Single Cell RNA-Seq Data Analysis: Unveiling Single Cell Expression
Single cell RNA-seq or “scRNA-seq” has been demonstrated as a powerful technique for classification of tissue-specific cells and is used to study cell differentiation using time-course experiments. However, specialized data preparation techniques and high noise-signal ratio of this type of data require specialized approaches to its analysis. In addition, resulting expression tables contain sparse data that need to be prepared for downstream analysis with various normalization and imputation techniques. Single-Cell RNA-Seq provides transcriptional profiling of thousands of individual cells. This level of analysis enables researchers to understand at the single-cell level what genes are expressed, in what quantities, and how they differ across thousands of cells within a heterogeneous sample. The analysis consists of methods for dimensionality reduction, clustering and annotation of cells by type. To achieve this objective, the data has to be ready for analysis. Most of the time, samples are integrated, placed in a smaller number of dimensions and then clustered. Clusters represent some biologically distinct groups of cells that can be assigned with a known cell type by expression levels of marker genes.
scRNA-seq can reveal much more detail about what is going on in each cell and that can be a significant improvement from bulk RNA-seq. Single cell transcriptomics (SCT) entails the profiling of all messenger RNAs present in a single cell and constitutes the most widely-used sc profiling technology. Unlike bulk RNA-seq profiling where sequencing libraries are generated from thousands of cells, scRNA-seq technologies isolate single cells and generate cell-specific sequencing libraries to mark RNA content with a cell-specific molecular barcode. Analysis of this data generates gene expression estimates at the single cell level. SCT enables the measurement of the transcriptomic information for a range of thousands up to millions of single cells in a single experiment.
Rise in Single Cell Data Analysis
The last several years have seen rapid development of technologies and methods that permit a detailed analysis of the genome and transcriptome of a single cell. Recent evidence from studies of single cells reveals that each cell type has a distinct lineage and function. The lineage and stage of development of each cell determine how they respond to each other and the environment. Experimental approaches that utilize single-cell analysis are effective means to understand how cell networks work in concert to coordinate a response at the population level; recent progress in single-cell analysis is offering a glimpse at the future.
Now let's have a look at what a Single Cell Transcriptomics pipeline looks like on the T-Bioinfo server.
Single Cell Data Analysis on T-Bioinfo Server
To run the pipeline we need to follow the defined workflow:
Start > QC > SCT Transformation > PCA > Find Clusters > UMAP > Find All Markers > Marker Plots > CellDex for Humans/Mouse > DE between clusters
After the pipeline has completed its processing, you will obtain a list of output files that could be downloaded to carry out statistical analysis and interpret biological insights. You will also obtain data visualizations in your output files that make sense to understand meaningful patterns or significant results.
To understand what type of data is required to process the pipeline, what are the different algorithms involved and finally what does the output files include, please visit Single Cell RNA-Seq on T-Bioinfo Server (omicslogic.com)
On the OmicsLogic Learn Portal, experts have put together an example project which explains the application of single cell data analysis. The aim of this project is to understand the tumor heterogeneity in breast cancer where we will be analyzing “A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer from Ayse Bassez et al.. The data was generated through single-cell transcriptome, T cell receptor and proteome profiling to understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative breast cancer were treated with anti-PD1 before surgery.
Example Project on OmicsLogic Learn Portal : Tumor Heterogeneity of Breast Cancer
Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed and these methods possess unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.
“Immunotherapy has demonstrated significant improvements in various other types of cancers. However, breast cancer remains one of the tumors that have not experienced the explosion of immunotherapy yet. Indeed, breast cancer was traditionally considered as being weakly immunogenic with a lower mutational load compared to other tumor types. In the last few years, anti-PD 1/PD-L1 (Programmed death-ligand 1) agents have been evaluated in breast cancer, particularly in the triple negative subtype, with promising results observed when delivered as monotherapy or in combination with conventional treatments.”
In this example project we will look at the single cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer.
To learn more about the “Role of high fat diet on Obesity”, visit OmicsLogic Learn Portal: Project 15: https://learn.omicslogic.com/courses/course/project-15-tumor-heterogeneity-of-breast-cancer and get a good hands on practical experience with real time data, run the pipeline yourself, learn to analyze and interpret biological insights to understand tumor heterogeneity in breast cancer.
For any questions, you can reach out to us at firstname.lastname@example.org or email@example.com