This course provides comprehensive end-to-end training on how to perform and analyse data from long-read single-cell RNA-seq experiments. 

Consisting of a mixture of conceptual and methodological lectures and hands-on bioinformatics sessions, we will provide an overview of the current long-read methods for single-cell experiments, data processing, through to data interpretation and presentation - including best practice and tips as learned first-hand by Earlham Institute’s faculty.   

What will I learn?

• Detailed overview of single-cell long-read sequencing technologies
• Optimal method selection across diverse experimental applications
• Best practices in experimental design and data management
• End-to-end workflow for generating, processing, and interpreting long-read RNA-seq data - specifically in the context of single-cell experiments, including:
  • Quality control,
  • Mapping,
  • Annotation,
  • Quantification,
  • Dataset Integration,
  • Single Cell Clustering and Annotation,
  • Data Visualisation. 
     

Who is this training for? 

Researchers who are new to and are involved in design or delivery of project(s) centred around single-cell long-read sequencing. We particularly encourage those in the grant application or experimental design stages. 

Course prerequisites 

Basic experience with the programming language R: such as through completion of activities as they appear in Software Carpentry lesson(s): ‘Programming with R’ and ‘R for Reproducible Scientific Analysis’.  

Basic experience in using the command line interface: such as through completion of activities as they appear in the Software Carpentry lesson ‘The Unix Shell’.  

Familiarity with fundamental RNA-seq concepts: such as through completion of activities as they appear in the Data Camp ‘RNA-seq with Bioconductor in R’ course, chapter ‘Introduction to RNA-Seq theory and workflow’.

Once accepted, participants will be granted access to our pre-requisite module to aid their progress in the course. 

Spatial transcriptomics is becoming increasingly popular in all species and tissue types and is a powerful tool to analyse gene expression at a single cellular level.  

Traditional histology techniques focus only on the morphology of your sample, often without consideration of maintenance of high-quality RNA needed for a successful spatial transcriptomics experiment. 

This course will give you a good understanding of the different stages of histological sample preparation to optimise for spatial transcriptomics experiments. 

We will share our own experiences to enable you to prepare your own samples. Through a series of case studies, we will present a variety of sample preparation methods that span different species and tissue types. 

What will I learn?  

• Understand the theory of mechanisms of fixation, dehydration, embedding and sectioning, and when it is/is not appropriate to use these for your sample
• Gain hands-on experience in handling samples for spatial transcriptomics: embedding and sectioning, including differences with common platforms
• What a good sample looks like, and how to troubleshoot challenging samples
• Understand a variety of sample preparation methods available to you for spatial transcriptomics and how these perform more or less well for different sample types  

Who is this training for?  

Anyone in the process of planning a spatial transcriptomics experiment and wanting to learn about optimising sample preparation. If you are thinking about a grant submission and want to understand the various timings required for preparing and optimising samples, this is definitely for you. 

This training is for all roles who might be working with sample preparation for spatial transcriptomics, including but not limited to: technicians, postdoctoral researchers, research assistants, group leaders, PhD students and more

Prerequisites:    

Good laboratory practice and familiarity with common molecular biology equipment. Previous experience with histology is not required.