Complex Biological Systems Research Articles

Combining SDS-PAGE to capillary zone electrophoresis-tandem mass spectrometry for high-resolution top-down proteomics analysis of intact histone proteoforms.

Mass spectrometry (MS)-based top-down proteomics (TDP) analysis of histone proteoforms provides critical information about combinatorial post-translational modifications (PTMs), which is vital for pursuing a better understanding of epigenetic regulation of gene expression. It requires high-resolution separations of histone proteoforms before MS and tandem MS (MS/MS) analysis. In this work, for the first time, we combined SDS-PAGE-based protein fractionation (passively eluting proteins from polyacrylamide gels as intact species for mass spectrometry, PEPPI-MS) with capillary zone electrophoresis (CZE)-MS/MS for high-resolution characterization of histone proteoforms. We systematically studied the histone proteoform extraction from SDS-PAGE gel and follow-up cleanup as well as CZE-MS/MS, to determine an optimal procedure. The optimal procedure showed reproducible and high-resolution separation and characterization of histone proteoforms. SDS-PAGE separated histone proteins (H1, H2, H3, and H4) based on their molecular weight and CZE provided additional separations of proteoforms of each histone protein based on their electrophoretic mobility, which was affected by PTMs, for example, acetylation and phosphorylation. Using the technique, we identified over 200 histone proteoforms from a commercial calf thymus histone sample with good reproducibility. The orthogonal and high-resolution separations of SDS-PAGE and CZE made our technique attractive for the delineation of histone proteoforms extracted from complex biological systems.

Computing macroscopic reaction rates in reaction-diffusion systems using Monte Carlo simulations.

Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale, long-time kinetics in such systems are effective, scale-dependent renormalized parameters that need to be either measured experimentally or computed by means of a microscopic model. In a Monte Carlo simulation of stochastic reaction-diffusion systems, microscopic probabilities for specific events to happen serve as the input control parameters. To match the results of any computer simulation to observations or experiments carried out on the macroscale, a mapping is required between the microscopic probabilities that define the Monte Carlo algorithm and the macroscopic reaction rates that are experimentally measured. Finding the functional dependence of emergent macroscopic rates on the microscopic probabilities (subject to specific rules of interaction) is a very difficult problem, and there is currently no systematic, accurate analytical way to achieve this goal. Therefore, we introduce a straightforward numerical method of using lattice Monte Carlo simulations to evaluate the macroscopic reaction rates by directly obtaining the count statistics of how many events occur per simulation time step. Our technique is first tested on well-understood fundamental examples, namely, restricted birth processes, diffusion-limited two-particle coagulation, and two-species pair annihilation kinetics. Next we utilize the thus gained experience to investigate how the microscopic algorithmic probabilities become coarse-grained into effective macroscopic rates in more complex model systems such as the Lotka-Volterra model for predator-prey competition and coexistence, as well as the rock-paper-scissors or cyclic Lotka-Volterra model and its May-Leonard variant that capture population dynamics with cyclic dominance motifs. Thereby we achieve a more thorough and deeper understanding of coarse graining in spatially extended stochastic reaction-diffusion systems and the nontrivial relationships between the associated microscopic and macroscopic model parameters, with a focus on ecological systems. The proposed technique should generally provide a useful means to better fit Monte Carlo simulation results to experimental or observational data.

Bidirectional path-based non-equilibrium simulations for binding free energy

Estimating free energy is a fundamental computational challenge, especially in complex biological systems characterised by numerous degrees of freedom. In this study, we investigate the potential of leveraging non-equilibrium free energy estimators within path-based approaches; this offers an appealing feature of inherent parallelism. Building upon our prior work on protein-ligand binding free energy calculations, we develop its non-equilibrium counterpart. We begin by validating our computational strategy on a simple toy model and then extend our analysis to the well-established trypsin-benzamidine complex, serving as a benchmark system. Subsequently, we apply this method to a more intricate, relevant pharmaceutical system to evaluate the performance of our computational pipeline on this complex system. Our results not only demonstrate the feasibility of this approach but also shed light on potential limitations. Furthermore, we showcase the capabilities of the Jarzynski and Crooks estimators employed in our study.

Luminescence Probes in Bio-Applications: From Principle to Practice.

Bioanalysis based on optical imaging has gained significant progress in the last few decades. Luminescence probes are capable of detecting, monitoring, and tracing particular biomolecules in complex biological systems to figure out the roles of these molecules in organisms. Considering the rapid development of luminescence probes for bio-applications and their promising future, we have attempted to explore the working principles and recent advances in bio-applications of luminescence probes, in the hope of helping readers gain a detailed understanding of luminescence probes developed in recent years. In this review, we first focus on the current widely used luminescence probes, including fluorescence probes, bioluminescence probes, chemiluminescence probes, afterglow probes, photoacoustic probes, and Cerenkov luminescence probes. The working principles for each type of luminescence probe are concisely described and the bio-application of the luminescence probes is summarized by category, including metal ions detection, secretion detection, imaging, and therapy.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

The Impact of Centrality Measures in Protein–Protein Interaction Networks: Tools, Databases, Challenges and Future Directions

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively.

Visualization of renal rotenone accumulation after oral administration and in situ detection of kidney injury biomarkers via MALDI mass spectrometry imaging.

The examination of drug accumulation within complex biological systems offers valuable insights into the molecular aspects of drug metabolism and toxicity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an innovative methodology that enables the spatial visualization and quantification of biomolecules as well as drug and its metabolites in complex biological system. Hence, this method provides valuable insights into the metabolic profile and any molecular changes that may occur as a result of drug treatment. The renal system is particularly vulnerable to adverse effects of drug-induced harm and toxicity. In this study, MALDI MSI was utilized to examine the spatial distribution of drug and renal metabolites within kidney tissues subsequent to a single oral dosage of the anticancer compound rotenone. The integration of ion mobility spectrometry with MALDI MSI enhanced the data acquisition and analysis, resulting to improved mass resolution. Subsequently, the MS/MS fragment ions of rotenone reference drug were detected and characterized using MALDI HDMS/MS imaging. Notably, drug accumulation was observed in the cortical region of the representative kidney tissue sections treated with rotenone. The histological examination of treated kidney tissues did not reveal any observable changes. Differential ion intensity of renal endogenous metabolites was observed between untreated and rotenone-treated tissues. In the context of treated kidney tissues, the ion intensity level of sphingomyelin (D18:1/16:0), a sphingolipid indicator of glomerular cell injury and renal damage, was found to be elevated significantly compared to untreated kidney tissues. Conversely, the ion intensities of choline, glycero-3-phosphocholine (GPC), inosine, and a lysophosphatidylcholine LysoPC(18:0) exhibited a significant decrease. The results of this study demonstrate the potential of MALDI MSI as a novel technique for investigating the in situ spatial distribution of drugs and renal endogenous molecules while preserving the anatomical integrity of the kidney tissue. This technique can be used to study drug-induced metabolism and toxicity in a dynamic manner.

The construction and theoretical investigation of compound-protein target-pathway network for Radix Pueraria

The complex network approach is an effective method to analyze the global properties of complex biological systems, which can be used to explore the interactions between multiple compounds and protein targets of drug. Radix Pueraria has a long history of efficacy in different diseases, containing a variety of compounds that can produce multiple therapeutic effects via multiple targets. To explore the interrelationship between compounds of Radix Pueraria, protein targets, and pathways, the present investigation used the complex network approach to construct the compound-protein target-pathway network of Radix Pueraria. The analysis of the statistical and topological characteristics shows that the network has small-world and scale-free characteristics. The diameter of the network is 7, indicating that the information exchange between two nodes could quickly spread to the whole network. With the rank of degree value and integrated centrality as identification indicators, by taking the threshold of integrated centrality as 0.70, a total of nine key hub nodes containing three active compounds, two protein targets, and four biological pathways were identified: genistein, daidzein, puerarin, MAPK3, MAPK1, hsa01100, hsa05200, hsa05417, and hsa05207. The network analysis suggests these key hub nodes should play an important role in the pharmacological action of Radix Pueraria. The results of this study provide useful information and knowledge for further exploring the pharmacological mechanism of Radix Pueraria in the course of disease treatment.

Single-cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of irreversible blindness in the working-age populations. Despite decades of research on the pathogenesis of DR for clinical care, a comprehensive understanding of the condition is still lacking due to the intricate cellular diversity and molecular heterogeneity involved. Single-cell RNA sequencing (scRNA-seq) has made the high-throughput molecular profiling of cells across modalities possible which has provided valuable insights into complex biological systems. In this review, we summarise the application of scRNA-seq in investigating the pathogenesis of DR, focusing on four aspects. These include the identification of differentially expressed genes, characterisation of key cell subpopulations and reconstruction of developmental 'trajectories' to unveil their state transition, exploration of complex cell‒cell communication in DR and integration of scRNA-seq with genome-wide association studies to identify cell types that are most closely related to DR risk genetic loci. Finally, we discuss the future challenges and expectations associated with studying DR using scRNA-seq. We anticipate that scRNA-seq will facilitate the discovery of mechanisms and new treatment targets in the clinical care landscape for patients with DR. KEY POINTS: Progress in scRNA-seq for diabetic retinopathy (DR) research includes studies on DR patients, non-human primates, and the prevalent mouse models. scRNA-seq facilitates the identification of differentially expressed genes, pivotal cell subpopulations, and complex cell-cell interactions in DR at single-cell level. Future scRNA-seq applications in DR should target specific patient subsets and integrate with single-cell and spatial multi-omics approaches.

