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Cicconet, M.

Publications and source records attributed to Cicconet, M..

3 recordsLinked to original sources

FIT2 is a lipid phosphate phosphatase crucial for endoplasmic reticulum homeostasis

The endoplasmic reticulum (ER) protein Fat-Induced Transcript 2 (FIT2) has emerged as a key factor in lipid droplet (LD) formation, although its molecular function is unknown. Highlighting its importance, FIT2 orthologs are essential in worms and mice, and FIT2 deficiency causes a deafness/dystonia syndrome in humans. Here we show that FIT2 is a lipid phosphate phosphatase (LPP) enzyme that is required for maintaining the normal structure of the ER. Recombinant FIT2 exhibits LPP activity in vitro and loss of this activity in cells leads to ER membrane morphological changes and ER stress. Defects in LD formation in FIT2 depletion appear to be secondary to membrane lipid abnormalities, possibly due to alterations in phospholipids required for coating forming LDs. Our findings uncover an enzymatic role for FIT2 in ER lipid metabolism that is crucial for ER membrane homeostasis.

cell biology

Bots for Software-Assisted Analysis of Image-Based Transcriptomics

We introduce software assistants - bots - for the task of analyzing image-based transcriptomic data. The key steps in this process are detecting nuclei, and counting associated puncta corresponding to labeled RNA. Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities. For challenging nuclei segmentation cases, we enable the user to train a stacked Random Forest, which includes novel circularity features that leverage prior knowledge regarding nuclei shape for better instance segmentation. This machine learning model can be trained on a modern CPU-only computer, yet performs comparably with respect to a more hardware-demanding state-of-the-art deep learning approach, as demonstrated through experiments. While the primary motivation for the bots was image-based transcriptomics, we also demonstrate their applicability to the more general problem of scoring \"spots\" in nuclei.

bioinformatics

Whole-Brain Serial-Section Electron Microscopy In Larval Zebrafish

Investigating the dense meshwork of wires and synapses that form neuronal circuits is possible with the high resolution of serial-section electron microscopy (ssEM)1. However, the imaging scale required to comprehensively reconstruct axons and dendrites is more than 10 orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons2--some of which span nearly the entire brain. The difficulties in generating and handling data for relatively large volumes at nanoscale resolution has thus restricted all studies in vertebrates to neuron fragments, thereby hindering investigations of complete circuits. These efforts were transformed by recent advances in computing, sample handling, and imaging techniques1, but examining entire brains at high resolution remains a challenge. Here we present ssEM data for a complete 5.5 days post-fertilisation larval zebrafish brain. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management. The resulting dataset can be analysed to reconstruct neuronal processes, allowing us to, for example, survey all the myelinated axons (the projectome). Further, our reconstructions enabled us to investigate the precise projections of neurons and their contralateral counterparts. In particular, we observed that myelinated axons of reticulospinal and lateral line afferent neurons exhibit remarkable bilateral symmetry. Additionally, we found that fasciculated reticulospinal axons maintain the same neighbour relations throughout the extent of their projections. Furthermore, we use the dataset to set the stage for whole-brain comparisons of structure and function by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. We provide the complete dataset and reconstructions as an open-access resource for neurobiologists and others interested in the ultrastructure of the larval zebrafish.

neuroscience