Single-cell sequencing technologies are powerful tools used to assess genomic, transcriptomic and proteomics information at the single-cell level. In recent years, the application of techniques that use single-cell sequencing have become increasingly common in several areas of research: including medicine, agriculture, and other life sciences disciplines. Single-cell sequencing may be used to study many aspects of an organism’s biology, both in health and disease, and the results of these studies contribute immensely to advancing the understanding of organisms as a whole.
Illustrated by the successful formation of scientific networks within the Human Cell Atlas, there is growing interest among scientists and increased need for building capacity for single cell technologies. However, there is a lack of infrastructure, limited funding, and expertise relevant for applying these advanced technologies in Latin America. Therefore, this short course offers hands-on laboratory training and bioinformatics analysis in single-cell technologies to contribute to capacity development in Latin America.
- Patricia Abrão Possik, National Institute of Cancer (INCA), Brazil
- David Adams, Wellcome Sanger Institute, UK
- Mariana Boroni, National Institute of Cancer (INCA), Brazil
- Vinicius Maracaja-Coutinho, Advanced Center for Chronic Diseases (ACCDiS), University of Chile, Chile
- Erick Armingol, University of California, San Diego, USA
- Lia Chappell, Wellcome Sanger Institute, UK
- Yesid Cuesta Astroz, Colombian Institute of Tropical Medicine (ICMT), Colombia
- Benilton de Sá Carvalho, Unicamp, Brazil
- Anita Scoones, Earlham Institute, UK
- Patricia Severino, Albert Einstein Research & Education Institute, Brazil
- Danielle Carvalho, National Institute of Cancer (INCA), Brazil
- Flavia Aguiar, National Institute of Cancer (INCA), Brazil
Below, you will find the links to the pre-course videos and Google Colab notebooks for the practical modules.
- Get started with Google Colaboratory: This video provides a basic understanding of Google Colaboratory or Google Colab and their notebooks structure, which is the web-based interactive computing platform that we will be using along with our bioinformatics practical work.
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Notebook 01 - Processing raw scRNA-seq data: Processing raw single-cell sequencing data (scRNA-seq) is a crucial step in the whole pipeline analysis of scRNA-Seq experiments. Depending on the library preparation method used, the RNA sequences will be acquired either from 3’ ends (or 5’ ends) of the transcripts (10X Genomics, CEL-seq2, Drop-seq, inDrops) or from full-length transcripts (Smart-seq). The choice of a specific method will depend entirely on the biological question and the downstream analysis to be implemented from a count matrix. In this notebook we will cover theoretical and practical steps in setting up from raw sequences (reads)to count matrix analysis pipelines, as well as explore the basic output of the Cell Ranger tool. Related Slides 01: Processing raw seqs.
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Notebook 02 - Quality Control, Exploratory Analysis, Data Normalization, and Clustering in scRNA-seq experiments: In that part of the course, we will guide you through the initial steps of scRNA-seq data analysis, including data importing and organization, filtering, and preliminary visualization. These essential steps ensure quality and metadata information control over heterogeneous datasets before using the Seurat package and Bioconductor infrastructure. Once the data is imported, we will focus on assessing dataset quality through various metrics and visualizations, enabling the identification and removal of poor-quality cells. Furthermore, we'll delve into normalization techniques to address technical factors and clustering methods to group cells based on expression similarity, facilitating the interpretation of results and characterization of heterogeneity. Related Slides 02: Quality control and exploration of scRNA-seq datasets; Related Slides 03: Normalization, clustering and other concepts.
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Notebook 03 - Differential expression, cell type annotation and functional data analysis: Identifying the set of features (genes/transcripts) that show distinct patterns of expression when comparing different conditions is an essential part of scRNA-seq analysis. This enables one to explore what processes might be involved in the differentiation between these circumstances. In this notebook, we will discuss mechanisms of analysis that combine differential expression, cell type annotation, and functional analyses to address this issue. Related Slides 04: Differential expression and cell annotation.
