Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit.

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023.

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

A Direct Approach for Living Biomembrane Printing on a Nanoparticle.

Biomembrane coating technologies have been developed to equip synthetic nanomaterials with natural biointerfaces. We report a one-step method for nondestructively coating the biomembranes of "living" cells onto nanoparticle surfaces. By using simple centrifugation, nanoparticles pass through a concentrated layer of living cells. This process mimics exosome release via endocytosis and exocytosis, preserving the membrane integrity of the source cells. The resulting silica nanoparticles were efficiently coated with membrane components from Raw264.7 cells. Nanoflow-liquid chromatography-tandem mass spectrometry confirmed that the proteins composing the membrane originated from the source cells. Additionally, the biomembrane coating suppressed the phagocytosis of silica nanoparticles by Raw264.7 cells while enhancing their uptake by HeLa cells. Our simple and efficient method for living biomembrane coating holds promise for the development of nanoparticles for medical and pharmaceutical applications.

Template Selection Strategy for Synthesis of One-Dimensional CrSbSe3 Ferromagnetic Semiconductor Nanoribbons.

CrSbSe3─the only experimentally validated one-dimensional (1D) ferromagnetic semiconductor─has recently attracted significant attention. However, all reported synthesis methods for CrSbSe3 nanocrystals are based on top-down methods. Here we report a template selection strategy for the bottom-up synthesis of CrSbSe3 nanoribbons. This strategy relies on comparing the formation energies of potential binary templates to the ternary target product. It enables us to select Sb2Se3 with the highest formation energy, along with its 1D crystal structure, as the template instead of Cr2Se3 with the lowest formation energy, thereby facilitating the transformation from Sb2Se3 to CrSbSe3 by replacing half of the Sb atoms in Sb2Se3 with Cr atoms. The as-prepared CrSbSe3 nanoribbons exhibit a length of approximately 5 µm, a width ranging from 80 to 120 nm, and a thickness of about 5 nm. The single CrSbSe3 nanoribbon presents typical semiconductor behavior and ferromagnetism, confirming the intrinsic ferromagnetism in the 1D CrSbSe3 semiconductor.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples.

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Accelerating Proteomics Using Broad Specificity Proteases.

Trypsin is the gold-standard protease in bottom-up proteomics, but many sequence stretches of the proteome are inaccessible to trypsin and standard LC-MS approaches. Thus, multienzyme strategies are used to maximize sequence coverage in post-translational modification profiling. We present fast and robust SP3- and STRAP-based protocols for the broad-specificity proteases subtilisin, proteinase K, and thermolysin. All three enzymes are remarkably fast, producing near-complete digests in 1-5 min, and cost 200-1000× less than proteomics-grade trypsin. Using FragPipe resolved a major challenge by drastically reducing the duration of the required "unspecific" searches. In-depth analyses of proteinase K, subtilisin, and thermolysin Jurkat digests identified 7374, 8178, and 8753 unique proteins with average sequence coverages of 21, 29, and 37%, including 10,000s of amino acids not reported in PeptideAtlas' >2400 experiments. While we could not identify distinct cleavage patterns, machine learning could distinguish true protease products from random cleavages, potentially enabling the prediction of cleavage products. Finally, proteinase K, subtilisin, and thermolysin enabled label-free quantitation of 3111, 3659, and 4196 unique Jurkat proteins, which in our hands is comparable to trypsin. Our data demonstrate that broad-specificity proteases enable quantitative proteomics of uncharted areas of the proteome. Their fast kinetics may allow "on-the-fly" digestion of samples in the future.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics.

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

Site-Specific Stability Evaluation of Antibody-Drug Conjugate in Serum Using a Validated Liquid Chromatography-Mass Spectrometry Method.

Antibody-drug conjugate (ADC) consists of engineered antibodies and cytotoxic drugs linked via a chemical linker, and the stability of ADC plays a crucial role in ensuring its safety and efficacy. The stability of ADC is closely related to the conjugation site; however, no method has been developed to assess the stability of different conjugation sites due to the low response of conjugated peptides. In this study, an integrated strategy was developed and validated to assess the stability of different conjugation sites on ADC in serum. Initial identification of the conjugated peptides of the model drug ado-trastuzumab emtansine (T-DM1) was achieved by the proteomic method. Subsequently, a semiquantitative method for conjugated peptides was established in liquid chromatography-hybrid linear ion trap triple quadrupole mass spectrometry (LC-QTRAP-MS/MS) based on the qualitative information. The pretreatment method of the serum sample was optimized to reduce matrix interference. The method was then validated and applied to evaluate the stability of the conjugation sites on T-DM1. The results highlighted differences in stability among the different conjugation sites on T-DM1. This is the first study to assess the stability of different conjugation sites on the ADC in serum, which will be helpful for the design and screening of ADCs in the early stages of development.

Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.

The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the "Histone Code". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K0) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me1-3) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.

Impact of Surfactants on Cumulative Trypsin Activity in Bottom-Up Proteome Analysis.

