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Protein complexes are essential for proteins' folding and biological function. Currently, native analysis of large multimeric protein complexes remains challenging. Structural biology techniques are time‐consuming and often cannot monitor the proteins' dynamics in solution. Here, a capillary electrophoresis‐mass spectrometry (CE–MS) method is reported to characterize, under near‐physiological conditions, the conformational rearrangements of ∽1 MDa GroEL upon complexation with binding partners involved in a protein folding cycle. The developed CE–MS method is fast (30 min per run), highly sensitive (low‐amol level), and requires ∽10 000‐fold fewer samples compared to biochemical/biophysical techniques. The method successfully separates GroEL14 (∽800 kDa), GroEL7 (∽400 kDa), GroES7 (∽73 kDa), and NanA4 (∽130 kDa) oligomers. The non‐covalent binding of natural substrate proteins with GroEL14 can be detected and quantified. The technique allows monitoring of GroEL14 conformational changes upon complexation with (ATPγS)4–14 and GroES7 (∽876 kDa). Native CE‐pseudo‐MS3 analyses of wild‐type (WT) GroEL and two GroEL mutants result in up to 60% sequence coverage and highlight subtle structural differences between WT and mutated GroEL. The presented results demonstrate the superior CE–MS performance for multimeric complexes' characterization versus direct infusion ESI–MS. This study shows the CE–MS potential to provide information on binding stoichiometry and kinetics for various protein complexes. High‐sensitivity capillary electrophoresis‐mass spectrometry (CE–MS) method to quickly characterize ≈1 MDa GroEL14 multimeric protein assembly under near‐physiological conditions is presented. The CE–MS method enables assessment of non‐covalent binding of substrate proteins to GroEL14 chaperon and its conformational changes upon complexation with nucleotides and GroES7 involved in a protein folding cycle, and in‐depth structural characterization of wild‐type and mutant GroEL14 complexes.

One gene can give rise to many functionally distinct proteoforms, each of which has a characteristic molecular mass. Top-down mass spectrometry enables the analysis of intact proteins and proteoforms. Here members of the Consortium for Top-Down Proteomics provide a decision tree that guides researchers to robust protocols for mass analysis of intact proteins (antibodies, membrane proteins and others) from mixtures of varying complexity. We also present cross-platform analytical benchmarks using a protein standard sample, to allow users to gauge their proficiency.

Background: We examined recent trends and interhospital variation in use of indomethacin, ibuprofen, and surgical ligation for patent ductus arteriosus (PDA) in very-low-birth-weight (VLBW) infants. Methods: Included in this retrospective study of the Pediatric Hospital Information System database were 13,853 VLBW infants from 19 US children’s hospitals, admitted at age < 3 d between 1 January 2005 and 31 December 2014. PDA management and in-hospital outcomes were examined for trends and variation. Results: PDA was diagnosed in 5,719 (42%) VLBW infants. Cyclooxygenase inhibitors and/or ligation were used in 74% of infants with PDA overall, however studied hospitals varied greatly in PDA management. Odds of any cyclooxygenase inhibitor or surgical treatment for PDA decreased 11% per year during the study period. This was temporally associated with improved survival but also with increasing bronchopulmonary dysplasia, periventricular leukomalacia, retinopathy of prematurity, and acute renal failure in unadjusted analyses. There was no detectable correlation between hospital-specific changes in PDA management and hospital-specific changes in outcomes of preterm birth during the study period. Conclusion: Use of cyclooxygenase inhibitors and ligation for PDA in VLBW infants decreased over a 10-y period at the studied hospitals. Further evidence is needed to assess the impact of this change in PDA management.

