Dynamical topological phase realized in a trapped-ion quantum simulator.

Nascent platforms for programmable quantum simulation offer unprecedented access to new regimes of far-from-equilibrium quantum many-body dynamics in almost isolated systems. Here achieving precise control over quantum many-body entanglement is an essential task for quantum sensing and computation. Extensive theoretical work indicates that these capabilities can enable dynamical phases and critical phenomena that show topologically robust methods to create, protect and manipulate quantum entanglement that self-correct against large classes of errors. However, so far, experimental realizations have been confined to classical (non-entangled) symmetry-breaking orders1-5. In this work, we demonstrate an emergent dynamical symmetry-protected topological phase6, in a quasiperiodically driven array of ten 171Yb+ hyperfine qubits in Quantinuum's System Model H1 trapped-ion quantum processor7. This phase shows edge qubits that are dynamically protected from control errors, cross-talk and stray fields. Crucially, this edge protection relies purely on emergent dynamical symmetries that are absolutely stable to generic coherent perturbations. This property is special to quasiperiodically driven systems: as we demonstrate, the analogous edge states of a periodically driven qubit array are vulnerable to symmetry-breaking errors and quickly decohere. Our work paves the way for implementation of more complex dynamical topological orders8,9 that would enable error-resilient manipulation of quantum information.

Cerebral Glucose Metabolism Is a Valuable Predictor of Survival in Patients with Lewy Body Diseases.

OBJECTIVE: Patients with Lewy body diseases have an increased risk of dementia, which is a significant predictor for survival. Posterior cortical hypometabolism on [18F]fluorodeoxyglucose positron emission tomography (PET) precedes the development of dementia by years. We therefore examined the prognostic value of cerebral glucose metabolism for survival. METHODS: We enrolled patients diagnosed with Parkinson's disease (PD), Parkinson's disease with dementia, or dementia with Lewy bodies who underwent [18F]fluorodeoxyglucose PET. Regional cerebral metabolism of each patient was analyzed by determining the expression of the PD-related cognitive pattern (Z-score) and by visual PET rating. We analyzed the predictive value of PET for overall survival using Cox regression analyses (age- and sex-corrected) and calculated prognostic indices for the best model. RESULTS: Glucose metabolism was a significant predictor of survival in 259 included patients (n = 118 events; hazard ratio: 1.4 [1.2-1.6] per Z-score; hazard ratio: 1.8 [1.5-2.2] per visual PET rating score; both p < 0.0001). Risk stratification with visual PET rating scores yielded a median survival of 4.8, 6.8, and 12.9 years for patients with severe, moderate, and mild posterior cortical hypometabolism (median survival not reached for normal cortical metabolism). Stratification into 5 groups based on the prognostic index revealed 10-year survival rates of 94.1%, 78.3%, 34.7%, 0.0%, and 0.0%. INTERPRETATION: Regional cerebral glucose metabolism is a significant predictor of survival in Lewy body diseases and may allow an earlier survival prediction than the clinical milestone "dementia." Thus, [18F]fluorodeoxyglucose PET may improve the basis for therapy decisions, especially for invasive therapeutic procedures like deep brain stimulation in Parkinson's disease. ANN NEUROL 2024.

Identification of proteoforms by top-down proteomics using two-dimensional low/low pH reversed-phase liquid chromatography-mass spectrometry.

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20 kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics.

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

GeLC-FAIMS-MS workflow for in-depth middle-down proteomics.

Middle-down proteomics (MDP) is an analytical approach in which protein samples are digested with proteases such as Glu-C to generate large peptides (>3 kDa) that are analyzed by mass spectrometry (MS). This method is useful for characterizing high-molecular-weight proteins that are difficult to detect by top-down proteomics (TDP), in which intact proteins are analyzed by MS. In this study, we applied GeLC-FAIMS-MS, a multidimensional separation workflow that combines gel-based prefractionation with LC-FAIMS MS, for deep MDP. Middle-down peptides generated by optimized limited Glu-C digestion conditions were first size-fractionated by polyacrylamide gel electrophoresis, followed by C4 reversed-phase liquid chromatography separation and additional ion mobility fractionation, resulting in a significant increase in peptide length detectable by MS. In addition to global analysis, the GeLC-FAIMS-MS concept can also be applied to targeted MDP, where only proteins in the desired molecular weight range are gel-fractionated and their Glu-C digestion products are analyzed, as demonstrated by targeted analysis of integrins in exosomes. In-depth MDP achieved by global and targeted GeLC-FAIMS-MS supports the exploration of proteoform information not covered by conventional TDP by increasing the number of detectable protein groups or post-translational modifications (PTMs) and improving the sequence coverage.

Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes.

BACKGROUND: In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis. RESULTS: In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant. CONCLUSIONS: This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.

Connection Failure: Differences in White Matter Microstructure Are Associated with 5-HTTLPR but Not with Risk Seeking for Losses.

S/S carriers of 5-HTTLPR have been found to be more risk seeking for losses compared to L/L carriers. This finding may be the result of reduced top-down control from the frontal cortex due to altered signal pathways involving the amygdala and ventral striatum. The serotonergic system is known to be involved in neurodevelopment and neuroplasticity. Therefore, the aim of this study was to investigate whether structural differences in white matter can explain the differences in risk-seeking behaviour. Lower structural connectivity in S/S compared to L/L carriers and a negative relationship between risk seeking for losses and connectivity were assumed. Diffusion-weighted imaging was used to compute diffusion parameters for the frontostriatal and uncinate tract in 175 genotyped individuals. The results showed no significant relationship between diffusion parameters and risk seeking for losses. Furthermore, we did not find significant differences in diffusion parameters of the S/S vs. L/L group. There were only group differences in the frontostriatal tract showing stronger structural connectivity in the S/L group, which is also reflected in the whole brain approach. Therefore, the data do not support the hypothesis that the association between 5-HTTLPR and risk seeking for losses is related to differences in white matter pathways implicated in decision-making.

[Dementia with Lewy bodies: old and new knowledge - Part 1: clinical aspects and diagnostics]. / Demenz mit Lewy-Körpern: alte und neue Erkenntnisse ­ Teil 1: Klinik und Diagnostik.

BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia after Alzheimer's disease. Patients with DLB often have a poor prognosis, with worse outcomes than patients with Alzheimer's disease in terms of important parameters, such as quality of life, caregiver burden, health-related costs, frequency of hospital and nursing home admissions, shorter time to severe dementia, and lower survival. The DLB is frequently misdiagnosed and often undertreated. Therefore, it is critical to diagnose DLB as early as possible to ensure optimal care and treatment. OBJECTIVE: The aim of this review article is to summarize the main recent findings on diagnostic tools, epidemiology and genetics of DLB. RESULTS: Precise clinical diagnostic criteria exist for DLB that enable an etiologic assignment. Imaging techniques are used as standard in DLB, especially also to exclude non-neurodegenerative causes. In particular, procedures in nuclear medicine have a high diagnostic value. DISCUSSION: The diagnosis is primarily based on clinical symptoms, although the development of in vivo neuroimaging and biomarkers is changing the scope of clinical diagnosis as well as research into this devastating disease.

[Dementia with Lewy bodies: old and new knowledge-Part 2: treatment]. / Demenz mit Lewy-Körpern: alte und neue Erkenntnisse ­ Teil 2: Behandlung.

BACKGROUND: The treatment of patients with dementia with Lewy bodies (DLB) is multifaceted, as motor symptoms, cognitive symptoms, behavioral and psychological symptoms can occur in different constellations. In addition, the use of certain medications is limited (e.g., neuroleptics). OBJECTIVE: To summarize the main recent findings on the treatment of DLB. RESULTS: To date, there is no approved therapeutic option for the treatment of patients with DLB in Germany; moreover, the evidence base for pharmacological and non-pharmacological treatment is sparse. The currently consented treatment options are based on the treatment of motor symptoms in the same way as the treatment of Parkinson's disease and for behavioral symptoms based on the treatment for Alzheimer's disease. DISCUSSION: The treatment of DLB with its various symptoms is difficult and often can only be adequately achieved for the patient in close cooperation with a specialist.

Detection of Post-COVID-19 Lung Abnormalities: Photon-counting CT versus Same-Day Energy-integrating Detector CT.

