that PI3Kδ inhibitors could alter the tumor microenvironment.


Next, the authors further analyzed the mechanism of PI3Kδ inhibitor side effects by using a mouse solid tumor model. The authors examined the levels of Treg in different organs of mice and found that Treg levels were significantly decreased in both spleen and intestine.


Since drug toxicities in patients were most severe in the GI tract, the authors hypothesized that Treg in the intestine might be most sensitive to PI3Kδ inhibitors. To test this hypothesis, the authors performed single-cell sequencing of tumor-infiltrating T cells from the patient and Treg cells from the GI tract in a mouse model and found that specific Treg subpopulations in the gut were highly sensitive to PI3Kδ inhibitors. after PI3Kδ inhibitor administration, the organism caused enterocolitis due to loss of Treg regulation.


To further determine the relationship between PI3Kδ inhibitors and enteritis, the authors established a mouse model and found that enteritis was more severe in the administered group of mice compared to the control group.


Finally, the authors explored how the frequency of dosing could be adjusted to reduce toxicities. The authors tested both continuous and intermittent dosing and found that intermittent dosing could reduce toxic side effects to some extent, providing a possible improvement in clinical dosing.



This article used single-cell sequencing combined with bulk RNA seq, immunohistochemistry, animal models, and other methods to provide multiple levels of evidence for analyzing the mechanisms of toxic effects of PI3Kδ inhibitors. Meanwhile, in terms of single-cell sequencing, the authors not only performed single-cell sequencing of clinical samples but also analyzed single-cell sequencing of mouse model samples, enabling the study to balance clinical evidence and in-depth mechanistic analysis. Finally, the authors also explored and validated the possible methods to address the toxic side effects, so as to further enhance the clinical value of the study by "coming from the clinic and going to the clinic".


Article 2: Analyzing the tumor immune microenvironment

Article title: Extricating human tumour immune alterations from tissue inflammation




Research idea:

This study investigates the role of cancer cells in "turning back" T cells to protect solid tumors from immune attack.


The authors collected non-malignant oral inflammatory tissue, as well as tumor tissue and peripheral blood samples from patients with head and neck squamous carcinoma, and performed flow analysis and high-throughput single-cell sequencing of T cells and antigen-presenting cells.

In doing so, the authors found that while most immune cells in tumor tissue are also present in non-malignant inflammatory tissues, a specific type of Treg cells that are unique to tumor tissue.

This Treg cell has a stronger immunosuppressive capacity than normal Treg cells and protects cancer cells in tumor tissue from attack by other immune cells.

Compared with other Treg cells, this Treg cell is characterized by expressing both ICOS receptor and IL1R1 receptor - general Treg cells basically do not express both receptors, so it is possible to use both receptors as markers to screen out this particular Treg cell.

Finally, the authors also used a single-cell public dataset to determine that such Treg cells are not only found in head and neck squamous carcinoma, but also in other solid tumors - which may be a breakthrough point for future treatment of solid tumors.


The idea of this article is clever in that it studies immune cells infiltrating in malignant tumors and its control uses non-malignant inflammatory tissues - many other single cell sequencing studies of tumors tend to use paraneoplastic or normal tissues as controls, and the choice of control in this article certainly expands new ideas for extracting disease-specific changes from general inflammation-related The selection of controls in this article certainly expands our thinking and provides an excellent example of how to extract disease-specific changes from general inflammation-related patterns.


At the same time, the authors have employed a variety of research tools, including flow analysis, in vitro immunostimulation and immunosuppression assays, cytokine analysis, and humanized mouse models in addition to single cell sequencing, which gives the article a rich and comprehensive set of evidence from all different levels and perspectives. Moreover, the article concludes by expanding the key findings to other solid tumors, further enhancing the biological significance of its findings.


Article 3: Understanding the mechanism of carcinogenesis

Article title: Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

Published in: Nature Genetics

DOI: 10.1038/s41588-022-01088-x


Research idea:

This study focused on the mechanisms of carcinogenesis during the transition from healthy colon to invasive colorectal cancer. Therefore, the authors performed snRNA-seq (single-cell nuclear transcriptome sequencing) and scATAC-seq (single-cell ATAC sequencing) on healthy colon, adenomatous polyp and colorectal cancer samples, respectively, to map gene expression regulation and transcriptome changes during tumorigenesis development.


First, the authors analyzed the cell types in different types of samples and found that Treg cells were enriched in both intestinal polyps and colorectal cancer, but only depleted T cells were enriched in colorectal cancer samples.


