Artificial Fingertip with Embedded Fiber-Shaped Sensing Arrays for High Resolution Tactile Sensing.

Replication of the human sense of touch would be highly advantageous for robots or prostheses as it would allow an agile and dexterous interaction with the environment. The article presents an approach for the integration of a micro-electromechanical system sensing skin with 144 tactile sensors on a soft, human-sized artificial fingertip. The sensing technology consists of thin, 1D sensing strips which are wrapped around the soft and curved fingertip. The sensing strips include 0.5 mm diameter capacitive sensors which measure touch, vibrations, and strain at a resolution of 1 sensor/mm2. The method allows to leverage the advantages of sensing skins over other tactile sensing technologies while showing a solution to integrate such skins on a soft three-dimensional body. The adaptable sensing characteristics are dominated by the thickness of a spray coated silicone layer, encapsulating the sensors in a sturdy material. We characterized the static and dynamic sensing capabilities of the encapsulated taxels up to skin thicknesses of 600 µm. Taxels with 600 µm skin layers have a sensitivity of 6 fF/mN, corresponding to an ∼5 times higher sensitivity than a human finger if combined with the developed electronics. They can detect vibrations in the full tested range of 0-600 Hz. The softness of a human finger was measured to build an artificial sensing finger of similar conformity. Miniaturized readout electronics allow the readout of the full finger with 220 Hz, which enables the observation of touch and slipping events on the artificial finger, as well as the estimation of the contact force. Slipping events can be observed as vibrations registered by single sensors, whereas the contact force can be extracted by averaging sensor array readouts. We verified the sturdiness of the sensing technology by testing single coated sensors on a chip, as well as the completely integrated sensing fingertip by applying 15 N for 10,000 times. Qualitative datasets show the response of the fingertip to the touch of various objects. The focus of this article is the development of the sensing hardware and the basic characterization of the sensing performance.

Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is estimated to affect 30% of the world's population, and its prevalence is increasing in line with obesity. Liver fibrosis is closely related to mortality, making it the most important clinical parameter for MASLD. It is currently assessed by liver biopsy - an invasive procedure that has some limitations. There is thus an urgent need for a reliable non-invasive means to diagnose earlier MASLD stages. METHODS: A discovery study was performed on 158 plasma samples from histologically-characterised MASLD patients using mass spectrometry (MS)-based quantitative proteomics. Differentially abundant proteins were selected for verification by ELISA in the same cohort. They were subsequently validated in an independent MASLD cohort (n = 200). RESULTS: From the 72 proteins differentially abundant between patients with early (F0-2) and advanced fibrosis (F3-4), we selected Insulin-like growth factor-binding protein complex acid labile subunit (ALS) and Galectin-3-binding protein (Gal-3BP) for further study. In our validation cohort, AUROCs with 95% CIs of 0.744 [0.673 - 0.816] and 0.735 [0.661 - 0.81] were obtained for ALS and Gal-3BP, respectively. Combining ALS and Gal-3BP improved the assessment of advanced liver fibrosis, giving an AUROC of 0.796 [0.731. 0.862]. The {ALS; Gal-3BP} model surpassed classic fibrosis panels in predicting advanced liver fibrosis. CONCLUSIONS: Further investigations with complementary cohorts will be needed to confirm the usefulness of ALS and Gal-3BP individually and in combination with other biomarkers for diagnosis of liver fibrosis. With the availability of ELISA assays, these findings could be rapidly clinically translated, providing direct benefits for patients.

Validity of Ultrasound for the Diagnosis of Arterial Thoracic Outlet Syndrome.

Objective: Thoracic outlet syndrome (TOS) is a rare disorder mostly seen in younger individuals. Although patient wellbeing is relevantly impaired, it often takes a long time before the diagnosis is made. Digital subtraction angiography (DSA) is routinely used despite its radiation exposure, which is a major concern in this young patient population. Moreover, DSA offers limited opportunities for functional assessment. By contrast, ultrasonography is widely accessible without causing radiation exposure and allows for flexible functional assessment. The main goal of the study was to investigate whether ultrasound (US) was a viable alternative to DSA in diagnosing arterial TOS (aTOS). Methods: Patients, referred to a tertiary centre for evaluation of suspected TOS, were recruited into the study. DSA was routinely performed with the patient's arms both in the raised (abducted) and neutral (adducted) position. Two vascular surgeons and two radiologists assessed the resulting images for the presence of aTOS. Additionally, two examiners performed US according to a standardised protocol. The reference for presence of aTOS was the DSA based interdisciplinary vascular conference consensus. Inter-rater agreement and latent class analysis (LCA) were performed between assessors and diagnostic methods. Results: Fifty one consecutive patients (two thirds female) aged 39.3 ± 13.0 years were included within 11 months. US agreement was excellent at 0.94 (0.841-0.980), DSA agreement for vascular surgeons was good at 0.779 (0.479-1.000), whereas it was moderate at 0.546 (0.046-1.000) for radiologists. Results suggest that DSA is untenable as the gold standard for aTOS diagnosis. In LCA, US was shown to be a reliable diagnostic tool for the detection of aTOS. Conclusion: US examination is a valid test for the detection of haemodynamically relevant compression of arteries in the diagnostic work up of aTOS using a standardised protocol. The role of DSA as the gold standard should be reviewed and needs to be reconsidered.

