Nicolas Ruffini

About me

Nicolas Ruffini

Current projects

Common & Unique Factors in Neurodegeneration

Neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases (NDDs) for many years, thus we gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on these three omics-layers. Our meta-study reveals highly significant processes in the identified set of 139 genes, common to all analyzed NDDs and might therefore contribute to the development of pharmaceutical measures against neurodegeneration in general. Regarding future research on this topic, we want to expand the repertoire of omics-layers by epigenomics and concentrate further on the differences between these separate NDDs according to the regulation of their common genes. Additional analyses of the overlaps between different omics layers at the level of individual diseases, as well as differences in the intersection of AD, PD, and ALS in contrast with the autosomal dominant disorder HD, could also provide new insight in light of the knowledge of the processes common to all NDDs.

IntelliPy – A Python GUI for analyzing long term behavior monitoring of mice in IntelliCages

The differentiation between susceptible and resilient individuals in mouse models is neither trivial nor indisputably defined. Long-term surveillance of mice can give more insights into mice behavior and can consequently help classifying individuals as resilient or susceptible. The IntelliCage is a system for long term surveillance of mice without human intervention. It comes with software for data analysis, but some aspects of analysis are not covered by this software. In order to supplement the analysis with further steps like computing the learning rate, I developed a graphical user interface, IntelliPy, to expand the current possibilities of data analysis in this new GUI. It is aimed to be usable also by scientists without any programming experience in order to make IntelliCage data analysis easier, faster, and more applicable.


IDSAIR - Optimization of the Calcium Imaging Analysis Pipeline

Calcium Imaging Analysis enables researchers to track the activity of hundreds and thousands of neurons within the brains of living animals. However, analyzing this huge amount of data raises some difficulties. In order to improve and accelerate the analysis of calcium imaging data, we collaborate with the Fraunhofer ITWM ( and the group of Prof. Dr. Albrecht Stroh ( to automatically label neurons in the produced image files using Deep Learning. Following this part of the analysis, the peaks within the transients of the identified neurons must be identified. By optimizing the whole analysis pipeline and making the procedure available and easily useable for other researchers, the effort and time spent with such analyses can be drastically reduced. Furthermore, results will be better comparable as the analysis will be less dependent on personal decisions.

Other Projects:

  • A large data set of human proteome data in combination with a score to represent their current state of stress resilience gave us the opportunity to try out the prediction of resilience scores of subjects by their proteome. By applying machine learning techniques to extract sets of highly predictive proteins for this resilience score, we were able to find small sets of proteins that have a highly significant predictive power of these resilience scores. The understanding of the biological background of these protein sets is not yet complete and represents an exciting task yet to be solved.

Collaboration with Raffael Kalisch


  • Collaboration with Monika Chongtham on behavioral experiments with mice with the aim of finding factors that can predict if one mouse will act resilient or susceptible after being stressed. In order to do this, we also need a better way of classifying the mice into resilient and susceptible than the current system (using the SI-score) so we try to combine subjective human perception of the mice behavior with video-tracking. The aim is to extract objective parameters from the trajectory of the mice to create parameters like counting shy away in order to represent the mouse's behavior with better parameters than just a quotient of two time periods (current system). This project also led to the Bachelor thesis of Vincent Dietrich and his development of the Resilipy GUI.