Spatial Transcriptomic Technologies and Data Analysis

Semester: Spring
|
Year offered: 2025

THE ROOM LOCATION FOR ALL 3 DAYS HAS CHANGED TO: Warren Alpert Building room 436

COURSE DESCRIPTION AND ASSIGNMENTS

This course provides an introduction to the core concepts of spatial transcriptomics and various spatial technologies. Students will learn how to choose the most suitable methods for their research questions, engage with practical applications through case studies and paper discussions, and gain hands-on experience using bioinformatics tools and software for data analysis.

Students will be assessed based on their ability to analyze a provided dataset and address the given questions. They are required to submit a written report and deliver a presentation on their findings during the final day of the course.

REGISTRATION LINK AND DEADLINE

Registration link- https://hms.az1.qualtrics.com/jfe/form/SV_9RHfz7DNVBP3w34

Registration deadline- February 7, 2025

Minimum student number- 6

Maximum student number- 15

Priority will be given to graduate students taking the course for credit. Postdocs may register but will only be granted access to the course as space allows. All sessions will be in-person only. 

MILESTONE CREDIT

In order to receive a Milestone, students must attend all three sessions and submit the final assignment before session 3. Participants (in groups or individually) will be given few spatial transcriptomic datasets to choose from and will select one to analyze, answering a set of specific questions provided in advance. Based on their analysis, they will be asked to write a report summarizing their findings and insights from the data.

Additional information about Milestone credit can be found HERE.

REQUIREMENT FOR THE COURSE

Students are expected to have a working knowledge of R.

Attendees will be required to bring their own laptops. They will be provided with instructions to download and install the necessary packages on their own laptops prior to the course. 

INSTRUCTORS

Jeffrey Moffitt, PhD

Assistant Professor, Program in Cellular and Molecular Medicine, Boston Children's Hospital

Assistant Professor, Department of Microbiology, Harvard Medical School

 

Single Cell Core Team, Harvard Medical School

Pratyusha Bala, PhD

Associate Director- Spatial Transcriptomics

Mandovi Chatterjee, PhD

Director

 

Harvard Chan Bioinformatics Core Team, Harvard School of Public Health

Alexandra Bartlett, MS

Bioinformatician II

Meeta Mistry, PhD

Associate Director

Senior Research Scientist

 

Core for Computational Biomedicine Team, Harvard Medical School

Andrew Ghazi, PhD

Statistical Geneticist

Anthony Christidis, PhD

Computational Scientist

Ludwig Geistlinger, PhD

Director of Computational Biology

 

SESSION 1- Wednesday, February 19th, 2025; Time- 1-5 pm; Location- Warren Alpert Building room 436

Day 1 Module 1 (1-2:30PM): Overview of spatial transcriptomics with research examples

Objectives: 

  • Understand the evolution and principles of spatial transcriptomic technologies

  • Use research examples of spatial transcriptomics to understand how it has been historically used

Day 1 Module 2 (2:45PM -5PM): Image-Based spatial transcriptomics data analysis

Objectives:

  • Use example data sets showcasing spatial transcriptomics to understand how to perform data analysis 

  • Perform basics quality control, segmentation and visualization of data 

 

SESSION 2- Wednesday, February 26th, 2025; Time- 1-5 pm; Location- Warren Alpert Building room 436

Day 2 Module 1 (1-2 PM): Key considerations when designing a spatial transcriptomics experiment 

Objectives: 

  • Different sample preparation methods and experimental design for spatial transcriptomics data collection 

  • Analyze experimental scenarios and their effects on spatial transcriptomics data 

Day 2 Module 2 (2:15-5 PM) Basics of analyzing NGS based spatial transcriptomics data

Objectives:

  • Students will be able to analyze NGS-based spatial transcriptomics data through guided analysis of provided data sets using the R Modules 

  • Students will apply quality control and data visualization to the provided data sets 

 

SESSION 3- Wednesday, March 5th, 2025; Time- 1-5 pm; Location- Warren Alpert Building room 436

Hands on spatial transcriptomics analysis by the participants 

Participants (in groups) will be given few spatial transcriptomic datasets to choose from and will select one to analyze, answering a set of specific questions provided in advance. Based on their analysis, they will be asked to write a report summarizing their findings and insights from the data.

Objectives 

  • Apply knowledge and skills to analyze a complete spatial transcriptomics set 

  • Collaborate with peers to present findings

  • Write a one-page report documenting analysis, methodologies and conclusion 

See also: