# Computational Discovery of Cell Phenotypes and Subtypes from Single-Cell Imaging and Transcriptomic Data Semester: Summer | Year offered: 2026 ### Course Description Cellular heterogeneity is a fundamental feature of biological systems and plays important roles in development, disease progression, and therapeutic response. This Nanocourse introduces computational strategies for discovering cell phenotypes from cellular images and identifying cell subtypes from transcriptomic data. Participants will learn core methods for image processing, segmentation, tracking, feature extraction, and quantitative analysis of cellular morphology and dynamics. The course will emphasize machine learning approaches for uncovering subtle, rare, or previously unrecognized phenotypic variation in cell populations from imaging data. In parallel, the course will introduce key concepts and analytical strategies for discovering cell subtypes from transcriptomic data, without covering a full scRNA-seq processing pipeline. The course will also discuss how imaging-derived phenotypes and transcriptomic subtypes can be related to improve biological interpretation of cellular heterogeneity. ### Course Objectives **Participants will be able to:** - Apply image analysis methods to extract quantitative features from cellular images - Use computational and machine learning approaches to discover heterogeneous cell phenotypes from imaging data. - Understand key concepts and analytical strategies for identifying cell subtypes from transcriptomic data. - Develop practical familiarity with commonly used tools for cellular image analysis and machine learning such as by MATLAB, CellProfiler, and ImageJ. ### Class Schedule **Time:** 1:00–3:00 PM **Dates:** - **Session 1:** June 15, 2026 - **Session 2:** June 16, 2026 - **Session 3:** June 18, 2026 - **Session 4:** June 19, 2026 **Classroom:** TMEC 128 Learning Studio (Castle) ### Required Materials Participants will need to bring their laptops with recommended software installed (instructor to provide additional details). ### Milestone Credit In order to receive Milestone Credit, students must: - Attend all sessions - Complete the image analysis workflow introduced in Session 2. - Submit a concise report detailing workflows, methods, and key outcomes in Session 4. **HMS-based PhD students and select HMS Masters students can combine Nanocourses for credit. More information about Milestone Credit can be found** [**here**](https://curriculumfellows.hms.harvard.edu/nanocourses)**.** ### Course Team **Course Directors / Instructors** Kwonmoo Lee, PhD, Assistant Professor, Vascular Biology Program, Boston Children's Hospital, Harvard Medical School, Hyung Joon Kim, PhD, Senior Staff Scientist, Vascular Biology Program, Boston Children's Hospital, Harvard Medical School, Tzu-Hsi Song, PhD, Post-doctoral Fellow, Vascular Biology Program, Boston Children's Hospital, Harvard Medical School, Daehyung Kim, PhD, Post-doctoral Fellow, Vascular Biology Program, Boston Children's Hospital, Harvard Medical School, ### Registration Enrollment is limited to 20 participants. Additional participants may be considered case by case. Registration priority will be given to HMS-based PhD students and select HMS Master’s students who are taking the course for credit. More information can be found[ here.](https://curriculumfellows.hms.harvard.edu/nanocourses) You will receive an email confirming your enrollment. If the course is full, you will be placed on a waitlist and notified by email. **Register Here:** [https://hms.az1.qualtrics.com/jfe/form/SV\_0kbNE3MwFcKb35c](https://hms.az1.qualtrics.com/jfe/form/SV_0kbNE3MwFcKb35c) **Registration Deadline:** Friday, June 5, 2026 **Participants will need to bring their laptops with recommended software installed (instructor to provide additional details).** *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 accommodations.* See also:- [ Upcoming ](/class-categories/upcoming)