Cell lineage specification and signalling pathway use during development of the lateral plate mesoderm and forelimb mesenchyme.

The lateral plate mesoderm (LPM) is a transient tissue that produces a diverse range of differentiated structures, including the limbs. However, the molecular mechanisms that drive early LPM specification and development are poorly understood. In this study, we use single-cell transcriptomics to define the cell-fate decisions directing LPM specification, subdivision and early initiation of the forelimb mesenchyme in chicken embryos. We establish a transcriptional atlas and global cell-cell signalling interactions in progenitor, transitional and mature cell types throughout the developing forelimb field. During LPM subdivision, somatic and splanchnic LPM fate is achieved through activation of lineage-specific gene modules. During the earliest stages of limb initiation, we identify activation of TWIST1 in the somatic LPM as a putative driver of limb bud epithelial-to-mesenchymal transition. Furthermore, we define a new role for BMP signalling during early limb development, revealing that it is necessary for inducing a somatic LPM fate and initiation of limb outgrowth, potentially through activation of TBX5. Together, these findings provide new insights into the mechanisms underlying LPM development, somatic LPM fate choice and early initiation of the vertebrate limb.

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Proteomic Profiling of Intra-Islet Features Reveals Substructure-Specific Protein Signatures.

Despite their diminutive size, islets of Langerhans play a large role in maintaining systemic energy balance in the body. New technologies have enabled us to go from studying the whole pancreas to isolated whole islets, to partial islet sections, and now to islet substructures isolated from within the islet. Using a microfluidic nanodroplet-based proteomics platform coupled with laser capture microdissection and field asymmetric waveform ion mobility spectrometry, we present an in-depth investigation of protein profiles specific to features within the islet. These features include the islet-acinar interface vascular tissue, inner islet vasculature, isolated endocrine cells, whole islet with vasculature, and acinar tissue from around the islet. Compared to interface vasculature, unique protein signatures observed in the inner vasculature indicate increased innervation and intra-islet neuron-like crosstalk. We also demonstrate the utility of these data for identifying localized structure-specific drug-target interactions using existing protein/drug binding databases.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics.

There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.

An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics.

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress.

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett's esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson's correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett's cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.

Picoflow Liquid Chromatography-Mass Spectrometry for Ultrasensitive Bottom-Up Proteomics Using 2-µm-i.d. Open Tubular Columns.

In many areas of application, key objectives of chemical separation and analysis are to minimize the sample quantity while maximizing the chemical information obtained. Increasing measurement sensitivity is especially critical for proteomics research, especially when processing trace samples and where multiple measurements are desired. A rich collection of technologies has been developed, but the resulting sensitivity remains insufficient for achieving in-depth coverage of proteomic samples as small as single cells. Here, we combine picoliter-scale liquid chromatography (picoLC) with mass spectrometry (MS) to address this issue. The picoLC employs a 2-µm-i.d. open tubular column to reduce the sample input needed to greatly increase the sensitivity achieved using electrospray ionization (ESI) with MS. With this picoLC-MS system, we show that we can identify ∼1000 proteins reliably using only 75 pg of tryptic peptides, representing a 10-100-fold sensitivity improvement compared with the state-of-the-art liquid chromatography (LC) or capillary electrophoresis (CE)-MS methods. PicoLC-MS extends the limit of separation science and is expected to be a powerful tool for single cell proteomics.

Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform.

Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant technical gap of top-down proteomics is the inability to analyze a low amount of biological samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample preparation system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-ß-d-maltopyranoside (DDM) with urea as protein extraction buffer significantly improved both protein extraction efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream analysis. The nanoPOTS-based top-down platform reproducibly and quantitatively identified ∼170 to ∼620 proteoforms from ∼70 to ∼770 HeLa cells containing ∼10 to ∼115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clinical specimens are not available in amounts amenable to standard workflows.

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell Proteomics.

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. We have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ∼77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

Comprehensive characterization of distinct states of human naive pluripotency generated by reprogramming.

Recent reports on the characteristics of naive human pluripotent stem cells (hPSCs) obtained using independent methods differ. Naive hPSCs have been mainly derived by conversion from primed hPSCs or by direct derivation from human embryos rather than by somatic cell reprogramming. To provide an unbiased molecular and functional reference, we derived genetically matched naive hPSCs by direct reprogramming of fibroblasts and by primed-to-naive conversion using different naive conditions (NHSM, RSeT, 5iLAF and t2iLGöY). Our results show that hPSCs obtained in these different conditions display a spectrum of naive characteristics. Furthermore, our characterization identifies KLF4 as sufficient for conversion of primed hPSCs into naive t2iLGöY hPSCs, underscoring the role that reprogramming factors can play for the derivation of bona fide naive hPSCs.

