A5005720694- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
- Cancer Genomics and Diagnostics
- Genomics and Chromatin Dynamics
- Radiomics and Machine Learning in Medical Imaging
- DNA Repair Mechanisms
- Advanced Proteomics Techniques and Applications
- Neuroscience of respiration and sleep
- AI in cancer detection
- Metabolomics and Mass Spectrometry Studies
- Quantum-Dot Cellular Automata
- Genomics and Phylogenetic Studies
- Cell Adhesion Molecules Research
- Evolution and Genetic Dynamics
- Neonatal Respiratory Health Research
- Congenital Diaphragmatic Hernia Studies
- RNA modifications and cancer
- Bioinformatics and Genomic Networks
- Immune cells in cancer
- Gene Regulatory Network Analysis
- RNA and protein synthesis mechanisms
- Metabolism and Genetic Disorders
- Molecular Biology Techniques and Applications
- Computational Drug Discovery Methods
Azienda Ospedaliera Pugliese Ciaccio
2024
IBM Research - Zurich
2018-2024
University of Lausanne
2024
University of Patras
2010-2021
ETH Zurich
2014-2021
Institut national de recherche en informatique et en automatique
2014
Magna Graecia University
2008-2013
Understanding protein dynamics is crucial in order to elucidate function and interactions. Advances modern microscopy facilitate the exploration of mobility fluorescently tagged proteins within living cells. Fluorescence recovery after photobleaching (FRAP) an increasingly popular functional live-cell imaging technique which enables study dynamic properties at a single-cell level. As increasing number labs generate FRAP datasets, there need for fast, interactive user-friendly applications...
Abstract Summary: We present easyFRAP, a versatile tool that assists quantitative and qualitative analysis of fluorescence recovery after photobleaching (FRAP) data. The user can handle simultaneously large data sets raw data, visualize curves, exclude low quality perform normalization, extract parameters, batch save the resulting figures for further use. Our is implemented as single-screen Graphical User Interface (GUI) highly interactive, it permits parameterization visual assessment at...
Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled tumor profiling, however computational resources that exploit the measurements to quantify in spatially aware manner are largely missing. We present ATHENA (Analysis HEterogeNeity from spAtial measurements), framework facilitates visualization, processing analysis measurements. uses graph representations tumors bundles...
Abstract Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation standard measures that hinders the development personalized – they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests using z-scored response mitigates these limitations and leads to meaningful predictions, opening door for sophisticated ML oncology models.
Abstract Recent studies have shown that cell cycle and volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental computational method, CellCycleTRACER, to account for these mass cytometry data. CellCycleTRACER is applied data collected on three different types during TNFα stimulation time-course. reveals signaling relationships heterogeneity were otherwise masked.
Once-per-cell cycle replication is regulated through the assembly onto chromatin of multisubunit protein complexes that license DNA for a further round replication. Licensing consists loading hexameric MCM2–7 complex during G1 phase and dependent on licensing factor Cdt1. In vitro experiments have suggested two-step binding mode minichromosome maintenance (MCM) proteins, with transient initial interactions converted to stable loading. Here, we assess MCM in live human cells using an vivo...
Abstract Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. interpretable accurately generalizes in unseen spatial transcriptomics data. Importantly, we offer critical view using complex algorithms ignoring...
Proliferation of cells under hypoxia is facilitated by metabolic adaptation, mediated the transcriptional activator Hypoxia Inducible Factor-1 (HIF-1). HIF-1α, inducible subunit HIF-1 regulated oxygen as well oxygen-independent mechanisms involving phosphorylation. We have previously shown that CK1δ phosphorylates HIF-1α in its N-terminus and reduces affinity for heterodimerization partner ARNT. To investigate importance this mechanism cell proliferation hypoxia, we visually monitored...
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to expectation that near future computers will be capable performing simulations for natural science applications, operations research, machine learning at scales mostly inaccessible classical computers. Whereas impact already started recognized fields such as cryptanalysis, simulations, optimization among others, very little is known about full potential...
Cytoskeleton-mediated forces regulate the assembly and function of integrin adhesions; however, underlying mechanisms remain unclear. The tripartite IPP complex, comprising ILK, Parvin, PINCH, mediates integrin-actin link at Drosophila embryo muscle attachment sites (MASs). Here, we demonstrate a developmentally earlier for complex: to reinforce integrin-extracellular matrix (ECM) adhesion in response tension. In IPP-complex mutants, integrin-ECM linkage MASs breaks intense contractility....
Abstract Understanding the interactions between biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances single-cell technologies have enabled simultaneous quantification of multiple same cell, opening new avenues for understanding complexity and heterogeneity. Still, resulting multimodal datasets present unique challenges arising from high dimensionality sources acquisition noise. Computational methods able to match cells across different...
Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, number analytical models have been developed. Simplifications are inherent these models, which may lead to inexhaustive or inaccurate exploitation experimental data. An appealing alternative offered by simulation biological processes realistic environments at particle level. However,...
Understanding the spatial heterogeneity of tumors and its links to disease is a cornerstone cancer biology. Emerging technologies offer unprecedented capabilities towards this goal, but several limitations hinder their clinical adoption. To date, histopathology workflows still heavily depend on hematoxylin & eosin (H&E) serial immunohistochemistry (IHC) staining, cumbersome tissue-exhaustive process that yields unaligned tissue images. We propose VirtualMultiplexer, generative AI...
Abstract Background The co-existence of two genetically distinct metabolic disorders in the same patient has rarely been reported. Phenylketonuria (PKU) is an inborn error metabolism resulting from a phenylalanine hydroxylase deficiency. Fabry disease (FD) X-linked lysosomal storage disorder due to deficiency enzyme alpha-galactosidase A. Case presentation We report case 3 year- old boy affected by classic PKU and FD, both confirmed molecular data. FD was suspected at age 21 months on...
© 2013 Wiley Periodicals, Inc.
Understanding the three-dimensional (3D) structure of genome is essential for elucidating vital biological processes and their links to human disease. To determine how folds within nucleus, chromosome conformation capture methods such as HiC have recently been employed. However, computational that exploit resulting high-throughput, high-resolution data are still suffering from important limitations. In this work, we explore idea manifold learning 3D chromatin inference present a novel...
A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor believed to be key therapy failure and differences prognosis cancer patients. Comprehending heterogeneity, especially at the protein level, critical tracking evolution, showing presence of different phenotypical variants their location with respect tissue architecture. Although a variety techniques available quantifying expression, observed rarely addressed. The proposed method validated breast...