Labeling Methodology Research Articles

Proteomics elucidating physiological and pathological functions of TDP-43.

Trans-activation response DNA binding protein of 43kDa (TDP-43) regulates a great variety of cellular processes in the nucleus and cytosol. In addition, a defined subset of neurodegenerative diseases is characterized by nuclear depletion of TDP-43 as well as cytosolic mislocalization and aggregation. To perform its diverse functions TDP-43 can associate with different ribonucleoprotein complexes. Combined with transcriptomics, MS interactome studies have unveiled associations between TDP-43 and the spliceosome machinery, polysomes and RNA granules. Moreover, the highly dynamic, low-valency interactions regulated by its low-complexity domain calls for innovative proximity labeling methodologies. In addition to protein partners, the analysis of post-translational modifications showed that they may play a role in the nucleocytoplasmic shuttling, RNA binding, liquid-liquid phase separation and protein aggregation of TDP-43. Here we review the various TDP-43 ribonucleoprotein complexes characterized so far, how they contribute to the diverse functions of TDP-43, and roles of post-translational modifications. Further understanding of the fluid dynamic properties of TDP-43 in ribonucleoprotein complexes, RNA granules, and self-assemblies will advance the understanding of RNA processing in cells and perhaps help to develop novel therapeutic approaches for TDPopathies.

An iterative labeling method for annotating marine life imagery

This paper presents a labeling methodology for marine life data using a weakly supervised learning framework. The methodology iteratively trains a deep learning model using non-expert labels obtained from crowdsourcing. This approach enables us to converge on a labeled image dataset through multiple training and production loops that leverage crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset consists of 10,505 images that were point annotated by NOAA biologists. This dataset allows us to validate the accuracy of our labeling process. We also apply our algorithm and methodology to a second dataset consisting of 3,968 completely unlabeled images. These image categories are challenging to label, such as sponges. Qualitatively, our results indicate that training with a tiny subset and iterating on those results allows us to converge to a large, highly annotated dataset with a small number of iterations. To demonstrate the effectiveness of our methodology quantitatively, we tabulate the mean average precision (mAP) of the model as the number of iterations increases.

Advances in the Specificity of Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Based Structural Characterisation Methods for Synthetic Oligonucleotides

Identity testing is a critical part in the development of a therapeutic synthetic oligonucleotide. Tandem Mass Spectrometry (MS/MS) is commonly used for the analysis of oligonucleotides to obtain structural and sequence information, however there are challenges resulting from chemical modifications introduced to improve their pharmacokinetics and stability. For these structurally complex oligonucleotides, Nuclear Magnetic Resonance (NMR) Spectroscopy has found limited use for characterisation and identity testing, as only partial NMR resonance assignment for oligonucleotides is achieved without isotopic labelling methodologies. Regardless of the choice of method used for oligonucleotide analysis, the specificity is of critical importance. In this work, in-source dissociation mass spectrometry and proton (1H) and carbon (13C) NMR at high temperature were used to analyse danvatirsen, a 16 nucleotide phosphorothioate antisense oligonucleotide, and its closely related switch sequences. Both approaches have shown specificity to distinguish danvatirsen from these similar sequences.

Digital traces and physical activities: opportunities, challenges and pitfalls

The strong integration of consumer electronics in everyday life offers many new investigative opportunities. In particular, digital traces from smartphones, smartwatches and activity trackers can now increasingly be used to infer information about actions performed by their users in the physical world that might not be obtainable from any other types of forensic evidence.While potentially very valuable from an investigative perspective, making forensically justifiable statements about such traces can sometimes be more difficult than expected. Requirements for this have not yet received much attention in the digital forensic literature. To help filling this gap, we describe the principles we use in determining the evidential value of such traces, which emphasize the need for experimental verification. For such research, aimed at determining the evidential value of these traces, we coin the term data2activity.In this paper, we devote attention to the potential and limitations of data2activity traces, focusing on challenges and giving two examples to illustrate potential pitfalls in interpreting data. Finally, future research directions into data2activity traces are indicated that, in our opinion, should be given attention. These include development of future-proof data acquisition and storage methodology, enabling division-of-effort and sharing of information, as well as development of labeling methodology for free-living experiments.