Trypsin digestion plays a pivotal role in successful bottom-up peptide characterization and quantitation. While denaturants are often incorporated to enhance protein solubility, surfactants are recognized to inhibit enzyme activity. However, several reports have suggested that incorporating surfactants or other solvent additives may enhance digestion and MS detection. Here, we assess the impacts of ionic surfactants on cumulative trypsin activity and subsequently evaluate the total digestion efficiency of a proteome mixture by quantitative MS. Although low surfactant concentrations, such as 0.01% SDS or 0.2% SDC, significantly enhanced the initial trypsin activity (by 14 or 42%, respectively), time course assays revealed accelerated enzyme deactivation, evident by 10- or 40-fold reductions in trypsin activity half-life at these respective surfactant concentrations. Despite enhanced initial tryptic activity, quantitative MS analysis of a common liver proteome extract, digested with various surfactants (0.01 or 0.1% SDS, 0.5% SDC), consistently revealed decreased peptide counts and signal intensity, indicative of a lower digestion efficiency compared to a nonsurfactant control. Furthermore, including detergents for digestion did not improve the detection of membrane proteins, nor hydrophobic peptides. These results stress the importance of assessing cumulative enzyme activity when optimizing the digestion of a proteome mixture, particularly in the presence of denaturants.

Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses.

Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task.

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

Interfacial Assembly of Bacterial Microcompartment Shell Proteins in Aqueous Multiphase Systems.

Compartments are a fundamental feature of life, based variously on lipid membranes, protein shells, or biopolymer phase separation. Here, this combines self-assembling bacterial microcompartment (BMC) shell proteins and liquid-liquid phase separation (LLPS) to develop new forms of compartmentalization. It is found that BMC shell proteins assemble at the liquid-liquid interfaces between either 1) the dextran-rich droplets and PEG-rich continuous phase of a poly(ethyleneglycol)(PEG)/dextran aqueous two-phase system, or 2) the polypeptide-rich coacervate droplets and continuous dilute phase of a polylysine/polyaspartate complex coacervate system. Interfacial protein assemblies in the coacervate system are sensitive to the ratio of cationic to anionic polypeptides, consistent with electrostatically-driven assembly. In both systems, interfacial protein assembly competes with aggregation, with protein concentration and polycation availability impacting coating. These two LLPS systems are then combined to form a three-phase system wherein coacervate droplets are contained within dextran-rich phase droplets. Interfacial localization of BMC hexameric shell proteins is tunable in a three-phase system by changing the polyelectrolyte charge ratio. The tens-of-micron scale BMC shell protein-coated droplets introduced here can accommodate bioactive cargo such as enzymes or RNA and represent a new synthetic cell strategy for organizing biomimetic functionality.

Insulated π-Conjugated Azido Scaffolds for Stepwise Functionalization via Huisgen Cycloaddition on Metal Oxide Surfaces.

In organic-inorganic hybrid devices, fine interfacial controls by organic components directly affect the device performance. However, fabrication of uniformed interfaces using π-conjugated molecules remains challenging due to facile aggregation by their strong π-π interaction. In this report, a π-conjugated scaffold insulated by covalently linked permethylated α-cyclodextrin moiety with an azido group is synthesized for surface Huisgen cycloaddition on metal oxides. Fourier-transformed infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy confirm the successful immobilization of the insulated azido scaffold on ZnO nanowire array surfaces. Owing to the highly independent immobilization, the scaffold allows rapid and complete conversion of the surface azido group in Huisgen cycloaddition reactions with ethynyl-terminated molecules, as confirmed by FT-IR spectroscopy monitoring. Cyclic voltammetry analysis of modified indium tin oxide substrates shows the positive effects of cyclic insulation toward suppression of intermolecular interaction between molecules introduced by the surface Huisgen cycloaddition reactions. The utility of the scaffold for heterogeneous catalysis is demonstrated in electrocatalytic selective O2 reduction to H2O2 with cobalt(II) chlorin modified fluorine doped tin oxide electrode and photocatalytic H2 generation with iridium(III) dye-sensitized Pt-loaded TiO2 nanoparticle. These results highlight the potential of the insulated azido scaffold for a stepwise functionalization process, enabling precise and well-defined hybrid interfaces.

Soft Synthetic Cells with Mobile Membrane Ligands for Ex Vivo Expansion of Therapy-Relevant T Cell Phenotypes.

The expansion of T cells ex vivo is crucial for effective immunotherapy but currently limited by a lack of expansion approaches that closely mimic in vivo T cell activation. Taking inspiration from bottom-up synthetic biology, a new synthetic cell technology is introduced based on dispersed liquid-liquid phase-separated droplet-supported lipid bilayers (dsLBs) with tunable biochemical and biophysical characteristics, as artificial antigen presenting cells (aAPCs) for ex vivo T cell expansion. These findings obtained with the dsLB technology reveal three key insights: first, introducing laterally mobile stimulatory ligands on soft aAPCs promotes expansion of IL-4/IL-10 secreting regulatory CD8+ T cells, with a PD-1 negative phenotype, less prone to immune suppression. Second, it is demonstrated that lateral ligand mobility can mask differential T cell activation observed on substrates of varying stiffness. Third, dsLBs are applied to reveal a mechanosensitive component in bispecific Her2/CD3 T cell engager-mediated T cell activation. Based on these three insights, lateral ligand mobility, alongside receptor- and mechanosignaling, is proposed to be considered as a third crucial dimension for the design of ex vivo T cell expansion technologies.

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

Collision energies: Optimization strategies for bottom-up proteomics.

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.