Pulse-SILAC and Interactomics Reveal Distinct DDB1-CUL4-Associated Factors, Cellular Functions, and Protein Substrates.

Cullin-RING finger ligases represent the largest family of ubiquitin ligases. They are responsible for the ubiquitination of ∼20% of cellular proteins degraded through the proteasome, by catalyzing the transfer of E2-loaded ubiquitin to a substrate. Seven cullins are described in vertebrates. Among them, cullin 4 (CUL4) associates with DNA damage-binding protein 1 (DDB1) to form the CUL4-DDB1 ubiquitin ligase complex, which is involved in protein ubiquitination and in the regulation of many cellular processes. Substrate recognition adaptors named DDB1/CUL4-associated factors (DCAFs) mediate the specificity of CUL4-DDB1 and have a short structural motif of approximately forty amino acids terminating in tryptophan (W)-aspartic acid (D) dipeptide, called the WD40 domain. Using different approaches (bioinformatics/structural analyses), independent studies suggested that at least sixty WD40-containing proteins could act as adaptors for the DDB1/CUL4 complex. To better define this association and classification, the interaction of each DCAFs with DDB1 was determined, and new partners and potential substrates were identified. Using BioID and affinity purification-mass spectrometry approaches, we demonstrated that seven WD40 proteins can be considered DCAFs with a high confidence level. Identifying protein interactions does not always lead to identifying protein substrates for E3-ubiquitin ligases, so we measured changes in protein stability or degradation by pulse-stable isotope labeling with amino acids in cell culture to identify changes in protein degradation, following the expression of each DCAF. In conclusion, these results provide new insights into the roles of DCAFs in regulating the activity of the DDB1-CUL4 complex, in protein targeting, and characterized the cellular processes involved.

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations.

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters.

In proteomic differential analysis, FDR control is often performed through a multiple test correction (i.e., the adjustment of the original p-values). In this protocol, we apply a recent and alternative method, based on so-called knockoff filters. It shares interesting conceptual similarities with the target-decoy competition procedure, classically used in proteomics for FDR control at peptide identification. To provide practitioners with a unified understanding of FDR control in proteomics, we apply the knockoff procedure on real and simulated quantitative datasets. Leveraging these comparisons, we propose to adapt the knockoff procedure to better fit the specificities of quantitative proteomic data (mainly very few samples). Performances of knockoff procedure are compared with those of the classical Benjamini-Hochberg procedure, hereby shedding a new light on the strengths and weaknesses of target-decoy competition.

Validation of MS/MS Identifications and Label-Free Quantification Using Proline.

In the proteomics field, the production and publication of reliable mass spectrometry (MS)-based label-free quantitative results is a major concern. Due to the intrinsic complexity of bottom-up proteomics experiments (requiring aggregation of data relating to both precursor and fragment peptide ions into protein information, and matching this data across samples), inaccuracies and errors can occur throughout the data-processing pipeline. In a classical label-free quantification workflow, the validation of identification results is critical since errors made at this first stage of the workflow may have an impact on the following steps and therefore on the final result. Although false discovery rate (FDR) of the identification is usually controlled by using the popular target-decoy method, it has been demonstrated that this method can sometimes lead to inaccurate FDR estimates. This protocol shows how Proline can be used to validate identification results by using the method based on the Benjamini-Hochberg procedure and then quantify the identified ions and proteins in a single software environment providing data curation capabilities and computational efficiency.

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.

Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Challenging Targets or Describing Mismatches? A Comment on Common Decoy Distribution by Madej et al.