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Notebook 04 - Integrating single-cell transcriptomes from multiple samples: With the increasing complexity of single-cell data, the integration of multiple datasets has become common. However, it is crucial to account for batch effects resulting from technical and biological variations to perform accurate analyses. These batch effects can stem from differences in sample handling, experimental protocols, sequencing platforms, as well as biological factors like the donor's genetic background and tissue origin. By employing computational methods to address these variations when comparing multiple samples, unwanted sources of variation can be eliminated, allowing researchers to focus on biologically meaningful signals. The process of removing batch effects involves making two important choices: selecting the appropriate method and parameterization, and determining the batch covariate. While the parameters are specific to the chosen method, the selection of the batch covariate depends on the goal of the integration task. This notebook will cover the key concepts and methods related to data integration and batch-effect correction, followed by hands-on activities that illustrate the integration of multiple datasets using methods from Seurat and Harmony. Related Slides 05: scRNA-seq dataset integration.
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Notebook 05 - Trajectory inference and pseudotemporal ordering: Gene expression changes in a dynamic way as cells transition from one state to another. These transitions occur during development and throughout life, which makes them of interest to understand changes in the cellular functions. In each of these states, some genes get activated and others silenced. By using scRNA-seq data, computational tools such as Monocle3 can infer the single-cell trajectories that cells undergo when transitioning across the different functional states. Thus, the developmental history (ontogeny) of differentiated cell types can be traced. This notebook will cover the key concepts and methods related to inferring cell-state trajectory and pseudotime ordering, followed by hands-on activities that illustrate the use of Monocle3, a tool devised for this purpose. Related Slides 06: Trajectory inference and pseudotime analysis.
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Notebook 06 - Deciphering cell-cell communication in single-cell transcriptomics data: Cell-cell communication plays a crucial role in coordinating cellular activities and maintaining the overall functionality of multicellular organisms. It allows cells to transmit signals, exchange information, and coordinate their behaviors, ultimately contributing to essential biological processes such as development, immune response, and tissue homeostasis. In this context, inferring cell-cell interactions from gene expression data becomes valuable for unraveling the multiple roles and coordination processes that cells perform within multicellular systems. In this notebook, main concepts and a general computational workflow will be covered, then hands-on activities will be performed using LIANA, a flexible tool implementing multiple state-of-the-art methods to study cell-cell interactions. Related Slides 07: Inferring cell-cell interactions and communication from gene expression.
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Notebook 07 - An introduction to spatial transcriptomics approaches: Spatial transcriptomics is a rapidly evolving field that aims to provide a spatially resolved gene expression profile of a tissue or organ. This technology has the potential to advance our understanding of complex biological processes and help identify new biomarkers for disease diagnosis and treatment. The main goal of spatial transcriptomics is to capture the gene expression profile of individual cells (or a mini mixture of cells in a given region) in their native tissue context, allowing for the identification of cell types and their spatial distribution. This information can then be used to create detailed maps of gene expression within tissues, providing insights into cellular interactions, developmental processes, and disease progression. In this notebook, we will cover practical steps in setting up a spatial transcriptomics analysis pipeline using the Seurat package. We will cover the basic analysis to recover gene expression in different regions as well as cell type deconvolution approaches. Related Slides 08: An introduction to spatial transcriptomics approaches.
We believe in hands-on learning and want to put your newly acquired bioinformatics skills to practice! At the end of our exciting week of classes, we've prepared a short project for you to dive into. This project will allow you to apply the knowledge gained throughout the course and further reinforce your understanding.
Please access the project instructions using this URL.
Feel free to explore, experiment, and ask questions as you work on the project. Remember, this is a fantastic opportunity to challenge yourself and gain practical experience in the fascinating world of single-cell transcriptomic RNA-seq data analysis.
This comprehensive material has been a result of collaborative efforts since 2021 and has been successfully employed in numerous courses organized by esteemed institutions like the Human Cell Atlas, LatinCells initiative, and Wellcome Connecting Sciences. We extend our heartfelt gratitude to all the individuals listed below, who have actively contributed to the development and refinement of this material over the years. Their dedication and expertise have been instrumental in making this resource valuable for the bioinformatics community.
We appreciate the continuous support and feedback from participants, mentors, and institutions that have made this endeavor possible. Together, we strive to advance the understanding and application of single-cell genomics in Latin America and the Caribbean.
List of Contributors - Listed Alphabetically:
- Adolfo Rojas
- Benilton S. Carvalho
- Carlos Alberto Oliveira de Biagi Júnior
- Cesar Prada
- Cristóvão Antunes
- Erick Armingol
- Gabriela Guimarães
- Leandro Santos
- Mariana Boroni
- Patricia Severino
- Raúl Arias-Carrasco
- Ricardo Khouri
- Vinicius Maracaja-Coutinho
- Yesid Cuesta
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
