Expression of coding and non-coding genes in mammalian genome

Challenges in measuring RNA expression

Capturing specific protein–DNA interactions in biological systems by ChIP 

DNA Cloning strategies

RNA-based therapies 

Working with mammalian genomic DNA

CRISPR technology and its applications in drug discovery

Protein expression and purification

Biochemical and biophysical protein characterization

Protein interactions in vitro

Measuring protein interactions in vivo 

Working with Antibodies

Protein-DNA interactions involved in DNA repair and DNA replication

Principles of enzymology 

Liquid-liquid phase separation

Proteins on DNA – just look at the thing. What can we learn from using single molecule approach

Case Studies

- Course Introduction
- Programming imprinting 
- Code writing

R basics: language structure, data types, object-oriented programming

- Data manipulation
- Performance testing

Writing R Extensions (by Uberto Pozzoli)

- Bioconductor & Data Visualization (by Fabio Iannelli)
- Memory Handling

Data Communication + Build interactive web app: Shiny

Introduction to Python/Jython programming languages:

• From ImageJ macro language to Jython programming language in Fiji
• Python basics: syntax, statements, expressions, list, loops, conditional statements, functions
• From Python to Jython: object oriented programming
• Jython main APIs and Classes for Fiji
• Python vs Jython: Pros and cons

Hands-on: practice on a nuclei segmentation macro and comparison with Jython language

• Implementation of nuclei segmentation
• Focus on Regions Of Interest Manager
• Creation of customized Results table

More complex image analysis and Analysis of Multiple Images

Hands-on: nuclei segmentation with spots count and intensity measurement

• Bio-formats handling
• User interaction
• Multiple channel handling
• Parent-children objects handling
• Multiple Results Table management
• Multiple images analysis

Introduction to machine learning and deep learning in image analysis

• Theoretical introduction about machine learning and deep learning
• Machine learning as tool for semantic segmentation/pixel classification
• Deep Learning as tool for image restoration and instance segmentation

Hands-on: Semantic segmentation/pixel classification with Labkit

Image restoration and instance segmentation using Deep Learning in Python

• Introduction to Jupyter (Python) notebooks and their use in cloud with ZeroCostDL4Mic
• Deep Learning algorithms for image restoration and instance

Hands-on: Image restoration and nuclear segmentation with Deep Learning algorithms

• Image restoration using Noise2Void algorithm; model training and application using Jupiter notebooks in ZeroCostDL4Mic
• Nuclear segmentation using 2D and 3D Stardist using Jupyter notebooks in ZeroCostDL4Mic

Segmentation in tissues using Stardist with QuPath software:

• Introduction to QuPath for tissue sections analysis
• Cell segmentation in tissues

Hands-on: Cell segmentation in tissues using Stardist and cell-type classification in different populations

Cell segmentation using Cellpose software in Python:

• Introduction to conda environments and their utility for using local Jupyter (Python) notebooks
• CellPose Deep Learning software for cell and nuclear segmentation

Hands-on: Segmentation of cells and nuclei using CellPose and evaluation of cytoplasmic signal using local Jupyter notebooks

General principles of qualitative analysis

Collecting and working with different qualitative data

The main approaches to qualitative analysis

The use of software and AI in qualitative research

Applications of qualitative analysis in medical humanities (with Valeria Sebri)

The Philosophical and Scientific Basis for the Integration of Medicine and Psychology.

PsychoNeuroEndocrineImmunology: Integration of Psychology, Neurology, Endocrinology and Immunology. An overview (part I).

PsychoNeuroEndocrineImmunology: Integration of Psychology, Neurology, Endocrinology and Immunology. An overview (part II).

Epigenetics and fetal programming of adulthood.

Principles of gut-microbiota-brain axis in human physiology and pathology.

• Aim of the Data management session
• Introduction: The Data Management Plan (DMP)
• Data management: who is involved?
• Analysis of a DPM, focus and relevant sections
• Open science & FAIR data
• Data

• Data storage
• Data Security
• Documenting your data
• Data management good practices.
• Research ethics and data protection,
• Writing a Data Management Plan, Conclusions

Critical reading of the literature:

• How to read a scientific manuscript
• How to prepare effective Journal Clubs
• Practical

Experimental Design

• From question to conclusions

Research integrity:

• Shared values in scientific research
• Running and recording experiments

Scientific integrity vs scientific misconduct:

• Generating and manipulating data - Standard of conduct
• Famous cases of alleged scientific misconduct
• Time management

Gender diversity in scientific experiments

Course introduction: Intro, aims, expectations

Development and functions of neocortex

Neuronal guidance and connectivity

Practical sessions – Demonstration

Microinjection into organoids/mouse cortical slices (Elena Taverna, Elena Restelli)

Electrophysiology (Filippo Mirabella)