An integrative analysis of non-coding regulatory DNA variations associated with autism spectrum disorder.

A number of genetic studies have identified rare protein-coding DNA variations associated with autism spectrum disorder (ASD), a neurodevelopmental disorder with significant genetic etiology and heterogeneity. In contrast, the contributions of functional, regulatory genetic variations that occur in the extensive non-protein-coding regions of the genome remain poorly understood. Here we developed a genome-wide analysis to identify the rare single nucleotide variants (SNVs) that occur in non-coding regions and determined the regulatory function and evolutionary conservation of these variants. Using publicly available datasets and computational predictions, we identified SNVs within putative regulatory regions in promoters, transcription factor binding sites, and microRNA genes and their target sites. Overall, we found that the regulatory variants in ASD cases were enriched in ASD-risk genes and genes involved in fetal neurodevelopment. As with previously reported coding mutations, we found an enrichment of the regulatory variants associated with dysregulation of neurodevelopmental and synaptic signaling pathways. Among these were several rare inherited SNVs found in the mature sequence of microRNAs predicted to affect the regulation of ASD-risk genes. We show a paternally inherited miR-873-5p variant with altered binding affinity for several risk-genes including NRXN2 and CNTNAP2 putatively overlay maternally inherited loss-of-function coding variations in NRXN1 and CNTNAP2 to likely increase the genetic liability in an idiopathic ASD case. Our analysis pipeline provides a new resource for identifying loss-of-function regulatory DNA variations that may contribute to the genetic etiology of complex disorders.

An Injectable Reverse Thermal Gel for Minimally Invasive Coverage of Mouse Myelomeningocele.

BACKGROUND: Myelomeningocele (MMC) results in lifelong neurologic and functional deficits. Currently, prenatal repair of MMC closes the defect, resulting in a 50% reduction in postnatal ventriculoperitoneal shunting. However, this invasive fetal surgery is associated with significant morbidities to mother and baby. We have pioneered a novel reverse thermal gel (RTG) to cover MMC defects in a minimally invasive manner. Here, we test in-vitro RTG long-term stability in amniotic fluid and in vivo application in the Grainy head-like 3 (Grhl3) mouse MMC model. MATERIALS AND METHODS: RTG stability in amniotic fluid (in-vitro) was monitored for 6 mo and measured using gel permeation chromatography and solution-gel transition temperature (lower critical solution temperature). E16.5 Grhl3 mouse fetuses were injected with the RTG or saline and harvested on E19.5. Tissue was assessed for RTG coverage of the gross defect and inflammatory response by immunohistochemistry for macrophages. RESULTS: Polymer backbone molecular weight and lower critical solution temperature remain stable in amniotic fluid after 6 mo. Needle injection over the MMC of Grhl3 fetuses successfully forms a stable gel that covers the entire defect. On harvest, some animals demonstrate >50% RTG coverage. RTG injection is not associated with inflammation. CONCLUSIONS: Our results demonstrate that the RTG is a promising candidate for a minimally invasive approach to patch MMC. We are now poised to test our RTG patch in the large preclinical ovine model used to evaluate prenatal repair of MMC.

Identification of the genomic mutation in Epha4(rb-2J/rb-2J) mice.

The EphA4 receptor tyrosine kinase is involved in numerous cell-signalling activities during embryonic development. EphA4 has the ability to bind to both types of ephrin ligands, the ephrinAs and ephrinBs. The C57BL/6J-Epha4rb-2J/GrsrJ strain, denoted Epha4(rb-2J/rb-2J), is a spontaneous mouse mutant that arose at The Jackson Laboratory. These mutants exhibited a synchronous hind limb locomotion defect or "hopping gait" phenotype, which is also characteristic of EphA4 null mice. Genetic complementation experiments suggested that Epha4(rb-2J) corresponds to an allele of EphA4, but details of the genomic defect in this mouse mutant are currently unavailable. We found a single base-pair deletion in exon 9 resulting in a frame shift mutation that subsequently resulted in a premature stop codon. Analysis of the predicted structure of the truncated protein suggests that both the kinase and sterile α motif (SAM) domains are absent. Definitive determination of genotype is needed for experimental studies of mice carrying the Epha4(rb-2J) allele, and we have also developed a method to ease detection of the mutation through RFLP. Eph-ephrin family members are reportedly expressed as numerous isoforms. Hence, delineation of the specific mutation in EphA4 in this strain is important for further functional studies, such as protein-protein interactions, immunostaining and gene compensatory studies, investigating the mechanism underlying the effects of altered function of Eph family of receptor tyrosine kinases on phenotype.

