Biology subjects

Dalmasso, G.

Publications and source records attributed to Dalmasso, G..

3 recordsLinked to original sources

bioRxiv2025

LimbLab: Pipeline for 3D Analysis and Visualisation of Limb Bud Gene Expression

MotivationAlthough some aspects of limb development can be treated as a 2D problem, a true understanding of the morphogenesis and patterning requires 3D analysis. Since the data on gene expression patterns are largely static 3D image stacks, a major challenge is an efficeint pipeline for staging each data-set, and then aligning and warping the data into a standard atlas for convenient visualisation. ResultsWe present a novel bioinformatic pipeline tailored for 3D visualization and analysis of developing limb buds. The pipeline integrates key steps such as data acquisition, volume cleaning, surface extraction, staging, alignment, and advanced visualization techniques. Its modular design allows researchers to customize workflows while maintaining compatibility with tools such as Fiji and Vedo. Our results highlight the pipelines effectiveness in managing complex 3D gene expression data, enhancing the accuracy and reproducibility of limb development studies. Availability and ImplementationLimbLab is released under the MIT license. The pipeline is implemented in Python and is available as an open-source package. It can be easily installed via pip and is accompanied by comprehensive documentation (https://limblab.embl.es/) to support users at various levels of expertise. The pipeline can be accessed and downloaded at https://github.com/LauAvinyo/limblab.

bioinformatics↗

bioRxiv2023

Spatially-resolved metabolomic identifies colibactin-specific principles of reprogrammed lipid metabolism to promote cancer progression.

Intratumoral bacteria locally contribute to cellular and molecular tumor heterogeneity that support cancer stemness through poorly understood mechanisms. This study aims to explore how Colibactin-producing Escherichia coli (CoPEC) flexibly alters the tumor microenvironment in right-sided colorectal cancer (CRC). Metabolomic and transcriptomic spatial profiling uncovered that CoPEC colonization establishes a high-glycerophospholipid microenvironment within the tumor that is conducive to exhaustion of infiltrated CD8+ T cell and has a lowered prognostic value in right-sided CRC. Mechanistically, the accumulation of lipid droplets in infected cancer cells relied on the production of colibactin as a measure to limit genotoxic stress and supply with sufficient energy for sustaining cell survival and lowering tumor immunogenicity. Specifically, a heightened phosphatidylcholine remodeling of CoPEC-infected cancer cells by the enzyme of the Lands cycle coincided with a lowered accumulation of proapoptotic ceramide and lysophosphatidylcholine. Consequently, a reduced infiltration of CD8+ T lymphocytes that produce the cytotoxic cytokines IFN-{gamma} was found where invading bacteria have been geolocated. By contrast, such an immunosuppressive dysmetabolic process was not observed when human colon cancer cells were infected with the mutant strain that did not produce colibactin (11G5{delta}ClbQ). This work revealed an unexpected property of CoPEC on lipid overload within tumors that could locally provide an inflammatory environment leading to immunosuppressive mechanisms and tumor expansion. This may pave the way for improving chemoresistance and subsequently outcome of CRC patients who are colonized by CoPEC.

cancer biology↗

bioRxiv2021

4D reconstruction of developmental trajectories using spherical harmonics

Although the full embryonic development of species such as Drosophila and zebrafish can be 3D imaged in real time, this is not true for mammalian organs, as normal organogenesis cannot be recapitulated in vitro. Currently available 3D data is therefore ex vivo images which provide only a snap shot of development at discrete moments in time. Here we propose a computer-based approach to recreate the continuous evolution in time and space of developmental stages from 3D volumetric images. Our method uses the mathematical approach of spherical harmonics to re-map discrete shape data into a space in which facilitates a smooth interpolation over time. We tested our approach on mouse limb buds (from E10 to E12.5) and embryonic hearts (from 10 to 29 somites). A key advantage of the method is that the resulting 4D trajectory takes advantage of all the available data (i.e. it is not dominated by the choice of a few "ideal" images), while also being able to interpolate well through time intervals for which there is little or no data. This method not only provides a quantitative basis for validating predictive models, but it also increases our understanding of morphogenetic processes. We believe this is the first data-driven quantitative 4D description of limb morphogenesis.

developmental biology↗