Biology subjects

Moreno-Andres, D.

Publications and source records attributed to Moreno-Andres, D..

5 recordsLinked to original sources

bioRxiv2023

SART1 localizes to spindle poles forming a SART1 cap and promotes spindle pole assembly

The nuclear protein SART1 has been associated with pre-mRNA splicing but SART1 RNAi knockdown results also in defects in mitotic progression, centrosome biogenesis and chromosome cohesion. The mitotic roles of SART1 have not been characterized in detail and it remains unclear whether SART1 functions in mitosis directly or indirectly via pre-mRNA splicing. Here, we identify SART1 as a direct, mitosis-specific microtubule-associated protein. SART1 downregulation in human cells leads to spindle assembly defects with reduced microtubule dynamics, lack of end-on attachment, and checkpoint activation, while microtubule dynamics remain unaffected in interphase. SART1 uniquely localizes to the distal surface of mitotic centrosomes along the spindle axis, forming a previously not described structure we refer to as SART1 cap. Immunoprecipitation of SART1 consistently identifies centrosomal proteins as interaction partners. Immunostaining of these shows that SART1 downregulation does not affect centriole duplication and centrosome-accumulation of {gamma}-tubulin but reduces the accumulation of selective pericentriolar material (PCM) proteins like Ninein. Depletion of SART1 from frog egg extracts disrupts spindle pole assembly around sperm nuclei and DNA-coated beads. Spindles formed around DNA-coated beads do not contain centrosomes but still recruits PCM proteins for spindle pole assembly. We finally show that the N-terminus of SART1 is its microtubule-binding region and essential for spindle assembly. Our data unravel a unique localization of SART1 and a novel function to recruit selective PCM proteins for spindle pole assembly in centrosomal and acentrosomal spindle assembly.

cell biology↗

bioRxiv2023

Automatic Detection of Cell-cycle Stages using Recurrent Neural Networks

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well, to quantify the amount of misclassification. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

bioinformatics↗

bioRxiv2022

END-TO-END CLASSIFICATION OF CELL-CYCLE STAGES WITH CENTER-CELL FOCUS TRACKER USING RECURRENT NEURAL NETWORKS

Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Consequently, a large number of manual and semi-automated time-lapse microscopy image analyses of mitosis have been carried out in recent years. In this paper, we propose a method for automatic detection of cell-cycle stages using a recurrent neural network (RNN). An end-to-end model with center-cell focus tracker loss, and classification loss is trained. The evaluation was conducted on two time-series datasets, with 6-stages and 3-stages of cell splitting labeled. The frame-to-frame accuracy was calculated and precision, recall, and F1-Score were measured for each cell-cycle stage. We also visualized the learned feature space. Image reconstruction from the center-cell focus module was performed which shows that the network was able to focus on the center-cell and classify it simultaneously. Our experiments validate the superior performance of the proposed network compared to a classifier baseline.

bioinformatics↗

bioRxiv2021

LiveCellMiner: A New Tool to Analyze Mitotic Progression

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

cell biology↗

bioRxiv2021

The nucleoporin Nup50 activates the Ran guanyl-nucleotide exchange factor RCC1 to promote mitotic NPC assembly

During mitotic exit, thousands of nuclear pore complexes (NPCs) assemble concomitant with the nuclear envelope to build a transport-competent nucleus. We show here that Nup50 plays a crucial role in NPC assembly that is independent of its well-established function in nuclear transport. RNAi-mediated downregulation in cells or immunodepletion of the protein in Xenopus egg extracts interferes with NPC assembly. We define a conserved central region of 46 residues in Nup50 that is crucial for Nup153 and MEL28/ELYS binding, and NPC interaction. Surprisingly, neither NPC interaction nor binding of Nup50 to importin , {beta}, the GTPase Ran or chromatin is crucial for its function in the assembly process. Instead, we discovered that an N-terminal fragment of Nup50 can stimulate the Ran guanine exchange factor RCC1 and NPC assembly, indicating that Nup50 acts via the Ran system in mitotic NPC reformation. In support of this conclusion, Nup50 mutants defective in RCC1 binding and stimulation cannot replace the wild type protein in in vitro NPC assembly assays.

cell biology↗