Peptide nucleic acids (PNAs) control function of SARS-CoV-2 frameshifting stimulatory element trough PNA-RNA-PNA triplex formation

The highly structured nature of the SARS-CoV-2 genome provides many promising antiviral drug targets. One particularly promising target is a cis-acting RNA pseudoknot found within a critical region called the frameshifting stimulatory element (FSE). In this study, peptide nucleic acids (PNAs) binding to stem 2 of FSE RNA inhibited protein translation and frameshifting, as measured by a cell-free dual luciferase assay, more effectively than PNAs binding to stem 1, stem 3, or the slippery site. Surprisingly, simple antisense PNAs were stronger disruptors of frameshifting than PNA tail-clamps, despite higher thermal stability of the PNA-RNA-PNA triplexes formed by the latter. Another unexpected result was a strong and sequence non-specific enhancement of frameshifting inhibition when using a cationic triplex-forming PNA in conjunction with an antisense PNA targeting key regions of the frameshifting element. Our results illustrate both the potential and the challenges of using antisense PNAs to target highly structured RNAs, such as SARS-CoV-2 pseudoknots. While triplex forming PNAs, including PNA tail-clamps, are emerging as promising ligands for RNA recognition, the binding affinity enhancements when using cationic modifications in triplex-forming PNAs must be carefully balanced to avoid loss of sequence specificity in complex biological systems.

Cross-Recurrence Quantification Of Cardiovascular Signals In Newborns Is A Sensitive Marker Of Health Status

Background — Analysis of the state of complex biological systems requires the use of sensitive methods for diagnosing interactions using experimental time series. Objective — To evaluate the possibility of using the technique of cross-recurrence quantification (CRQ) in studying the health status of newborns using low-frequency components of RR-interval signals and photoplethysmograms, reflecting the dynamics of the circuits in autonomic regulation of blood circulation. Methods — The study included two groups of neonates: 10 full-term newborns and 10 preterm neonates. We carried out simultaneous recording of electrocardiographic and photoplethysmographic signals. CRQ analysis was employed as the primary tool. Results — We established that some indices of CRQ analysis, characterizing the degree of interaction of the studied circuits, act as sensitive markers. They make it possible to distinguish the dynamics of the studied contours between healthy newborns and preterm neonates. Conclusion — The results of our study confirmed that CRQ is a promising tool in creating methods for diagnosing health conditions, including in newborns.

Proline Analogues.

Within the canonical repertoire of the amino acid involved in protein biogenesis, proline plays a unique role as an amino acid presenting a modified backbone rather than a side-chain. Chemical structures that mimic proline but introduce changes into its specific molecular features are defined as proline analogues. This review article summarizes the existing chemical, physicochemical, and biochemical knowledge about this peculiar family of structures. We group proline analogues from the following compounds: substituted prolines, unsaturated and fused structures, ring size homologues, heterocyclic, e.g., pseudoproline, and bridged proline-resembling structures. We overview (1) the occurrence of proline analogues in nature and their chemical synthesis, (2) physicochemical properties including ring conformation and cis/trans amide isomerization, (3) use in commercial drugs such as nirmatrelvir recently approved against COVID-19, (4) peptide and protein synthesis involving proline analogues, (5) specific opportunities created in peptide engineering, and (6) cases of protein engineering with the analogues. The review aims to provide a summary to anyone interested in using proline analogues in systems ranging from specific biochemical setups to complex biological systems.

Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging

Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain biomolecular information from multiple layers, thereby gaining a better understanding of complex biological systems. However, the curse of dimensionality is one of the most significant challenges when handling omics or biological data. Additionally, integrating multi-omics by transforming different omics types into a new representation can reduce a model’s interpretability, as the extracted features may lose the biological context. This paper proposes Iterative Similarity Bagging (ISB), assisted by Bayesian Ridge Regression (BRR). BRR serves as a domain-oriented supervised feature selection method, choosing essential features by calculating the coefficients for each feature. Despite this, the BRR output datasets contain many features, leading to complexity and high dimensionality. To address this, ISB was introduced to dynamically reduce dimensionality and complexity without losing the biological integrity of the omics data, which often occurs with transformation-based integration approaches. The evaluation measures employed were Root Mean Square Error (RMSE), the Pearson Correlation Coefficient (PCC), and the coefficient of determination (R2). The results demonstrate that the proposed method outperforms some current models in terms of regression performance, achieving an RMSE of 0.12, a PCC of 0.879, and an R2 of 0.77 for the CCLE. For the GDSC, it achieved an RMSE of 0.029, a PCC of 0.90, and an R2 of 0.80.

Prediction of the Change Rate of Tumor Cells, Healthy Host Cells, and Effector Immune Cells in a Three-Dimensional Cancer Model using Extended Kalman Filter

In this study, we develop and implement the Extended Kalman Filter (EKF) to forecast the rate of change in tumor cells, healthy host cells, and effector immune cells within the Itik-Banks model. This novel application of EKF in cancer dynamics modeling aims to provide precise real-time estimations of cellular interactions, especially in constructing a new state space representation from the Itik-Banks model. We use a first-order Taylor series to linearize the model. The numerical simulations were performed to analyze the accuracy of this new state space with data from William Gilpin’s GitHub repository. The results show that the EKF predictions strongly align with actual data, i.e., in the prior and posterior steps for tumor and healthy host cells, there is a strong agreement between the predictions and the actual data. The EKF captures the oscillatory nature of the tumor and healthy host cell population well. The peaks and troughs of the predictions align closely with the actual data, indicating the EKF’s effectiveness in modeling the dynamic behavior of the tumor and healthy host cells. However, for effector immune cells, the oscillatory nature of the data in these cells gives rise to slight deviations. This represents a significant challenge in the future for updating the state space representations. Despite minor discrepancies, the EKF demonstrates a strong performance in both the training and testing data, with the posterior step estimates significantly improving the prior step accuracy. This study emphasizes the importance of data availability for accurate predictions, noting a symmetric Mean Absolute Percentage Error (sMAPE) of 35.92% when data is unavailable. Prompt correction with new data is essential to maintain accuracy. This research underscores the EKF’s potential for real-time monitoring and prediction in complex biological systems.

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells.

Understanding mechanisms underlying various physiological and pathological processes often requires accurate and fully automated analysis of dense cell populations that collectively migrate. In such multicellular systems, there is a rising interest in the relations between biophysical and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework that is designed, unlike previous tools, for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. We integrated into ConfluentFUCCI's pipeline state-of-the-art tools such as Cellpose, TrackMate, and Napari, some of which incorporate deep learning, and we wrap the entire tool into an isolated computational environment termed container. This provides an easy installation and workflow that is independent of any specific operation system. ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

Data-driven identification of stable sparse differential operators using constrained regression

Identifying differential operators from data is essential for the mathematical modeling of complex physical and biological systems where massive datasets are available. These operators must be stable for accurate predictions for dynamics forecasting problems. In this article, we propose a novel methodology for learning differential operators that are theoretically linearly stable and have sparsity patterns of common discretization schemes. These differential operators are obtained by solving a constrained regression problem, involving local constraints to ensure the linear stability of the global dynamical system. We further extend this approach for learning nonlinear differential operators by determining linear stability constraints for linearized equations around an equilibrium point. The applicability of the proposed method is demonstrated for both linear and nonlinear partial differential equations such as 1-D scalar advection-diffusion equation, 1-D Burgers equation and 2-D advection equation. The results indicated that solutions to constrained regression problems with linear stability constraints provide accurate and linearly stable sparse differential operators.

Dual-reporter fluorescent probe for precise identification of liver cancer by sequentially responding to carboxylesterase and polarity

Early treatment significantly improves the survival rate of liver cancer patients, so the development of early diagnostic methods for liver cancer is urgent. Liver cancer can develop from viral hepatitis, alcoholic liver, and fatty liver, thus making the above diseases share common features such as elevated viscosity, reactive oxygen species, and reactive nitrogen species. Therefore, accurate differentiation between other liver diseases and liver cancer is both a paramount practical need and challenging. Numerous fluorescent probes have been reported for the diagnosis of liver cancer by detecting a single biomarker, but these probes lack specificity for liver cancer in complex biological systems. Obviously, using multiple liver cancer biomarkers as the basis for judgment can dramatically improve diagnostic accuracy. Herein, we report the first fluorescent probe, LD-TCE, that sequentially detects carboxylesterase (CE) and lipid droplet polarity in liver cancer cells with high sensitivity and selectivity, with linear detection of CE in the range of 0–6 U/mL and a 65-fold fluorescence enhancement in response to polarity. The probe first reacts with CE and releases weak fluorescence, which is then dramatically enhanced due to the decrease in lipid droplet polarity in liver cancer cells. This approach allows the probe to enable specific imaging of liver cancer with higher contrast and accuracy. The probe successfully achieved the screening of liver cancer cells and the precise identification of liver cancer in mice. More importantly, it is not disturbed by liver fibrosis, which is a common pathological feature of many liver diseases. We believe that the LD-TCE is expected to be a powerful tool for early diagnosis of liver cancer.

Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.

Online Mixed-Bed Ion Exchange Chromatography for Native Top-Down Proteomics of Complex Mixtures.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.