Introduction into human PSC models and neuroimmunology

PSC models to study evolutionary neurobiology

From neurodiversity to physiopathology: an introduction

Workshop and tutorial stem cells, reprogramming and organoids

• Reprogramming
• Stem cell culture
• Organoid automation

Neuroglial biology

Workshop and tutorial: Neurogenomics - Part I

Workshop and tutorial: Neurogenomics - Part II

Wrap-up & Discussion

Usable R fundamentals:

• Introduction and history of R
• IDEs, RStudio, packages and good practices
• Anatomy of a command, standard output/error, batch & interactive mode
• R data structures, basic operators, objects, classes
• Reading data into R objects
• The Tidy data
• Important functions in R for Tidy Data

The grammar of graphics with ggplot & data visualization:

• Introductory rules of data visualisation
• The generic R graphic devices, plot command, colours
• Colours in R
• The grammar of graphics
• The ggplot2 levels and hierarchy
• Different types of geoms for different types of data (histograms, bar plots, pie charts, density plots, scatter plots, box plots)
• Piping summarized data into ggplot
• Special purpose graphics: volcano, heatmap, bubble plot

Fundamentals of descriptive statistics:

• Different kind of data, how to distinguish them
• Basic descriptive indices
• How to evaluate data position, variability and symmetry.
• How to interpret data position, variability and symmetry.
• Correlation and covariance

Bases of statistical inferences:

• Research hypotheses and tests
• Definition of null and alternative hypothesis
• The p-value
• One and two-sided tests
• (optional) Type I and II errors and power of a test
• Evaluating performances: Sensitivity and Precision
• Differences between parametric and non-parametric tests
• Choosing the most fitting test to answer a question
• Differences between independent and paired tests
• How to choose a test

Introduction to medical data: characteristics, uncertainty, data taxonomy, qualitative vs quantitative data

Introduction to quantitative analysis: data types, measurement scales, data visualization, and descriptive statistics

Practical session: Data visualization and descriptive statistics

Inferential statistics and hypothesis tests, regression and correlation

Practical session: Case studies

Types of clinical studies and sample size determination

Measures of effects, measures of frequencies, diagnostic tests and ROC curves

Introduction to advanced quantitative analysis techniques using machine learning

Application examples

• Course introduction (Dario Parazzoli, IFOM)
• Sample preparation (Sara Barozzi, IFOM)
• Fundamentals of optics and wide-field microscopy (Mario Faretta, IEO)

• Tissue imaging: histology (Claudio Tripodo, IFOM)
• Optical sectioning – part 1 (Simona Rodighiero, IEO)
• Optical sectioning – part 2 (Dario Parazzoli, IFOM)
• Wrap-up discussion

• Big samples: sample preparation (Fabrizio Orsenigo, IFOM)
• Big samples: imaging (Zeno Lavagnino, IFOM)

• Live cell-imaging – part 1 (Sara Barozzi, IFOM)
• Lice cell-imaging – part 2 (Ambra Dondi, IEO)
• F-techniques (Zeno Lavagnino, IFOM)

• Introduction to electron microscopy (Simona Rodighiero, IEO)
• Correlative microscopy (Galina Beznusenko, IFOM)
• Introduction to super-resolution & cutting-edge microscopy (Nils Gauthier, IFOM)

• “Scientific group” seminar (Andrea Palamidessi, IFOM)
• Q&A

• General principles of scientific communication
• How to prepare an effective presentation

• How to prepare an effective poster
• Basic principles of Scientific writing
• Q&A

Discussion of the assignment, with class divided into two groups (one per day)

Principles of Effective Writing

Grammar in Scientific English

Exercises (performed by students in class)

Writing an original manuscript 

Plagiarism and Copyrights 

How to write a good CV/resume

Discussion of Abstracts and Presentations before/after

• Introduction to AI in medicine
• Overview of Artificial Intelligence and machine learning
• Introduction to the application of AI in medicine
• Regulatory and ethical considerations. The role of Xai

• Machine learning fundamentals
• Basic concepts of supervised, unsupervised, and reinforcement learning
• Applications of machine learning in medicine: diagnosis, prognosis, treatment selection, drug discovery, etc.
• Evaluation metrics for machine learning models in healthcare

• Introduction to Python programming language and its applications in data analysis
• Data manipulation and analysis using Python libraries such as Numpy, Pandas, and Matplotlib
• Hands-On exercises and coding assignments using Python for data preprocessing and exploratory data analysis with medical datasets

• Overview of orange data mining software and its functionalities
• Data preprocessing, feature engineering, and model building using orange
• Hands-on tutorials and case studies using orange for machine learning tasks with medical datasets

• Overview of ChatGPT and its applications in Natural Language Processing (NLP)
• Basics of text preprocessing

Hands-on workshop on using ChatGPT and medical text data

Introduction to digital images:

• How an image is obtained, sampling, monochromatic and color images, Fourier transforms

Introduction to Fiji and objects segmentation:

• Introduction to Fiji’s key functionalities: installation, basic options, opening of images (Bio-Formats Import), management of simple images, export of analysed images
• Measurements of images: image calibration, set measurements parameters, creation of selections and Region Of Interest
• Processing images: geometric transformations, filters, mathematical operations
• Binarization of images: image thresholding methods, processing of binary images
• Objects segmentation and detection: Analyse particles/Find Maxima functionalities

Hands-on part 1: Nuclear segmentation and spots detection:

• Step-by-step creation of an image analysis workflow for nuclear segmentation and nuclear spots detection

Hands-on part 2: Nuclear segmentation and spot detection:

• Step-by-step recording of macro commands within the analysis workflow

Introduction to Macro Language:

• Introduction to macro language: What is a macro? Why do we need macros?
• How can move from a set of recorded commands to a macro for batch processing?
• Built-in functions in FIJI: basics imaging analysis functions
• Basics of programming in Fiji macro language with examples: variables, arrays, loops, conditional statements
• Import and export of files: bio-formats and bio-formats macros extensions

Hands-on Macro Language:

• Hands-on: Generalization of a series of recorded commands to create a macro for batch nuclear segmentation

Introduction to Python/Jython programming languages:

• From ImageJ macro language to Jython programming language in Fiji
• Python basics: syntax, statements, expressions, list, loops, conditional statements, functions
• From Python to Jython: object oriented programming
• Jython main APIs and Classes for Fiji
• Python vs Jython: Pros and cons

Hands-on: practice on a nuclei segmentation macro and comparison with Jython language:

• Implementation of nuclei segmentation
• Focus on Regions Of Interest Manager
• Creation of customized Results table

More complex image analysis and Analysis of Multiple Images

Hands-on: nuclei segmentation with spots count and intensity measurement:

• Bio-formats handling
• User interaction
• Multiple channel handling
• Parent-children objects handling
• Multiple Results Table management
• Multiple images analysis

Segmentation in tissues with QuPath software:

• Introduction to QuPath for tissue sections analysis
• Cell segmentation in tissues

Hands-on: Cell segmentation in tissues using a pre-trained deep learning model and cell-type classification in different populations

Examples of Image Analysis in Fiji (Collective Motion and Tracking):

• Introduction to the concepts of collective cellular mobility and motivation
• Different ways and parameters to study collective cell motility:
  Wound Healing, Cell Streaming, Particle Image Velocimetry to evaluate speed of wound closure, velocity and coordination of movement

Images for publication and guidelines for best practices in image processing

Introduction to machine learning and deep learning in image analysis:

• Theoretical introduction about machine learning and deep learning
• Machine learning as tool for semantic segmentation/pixel classification
• Deep Learning as tool for image restoration and instance segmentation

Image restoration and instance segmentation using Deep Learning in Python:

• Introduction to Jupyter (Python) notebooks and their use in cloud with ZeroCostDL4Mic
• Deep Learning algorithms for image restoration and instance

Hands-on: Image restoration and nuclear segmentation with Deep Learning algorithms:

• Image restoration using Noise2Void algorithm; model training and application using Jupiter notebooks in ZeroCostDL4Mic
• Nuclear segmentation using 2D and 3D Stardist using Jupyter notebooks in ZeroCostDL4Mic

Cell segmentation using Cellpose software in Python:

• Introduction to conda environments and their utility for using local Jupyter (Python) notebooks
• Cellpose Deep Learning software for cell and nuclear segmentation

Hands-on: Segmentation of cells and nuclei using CellPose and evaluation of cytoplasmic signal using local Jupyter notebooks

• Fluorescence, fluorochromes and fluorescent dyes (Michela Cravedi, IFOM)
• How antibodies work (Michela Cravedi, IFOM)
• Sample preparation for flow cytometry experiments (Maria Grazia Totaro, IFOM)

• Introduction to flow cytometry - Conventional flow cytometry (Simona Ronzoni, IEO)
• A brief introduction to fluorescent activated cell sorting (TBD)

• Spectral flow cytometry (Sara Martone, IFOM)
• Panel design considerations (Maria Grazia Totaro, IFOM)
• Data analysis and data presentation (TBD)

• Troubleshooting and round table

Introduction to the course

The society of knowledge and the state of art in science communication

Basis of theory of communication: frame, emotion vs rationality 

Science and Society: risk communication

Scientific versus public communication of science

Storytelling and data in science communication (and pseudoscience) 

Science and values: a glimpse into Bioethics and Research Integrity

Science and social media: handle with care

Scientific journalism: cheerleader or watchdog?

Know your press office! (and few tips on how to survive interviews and press conferences) 

How to communicate to students: edutainment/serious games

Workshop SMOGVILLE

Influence of AI between production and communication of science

Workshop - Simulation of interview and/or simulation of press conference 

Workshop pratico (pitch, ecc. con stili e modalità diverse comunicate prima)

Conclusions

Genome and transcriptome organization and regulation

TBD

Spatiotemporal regulation of gene expression: from description to perturbation and back

TBD

Cell type-specific intronic RNAs shape genome architecture during neuronal lineage specification