Spatial Transcriptomic Technologies and Data Analysis

Semester: Spring
|
Year offered: 2026

Course Description & 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.

Course Requirements

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. 

Course Schedule

Session 1: Tuesday, February 3, 2026  —  1:00–5:00 PM

Session 2: Thursday, February 5, 2026  — 1:00–5:00 PM

Session 3: Thursday, February 26, 2026  —  1:00–5:00 PM

All classes meet in WAB 563, 210 Longwood Avenue.

 

Objectives

SESSION 1

Module 1: Overview of spatial transcriptomics and Key experimental considerations

  • Understand the evolution and principles of spatial transcriptomic technologies
  • Different sample preparation methods and experimental design for spatial transcriptomics data collection 

Module 2: Basics of analyzing NGS based spatial transcriptomics data

  • 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 2

Module 1: Research lecture on Spatial Transcriptomics

  • Use research examples of spatial transcriptomics to understand how it has been historically used
  • Analyze experimental scenarios and their effects on spatial transcriptomics data 

Module 2: Image-Based spatial transcriptomics data analysis

  • Use example data sets showcasing spatial transcriptomics to understand how to perform data analysis
  • Perform basics quality control, segmentation and visualization of data 

 

SESSION 3

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.

  • 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 

 

Instructors

Jeffrey Moffitt, PhD

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

Associate Professor, Department of Microbiology, Harvard Medical School

 

Single Cell Core Team, Harvard Medical School

Mandovi Chatterjee, PhD

Director, Single Cell Core, Harvard Medical School

Pratyusha Bala, PhD

Associate Director- Spatial Transcriptomics

Single Cell Core, Harvard Medical School

 

Harvard Chan Bioinformatics Core Team, Harvard School of Public Health

Noor Sohail

Bioinformatician II

Shannan Ho Sui, PhD

Director Harvard Chan Bioinformatics Core

Principal Research Scientist

Harvard Stem Cell Institute Affiliated Faculty

 

Core for Computational Biomedicine Team, Harvard Medical School

Andrew Ghazi, PhD

Senior Computational Biologist

Anthony Christidis, PhD

Computational Scientist

Ludwig Geistlinger, PhD

Director of Computational Biology, Core for Computational Biomedicine

Research Scientist, Department of Biomedical Informatics

Harvard Medical School

Milestone Credit

In order to receive Milestone credit, students must attend all sessions and complete the final assignment before session 3.

Students can combine Nanocourse Milestones for credit. For more information, please see HERE.

Registration

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. 

Minimum student number: 6

Maximum student number: 15

To register, please complete THIS FORM by January 20th, 2026.

https://hms.az1.qualtrics.com/jfe/form/SV_d6FTMqVTYIdtwG2 

Students requiring accommodations should contact the Disability Access Office upon admission to the nanocourse. Please provide the course name, instructor’s name and email, and course dates to ensure timely communication of accomodations.

 

 

See also: