DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes.

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

Probabilistic pathway-based multimodal factor analysis.

MOTIVATION: Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. RESULTS: We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Author Correction: Chimeric peptidomimetic antibiotics against Gram-negative bacteria.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Chimeric peptidomimetic antibiotics against Gram-negative bacteria.

There is an urgent need for new antibiotics against Gram-negative pathogens that are resistant to carbapenem and third-generation cephalosporins, against which antibiotics of last resort have lost most of their efficacy. Here we describe a class of synthetic antibiotics inspired by scaffolds derived from natural products. These chimeric antibiotics contain a ß-hairpin peptide macrocycle linked to the macrocycle found in the polymyxin and colistin family of natural products. They are bactericidal and have a mechanism of action that involves binding to both lipopolysaccharide and the main component (BamA) of the ß-barrel folding complex (BAM) that is required for the folding and insertion of ß-barrel proteins into the outer membrane of Gram-negative bacteria. Extensively optimized derivatives show potent activity against multidrug-resistant pathogens, including all of the Gram-negative members of the ESKAPE pathogens1. These derivatives also show favourable drug properties and overcome colistin resistance, both in vitro and in vivo. The lead candidate is currently in preclinical toxicology studies that-if successful-will allow progress into clinical studies that have the potential to address life-threatening infections by the Gram-negative pathogens, and thus to resolve a considerable unmet medical need.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

Poxviruses package viral redox proteins in lateral bodies and modulate the host oxidative response.

All poxviruses contain a set of proteinaceous structures termed lateral bodies (LB) that deliver viral effector proteins into the host cytosol during virus entry. To date, the spatial proteotype of LBs remains unknown. Using the prototypic poxvirus, vaccinia virus (VACV), we employed a quantitative comparative mass spectrometry strategy to determine the poxvirus LB proteome. We identified a large population of candidate cellular proteins, the majority being mitochondrial, and 15 candidate viral LB proteins. Strikingly, one-third of these are VACV redox proteins whose LB residency could be confirmed using super-resolution microscopy. We show that VACV infection exerts an anti-oxidative effect on host cells and that artificial induction of oxidative stress impacts early and late gene expression as well as virion production. Using targeted repression and/or deletion viruses we found that deletion of individual LB-redox proteins was insufficient for host redox modulation suggesting there may be functional redundancy. In addition to defining the spatial proteotype of VACV LBs, these findings implicate poxvirus redox proteins as potential modulators of host oxidative anti-viral responses and provide a solid starting point for future investigations into the role of LB resident proteins in host immunomodulation.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

CD20 as a gatekeeper of the resting state of human B cells.

CD20 is a B cell-specific membrane protein and represents an attractive target for therapeutic antibodies. Despite widespread usage of anti-CD20 antibodies for B cell depletion therapies, the biological function of their target remains unclear. Here, we demonstrate that CD20 controls the nanoscale organization of receptors on the surface of resting B lymphocytes. CRISPR/Cas9-mediated ablation of CD20 in resting B cells resulted in relocalization and interaction of the IgM-class B cell antigen receptor with the coreceptor CD19. This receptor rearrangement led to a transient activation of B cells, accompanied by the internalization of many B cell surface marker proteins. Reexpression of CD20 restored the expression of the B cell surface proteins and the resting state of Ramos B cells. Similarly, treatment of Ramos or naive human B cells with the anti-CD20 antibody rituximab induced nanoscale receptor rearrangements and transient B cell activation in vitro and in vivo. A departure from the resting B cell state followed by the loss of B cell identity of CD20-deficient Ramos B cells was accompanied by a PAX5 to BLIMP-1 transcriptional switch, metabolic reprogramming toward oxidative phosphorylation, and a shift toward plasma cell development. Thus, anti-CD20 engagement or the loss of CD20 disrupts membrane organization, profoundly altering the fate of human B cells.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.

BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Apicobasal Surfaceome Architecture Encodes for Polarized Epithelial Functionality and Depends on Tumor Suppressor PTEN.

The loss of apicobasal polarity during the epithelial-to-mesenchymal transition (EMT) is a hallmark of cancer and metastasis. The key feature of this polarity in epithelial cells is the subdivision of the plasma membrane into apical and basolateral domains, with each orchestrating specific intra- and extracellular functions. Epithelial transport and signaling capacities are thought to be determined largely by the quality, quantity, and nanoscale organization of proteins residing in these membrane domains, the apicobasal surfaceomes. Despite its implications for cancer, drug uptake, and infection, our current knowledge of how the polarized surfaceome is organized and maintained is limited. Here, we used chemoproteomic surfaceome scanning to establish proteotype maps of apicobasal surfaceomes and reveal quantitative distributions of, i.e., surface proteases, phosphatases, and tetraspanins as potential key regulators of polarized cell functionality. We show further that the tumor suppressor PTEN regulates polarized surfaceome architecture and uncover a potential role in collective cell migration. Our differential surfaceome analysis provides a molecular framework to elucidate polarized protein networks regulating epithelial functions and PTEN-associated cancer progression.

Decoding Functional High-Density Lipoprotein Particle Surfaceome Interactions.

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport (RCT) and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDL's journey throughout the body, its functions are mediated through interactions with cell surface receptors on different cell types. To characterize and better understand the functional interplay between HDL particles and tissue, we analyzed the surfaceome-residing receptor neighborhoods with which HDL potentially interacts. We applied a combination of chemoproteomic technologies including automated cell surface capturing (auto-CSC) and HATRIC-based ligand-receptor capturing (HATRIC-LRC) on four different cellular model systems mimicking tissues relevant for RCT. The surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAECs) revealed the main currently known HDL receptor scavenger receptor B1 (SCRB1), as well as 155 shared cell surface receptors representing potential HDL interaction candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF-modulated surfaceome of HAEC using the auto-CSC technology. VEGF-A treatment led to the remodeling of the surfaceome of HAEC cells, including the previously reported higher surfaceome abundance of SCRB1. In total, 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using the HATRIC-LRC technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK). Through RNA interference, we revealed its contribution to endothelial HDL binding and uptake. Furthermore, subsequent proximity ligation assays (PLAs) demonstrated the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence binding and uptake of HDL.

Reproducible Determination of High-Density Lipoprotein Proteotypes.

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

Phage resistance at the cost of virulence: Listeria monocytogenes serovar 4b requires galactosylated teichoic acids for InlB-mediated invasion.

The intracellular pathogen Listeria monocytogenes is distinguished by its ability to invade and replicate within mammalian cells. Remarkably, of the 15 serovars within the genus, strains belonging to serovar 4b cause the majority of listeriosis clinical cases and outbreaks. The Listeria O-antigens are defined by subtle structural differences amongst the peptidoglycan-associated wall-teichoic acids (WTAs), and their specific glycosylation patterns. Here, we outline the genetic determinants required for WTA decoration in serovar 4b L. monocytogenes, and demonstrate the exact nature of the 4b-specific antigen. We show that challenge by bacteriophages selects for surviving clones that feature mutations in genes involved in teichoic acid glycosylation, leading to a loss of galactose from both wall teichoic acid and lipoteichoic acid molecules, and a switch from serovar 4b to 4d. Surprisingly, loss of this galactose decoration not only prevents phage adsorption, but leads to a complete loss of surface-associated Internalin B (InlB),the inability to form actin tails, and a virulence attenuation in vivo. We show that InlB specifically recognizes and attaches to galactosylated teichoic acid polymers, and is secreted upon loss of this modification, leading to a drastically reduced cellular invasiveness. Consequently, these phage-insensitive bacteria are unable to interact with cMet and gC1q-R host cell receptors, which normally trigger cellular uptake upon interaction with InlB. Collectively, we provide detailed mechanistic insight into the dual role of a surface antigen crucial for both phage adsorption and cellular invasiveness, demonstrating a trade-off between phage resistance and virulence in this opportunistic pathogen.

The in silico human surfaceome.

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.

A Proteogenomic Resource Enabling Integrated Analysis of Listeria Genotype-Proteotype-Phenotype Relationships.

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here, we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publicly available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 °C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 °C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels did not change significantly upon exposure to bile salts at 37 °C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics.

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.

Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations.

Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. Here, we provide a systematic investigation of the influence of different cell isolation protocols on transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis of microglia. We show that standard enzymatic digestion of brain tissue at 37 °C induces profound and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared to an optimized mechanical dissociation protocol at 4 °C. These findings emphasize the risk of introducing technical biases and biological artifacts when implementing enzymatic digestion-based isolation methods for brain cell analyses.

In vitro quantification of botulinum neurotoxin type A1 using immobilized nerve cell-mimicking nanoreactors in a microfluidic platform.

The bacterial toxin botulinum neurotoxin A (BoNT/A) is not only an extremely toxic substance but also a potent pharmaceutical compound that is used in a wide spectrum of neurological disorders and cosmetic applications. The quantification of the toxin is extremely challenging due to its extraordinary high physiological potency and is further complicated by the toxin's three key functionalities that are necessary for its activity: receptor binding, internalization-translocation, and catalytic activity. So far, the industrial standard to measure the active toxin has been the mouse bioassay (MBA) that is considered today as outdated due to ethical issues. Therefore, recent introductions of cell-based assays were highly anticipated; their impact however remains limited due to their labor-intensive implementation. This report describes a new in vitro approach that combines a nanosensor based on the use of nerve cell-mimicking nanoreactors (NMN) with microfluidic technology. The nanosensor was able to measure all three key functionalities, and therefore suitable to quantify the amount of physiologically active BoNT/A. The integration of such a sensor in a microfluidic device allowed the detection and quantification of BoNT/A amounts in a much shorter time than the MBA (<10 h vs. 2-4 days). Lastly, the system was also able to reliably quantify physiologically active BoNT/A within a simple final pharmaceutical formulation. This complete in vitro testing system and its unique combination of a highly sensitive nanosensor and microfluidic technology represent a significant ethical advancement over in vivo measures and a possible alternative to cell-based in vitro detection methods.