The mass spectrometry (MS)-based ‘omics’ approach to protein analysis enables high-throughput profiling of the proteome, providing a rich data set of protein identifications and abundances to probe global protein expression levels from cells, tissue, or other biological samples. The depth of proteome coverage is generally correlated with the amount of sample that is analyzed, which in many cases comprises microgram (µg) to milligram (mg) levels of starting protein amount for most informative coverage. The downside to high sample requirements is that the resulting measurements reflect a weighted average of protein levels from the bulk sample and lose any assessment of heterogeneity within the sample. Single-cell proteomics (SCP) can provide more detailed information about individual cells and reveal subtleties in cellular heterogeneity that have previously been masked by bulk sampling approaches. Achieving sufficient proteome coverage at the single-cell level to perform impactful biological research is a challenge that has fueled significant advances in all aspects of single-cell proteomic analysis. Additionally, for some types of samples, such as microbiopsies and rare cells, it is not feasible to obtain the amounts required for bulk analysis, making profiling of these mass-limited samples largely inaccessible without high-sensitivity proteomic techniques. This dissertation aims to exploit the strengths of capillary electrophoresis coupled to MS (CE-MS)-based technologies to develop innovative methods with improved sensitivity that can expand the capabilities of limited sample and single-cell proteomic analyses. Current state-of-the-art methods and strategies for limited sample and SCP, including sample processing techniques and high-sensitivity separations, are reviewed in Chapter 1. Chapters 2 and 3 explore the benefits of ultrasensitive CE-MS applied to bottom-up proteomic (BUP) analysis of low nanogram and sub-nanogram samples. First, a novel CE-MS-based bottom-up proteomics approach optimized for high sensitivity was described and compared with alternative ultrasensitive methods to highlight specific advantages for profiling post-translational modifications (PTMs) in limited samples. We hypothesized that ion mobility separation coupled with CE-MS would enable higher sensitivity in proteomic profiling. In Chapter 3, coupling ultrasensitive CE-MS with high-field asymmetric waveform mobility spectrometry (FAIMS) was investigated to achieve greater levels of proteome coverage from a low nanogram sample. For the analysis of intact proteins using the top-down proteomic (TDP) approach, we hypothesized that CE-MS-based methods present an ideal opportunity to inject intact cells and lyse directly in the separation capillary to minimize sample losses. The fourth chapter tests two different modes of cell injection for analysis of <10 mammalian cells and single cells with on-capillary lysis followed by CE-MS analysis exhibiting significantly higher numbers of protein and proteoform identifications from a single mammalian cell than have previously been reported. The results shown in this work denote a substantial step forward for the field of single-cell top-down proteomics. The final chapter summarizes the work detailed in this dissertation and discusses promising future directions for SCP technologies and prospective applications for CE-MS-based methods in other areas of high-sensitivity protein characterization.

nformative and deep proteomic and glycomic characterization of limited availability biological and medical samples has been a significant challenge. Here, we describe our current and recent efforts in advancing sample preparation as well as miniaturized electric field- and pressure-driven separation approaches interfaced with high-end mass spectrometry (MS) to enhance the sensitivity and depth of proteomic and glycomic profiling of several types of limited biological and clinically relevant samples.

Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These ‘subtypes’ were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of ‘typical AD’ and a revisiting of tau pathological staging. Systematic characterization of longitudinal tau variability in human Alzheimer’s disease using an unbiased subtyping algorithm reveals four trajectories of tau deposition with distinct clinical features.

In Alzheimer’s diseases (AD), tau pathology is strongly associated with cognitive decline. Preclinical evidence suggests that tau spreads across connected neurons in an activity-dependent manner. Supporting this, cross-sectional AD studies show that tau deposition patterns resemble functional brain networks. However, whether higher functional connectivity is associated with higher rates of tau accumulation is unclear. Here, we combine resting-state fMRI with longitudinal tau-PET in two independent samples including 53 (ADNI) and 41 (BioFINDER) amyloid-biomarker defined AD subjects and 28 (ADNI) vs. 16 (BioFINDER) amyloid-negative healthy controls. In both samples, AD subjects show faster tau accumulation than controls. Second, in AD, higher fMRI-assessed connectivity between 400 regions of interest (ROIs) is associated with correlated tau-PET accumulation in corresponding ROIs. Third, we show that a model including baseline connectivity and tau-PET is associated with future tau-PET accumulation. Together, connectivity is associated with tau spread in AD, supporting the view of transneuronal tau propagation. Tau accumulation is associated with disease progression in Alzheimer’s disease. Here the authors use resting state fMRI and tau-PET to demonstrate that baseline connectivity in Alzheimer's disease is associated with tau spreading.

Tau is a hallmark pathology of Alzheimer’s disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β -amyloid (A β ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer’s disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain A β is present, but regions with greater A β burden show greater tau than predicted by connectivity patterns, suggesting a role of A β in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain A β . The tau protein is theorized to spread transneuronally in Alzheimers disease, though this theory remains unproven in humans. Our simulations of epidemic-like protein spreading across human brain networks support this theory, and suggest the spreading dynamics are modified by β -amyloid

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

Alzheimer’s disease (AD) is a leading cause of mortality in the elderly. While the coding change of APOE -ε4 is a key risk factor for late-onset AD and has been believed to be the only risk factor in the APOE locus, it does not fully explain the risk effect conferred by the locus. Here, we report the identification of AD causal variants in PVRL2 and APOC1 regions in proximity to APOE and define common risk haplotypes independent of APOE -ε4 coding change. These risk haplotypes are associated with changes of AD-related endophenotypes including cognitive performance, and altered expression of APOE and its nearby genes in the human brain and blood. High-throughput genome-wide chromosome conformation capture analysis further supports the roles of these risk haplotypes in modulating chromatin states and gene expression in the brain. Our findings provide compelling evidence for additional risk factors in the APOE locus that contribute to AD pathogenesis. Several studies show that APOE -ε4 coding variants are associated with Alzheimer’s disease (AD) risk. Here, Zhou et al. perform fine-mapping of the APOE region and find AD risk haplotypes with non-coding variants in the PVRL2 and APOC1 regions that are associated with relevant endophenotypes.