Also, very different fibroblast subtypes were present in intestinal polyps and colorectal cancers and may play a role in the tumorigenesis of precancerous lesions.


The researchers also found that the epithelial cells in intestinal polyps and colorectal cancer samples were enriched with stem cell-like epithelial cells that were not present in healthy intestinal samples, and the authors suggest that these stem cell-like epithelial cells may represent the "cancer" stem cells in the tissue. The proportion of stem cells in the samples increased as the disease became more malignant.


The authors then compared the gene expression and chromatin accessibility of stem cell-like cells in intestinal polyps and colorectal cancer samples with normal stem cells and found that the differences between stem cell-like cells and normal stem cells increased with increasing cellular malignancy. A series of genes, such as ASCL2, HNF4A, GPX2, etc., were also found to change in expression with increasing malignancy.


Finally, the authors combined the methylation data of normal and colorectal cancer samples in TCGA, and mined the single-cell sequencing data more deeply to reveal colorectal cancer methylation loci with early chromatin accessibility changes.


This is a article that focuses on single-cell sequencing and its analysis, and does not involve any wet experimental validation. However, this does not affect the richness of this article - it utilizes single-cell multi-omics technology, complemented by extensive in-depth bioinformatic analysis, to dig deeper into the data at both transcriptomic and epigenetic regulatory levels, providing meaningful insights into cell subpopulations, functional mechanisms, and the progression process of precancerous lesions. Meanwhile, the joint analysis with TCGA methylation sequencing data further expands the perspective and depth of the article.


Article 4: Tapping into tumor-infiltrating lymphocyte characteristics

Article title: An activation to memory differentiation trajectory of tumor-infiltrating lymphocytes informs metastatic melanoma outcomes

Published in: Cancer cell

DOI: 10.1016/j.ccell.2022.04.005


Research idea:

This study focused on tumor infiltrating lymphocytes (TIL) and performed a systematic analysis of TIL by single cell transcriptome sequencing combined with bulk RNA seq to summarize the characteristics of persistent resident memory TIL cells and identify signatures associated with melanoma prognosis.


The authors first used the bulk RNA seq public dataset to analyze T cell subsets after viral infection in multiple organs, establishing a perception of the "baseline" state of T cells in the immune response and providing a comparable baseline for subsequent experiments.


Subsequently, the authors analyzed 1500 TILs from 13 patients with metastatic melanoma by single-cell transcriptome sequencing and found that approximately 2/3 of the TILs were characterized as memory-resident T cells and highly expressed immune checkpoint genes, immune activation marker genes, and tissue-residue-related signature genes.


The authors further compared samples from monocytic melanoma patients who responded and did not respond to the PD-1 antibody drug, and modeling revealed that: patients who did not respond to the drug had more activated TILs in their samples, while patients who responded to the drug had a high proportion of memory/resident memory T cells in their samples.


The activated TIL subpopulation was very similar to the T cell state caused by viruses or vaccines, and the authors also found the same to be a conserved mechanism in other cancer species.


The persistent presence of resident memory T cells with siganature is a potential prognostic marker for melanoma, which could then stratify patients well in TCGA and distinguish those with good prognosis from those with poor prognosis. Therefore, this marker might be applied in the future to stratify patients for management and treatment.


This is also an article that focuses on single-cell sequencing analysis. However, the authors innovatively combined the research approach of systems biology with single-cell transcriptome sequencing to provide a comprehensive and in-depth analysis from multiple perspectives, which is a worthy idea. Also, the authors have provided possible application points for clinical use through the TCGA database, which further enhances the article.



Through the above several high-scoring articles, we can find that the most obvious common feature of them is: the use of multiple methodologies.


The first two articles focus on experimental validation, integrating a variety of experimental tools and trying to provide supporting evidence from multiple perspectives.


The last two articles focus on bioinformatic analysis, which not only conducts a variety of bioinformatic analysis but also enriches the perspective of evidence through a multi-omics approach. In addition, both articles use the TCGA database to dig deeper and take the article to the next level - a way that teachers who are good at dry experiments can learn from.


In short, for a good article to be published, a variety of methods cannot be missing. However, this does not mean that we have to do all the existing experimental methods or bioinformatic analysis processes, but rather we can choose the experimental design that best plays to our strengths (e.g., whether we are better at various experimental methods in wet experiments, or better at various types of bioinformatic analysis in dry experiments).