Class I HDACs regulate angiotensin II-dependent cardiac fibrosis via fibroblasts and circulating fibrocytes.

Fibrosis, which is defined as excessive accumulation of fibrous connective tissue, contributes to the pathogenesis of numerous diseases involving diverse organ systems. Cardiac fibrosis predisposes individuals to myocardial ischemia, arrhythmias and sudden death, and is commonly associated with diastolic dysfunction. Histone deacetylase (HDAC) inhibitors block cardiac fibrosis in pre-clinical models of heart failure. However, which HDAC isoforms govern cardiac fibrosis, and the mechanisms by which they do so, remains unclear. Here, we show that selective inhibition of class I HDACs potently suppresses angiotensin II (Ang II)-mediated cardiac fibrosis by targeting two key effector cell populations, cardiac fibroblasts and bone marrow-derived fibrocytes. Class I HDAC inhibition blocks cardiac fibroblast cell cycle progression through derepression of the genes encoding the cyclin-dependent kinase (CDK) inhibitors, p15 and p57. In contrast, class I HDAC inhibitors block agonist-dependent differentiation of fibrocytes through a mechanism involving repression of ERK1/2 signaling. These findings define novel roles for class I HDACs in the control of pathological cardiac fibrosis. Furthermore, since fibrocytes have been implicated in the pathogenesis of a variety of human diseases, including heart, lung and kidney failure, our results suggest broad utility for isoform-selective HDAC inhibitors as anti-fibrotic agents that function, in part, by targeting these circulating mesenchymal cells.

HDAC6 contributes to pathological responses of heart and skeletal muscle to chronic angiotensin-II signaling.

Little is known about the function of the cytoplasmic histone deacetylase HDAC6 in striated muscle. Here, we addressed the role of HDAC6 in cardiac and skeletal muscle remodeling induced by the peptide hormone angiotensin II (ANG II), which plays a central role in blood pressure control, heart failure, and associated skeletal muscle wasting. Comparable with wild-type (WT) mice, HDAC6 null mice developed cardiac hypertrophy and fibrosis in response to ANG II. However, whereas WT mice developed systolic dysfunction upon treatment with ANG II, cardiac function was maintained in HDAC6 null mice treated with ANG II for up to 8 wk. The cardioprotective effect of HDAC6 deletion was mimicked in WT mice treated with the small molecule HDAC6 inhibitor tubastatin A. HDAC6 null mice also exhibited improved left ventricular function in the setting of pressure overload mediated by transverse aortic constriction. HDAC6 inhibition appeared to preserve systolic function, in part, by enhancing cooperativity of myofibrillar force generation. Finally, we show that HDAC6 null mice are resistant to skeletal muscle wasting mediated by chronic ANG-II signaling. These findings define novel roles for HDAC6 in striated muscle and suggest potential for HDAC6-selective inhibitors for the treatment of cardiac dysfunction and muscle wasting in patients with heart failure.

Spatial top-down proteomics for the functional characterization of human kidney.

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Electroconvulsive therapy for the acute management of severe agitation in dementia (ECT-AD): A modified study protocol.

OBJECTIVE: This study began as a single-blind randomized controlled trial (RCT) to investigate the efficacy and safety of electroconvulsive therapy (ECT) for severe treatment-refractory agitation in advanced dementia. The aims are to assess agitation reduction using the Cohen-Mansfield Agitation Inventory (CMAI), evaluate tolerability and safety outcomes, and explore the long-term stability of agitation reduction and global functioning. Due to challenges encountered during implementation, including recruitment obstacles and operational difficulties, the study design was modified to an open-label format and other protocol amendments were implemented. METHODS: Initially, the RCT randomized participants 1:1 to either ECT plus usual care or simulated ECT plus usual care (S-ECT) groups. As patients were enrolled, data were collected from both ECT and simulated ECT (S-ECT) patients. The study now continues in an open-label study design where all patients receive actual ECT, reducing the targeted sample size from 200 to 50 participants. RESULTS: Study is ongoing and open to enrollment. CONCLUSION: The transition of the ECT-AD study design from an RCT to open-label design exemplifies adaptive research methodologies in response to real-world challenges. Data from both the RCT and open-label phases of the study will provide a unique perspective on the role of ECT in managing severe treatment-refractory agitation in dementia, potentially influencing future clinical practices and research approaches.