The course aims to ensure that students achieve the following learning outcomes:
1) Knowledge and understanding: Students will gain a comprehensive understanding of the fundamental principles of OMICS technologies in neuroscience. This includes genomics, transcriptomics, proteomics, and metabolomics, and their applications in understanding brain function and disease mechanisms.
2) Application of knowledge: Students will acquire the theoretical foundation and practical skills necessary to analyze and interpret OMICS data in the context of neuroscience research. They will be able to apply these methodologies to experimental design and problem-solving in neurobiology.
3) Judgment: Students will develop critical thinking and analytical skills to assess OMICS datasets and research findings. They will learn to evaluate the strengths and limitations of various OMICS approaches and make informed decisions in experimental planning and data interpretation.
4) Communication skills: Students will enhance their ability to present and discuss OMICS concepts and findings in a clear and scientifically rigorous manner. They will engage in interactive discussions, collaborate with peers, and communicate their research effectively to both specialized and general audiences.
5) Study skills: By the end of the course, students will have the knowledge and skills necessary for independent learning in the field of OMICS and neuroscience. They will be able to adapt to emerging technologies and continue their education in molecular and computational neuroscience.

Knowledge of fundamental cell biology and molecular biology.

This course offers a comprehensive and up-to-date exploration of OMICS technologies and their applications in neuroscience. Designed to equip students with both theoretical knowledge and practical skills, it provides a deep dive into the key methodologies and bioinformatics tools that are essential for understanding the molecular mechanisms governing brain function. By integrating multiple layers of molecular data, OMICS approaches have revolutionized neurobiological research, shedding light on the complex interplay of genes, proteins, and metabolites in health and disease.
Dr. Fornasiero will overview the entire course, while Prof. Sanges will take care of one credit (8h) where he will focus on transcriptomic technologies.
The course is structured to guide students through the fundamental principles of genomics, transcriptomics, proteomics, and metabolomics, gradually advancing toward their specialized applications in neuroscience. Through a combination of lectures, hands-on tutorials, and discussions, students will develop a solid understanding of these high-throughput technologies and their analytical pipelines. In addition, guest experts will lead panel sessions, offering insights into cutting-edge developments and real-world applications.
Throughout the course, several key topics will be explored in depth. The fundamentals of OMICS in neuroscience will serve as a foundation, introducing students to the overarching concepts and their relevance to neurobiological studies. The genomics section will delve into DNA modifications, nuclear organization, and state-of-the-art sequencing technologies, while transcriptomics will cover both conventional and single-cell approaches to studying gene expression. Proteomics will be examined in detail, encompassing standard techniques, specialized methodologies, and the critical role of post-translational modifications in protein function. Additionally, the course will introduce metabolomics and lipidomics, highlighting their significance in neuronal physiology and pathology.
Given the increasing reliance on computational methods in modern neuroscience, bioinformatics applications will be a central focus, with an emphasis on data analysis, population statistics, and the interpretation of large-scale datasets. Students will gain hands-on experience with practical research methodologies, learning how to navigate databases and apply analytical tools to real-world neurobiological questions.

Omics Approaches, Technologies And Applications. Preeti Arivaradarajan · Gauri Misra. Springer
Integrative Omics Concept, Methodology, and Application
1st Edition - May 3, 2024 Editors: Manish Kumar Gupta, Pramod Katara, Sukanta Mondal, Ram Lakhan Singh

The course begins with an introduction to OMICS technologies and their transformative role in neuroscience. Dr. Eugenio Fornasiero will provide an overview of how these high-throughput approaches have reshaped our understanding of molecular diversity in the brain, highlighting their applications in neurodevelopment, synaptic plasticity, and neurodegenerative diseases. This introductory session will lay the groundwork for the subsequent modules by exploring the principles of systems biology and the integration of large-scale molecular datasets.
Following this foundation, the course will delve into genomics, focusing on the molecular architecture of the genome and the latest sequencing technologies used to investigate DNA organization and modification. Topics will include the impact of epigenetic regulation on neuronal function, chromatin accessibility, and the role of nuclear compartmentalization in gene expression.
Transcriptomics will be explored in depth in a dedicated module led by Prof. Remo Sanges. This section will cover both conventional bulk RNA sequencing and single-cell transcriptomic approaches, providing students with insights into how gene expression dynamics shape neuronal identity and function. Special emphasis will be placed on cutting-edge methodologies, such as spatial transcriptomics, which allows for the mapping of gene expression patterns within specific brain regions. Students will also gain practical experience in analyzing transcriptomic data, using bioinformatics tools to process, visualize, and interpret gene expression profiles.
The course will then transition to proteomics, focusing on the complexity of the neuronal proteome and the techniques used to study protein expression, structure, and function. Students will explore mass spectrometry-based proteomics, targeted protein quantification methods, and specialized techniques for investigating post-translational modifications, which play a crucial role in synaptic plasticity and neuronal signaling. Theoretical sessions will be complemented by case studies demonstrating how proteomic analyses have contributed to discoveries in neuroscience.
Metabolomics and lipidomics will form another theme, highlighting the importance of metabolic pathways in neuronal physiology and pathology. This section will cover the principles of mass spectrometry and nuclear magnetic resonance spectroscopy for metabolite profiling, with a focus on brain-specific metabolic adaptations. The discussion will extend to lipidomics, emphasizing the role of lipid signaling in synaptic function and neurodegenerative diseases.
Throughout the course, bioinformatics will be a central component, integrated into each module to provide students with the necessary computational skills for OMICS data analysis.
Practical sessions will be designed to familiarize them with key bioinformatics tools and databases, enabling them to independently analyze and interpret data relevant to their research interests.
Guest experts will contribute to select sessions, offering perspectives on how OMICS technologies are currently being applied in neuroscience research. These discussions will cover topics such as the integration of multi-OMICS data. Panel discussions will encourage students to engage with experts, fostering critical thinking and interdisciplinary dialogue.
By the end of the course, students will have acquired a comprehensive understanding of OMICS technologies and their applications in neuroscience. They will be equipped with both theoretical knowledge and hands-on experience, allowing them to navigate the complexities of molecular neuroscience.

Lectures and integrative teaching seminars.

Any change to the methods described, will be communicated on the web sites of the Department and of the Study Program or provided by the faculty at the beginning of the course.

The exam will include a written assessment, which may consist of multiple-choice (true or false) questions or open-ended written questions with concise answers. To successfully pass the exam, students are required to demonstrate a satisfactory comprehension of all the topics covered in the course.

This course explores topics closely related to two goals of the United Nations 2030 Agenda for Sustainable Development (SDGs). Specifically,
N.3 Health and wellbeing
N.4 Education of quality