A global initiative for inclusive and accessible single-cell transcriptomics education

This notebook introduces essential command-line operations in Linux, covering fundamental commands that are broadly applicable across programming languages with minimal adaptations. These foundational skills will support efficient data management and analysis in computational biology. Additionally, we will explore the key steps in processing raw sequencing reads into count matrices using Cell Ranger, discussing its main outputs and role in single-cell transcriptomics. Processing scRNA-seq data is a crucial step in single-cell analysis. The chosen library preparation method determines whether RNA sequences are captured from transcript ends (e.g., 10X Genomics, Drop-seq) or full-length transcripts (e.g., Smart-seq), directly influencing downstream analysis and biological insights.

In this section, we will use the Seurat package to process and analyze scRNA-seq data, covering essential steps such as data import, filtering, and preliminary visualization to ensure proper quality control before downstream analysis. A key part of scRNA-seq analysis is identifying genes and transcripts with distinct expression patterns across different conditions. These differences can reveal underlying biological processes driving cellular heterogeneity. To refine the dataset, we will assess its quality using key metrics, apply normalization techniques to mitigate technical variability, and implement clustering methods to group cells based on gene expression patterns. Furthermore, we will do differential expression analysis, cell type annotation, and functional enrichment techniques to uncover gene regulation mechanisms, identify key markers, and explore pathways involved in cellular differentiation and disease states. Together, these approaches provide a comprehensive framework for interpreting single-cell transcriptomics data and extracting meaningful biological insights.

As single-cell data complexity grows, integrating multiple datasets has become standard. However, batch effects—arising from technical and biological variations—must be corrected for accurate analysis. These effects stem from differences in sample handling, protocols, sequencing platforms, and biological factors like donor background or tissue origin. Computational methods help eliminate unwanted variation, ensuring biologically meaningful signals. Batch correction requires two key decisions: selecting the appropriate method and its parameters, and defining the batch covariate based on the integration objective. In this notebook, we explore core concepts and methods for data integration and batch correction, with hands-on activities using Seurat and Harmony. Additionally, we perform benchmarking to compare integration strategies, helping select the most effective method while preserving biological relevance.

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.

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.

This notebook explores multimodal data integration at the single-cell level, combining transcriptomic measurements with protein quantification. Using a dataset of 8,617 umbilical cord blood mononuclear cells (CBMCs), we follow a Seurat tutorial to analyze the relationships between RNA and surface protein expression. By loading count matrices for RNA and antibody-derived tags (ADT), we investigate cellular expression patterns and their biological implications. In addition to theoretical concepts, this notebook includes practical activities for downloading data from NCBI GEO and executing key analyses.

T cell receptor (TCR) profiling and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) are pivotal techniques in single-cell research, offering unparalleled insights into the adaptive immune system and cellular heterogeneity. TCR profiling enables a deep dive into the repertoire and diversity of T cell populations, highlighting the specificity and uniqueness of T cell responses. On the other hand, CITE-Seq facilitates the concurrent assessment of transcriptomic data and protein expression within individual cells, creating a comprehensive portrayal of cellular states. In this module, participants will explore the profound implications of TCR profiling in understanding immune responses and the synergies it can achieve when coupled with CITE-Seq. We'll initiate with core concepts and theories, and swiftly transition into practical applications using advanced computational tools. Through this hands-on approach, attendees will master the nuances of TCR profiling and CITE-Seq, equipping them with valuable tools for their immunological and single-cell research pursuits.

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.

scATAC-seq is a technique used to study chromatin accessibility at the single-cell level. Unlike scRNA-seq, which focuses on gene expression, scATAC-seq identifies regions of the genome that are open and potentially active, meaning they can be bound by transcription factors to regulate gene activity.

This module focuses on the bioinformatic analysis of Alternative Polyadenylation (APA) using SCAPE-APA, a specialized computational tool designed for single-cell RNA-seq data. Learners will explore the principles behind APA detection, quantification, and interpretation in a high-throughput context. The module introduces the structure and workflow of SCAPE-APA, including input formats, preprocessing steps, and output interpretation. Through guided exercises, users will apply SCAPE-APA to real datasets, visualize APA dynamics across cell types, and extract biologically meaningful insights from polyadenylation site usage. This hands-on approach equips learners with essential skills for analyzing transcriptomic complexity at single-cell resolution.