Background Photon-counting detector (PCD) CT enables ultra-high-resolution lung imaging and may shed light on morphologic correlates of persistent symptoms after COVID-19. Purpose To compare PCD CT with energy-integrating detector (EID) CT for noninvasive assessment of post-COVID-19 lung abnormalities. Materials and Methods For this prospective study, adult participants with one or more COVID-19-related persisting symptoms (resting or exertional dyspnea, cough, fatigue) underwent same-day EID and PCD CT between April 2022 and June 2022. The 1.0-mm EID CT images and, subsequently, 1.0-, 0.4-, and 0.2-mm PCD CT images were reviewed for the presence of lung abnormalities. Subjective and objective EID and PCD CT image quality were evaluated using a five-point Likert scale (-2 to 2) and lung signal-to-noise ratios (SNRs). Results Twenty participants (mean age, 54 years ± 16 [SD]; 10 men) were included. EID CT showed post-COVID-19 lung abnormalities in 15 of 20 (75%) participants, with a median involvement of 10% of lung volume [IQR, 0%-45%] and 3.5 lobes [IQR, 0-5]. Ground-glass opacities and linear bands (10 of 20 participants [50%] for both) were the most frequent findings at EID CT. PCD CT revealed additional lung abnormalities in 10 of 20 (50%) participants, with the most common being bronchiectasis (10 of 20 [50%]). Subjective image quality was improved for 1.0-mm PCD versus 1.0-mm EID CT images (median, 1; IQR, 1-2; P < .001) and 0.4-mm versus 1.0-mm PCD CT images (median, 1; IQR, 1-1; P < .001) but not for 0.4-mm versus 0.2-mm PCD CT images (median, 0; IQR, 0-0.5; P = .26). PCD CT delivered higher lung SNR versus EID CT for 1.0-mm images (mean difference, 0.53 ± 0.96; P = .03) but lower SNR for 0.4-mm versus 1.0-mm images and 0.2-mm vs 0.4-mm images (-1.52 ± 0.68 [P < .001] and -1.15 ± 0.43 [P < .001], respectively). Conclusion Photon-counting detector CT outperformed energy-integrating detector CT in the visualization of subtle post-COVID-19 lung abnormalities and image quality. © RSNA, 2023 Supplemental material is available for this article.

Evaluation of perivascular fat attenuation with coronary CT angiography in cardiac transplantation patients: an imaging biomarker candidate for prediction of cardiac mortality and re-transplantation.

OBJECTIVES: In cardiac transplant recipients, non-invasive allograft surveillance for identifying patients at risk for graft failure remains challenging. The fat attenuation index (FAI) of the perivascular adipose tissue in coronary computed tomography angiography (CCTA) predicts outcomes in coronary artery disease in non-transplanted hearts; however, it has not been evaluated in cardiac transplant patients. METHODS: We followed 39 cardiac transplant patients with two or more CCTAs obtained between 2010 and 2021. We performed FAI measurements around the proximal 4 cm segments of the left anterior descending (LAD), right coronary artery (RCA), and left circumflex artery (LCx) using a previously validated methodology. The FAI was analyzed at a threshold of - 30 to - 190 Hounsfield units. RESULTS: FAI measurements were completed in 113 CCTAs, obtained on two same-vendor CT models. Within each CCTA, the FAI values between coronary vessels were strongly correlated (RCA and LAD R = 0.67 (p < 0.0001), RCA and LCx R = 0.58 (p < 0.0001), LAD and LCx R = 0.67 (p < 0.0001)). The FAIs of each coronary vessel between the patient's first and last CCTA completed at 120 kV were also correlated (RCA R = 0.73 (p < 0.0001), LAD R = 0.81 (p < 0.0001), LCx R = 0.55 (p = 0.0069). Finally, a high mean FAI value of all three coronary vessels at baseline (mean ≥ - 71 HU) was predictive of cardiac mortality or re-transplantation, however, not predictive of all cause-mortality. CONCLUSION: High baseline FAI values may identify a higher-risk cardiac transplant population; thus, FAI may support the implementation of CCTA in post-transplant surveillance. KEY POINT: • Perivascular fat attenuation measured with coronary CT is feasible in cardiac transplant patients and may predict cardiac mortality or need for re-transplantation.

Towards Improving the Efficacy of PSMA-Targeting Radionuclide Therapy for Late-Stage Prostate Cancer-Combination Strategies.

