Biology subjects

Tedeschi, G.

Publications and source records attributed to Tedeschi, G..

6 recordsLinked to original sources

bioRxiv2025

An open, integrated platform for multiplexed bioluminescence microscopy

We report an imaging package to democratize all-in-one bioluminescence and fluorescence microscopy. The platform comprises three tools: PhasorViewer, a visualization suite to design experiments and identify optimal probe combinations; PhasorScope, an open-source, cost-effective microscopy framework to upgrade conventional microscopes; and PhasorAnalysis, a dedicated, user-friendly analysis pipeline to quantify phasor imaging data. We demonstrate the utility of the platform for multiplexed, simultaneous fluorescence and bioluminescence imaging with readily accessible optical reporters.

bioengineering↗

bioRxiv2025

Highly multiplexed spectral FLIM via physics informed data analysis

Spectral fluorescence lifetime imaging (S-FLIM) allows for the simultaneous deconvolution of signal from multiple fluorophore species by leveraging both spectral and lifetime information. However, existing analyses still face multiple difficulties in decoding information collected from typical S-FLIM experiments. These include: using information from pre-calibrated spectra in environments that may differ from the cellular context in which S-FLIM experiments are performed; limitations in the ability to deconvolute species due to overlapping spectra; high photon budget requirements, typically about a hundred photons per pixel per species. Yet information on the spectra themselves are already encoded in the data and do not require pre-calibration. What is more, efficient photon-by-photon analyses are possible reducing both the required photon budget and making it possible to use larger budgets in order to discriminate small differences in spectra to resolve spatially co-localized fluorophore species. To achieve this, we propose a Bayesian S-FLIM framework capable of simultaneously learning spectra and lifetimes photon-by-photon ultimately using limited photon counts and being highly data efficient. We demonstrate the proposed framework using a range of synthetic and experimental data and show that it can deconvolve up to 9 species with heavily overlapped spectra.

biophysics↗

bioRxiv2024

ESPRESSO: Spatiotemporal omics based on organelle phenotyping

Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have been instrumental to improving our understanding of complex biological systems. Despite fast-pace advancements, a crucial dimension is still left to explore: time. To capture this key parameter, we introduce ESPRESSO (Environmental Sensor Phenotyping RElayed by Subcellular Structures and Organelles), a pioneering technique that provides high-dimensional phenotyping resolved in space and time. Through a novel paradigm, ESPRESSO combines fluorescent labeling, advanced microscopy and bioimage and data analysis to extract morphological and functional information of the organelle network unveiling phenotypic changes over time at the single-cell level. In this work, we present ESPRESSOs methodology and its application across numerous cellular systems, showcasing its ability to discern cell types, stress response, differentiation and immune cells polarization. We then correlate ESPRESSO phenotypic changes with gene expression and demonstrate its applicability to 3D cultures, offering a path to revolutionizing biological exploration, providing invaluable insights into cellular states in both space and time.

bioengineering↗

bioRxiv2024

Monitoring macrophage polarization with gene expression reporters and bioluminescence phasor analysis

Macrophages exhibit a spectrum of behaviors upon activation and are generally classified as one of two types: inflammatory (M1) or anti-inflammatory (M2). Tracking these phenotypes in living cells can provide insight into immune function, but remains a challenging pursuit. Existing methods are mostly limited to static readouts or difficult to employ for multiplexed imaging in complex 3D environments while maintaining cellular resolution. We aimed to fill this void using bioluminescent technologies. Here we report genetically engineered luciferase reporters for long-term monitoring of macrophage polarization via spectral phasor analysis. M1- and M2-specific promoters were used to drive the expression of bioluminescent enzymes in macrophage cell lines. The readouts were multiplexed and discernable in both 2D and 3D formats with single cell resolution in living samples. Collectively, this work expands the toolbox of methods for monitoring macrophage polarization and provides a blueprint for monitoring other multifaceted networks in heterogeneous environments.

bioengineering↗

bioRxiv2024

Multimodal Phasor Approach to study breast cancer cells invasion in 3D spheroid model

We implemented a multimodal set of functional imaging techniques optimized for deep-tissue imaging to investigate how cancer cells invade surrounding tissues and how their physiological properties change in the process. As a model for cancer invasion of the extracellular matrix, we created 3D spheroids from triple-negative breast cancer cells (MDA-MB-231) and non-tumorigenic breast epithelial cells (MCF-10A). We analyzed multiple hallmarks of cancer within the same spheroid by combining a number of imaging techniques, such as metabolic imaging of NADH by Fluorescence Lifetime Imaging Microscopy (NADH-FLIM), hyperspectral imaging of a solvatochromic lipophilic dye (Nile Red) and extracellular matrix imaging by Second Harmonic Generation (SHG). We included phasor-based bioimage analysis of spheroids at three different time points, tracking both morphological and biological properties, including cellular metabolism, fatty acids storage, and collagen organization. Employing this multimodal deep-imaging framework, we observed and quantified cancer cell plasticity in response to changes in the environment composition.

cancer biology↗

bioRxiv2018

Stochastic rank aggregation for the identification of functional neuromarkers

Background and aimsThe main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N>100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets.\n\nMethodsBrain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation.\n\nResultsWe found parcels for which the rankings were different among control subjects and subjects affected by Parkinsons disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity.\n\nConclusionsThe proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases.

neuroscience↗