/Single_Cell_Genomics_2024

Hands-on training on state-of-the-art approaches for eukaryotic single-cell RNA sequencing.

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Single Cell Genomics - Latin America & the Caribbean

9–15 August 2024, INCA, Rio de Janeiro, Brazil

Wellcome Connecting Science Course Website Link
Course Timetable 2024

Summary

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 single-cell RNA sequencing (scRNA-seq) methodologies has become increasingly common across diverse fields of life sciences research. In medicine, single-cell approaches used to investigate organisms in both health and disease conditions have advanced unprecedented understanding of organisms’ biology as a whole.

Successful scientific networks promoted by the Human Cells Atlas consortium highlighted the growing interest among scientists and an urgent need for developing capacity in single-cell technologies worldwide. However, there is still a lack of infrastructure, limited funding, and insufficient expertise to apply these cutting-edge technologies in Latin America.

Aiming to expand the region’s capacity, our short course will offer hands-on laboratory and bioinformatics analysis training, tailored to empower researchers in Latin America with essential skills to employ single-cell technologies.

Following our successful first edition, this course will mark a revised second version, reflecting the high demand and positive feedback received previously. Building on the foundation laid by the inaugural course, we aim to delve deeper into the realm of single-cell technologies. This time, we are dedicated to exploring a broader spectrum of cutting-edge methodologies. These advancements promise to further enhance the capacity of researchers in Latin America, equipping them with the knowledge and skills needed to leverage the full potential of single-cell technologies in their scientific endeavours.

Target Audience

Early and mid-career scientists, PhD students, and postdoctoral researchers based in Latin American countries who are engaged or planning to engage in single-cell research.

Prerequisites

We recommend that participants be familiar with the basics of genomics and molecular Biology. The hands-on bioinformatics modules will be taught using R. Therefore, participants are required to have some familiarity with the R language. This will be essential for participants to fully benefit from the course. All candidates will be requested to undertake pre-course activities to gain the fundamental knowledge needed during the course.

The course will be taught in English but there will be support from Portuguese and Spanish-speaking instructors.

Course Content

This hands-on laboratory and bioinformatics course will offer a series of lectures and practical sessions including the following topics:

  • Introduction to single-cell sequencing: applications and analysis
  • Single-cell RNA sequencing methods: detailed state-of-the-art molecular biology protocols for sample preparation
  • Hands-on single-cell techniques: from cell preparation to data analysis
  • Best practices for planning and executing single-cell experiments, quality control and troubleshooting
  • Bioinformatics training: workflows and tools for data processing, visualization, and cellular structure identification
  • Group projects: bioinformatics analyses and grant writing

Learning Outcomes

At the end of this course, participants will be able to:

  • Compare single-cell RNA sequencing (scRNA-seq) methodologies to identify suitable applications.
  • Plan and perform scRNA-seq laboratory techniques and data analysis to obtain high-quality outputs.
  • Apply R scripting and publicly available data repositories to analyse scRNA-seq data.
  • Design and execute a bioinformatics analysis pipeline for scRNA-seq data.
  • Interpret the outcomes of a single-cell RNA-seq pipeline in light of a biological question.

Scientific Organising Committee

Instructors & Assistants

Wellcome Connecting Science Team

Bioinformatics Material

Module 1: Single Cell Resources

This module provides an overview of key resources and tools available for single-cell research, including databases, repositories, and software platforms that facilitate the exploration and analysis of single-cell data.

Module 2: Hands-on Databases

n this hands-on module, participants will explore various databases relevant to single-cell research, learning how to access, navigate, and extract valuable information for their studies.

Module 3: Data Processing

This module covers the fundamental steps of single-cell data processing, including quality assessment, read alignment, and the generation of expression matrices. Participants will gain practical experience with common data processing pipelines.

Module 4: Single-cell RNASeq Data Structure

Participants will learn about the structure and organization of single-cell RNA sequencing data. The module covers data formats, annotation standards, and how to manage and manipulate large-scale datasets.

Module 5: Data Quality Control

This module focuses on techniques for assessing and ensuring the quality of single-cell RNA sequencing data. Topics include identifying and mitigating sources of technical noise, filtering out low-quality cells, and standardizing quality control metrics.

Module 6: Data Normalization and Clustering

n this module, participants will learn methods for normalizing single-cell RNA sequencing data to account for technical variability. The module also covers clustering techniques to identify distinct cell populations within a dataset.

Module 7: Differential Expression and Cell Type Annotation

This module teaches methods for identifying differentially expressed genes between cell populations and annotating cell types based on gene expression profiles. Techniques for statistical testing and functional annotation will be discussed.

Module 8: Functional Analysis

Participants will explore various approaches to functional analysis of single-cell RNA sequencing data, including pathway analysis, gene set enrichment, and the integration of external biological knowledge to interpret cellular functions.

Module 9: scRNA-seq Dataset Integration

This module covers strategies for integrating multiple single-cell RNA sequencing datasets to achieve a comprehensive understanding of cellular heterogeneity. Participants will learn about batch effect correction, data harmonization, and integration algorithms.

Module 10: Integration Benchmarks

In this module, participants will evaluate different integration methods through benchmark datasets. They will learn how to assess the performance of integration techniques and select the appropriate method for their specific research questions.

Project: Project Development

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 here.

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.

Authorship and Acknowledgments

This comprehensive material has resulted from collaborative efforts since 2021. It has been successfully employed in numerous courses organized by esteemed institutions like the Human Cell Atlas, the 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
  • Jacqueline Marcia Boccacino
  • Leandro Santos
  • Mariana Boroni
  • Patricia Severino
  • Raúl Arias-Carrasco
  • Ricardo Khouri
  • Vinicius Maracaja-Coutinho
  • Yesid Cuesta

Citing and Re-using Course Material

The course data are free to reuse and adapt with appropriate attribution. All course data in these repositories are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Creative Commons Licence

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