Single Cell Technologies and Analysis

15–22 July 2022

Wellcome Genome Campus, UK

Learn the latest methodologies and applications for the analysis of nucleic acids from eukaryotic single cells


The development of robust protocols sensitive enough to measure nucleic acids from single cells is revolutionising biology, enabling the interrogation of molecular mechanisms that are not evident from measurements that represent the average of thousands of cells.

Currently, established plate-based protocols (such as Smart-seq2) provide scalable and robust measurements of mRNA. However, technologies in this field are rapidly evolving, and have recently enabled RNA-seq to be conducted on thousands of single cells in parallel (e.g. 3’ end sequencing of mRNA in droplets, either through open source protocols like Drop-seq or kits from commercial vendors like 10x) and for multiple classes of nucleic acid to be captured from the same cell (e.g. DNA and RNA with G&T-seq). Reliable statistical methods for analysing these data are also being developed. However, identifying tools that are most suitable for the analysis of their data may be challenging for those new in the field. This exciting course is taught collaboratively by researchers from the Wellcome Sanger Institute and Earlham Institute. The programme will provide participants with a broad overview of established and cutting-edge single cell methodologies, practical experience in the relevant molecular biology and laboratory skills, and the computational and statistical skills needed to interpret these large data sets.

The course will primarily provide intensive hands-on training on widely applicable plate-based full-length mRNA sequencing (Smart-seq2) and an overview of performing higher throughput 3’ end based protocol (10x chromium/Drop-seq/Seq-well). The participants will also learn analysis of single-cell RNA-sequencing datasets including most widely used tools and methods. We will touch on new “spatial” approaches to capture RNA from solid tissues, and how this can be combined with single cell approaches.

Course content will include demonstration on cell handling, flow cytometry (FACS) microfluidics systems and additional single-cell sequencing technologies. The integration of data analysis within the course will allow participants to critically evaluate both the technical performance and the biological interpretation of single cell data sets that they have generated. The bioinformatics component in the course, will empower wet-lab based researchers to understand the key QC metrics for single cell data, evaluation and protocol optimisation. The practical programme will be complemented by distinguished guest speakers, who will present the latest research in this fast-moving field, along with opportunities for informal discussions.

Target audience: The course is primarily aimed at PhD students and Postdoctoral scientists with some wet lab experience and enthusiasm to learn the basics of computational analysis. Previous courses have also hosted bioinformaticians and medical staff who were seeking a better understanding of the laboratory and computational aspects of these new technologies. As a resident course, participants will have an opportunity to network with other scientists and engage with instructors on related topics of their interest.


The course will include laboratory and computational practical sessions, along with lectures and discussions, covering the following topics:

  • Preparation and isolation of single cells (including preparation of suspensions, single cell sorting with FACS and brief overview of tissue dissociation methods)
  • Cell counting and quality control
  • Full length single cell mRNA sequencing in 96-well plates (Smart-seq2 protocol)
  • 3’ end single cell mRNA sequencing from thousands of cells (10x genomics) and an overview of Drop-seq and Seq-Well methods
  • Demonstration and overview of multi-omics for single cells (including G&T-seq for DNA/RNA)
  • Automation of single cell protocols
  • Illumina sequencing of single cell libraries
  • Overview of “spatial” gene expression techologies
  • Data processing, data handling and QC of single cell data sets
  • Downstream analysis of single-cell data (e.g. differential gene expression analyses and clustering) including widely used methodological concepts and tools for data analysis.

Learning Outcomes
After attending this course, participants will able to:

  • Evaluate advanced methodologies for the analysis of nucleic acids from eukaryotic single cells, along with their applications
  • Appreciate different approaches currently in use in the field to address specific research questions
  • Assess the strengths, weaknesses and limitations of different methodologies and approaches, experimental and computational.
  • Integrate and apply the knowledge and training from the course to their own research interests

Instructors and speakers

Course instructors

Lia Chappell
Wellcome Sanger Institute, UK

Kedar Natarajan
Department of Bioengineering, Technical University of Denmark

Alex Predeus
Wellcome Sanger Institute

Iain Macaulay
Earlham Institute

Simon Murray
Wellcome Sanger Institute

Raheleh Rahbasri
Wellcome Sanger Institute

Pasha Mazin
Wellcome Sanger Institute

How to apply

Applicants should be researchers interested in applying advanced laboratory and computational methodologies for the analysis of nucleic acids from eukaryotic single cells, with a strong emphasis on mammalian systems. It is suitable for PhD students, postdocs and clinician scientists, as well as staff from Core Facilities.

It is expected that participants have, at a minimum, familiarity with steps involved in analysis of sequencing data and basic knowledge of Linux/Unix as well as the R programming environment. There are numerous online tutorials available for these, including:


How to Apply
Please click the Apply button above to begin the online application process. Places are limited and will be awarded on merit. If you have any problems with the online application process, please contact us.

Please note: Applications must be supported by a recommendation from a scientific or clinical sponsor (e.g. supervisor, line manager or head of department). A request for a supporting statement will be sent to your nominated sponsor automatically during the application process. Applicants must ensure that their sponsor provides this supporting statement by the application deadline. Applications without a supporting statement cannot be considered.


*Course fee £1100 This is a residential course and the fee includes all accommodation and meals.

*The course fee is subsidised by Wellcome Genome Campus Advanced Courses and Scientific Conferences and applies to non-commercial applicants. Please contact us for the commercial fee.

Limited bursaries are available (up to 50% reduction on the course fee) and are awarded on merit. If you would like to apply for a bursary, please complete the bursary section of the online application form.

Where there are many bursary applications, the selection committee may issue smaller amounts.

Bursaries can be applied for as part of the course application form. Applicants will be notified of a bursary award along with their place on the course, usually within one month of the application deadline. The decision of the selection committee is final.

Please note that both the applicant and sponsor are required to provide a justification for the bursary as part of the application.

Additional funding opportunities
Visit our support page for additional financial support currently available.

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Feedback from the 2019 course:

“I thoroughly enjoyed this course and it surpassed my expectations. The level of teaching was exceptional and all the instructors were very approachable and keen to answer all my questions.Truly an excellent course, thank you.”

“All the topics that I was looking for were covered during the course. I think that the organizers did an amazing job to cover all the aspect of Single Cell Technologies including Single Cell transcriptomics, genomics, epigenomics and spatial transcriptomics.”

“Thank you so much, this was the best course I have attended. I have learnt so much and it has really consolidated my work.”

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