PURPOSE OF REVIEW: [177Lu]Lu-PSMA-617 is a radiopharmaceutical that emits beta-minus radiation and targets prostate-specific membrane antigen (PSMA)-positive prostate cancer. Despite its clinical success, there are still patients not showing sufficient response rates. This review compiles latest studies aiming at therapy improvement in [177Lu]Lu-PSMA-617-naïve and -resistant patients by alternative or combination treatments. RECENT FINDINGS: A variety of agents to combine with [177Lu]Lu-PSMA-617 are currently under investigation including alpha radiation-emitting pharmaceuticals, radiosensitizers, taxane chemotherapeutics, androgen receptor pathway inhibitors, immune checkpoint inhibitors, and external beam radiation. Actinium-225 (225Ac)-labeled PSMA-targeting inhibitors are the most studied pharmaceuticals for combination therapy or as an alternative for treatment after progression under [177Lu]Lu-PSMA-617 therapy. Alpha emitters seem to have a potential of achieving a response to PSMA-targeting radionuclide therapy in both initial non-responders or responders to [177Lu]Lu-PSMA-617 later developing treatment resistance. Emerging evidence for immunostimulatory effects of radiopharmaceuticals and first prospective studies support the combination of [177Lu]Lu-PSMA-617 and immune checkpoint inhibition for late-stage prostate cancer.

Widespread white matter oedema in subacute COVID-19 patients with neurological symptoms.

While neuropathological examinations in patients who died from COVID-19 revealed inflammatory changes in cerebral white matter, cerebral MRI frequently fails to detect abnormalities even in the presence of neurological symptoms. Application of multi-compartment diffusion microstructure imaging (DMI), that detects even small volume shifts between the compartments (intra-axonal, extra-axonal and free water/CSF) of a white matter model, is a promising approach to overcome this discrepancy. In this monocentric prospective study, a cohort of 20 COVID-19 inpatients (57.3 ± 17.1 years) with neurological symptoms (e.g. delirium, cranial nerve palsies) and cognitive impairments measured by the Montreal Cognitive Assessment (MoCA test; 22.4 ± 4.9; 70% below the cut-off value <26/30 points) underwent DMI in the subacute stage of the disease (29.3 ± 14.8 days after positive PCR). A comparison of whole-brain white matter DMI parameters with a matched healthy control group (n = 35) revealed a volume shift from the intra- and extra-axonal space into the free water fraction (V-CSF). This widespread COVID-related V-CSF increase affected the entire supratentorial white matter with maxima in frontal and parietal regions. Streamline-wise comparisons between COVID-19 patients and controls further revealed a network of most affected white matter fibres connecting widespread cortical regions in all cerebral lobes. The magnitude of these white matter changes (V-CSF) was associated with cognitive impairment measured by the MoCA test (r = -0.64, P = 0.006) but not with olfactory performance (r = 0.29, P = 0.12). Furthermore, a non-significant trend for an association between V-CSF and interleukin-6 emerged (r = 0.48, P = 0.068), a prominent marker of the COVID-19 related inflammatory response. In 14/20 patients who also received cerebral 18F-FDG PET, V-CSF increase was associated with the expression of the previously defined COVID-19-related metabolic spatial covariance pattern (r = 0.57; P = 0.039). In addition, the frontoparietal-dominant pattern of neocortical glucose hypometabolism matched well to the frontal and parietal focus of V-CSF increase. In summary, DMI in subacute COVID-19 patients revealed widespread volume shifts compatible with vasogenic oedema, affecting various supratentorial white matter tracts. These changes were associated with cognitive impairment and COVID-19 related changes in 18F-FDG PET imaging.

Evaluation of the cerebrovascular reactivity in patients with Moyamoya Angiopathy by use of breath-hold fMRI: investigation of voxel-wise hemodynamic delay correction in comparison to [15O]water PET.

