Single-cell profiling methods are developed to dissect heterogeneity of cell populations. The study was performed on the lung adenocarcinoma samples from early-stage tissues to advanced stage biopsies including both primary & metastatic sites with diverse histological & molecular phenotypes & treatment history. Following multiple quality control and filtering steps, a total of 90,406 cells were analyzed with respect to their transcriptomes. By characteristic canonical cell markers, 11 major cell types were detected, classified as carcinoma cell types, epithelial cells others than carcinoma cells, immune cell types & stromal cell types. In this project, we will deal with the lung cancer data for adenocarcinoma & squamous cell carcinoma & run pipeline, R and python scripts to obtain marker genes specific to different cell types & clusters.
NSCLC
The lungs are a pair of spongy, air-filled organs that facilitate gas exchange from the environment into the bloodstream. When the cells of the lungs grow and divide uncontrollably, they are referred to as lung cancer. Based on how these cancer cells look under the microscope, there are two subtypes of lung cancer: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Non-small cell lung cancer can be further divided into additional subtypes, such as squamous cell carcinoma and adenocarcinoma.
Source: American Lung Association (https://www.lung.org)
From the study, the authors were able to identify 11 major cell types from advanced NSCLC, including 48 subtypes besides cancer cells. The study also reported that lung squamous carcinoma has higher intra-tumoral heterogeneity than lung adenocarcinoma. Further, the study was also successful in identifying rare cell types such as FDC and Th17-like lymphocytes. In addition to this, the study also revealed a correlation of tumor heterogeneity and neutrophil contents.
Project Objectives:
Objective 1: To compare the cell heterogeneity in tumor microenvironment for Adenocarcinoma & Squamous cell carcinoma
Objective 2: Determine 11 major cell types through manual and automated annotation
Objective 3: Select immune cells and categorize them by type
Link to the demo pipeline: https://server.t-bio.info/pipelinessinglecellrnaanalysisinseurat/3932521?time=1657716591
Link to the dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148071
For this case study we will refer to the following GEO dataset
GSE148071: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148071
This dataset has 42 tissue biopsy samples from stage III/IV NSCLC patients collected by single cell RNA sequencing and presents the large-scale, single cell resolution profiles of advanced NSCLCs. To run the pipeline we picked only 5 samples from the dataset which were defined in the research article for lung adenocarcinoma & squamous cell carcinoma. The data files contain the expression matrix of RNA raw count.
Link to the Samples:
Lung Adenocarcinoma Samples:
P16: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4453591
P20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4453595
P32: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4453607
Lung Squamous Cell Carcinoma Samples:
P27: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4453602
P37: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4453612
You can run the demo pipeline on the T-Bioinfo Server to learn the flow of steps in the pipeline and visualize the results obtained: https://server.t-bio.info/pipelinessinglecellrnaanalysisinseurat/demopipelines/demo-lung-cancer-seurat-10x
To get more insights about the project, run the pipelines and learn to interpret results, you can visit the example project on the OmicsLogic Learn Portal: https://learn.omicslogic.com/courses/course/project-18-single-cell-rna-seq-for-lung-cancer-nsclc