Biotinylated Fluorescent Polymeric Nanoparticles for Enhanced Immunostaining.

The performance of fluorescence immunostaining is physically limited by the brightness of organic dyes, whereas fluorescence labeling with multiple dyes per antibody can lead to dye self-quenching. The present work reports a methodology of antibody labeling by biotinylated zwitterionic dye-loaded polymeric nanoparticles (NPs). A rationally designed hydrophobic polymer, poly(ethyl methacrylate) bearing charged, zwitterionic and biotin groups (PEMA-ZI-biotin), enables preparation of small (14nm) and bright fluorescent biotinylated NPs loaded with large quantities of cationic rhodamine dye with bulky hydrophobic counterion (fluorinated tetraphenylborate). The biotin exposure at the particle surface is confirmed by Förster resonance energy transfer with dye-streptavidin conjugate. Single-particle microscopy validates specific binding to biotinylated surfaces, with particle brightness 21-fold higher than quantum dot-585 (QD-585) at 550nm excitation. The nanoimmunostaining method, which couples biotinylated antibody (cetuximab) with bright biotinylated zwitterionic NPs through streptavidin, significantly improves fluorescence imaging of target epidermal growth factor receptors (EGFR) on the cell surface compared to a dye-based labeling. Importantly, cetuximab labeled with PEMA-ZI-biotin NPs can differentiate cells with distinct expression levels of EGFR cancer marker. The developed nanoprobes can greatly amplify the signal from labeled antibodies, and thus become a useful tool in the high-sensitivity detection of disease biomarkers.

Spotlight on the Winners of the DCO‐SCF 2021 Awards – An Ever Valuable Collaboration with EurJOC

Spotlight on the Winners of the DCO‐SCF 2021 Awards – An Ever Valuable Collaboration with EurJOC

Functionalization of Bacterial Cellulose and Related Surfaces Using a Facile Coupling Reaction by Thermoresponsive Catalyst.

Recently, bacterial cellulose and related materials attracted significant attention for applications such as leather-like materials, wound healing materials, etc., due to their abundance in pure form and excellent biocompatibility. Chemical modification of bacterial cellulose further helps to improve specific properties for practical utility and economic viability. However, in most cases, chemical modification of cellulose materials involves harsh experimental conditions such as higher temperatures or organic solvents, which may destroy the 3-dimensional network of bacterial cellulose, thereby altering its characteristic properties. Hence, in this work, we have adopted the Suzuki coupling methodology, which is relatively unexplored for chemically modifying cellulose materials. As the Suzuki coupling reaction is tolerable against air and water, modification can be done under mild conditions so that the covalently modified cellulose materials remain intact without destroying their 3-dimensional form. We performed Suzuki coupling reactions on cellulose surfaces using a recently developed thermoresponsive catalyst consisting of poly(N-isopropylacrylamide) (PNIPAM)-tagged N-heterocyclic carbene (NHC)-based palladium(II) complex. The thermoresponsive nature of the catalyst particularly helped to perform reactions in a water medium under mild conditions considering the biological nature of the substrates, where separation of the catalyst can be easily achieved by tuning temperature. The boronic acid derivatives have been chosen to alter the wettability behavior of bacterial cellulose. Bacterial cellulose (BC) obtained from fermentation on a lab scale using a cellulose-producing bacterium called Gluconacetobacter kombuchae (MTCC 6913) under Hestrin-Schramm (HS) medium, or kombucha-derived bacterial cellulose (KBC) obtained from kombucha available in the market or cotton-cellulose (CC) was chosen for the surface functionalization to find the methodology's diversity. Movie files in the Supporting Information and figures in the manuscript demonstrated the utility of the methodology for fluorescent labeling of bacterial cellulose and related materials. Finally, contact angle analysis of the surfaces showed the hydrophobic natures of some functionalized BC-based materials, which are important for the practical use of biomaterials in wet climatic conditions.

Differentiating between the sequence of structural events on alternative pathways of folding of a heterodimeric protein.