PURPOSE: Patients with Moyamoya Angiopathy (MMA) require hemodynamic assessment to evaluate the risk of stroke. Hemodynamic evaluation by use of breath-hold-triggered fMRI (bh-fMRI) was proposed as a readily available alternative to the diagnostic standard [15O]water PET. Recent studies suggest voxel-wise hemodynamic delay correction in hypercapnia-triggered fMRI. The aim of this study was to evaluate the effect of delay correction of bh-fMRI in patients with MMA and to compare the results with [15O]water PET. METHODS: bh-fMRI data sets of 22 patients with MMA were evaluated without and with voxel-wise delay correction within different shift ranges and compared to the corresponding [15O]water PET data sets. The effects were evaluated combined and in subgroups of data sets with most severely impaired CVR (apparent steal phenomenon), data sets with territorial time delay, and data sets with neither steal phenomenon nor delay between vascular territories. RESULTS: The study revealed a high mean cross-correlation (r = 0.79, p < 0.001) between bh-fMRI and [15O]water PET. The correlation was strongly dependent on the choice of the shift range. Overall, no shift range revealed a significantly improved correlation between bh-fMRI and [15O]water PET compared to the correlation without delay correction. Delay correction within shift ranges with positive high high cutoff revealed a lower agreement between bh-fMRI and PET overall and in all subgroups. CONCLUSION: Voxel-wise delay correction, in particular with shift ranges with high cutoff, should be used critically as it can lead to false-negative results in regions with impaired CVR and a lower correlation to the diagnostic standard [15O]water PET.

Digital Microfluidics and Magnetic Bead-Based Intact Proteoform Elution for Quantitative Top-down Nanoproteomics of Single C.†elegans Nematodes.

While most nanoproteomics approaches for the analysis of low-input samples are based on bottom-up proteomics workflows, top-down approaches enabling proteoform characterization are still underrepresented. Using mammalian cell proteomes, we established a facile one-pot sample preparation protocol based on protein aggregation on magnetic beads and intact proteoform elution using 40 % formic acid. Performed on a digital microfluidics device, the workflow enabled sensitive analyses of single Caenorhabditis elegans nematodes, thereby increasing the number of proteoform identifications compared to in-tube sample preparation by 46 %. Label-free quantification of single nematodes grown under different conditions allowed to identify changes in the abundance of proteoforms not distinguishable by bottom-up proteomics. The presented workflow will facilitate proteoform-directed analysis on samples of limited availability.

Validation of Top-Down Proteomics Data by Bottom-Up-Based N-Terminomics Reveals Pitfalls in Top-Down-Based Terminomics Workflows.

Bottom-up proteomics (BUP)-based N-terminomics techniques have become standard to identify protein N-termini. While these methods rely on the identification of N-terminal peptides only, top-down proteomics (TDP) comes with the promise to provide additional information about post-translational modifications and the respective C-termini. To evaluate the potential of TDP for terminomics, two established TDP workflows were employed for the proteome analysis of the nematode Caenorhabditis elegans. The N-termini of the identified proteoforms were validated using a BUP-based N-terminomics approach. The TDP workflows used here identified 1658 proteoforms, the N-termini of which were verified by BUP in 25% of entities only. Caveats in both the BUP- and TDP-based workflows were shown to contribute to this low overlap. In BUP, the use of trypsin prohibits the detection of arginine-rich or arginine-deficient N-termini, while in TDP, the formation of artificially generated termini was observed in particular in a workflow encompassing sample treatment with high acid concentrations. Furthermore, we demonstrate the applicability of reductive dimethylation in TDP to confirm biological N-termini. Overall, our study shows not only the potential but also current limitations of TDP for terminomics studies and also presents suggestions for future developments, for example, for data quality control, allowing improvement of the detection of protein termini by TDP.

MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications.

Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.

Improved Identification of Proteoforms in Top-Down Proteomics Using FAIMS with Internal CV Stepping.

In top-down (TD) proteomics, prefractionation prior to mass spectrometric (MS) analysis is a crucial step for both the high confidence identification of proteoforms and increased proteome coverage. In addition to liquid-phase separations, gas-phase fractionation strategies such as field asymmetric ion mobility spectrometry (FAIMS) have been shown to be highly beneficial in TD proteomics. However, so far, only external compensation voltage (CV) stepping has been demonstrated for TD proteomics, i.e., single CVs were applied for each run. Here, we investigated the use of internal CV stepping (multiple CVs per acquisition) for single-shot TD analysis, which has huge advantages in terms of measurement time and the amount of sample required. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with more negative CVs can be identified with lower resolution and number of microscans than larger proteins identified primarily via less negative CVs. We investigated the optimal combination and number of CVs for different gradient lengths and validated the optimized settings with the low-molecular-weight proteome of CaCo-2 cells obtained using a range of different sample preparation techniques. Compared to measurements without FAIMS, both the number of identified protein groups (+60-94%) and proteoforms (+46-127%) and their confidence were significantly increased, while the measurement time remained identical. In total, we identified 684 protein groups and 2675 proteoforms from CaCo-2 cells in less than 24 h using the optimized multi-CV method.

