Biology subjects

Castelli, D.

Publications and source records attributed to Castelli, D..

3 recordsLinked to original sources

bioRxiv2021

Hypervariable-Locus Melting Typing (HLMT): a novel, fast and inexpensive sequencing-free approach to pathogen typing based on High Resolution Melting (HRM) analysis

ObjectivesSubspecies pathogen typing is a pivotal tool to detect the emergence of high-risk clones in hospital settings and to limit their spreading among patients. Unfortunately, the most used subspecies typing methods (i.e. Pulsed-field Gel Electrophoresis - PFGE, Multi-Locus Sequence Typing - MLST and Whole Genome Sequencing - WGS) are too expensive and time consuming to be suitable for real-time surveillance. Here we present Hypervariable-Locus Melting Typing (HLMT), a novel subspecies typing approach based on High Resolution Melting (HRM) analysis, which allows pathogen typing in a few hours and with [~]5 euros per sample. MethodsHLMT types the strains by clustering them using melting temperatures (HLMT-clustering) and/or by assigning them to Melting Types (MTs) on the basis of a reference dataset (HLMT-assignment). We applied HLMT (clustering and typing) to 134 Klebsiella pneumoniae strains collected during outbreaks or surveillance programs in four hospitals. Then, we compared HLMT typing results to PFGE, MLST and WGS. ResultsHLMT-clustering distinguishes most of the K. pneumoniae high-risk clones with a sensitivity comparable to PFGE and MLST. It also drawed surveillance epidemiological curves comparable to those obtained by MLST, PFGE and WGS typing. Furthermore, the results obtained by HLMT-assignment were coherent to MLST for 96% of the typed strains with a Jaccard index of 0.912. ConclusionsHLMT is a fast and scalable method for pathogen typing, suitable for real-time hospital microbiological surveillance. HLMT is also inexpensive and thus it is applicable to infection control programs in low-middle income countries.

microbiology↗

bioRxiv2019

A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy

Serial-section electron microscopy is the method of choice for studying cellular structure and network connectivity in the brain. We have built a pipeline of parallel imaging using transmission electron automated microscopes (piTEAM) that scales this technology and enables the acquisition of petascale datasets containing local cortical microcircuits. The distributed platform is composed of multiple transmission electron microscopes that image, in parallel, different sections from the same block of tissue, all under control of a custom acquisition software (pyTEM) that implements 24/7 continuous autonomous imaging. The suitability of this architecture for large scale electron microscopy imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex spanning four different visual areas. Over 26,500 ultrathin tissue sections were imaged, yielding a dataset of more than 2 petabytes. Our current burst imaging rate is 500 Mpixel/s (image capture only) per microscope and net imaging rate is 100 Mpixel/s (including stage movement, image capture, quality control, and post processing). This brings the combined burst acquisition rate of the pipeline to 3 Gpixel/s and the net rate to 600 Mpixel/s with six microscopes running acquisition in parallel, which allowed imaging a cubic millimeter of mouse visual cortex at synaptic resolution in less than 6 months.

neuroscience↗

bioRxiv2019

Separation of hemodynamic signals from GCaMP fluorescence measured with widefield imaging

Widefield calcium imaging is often used to measure brain dynamics in behaving mice. With a large field of view and a high sampling rate, widefield imaging can monitor activity from several distant cortical areas simultaneously, revealing cortical interactions. Interpretation of widefield images is complicated, however, by the absorption of light by hemoglobin, which can substantially affect the measured fluorescence. One approach to separating hemodynamics and calcium signals is to use multi-wavelength backscatter recordings to measure light absorption by hemoglobin. Following this approach, we develop a spatially-detailed regression-based method to estimate hemodynamics. The spatially-detailed model is based on a linear form of the Beer-Lambert relationship, but is fit at every pixel in the image and does not rely on the estimation of physical parameters. In awake mice of three transgenic lines, the Spatial Model offers improved separation of hemodynamics and changes in GCaMP fluorescence. The improvement is pronounced near blood vessels and, in contrast with other models based on regression or the Beer-Lambert law, can remove vascular artifacts along the sagittal midline. Compared to other separation approaches, the spatially-detailed model permits more accurate fluorescence-based determination of neuronal activity across the cortex.\n\nNEW & NOTEWORTHYThis manuscript addresses a well-known and strong source of contamination in widefield calcium imaging data: hemodynamics. To guide researchers towards the best method to separate calcium signals from hemodynamics, we compare the performance of several commonly used methods in three commonly-used Cre-driver lines, and we present a novel regression model that out-performs the other techniques we consider.

neuroscience↗