Sanja Vickovic's research projects
Multimodal functional genomics of ALS
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that affects motor neurons and leads to progressive muscle weakness. Despite intensive research, effective treatments remain limited.
In this project, we study how cellular stress damages neurons in ALS and how this process differs between patients. We focus on stress related to mitochondria, protein quality control, and ribosome function — processes that are essential for healthy brain cells.
New technology platform
We have developed a new technology platform, which combines spatial transcriptomics, high-content imaging, and CRISPR-based gene perturbation at single-cell resolution. Using patient-derived stem cells, we generate aged motor neurons in the laboratory that closely resemble those found in patients.
By integrating molecular, cellular, and morphological data using computational models, we systematically link genetic changes to disease-related cell states. A key innovation is our reversible CRISPR system, which allows us to turn genes off and on within the same experiment, enabling precise validation of therapeutic targets.
This project will create a comprehensive functional atlas of ALS, helping us identify new drug targets, improve patient stratification, and accelerate the development of future treatments.
Understanding colorectal cancer plasticity
Colorectal cancer remains one of the leading causes of cancer-related deaths, largely because metastatic tumors are difficult to treat. A major reason is that cancer cells are highly adaptable: they can shift between different biological states, becoming more aggressive or more therapy-resistant over time.
In this project, we study how colorectal cancer cells change their identity and behavior during disease progression. Rather than being driven only by genetic mutations, these transitions are controlled by epigenetic mechanisms and interactions with the tumor microenvironment.
Tool to guide cancer treatment
We combine single-cell sequencing, spatial transcriptomics, functional genomics, and real-time sensors to track cancer cell states in living systems. Using large-scale CRISPR screens and drug libraries, we systematically identify genes and molecular pathways that drive these transitions.
Advanced computational models and machine learning approaches integrate molecular and spatial data to predict how tumors evolve and respond to therapy. This enables us to build dynamic, patient-specific models of cancer progression.
Our goal is to develop predictive and functional tools that can guide personalized cancer treatment and help shift tumors toward more treatable states.
Disease trafficking in the inflammatory tumour microenvironment dissected with deep learning
Cancer develops within a complex tissue environment composed of immune cells, stromal cells, and inflammatory signals that strongly influence tumour growth, metastasis, and treatment response. In colorectal cancer, chronic inflammation plays a major role in disease progression, yet the underlying cellular mechanisms remain poorly understood.
In this project, we map how tumour cells interact with immune and inflammatory cells in space and time, with a particular focus on responses to immunotherapy. Using high-definition spatial transcriptomics and spatial multi-omics, we measure gene and protein expression directly within intact tissue sections, preserving cellular organization and tissue architecture.
Improved cancer diagnostics and more precise therapies
These molecular maps are integrated with high-resolution microscopy images to create a next-generation digital pathology platform. We apply computer vision and deep learning to identify spatial patterns and cell–cell interactions associated with treatment sensitivity and resistance.
By combining spatial biology, advanced imaging, and artificial intelligence, this project establishes a predictive framework for understanding tumour microenvironments. Our long-term goal is to improve cancer diagnostics and enable more precise, personalized treatment strategies for patients with colorectal cancer.