Size-Based Proteome Fractionation through Polyacrylamide Gel Electrophoresis Combined with LC-FAIMS-MS for In-Depth Top-Down Proteomics.

The combination of liquid chromatography (LC) and gas-phase separation by field-asymmetric ion mobility spectrometry (FAIMS) is a powerful proteoform separation system for top-down proteomics. Here, we present an in-depth top-down proteomics workflow, GeLC-FAIMS-MS, in which a molecular-weight-based proteome fractionation approach using SDS-polyacrylamide gel electrophoresis is performed prior to LC-FAIMS-MS. Since individual bands and their corresponding mass ranges require different compensating voltages (CVs), the MS parameters for each gel band and CV were optimized to increase the number and reliability of proteoform identifications further. We developed an easy-to-implement and inexpensive procedure combining the earlier established Passively Eluting Proteins from Polyacrylamide gels as Intact species (PEPPI) protocol with an optimized Anion-Exchange disk-assisted Sequential sample Preparation (AnExSP) method for the removal of stains and SDS. The protocol was compared with a methanol-chloroform-water (MCW)-based protein precipitation protocol. The results show that the PEPPI-AnExSP procedure is better suited for the identification of low-molecular-weight proteoforms, whereas the MCW-based protocol showed advantages for higher-molecular-weight proteoforms. Moreover, complementary results were observed with the two methods in terms of hydrophobicity and isoelectric points of the identified proteoforms. In total, 8500 proteoforms could be identified in a human proteome standard, showing the effectiveness of the gel-based sample fractionation approaches in combination with LC-FAIMS-MS.

Cognitive impairment and altered cerebral glucose metabolism in the subacute stage of COVID-19.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, neurological symptoms increasingly moved into the focus of interest. In this prospective cohort study, we assessed neurological and cognitive symptoms in hospitalized coronavirus disease-19 (COVID-19) patients and aimed to determine their neuronal correlates. Patients with reverse transcription-PCR-confirmed COVID-19 infection who required inpatient treatment primarily because of non-neurological complications were screened between 20 April 2020 and 12 May 2020. Patients (age > 18 years) were included in our cohort when presenting with at least one new neurological symptom (defined as impaired gustation and/or olfaction, performance < 26 points on a Montreal Cognitive Assessment and/or pathological findings on clinical neurological examination). Patients with ≥2 new symptoms were eligible for further diagnostics using comprehensive neuropsychological tests, cerebral MRI and 18fluorodeoxyglucose (FDG) PET as soon as infectivity was no longer present. Exclusion criteria were: premorbid diagnosis of cognitive impairment, neurodegenerative diseases or intensive care unit treatment. Of 41 COVID-19 inpatients screened, 29 patients (65.2 ± 14.4 years; 38% female) in the subacute stage of disease were included in the register. Most frequently, gustation and olfaction were disturbed in 29/29 and 25/29 patients, respectively. Montreal Cognitive Assessment performance was impaired in 18/26 patients (mean score 21.8/30) with emphasis on frontoparietal cognitive functions. This was confirmed by detailed neuropsychological testing in 15 patients. 18FDG PET revealed pathological results in 10/15 patients with predominant frontoparietal hypometabolism. This pattern was confirmed by comparison with a control sample using voxel-wise principal components analysis, which showed a high correlation (R2 = 0.62) with the Montreal Cognitive Assessment performance. Post-mortem examination of one patient revealed white matter microglia activation but no signs of neuroinflammation. Neocortical dysfunction accompanied by cognitive decline was detected in a relevant fraction of patients with subacute COVID-19 initially requiring inpatient treatment. This is of major rehabilitative and socioeconomic relevance.