In their recent article, Madej et al. (Madej, D.; Wu, L.; Lam, H.Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics. J. Proteome Res.2022, 21 (2), 339-348) proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-only search. Conceptually, this approach is appealing as it takes the best of two worlds, i.e., decoy-based approaches (which leverage a large-scale collection of empirical mismatches) and decoy-free approaches (which are not subject to the randomness of decoy generation while sparing an additional database search). Interestingly, CDD also corresponds to a middle-of-the-road approach in statistics with respect to the two main families of FDR control procedures: Although historically based on estimating the false-positive distribution, FDR control has recently been demonstrated to be possible thanks to competition between the original variables (in proteomics, target sequences) and their fictional counterparts (in proteomics, decoys). Discriminating between these two theoretical trends is of prime importance for computational proteomics. In addition to highlighting why proteomics was a source of inspiration for theoretical biostatistics, it provides practical insights into the improvements that can be made to FDR control methods used in proteomics, including CDD.

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics.

BACKGROUND: Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach. RESULTS: We establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible. CONCLUSIONS: In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications.

Can Omics Biology Go Subjective because of Artificial Intelligence? A Comment on "Challenges and Opportunities for Bayesian Statistics in Proteomics" by Crook et al.

In their recent review ( J. Proteome Res. 2022, 21 (4), 849-864), Crook et al. diligently discuss the basics (and less basics) of Bayesian modeling, survey its various applications to proteomics, and highlight its potential for the improvement of computational proteomic tools. Despite its interest and comprehensiveness on these aspects, the pitfalls and risks of Bayesian approaches are hardly introduced to proteomic investigators. Among them, one is sufficiently important to be brought to attention: namely, the possibility that priors introduced at an early stage of the computational investigations detrimentally influence the final statistical significance.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Well Plate Maker: a user-friendly randomized block design application to limit batch effects in large-scale biomedical studies.

SUMMARY: Many factors can influence results in clinical research, in particular bias in the distribution of samples prior to biochemical preparation. Well Plate Maker is a user-friendly application to design single- or multiple-well plate assays. It allows multiple group experiments to be randomized and therefore helps to reduce possible batch effects. Although primarily fathered to optimize the design of clinical sample analysis by high throughput mass spectrometry (e.g. proteomics or metabolomics), it includes multiple options to limit edge-of-plate effects, to incorporate control samples or to limit cross-contamination. It thus fits the constraints of many experimental fields. AVAILABILITY AND IMPLEMENTATION: Well Plate Maker is implemented in R and available at Bioconductor repository (https://bioconductor.org/packages/wpm) under the open source Artistic 2.0 license. In addition to classical scripting, it can be used through a graphical user interface, developed with Shiny technology.

CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis.

BACKGROUND: The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. RESULTS: We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. CONCLUSIONS: Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics.

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Comprehensive and comparative exploration of the Atp7b-/- mouse plasma proteome.

Wilson's disease (WD), a rare genetic disease caused by mutations in the ATP7B gene, is associated with altered expression and/or function of the copper-transporting ATP7B protein, leading to massive toxic accumulation of copper in the liver and brain. The Atp7b-/- mouse, a genetic and phenotypic model of WD, was developed to provide new insights into the pathogenic mechanisms of WD. Many plasma proteins are secreted by the liver, and impairment of liver function can trigger changes to the plasma proteome. High standard proteomics workflows can identify such changes. Here, we explored the plasma proteome of the Atp7b-/- mouse using a mass spectrometry (MS)-based proteomics workflow combining unbiased discovery analysis followed by targeted quantification. Among the 367 unique plasma proteins identified, 7 proteins were confirmed as differentially abundant between Atp7b-/- mice and wild-type littermates, and were directly linked to WD pathophysiology (regeneration of liver parenchyma, plasma iron depletion, etc.). We then adapted our targeted proteomics assay to quantify human orthologues of these proteins in plasma from copper-chelator-treated WD patients. The plasma proteome changes observed in the Atp7b-/- mouse were not confirmed in these samples, except for alpha-1 antichymotrypsin, levels of which were decreased in WD patients compared to healthy individuals. Plasma ceruloplasmin was investigated in both the Atp7b-/- mouse model and human patients; it was significantly decreased in the human form of WD only. In conclusion, MS-based proteomics is a method of choice to identify proteome changes in murine models of disrupted metal homeostasis, and allows their validation in human cohorts.

Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analyses.

Results from mass spectrometry based quantitative proteomics analysis correspond to a subset of proteins which are considered differentially abundant relative to a control. Their selection is delicate and often requires some statistical expertise in addition to a refined knowledge of the experimental data. To facilitate the selection process, we have considered differential analysis as a five-step process, and here we present the practical aspects of the different steps. Prostar software is used throughout this article for illustration, but the general methodology is applicable with many other tools. By applying the approach detailed here, researchers who may be less familiar with statistical considerations can be more confident in the results they present.