Distinguishing between competing pathways of folding of a protein, on the basis of how they differ in their progress of structure acquisition, remains an important challenge in protein folding studies. A previous study had shown that the heterodimeric protein, double chain monellin (dcMN) switches between alternative folding pathways upon a change in guanidine hydrochloride (GdnHCl) concentration. In the current study, the folding of dcMN has been characterized by the pulsed hydrogen exchange (HX) labeling methodology used in conjunction with mass spectrometry. Quantification of the extent to which folding intermediates accumulate and then disappear with time of folding at both low and high GdnHCl concentrations, where the folding pathways are known to be different, shows that the folding mechanism is describable by a triangular three-state mechanism. Structural characterization of the productive folding intermediates populated on the alternative pathways has enabled the pathways to be differentiated on the basis of the progress of structure acquisition that occurs on them. The intermediates on the two pathways differ in the extent to which the α-helix and the rest of the β-sheet have acquired structure that is protective against HX. The major difference is, however, that β2 has not acquired any protective structure in the intermediate formed on one pathway, but it has acquired significant protective structure in the intermediate formed on the alternative pathway. Hence, the sequence of structural events is different on the two alternative pathways.

Use of Nuclear and Chromatin Enrichment Procedures for Quantitation of Yeast DNA Replication Proteins Using SILAC.

DNA replication is a highly complex process that achieves the faithful transmission of genetic information from parent to progeny. Recruitment of DNA replication proteins to DNA is dynamically regulated during the cell cycle and in response to replication stresses. For a large-scale analysis of DNA replication proteins, I established a method for analysis of chromatin-bound proteins by SILAC (stable isotope labeling by amino acids in cell culture)-based quantitative proteomics. Here I describe a detailed methodology for SILAC labeling of budding yeast Saccharomyces cerevisiae, then nuclear isolation and chromatin preparation from synchronized yeast cells, prior to quantitative proteomic analysis of DNA replication proteins.

Cover Feature: Measurement of Protein Dynamics from Site Directed Cu(II) Labeling (Anal. Sens. 1/2023)

The cover feature image illustrates two sites of a protein with different site specific reorientational dynamics (purple). Such differences can be measured by newly developed site-directed Cu(II) labeling methodology. The resultant EPR lineshape at physiological temperatures is sensitive to the timescale of backbone motion. Importantly, this methodology enables site-specific detection on both α-helices and β-sheets via EPR. Thus, the role of protein dynamics to protein function can be elucidated. More information can be found in the Review by Sunil Saxena and co-workers.

Synthesis of cholera toxin B subunit glycoconjugates using site-specific orthogonal oxime and sortase ligation reactions.

The chemoenzymatic synthesis of a series of dual N- and C-terminal–functionalized cholera toxin B subunit (CTB) glycoconjugates is described. Mucin 1 peptides bearing different levels of Tn antigen glycosylation [MUC1(Tn)] were prepared via solid-phase peptide synthesis. Using sortase-mediated ligation, the MUC1(Tn) epitopes were conjugated to the C-terminus of CTB in a well-defined manner allowing for high-density display of the MUC1(Tn) epitopes. This work explores the challenges of using sortase-mediated ligation in combination with glycopeptides and the practical considerations to obtain high levels of conjugation. Furthermore, we describe methods to combine two orthogonal labeling methodologies, oxime- and sortase-mediated ligation, to expand the biochemical toolkit and produce dual N- and C-terminal–labeled conjugates.

TurboID Identification of Evolutionarily Divergent Components of the Nuclear Pore Complex in the Malaria Model Plasmodium berghei.

ABSTRACTTwenty years since the publication of the Plasmodium falciparum and P. berghei genomes one-third of their protein-coding genes still lack functional annotation. In the absence of sequence and structural homology, protein-protein interactions can facilitate functional prediction of such orphan genes by mapping protein complexes in their natural cellular environment. The Plasmodium nuclear pore complex (NPC) is a case in point: it remains poorly defined; its constituents lack conservation with the 30+ proteins described in the NPC of many opisthokonts, a clade of eukaryotes that includes fungi and animals, but not Plasmodium. Here, we developed a labeling methodology based on TurboID fusion proteins, which allows visualization of the P. berghei NPC and facilitates the identification of its components. Following affinity purification and mass spectrometry, we identified 4 known nucleoporins (Nups) (138, 205, 221, and the bait 313), and verify interaction with the putative phenylalanine-glycine (FG) Nup637; we assigned 5 proteins lacking annotation (and therefore meaningful homology with proteins outside the genus) to the NPC, which is confirmed by green fluorescent protein (GFP) tagging. Based on gene deletion attempts, all new Nups — Nup176, 269, 335, 390, and 434 — are essential to parasite survival. They lack primary sequence homology with proteins outside the Plasmodium genus; albeit 2 incorporate short domains with structural homology to human Nup155 and yeast Nup157, and the condensin SMC (Structural Maintenance Of Chromosomes 4). The protocols developed here showcase the power of proximity labeling for elucidating protein complex composition and annotation of taxonomically restricted genes in Plasmodium. It opens the door to exploring the function of the Plasmodium NPC and understanding its evolutionary position.

