# 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**](https://curriculumfellows.hms.harvard.edu/nanocourses)**.** #### **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](https://hms.az1.qualtrics.com/jfe/form/SV_d6FTMqVTYIdtwG2) by **January 20th, 2026**. [https://hms.az1.qualtrics.com/jfe/form/SV\_d6FTMqVTYIdtwG2](https://hms.az1.qualtrics.com/jfe/form/SV_d6FTMqVTYIdtwG2) *Students requiring accommodations should contact the* [*Disability Access Office*](https://dao.fas.harvard.edu/) *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:- [ Past ](/class-categories/past)