BIOENG-420: Single-cell biology

Teaching Assistants: Jiayan Lin, Elisa Bugani

The course starts on Mon 17.02

Time: 16:15 - 19:00

Room: DIA003

Course schedule:

• Week 1 (17.02): Introductory lecture. Cell-to-cell heterogeneity, inter-cellular noise, teleonomic circuits - Lecturer: Giovanni D'Angelo
• Weeks 2-5 (24.02 to 17.03): Basics of single-cell RNA-seq data analysis - Lecturer: Vincent Gardeux [Project, 16.67% of final grade]
• Weeks 6-8 (24.03 to 07.04): Single-cell and spatial metabolomics - Lecturer: Giovanni D'Angelo [Journal Club, 16.67% of final grade]
• Weeks 9-12 (14.04 to 12.05): Single-cell epigenomics. Organoids. - Lecturer: Fides Zenk [Journal Club, 16.67% of final grade]
• Week 13 (19.05): Mini-symposium
  • Event: Gathering in Lausanne of Ph.D. students and postdocs from leading labs in single-cell biology providing lectures/open discussions on their current projects
• Week 14 (26.05): Final exam [Written exam, 50% of final grade]

(in blue is the evaluation method for each part of the course, if any)

Introductory lecture

Week 01

Lecture:

• BIOENG420-01: Cell-to-cell heterogeneity, inter-cellular noise, teleonomic circuits

Basics of single-cell RNA-seq data analysis

Week 02

Lecture:

• BIOENG420-02: Introduction to single-cell transcriptomics

• Installing R or Python environment.

• Picking a working group and starting the project.

Basics of single-cell RNA-seq data analysis

Week 03

Lecture:

• BIOENG420-03: Single-cell RNA-seq data analysis (I)

• Project: single-cell RNA-seq data analysis hands-on

Basics of single-cell RNA-seq data analysis

Week 04

Lecture:

• BIOENG420-04: Single-cell RNA-seq data analysis (II)

• Project: single-cell RNA-seq data analysis hands-on

Basics of single-cell RNA-seq data analysis

Week 05

Lecture:

• BIOENG420-05: Single-cell genomics applications

• Project: Wrapping single-cell RNA-seq data analysis and final questions

Single-cell and spatial metabolomics

Week 06

Lecture:

• BIOENG420-06: Single-cell and spatial metabolomics

Single-cell and spatial metabolomics

Week 07

Lecture:

• BIOENG420-07: From cell-to-cell heterogeneity to cell sociology

Single-cell and spatial metabolomics

Week 08

Journal Club

Single-cell epigenomics

Week 09

Lecture:

• BIOENG420-09: Single-cell epigenomics

Guest Lecture from Fides Zenk

Email fides.zenk@epfl.ch with any questions

Easter Monday

Single-cell multi-omics to understand brain organoid development

Week 10

Lecture:

• BIOENG420-10: In this week you will work on multiome data and try to understand the role of a transcription factor in shaping cell fate decisions. 

Single-cell epigenomics current trends and developments

Week 11

Lecture:

• BIOENG420-11: This week we will discuss the latest technologies genome function and regulation in single cells at different hierachical levels.

Single-cell epigenomics

Week 12

Journal Club

Please check the papers below we can distribute them together on the 14.04 or you can already sign up with 2 partners for a presentation. 

Here is the link to sign up:

https://docs.google.com/spreadsheets/d/1ROGQ85DeZbiT4B162uLgp8Fu0qcnTV0KRHxmaH3k9os/edit?usp=sharing

Please prepare 10-minute presentations in groups of 3. Then, we will discuss each paper together. 

fides.zenk@epfl.ch

Mini Symposium

Week 13

Lectures:

• 16:15-16:35 - Short research talk: Giulia Santoni (UPZENK) - Chromatin plasticity predetermines neuronal eligibility for memory trace formation
• 16:40-17:00  - Short research talk: Hannah Schede (UPDANGELO) - A tool for the creation of spatial metabolic atlases: uMAIA
  
• 
  
• 17:00-18:00 - Plenary Lecture: Charlotte Bunne - Virtual Cells and Digital Twins: AI in Personalized Oncology

Concretely, I will introduce the Virtual Tissues (VirTues) platform, a foundation model framework that transforms how we analyze multiplexed tissue data and seamlessly integrates molecular, cellular, and tissue-scale information to increase diagnostic precision and biological understanding in personalized oncology. VirTues employs a multi-modal vision transformer architecture designed to learn from heterogeneous, high-dimensional datasets spanning different biological markers, measurement characteristics, and variable clinical annotations. While existing approaches often focus on H&E-stained slides, our framework incorporates highly multiplexed imaging techniques that capture hundreds of proteins within single tissue sections. Through unsupervised learning and a multi-scale neural network architecture, VirTues unifies these diverse data sources into a coherent virtual tissue space. As a result, new patient biopsy samples can be automatically mapped into this common representation. This enables integrative analyses of morphological, molecular and spatial complexity while facilitating clinically relevant predictions.

To bridge insights from the analysis of patient samples with personalized treatment, we employ generative models trained on large biomedical datasets. These models predict treatment responses of biopsied cells from metastatic melanoma patients by revealing patterns of signaling pathway modulation associated with driver mutations and metastasis sites. Together, these approaches enable a multi-scale understanding of cancer biology and treatment response, advancing the development of personalized therapies guided by comprehensive digital twins of patient biology.

Exam

Week 14