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Diosdi, A.

Publications and source records attributed to Diosdi, A..

5 recordsLinked to original sources

Spheroid surgery: a first-generation micromanipulation platform for extracting viable cell clusters from the outer layers of hydrogel-embedded spheroids

Three-dimensional (3D) cell culture systems have gained attention as in vitro models capable of reproducing the tissue-like architecture, spatial organization, and physiological morphology of in vivo tumors. Their ability to mimic native cell-cell and cell-matrix interactions has positioned 3D cell cultures as powerful models for studying tumor biology and drug responses. However, methods for the localized extraction of viable cells remain limited, as existing approaches often disrupt the original 3D structure, limit access to defined regions, or compromise cell viability, restricting the biological insight that can be achieved. We present a manual, micropipette-based manipulation system designed to extract viable multicellular clusters from the outer layers of compact 3D tumor spheroids, embedded in biocompatible hydrogels. The extracted clusters remained fully functional, demonstrating that the procedure is sufficiently gentle to remove small multicellular units. This technology enables the localized extraction of viable multicellular clusters from 3D in vitro models without complete dissociation of the parent spheroid. The method opens new opportunities for longitudinal studies, spatially targeted sampling and controlled manipulation of living 3D cultures. This capability marks a step toward a new era of 3D model manipulation, where researchers can add, remove, or replace specific regions of living multicellular systems with precision.

bioengineering↗

HCS-3DX, a next-generation AI-driven automated 3D high-content screening system

Self-organized three-dimensional (3D) cell cultures, collectively called 3D-oids, include spheroids, organoids and other co-culture models. Systematic evaluation of these models forms a critical new generation of high-content screening (HCS) systems for patient-specific drug analysis and cancer research. However, the standardisation of working with 3D-oids remains challenging and lacks convincing implementation. This study develops and tests HCS-3DX, a next-generation system revolutionising HCS analysis in 3D imaging and image evaluation. HCS-3DX is based on three main components: an automated Artificial Intelligence (AI)-driven micromanipulator for 3D-oid selection, an HCS foil multiwell plate for optimised imaging, and image-based AI software for single-cell data analysis. We validated HCS-3DX directly on 3D tumour models, including tumour-stroma co-cultures. Our data demonstrate that HCS-3DX achieves a resolution that overcomes the limitations of current systems and reliably and effectively performs 3D HCS at the single-cell level. Its application will enhance the accuracy and efficiency of drug screening processes, support personalised medicine approaches, and facilitate more detailed investigations into cellular behaviour within 3D structures.

bioinformatics↗

When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling

Data is the driving engine of learning-based algorithms, the creation of which fundamentally determines the performance, accuracy, generalizability and quality of any model or method trained on it. When only skilled or trained personnel can create reliable annotations, assisted software solutions are desirable to reduce the time and effort the expert must spend on labelling. Herein is proposed an automated annotation helper software package in napari that offers multiple methods to assist the annotator in creating object-based labels on 2D or 3D images.

bioinformatics↗

SpheroidPicker: An Automated 3D cell culture manipulator robot using deep learning

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20 % of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, ex vivo 3D cell cultures from the patients own cancer cells have gained importance. We recently evaluated the major advancements and needs of the 3D cell cultures screening field, and we concluded that strictly standardized sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and transfers the most appropriate ones between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.

bioengineering↗

3D-Cell-Annotator: an open-source active surface tool for single cell segmentation in 3D microscopy images

SummarySegmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a fully- and semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids, embryos) show that the precision of the segmentation reaches the level of a human expert.\n\nAvailability and implementation3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers.\n\nContactsfilippo.piccinini@irst.emr.it and horvath.peter@brc.mta.hu

bioinformatics↗