Novel Real-Time Library Search driven data acquisition strategy for identification and characterization of metabolites.

Identification and structural characterization of novel metabolites in drug discovery or metabolomics experiments is one of the most challenging tasks. Multi-level fragmentation (MSn) based approaches combined with various dissociation modes are frequently utilized for facilitating structure assignment of the unknown compounds. As each of the MS precursors undergoes MSn, the instrument cycle time can limit the total number of precursors analyzed in a single run for complex samples. This necessitates splitting data acquisition into several LC/MS analyses where the results obtained in one acquisition inform the experimental design for the successive experiment. Here we present a new LC/MS data acquisition strategy, termed Met-IQ, where the decision to perform an MSn acquisition is automatically made in real time based on the similarity between an acquired experimental MS2 spectrum and a spectrum in a reference spectral library. Each MS2 spectrum is searched in real time against the spectra for the known compounds of interest. If a similarity to a spectrum in the library is found, the instrument performs a decision-dependent event, such as an MS3 scan. Compared to an intensity-based, data-dependent MSn experiment, only a selective number of MS3 are triggered using Met-IQ, increasing the overall MS2 instrument sampling rate. We applied this strategy to an Amprenavir sample incubated with human liver microsomes. The number of MS2 scan events increased nearly 3.5-fold compared to the standard data dependent experiment where MS3 was triggered for each precursor ion, resulting in identification and structural characterization of 55% more unique metabolites. Furthermore, the MS3 precursor fragments were selected intelligently, focusing on higher mass fragments of sufficient intensity to maximize acquisition of MS3 data relevant for structure assignment. The described Met-IQ strategy is not limited to metabolism experiments, and can be applied to analytical samples where the detection of unknown compounds structurally related to a known compound(s) is sought.

A Data-Dependent Acquisition Ladder for Ultrasensitive (Neuro)Proteomics

Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in high-resolution mass spectrometry (HRMS) sensitivity. Microanalytical capillary electrophoresis electrospray ionization (microCE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. We report here a data acquisition strategy that expands the detectable and quantifiable proteome in trace amounts of digests using microCE-ESI-HRMS. Data-dependent acquisition (DDA) was programmed to progressively exclude high-intensity peptide signals during repeated measurements. These nested experiments formed rungs of our DDA ladder. The method was tested for replicates analyzing ~500 pg of protein digest from cultured hippocampal (primary) neurons (mouse), which estimates to the total amount of protein from a single neuron. Analysis of net amounts approximating to ~10 neurons identified 428 nonredundant proteins (415 quantified), an ~35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder deepens the detectable proteome from trace amounts of proteins, expanding the analytical toolbox of neuroscience.

Brief review on learning-based methods for optical tomography

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

3D RECONSTRUCTION OF THE <i>ROMAN DOMUS</i> IN THE ARCHAEOLOGICAL SITE OF <i>LYLIBAEUM</i> (MARSALA, ITALY)

<p><strong>Abstract.</strong> Generally, terrestrial laser scanning surveys involve a rather large number of scans to ensure a high percentage of overlap required for the scan registration phase (target-based or point-based registration, cloud-to-cloud registration). These approaches result in data redundancy that could slow down both the acquisition and post-processing phases. In recent years, the technological evolution in the field of laser scanners has been directed to the development of devices that are able to perform an onsite pre-registration, to optimize the survey procedures and the reliability of the registration of the scan. The paper presents the results achieved during a terrestrial laser scanning survey carried out for the documentation and 3D reconstruction of the large and complex archaeological remains of the so-called Roman <i>Domus</i> in the archaeological site of <i>Lylibaeum</i> (Marsala, Italy). The survey was also conducted using a terrestrial laser scanner capable of pre-registering scans using a topographic approach. The pre-registration procedure and the data acquisition strategy have allowed to optimize the workflow and to obtain a 3D model of the Roman <i>Domus</i> with a high level of detail and area coverage.</p>

Fast detection and structural identification of carbocations on zeolites by dynamic nuclear polarization enhanced solid-state NMR

A fast NMR data acquisition strategy is explored to detect and characterize carbocations on solid zeolites.