This event offers a comprehensive overview of single-cell genomics, designed for researchers in the experimental planning stages of a project involving single-cell genomics.
Attendees will gain insights into best practices for experimental design, participate in hands-on sample preparation, and explore automated laboratory workflows. The course will also cover single-cell data pre-processing and quality control, followed by practical exercises in downstream analyses and visualisations of single-cell data.
Introducing Single Cell Genomics, this course covers several aspects of single cell workflows, from experimental design to laboratory hands-on, including cell sorting and processing for production of quality samples for sequencing, generation of sequencing data.
We will provide an overview of tools and metrics for assessing the data quality, with a focus on interpreting several different data QC reports.
Laboratory practical sessions will involve cell sorting using FACS (BD S8 Discover Spectral sorter), including considerations for different cell types, preparation of samples for Smart-seq2 cDNA library construction, and QC.
We will also include an overview of the differences in sample preparation for sequencing of 10X genomics libraries, including demonstrations of set-up and loading of the 10X Genomics Chromium Controller.
Delegates will have the opportunity to interpret and troubleshoot sequencing reports and discuss to discuss the key metrics for high-quality single cell data, understand the workflow for both library and sequencing data QC, generate gene expression matrices, and learn how to identify any filtering required with subsequent demultiplexing.
All theory lectures and hands-on sessions will include best practices and tips as learned first-hand by EI's own faculty.
The course will consist of a mixture of conceptual lectures, methodological lectures, hands-on sessions in the laboratory and with data analysis, with walk-throughs of initial quality control, common single-cell RNAseq workflows, as well as ample time for group discussions.
What will I learn?
- Cell sorting, including an introduction to different platforms at EI
- How to generate cDNA for single cell RNASeq using the Smart-seq2 protocol
- Overview of library construction using Nextera XT protocol
- Quality control at cDNA and library stages with examples of good and bad libraries
- 10X genomics chip loading demonstration
- Understand how to assess sample and sequence quality, troubleshoot problematic samples and optimise experimental design
- Hands-on experience in available tools and resources for analysing and visualising single-cell data
- Introduction to data analysis; cell type identification, data integration, differential expression and pseudotime analyses
- Overview of spatial transcriptomics platforms, experimental design and demonstration of visualiser software capabilities
Course Prerequisites
Delegates are expected to be confident and competent in Good Laboratory Practice and with standard laboratory equipment, for example familiarity with best practice for pipetting.
If you have certificates for competency training, please email these to training@earlham.ac.uk or we ask that your line manager emails us with a description of techniques/equipment that you are familiar with. A basic understanding of NGS including indexes and pooling would be beneficial.
For those with minimal laboratory experience, we will ask you to complete a short laboratory induction. For more information on requirements, please email training@earlham.ac.uk.
Some previous experience with programming is recommended. Familiarity with the command line and actions as in the Software Carpentry lesson on The Unix Shell would be beneficial alongside basic experience with R, such as being able to complete activities as they appear in Software Carpentry lesson Programming with R.
Who is this event for?
Those in the process of writing grant applications, or those in experimental planning stages of a project involving single-cell genomics, who want to gain wet lab experience in single-cell techniques and wanting to embark on some initial data analysis to help identify data quality and further inform experimental design.
Early career researchers (PhD and early career postdocs), research assistants, technicians and group leaders are all welcome.