Datasets are not enough: Challenges in labeling network traffic

In contrast to previous surveys, the present work is not focused on reviewing the datasets used in the network security field. The fact is that many of the available public labeled datasets represent the network behavior just for a particular time period. Given the rate of change in malicious behavior and the serious challenge to label, and maintain these datasets, they become quickly obsolete. Therefore, this work is focused on the analysis of current labeling methodologies applied to network-based data. In the field of network security, the process of labeling a representative network traffic dataset is particularly challenging and costly since very specialized knowledge is required to classify network traces. Consequently, most of the current traffic labeling methods are based on the automatic generation of synthetic network traces, which hides many of the essential aspects necessary for a correct differentiation between normal and malicious behavior. Alternatively, a few other methods incorporate non-experts users in the labeling process of real traffic with the help of visual and statistical tools. However, after conducting an in-depth analysis, it seems that all current methods for labeling suffer from fundamental drawbacks regarding the quality, volume, and speed of the resulting dataset. This lack of consistent methods for continuously generating a representative dataset with an accurate and validated methodology must be addressed by the network security research community. Moreover, a consistent label methodology is a fundamental condition for helping in the acceptance of novel detection approaches based on statistical and machine learning techniques.

A labelling strategy to define airtightness performance ranges of naturally ventilated dwellings: An application in southern Europe

Energy efficiency and indoor air quality are frequently-two conflicting objectives when establishing the air change rate (ACH) of a dwelling. In Europe, the northern countries have a clear focus on energy conservation, leading to an obvious awareness of the importance of airtightness, which translates into a high level of regulation and implementation. Meanwhile, the southern counterparts experience a more complex challenge by having predominantly passive ventilation strategies and milder climates, which often results in a more permissive approach.This work proposes an innovative labelling methodology to classify the performance of naturally ventilated dwellings. A representative sample of a southern European national built stock is used in a stochastic process to create a pool of 43,200 unique dwellings. The simulation period refers to a month of the typical heating season in the southern European mild conditions. The results test the labelling methodology. With feature selection, ACH limits, and a labelling strategy, dwellings classify according to their ability to provide adequate ACHs.The terrain was the best splitter of the dataset from the applied categorical variables. Regarding continuous variables, the airtightness was the one explaining most of the variability of the outputted ACHs, followed by the floor area. From the best performing dwellings labelled as compliant (Com), the average airtightness level was 5.3 h−1, with 4.9 h−1 and 5.8 h−1 in rural and urban locations.

Lateral flow assays (LFA) for detection of pathogenic bacteria: A small point-of-care platform for diagnosis of human infectious diseases.

Up-to-date diagnostics is globally improved by point-of-care testing (POCT) analysis and bedside research works. Development in POCT analysis has been provided mostly by forward-looking engineering technology for biosensing and sensing assessments. Lately, lateral flow assays (LFAs) have attracted a lot of interest as a result of their noteworthy benefits including cost-effectiveness, better portability, being operator friendly and rapid detection. This technique has been employed broadly for monitoring diverse biomarkers linked to ultrasensitive detection of pathogenic bacteria, ecological monitoring, consumer protection, and infectious diseases. LFA analyses established on qualitative and optical outcomes have boosted the objectivity and data efficiency of the assessments. Therefore, developing novel methods with the capability of providing reliable and quantitative information regarding a target analyte in a model and preserving the qualities of LFAs is of great necessity. In this review, the main principles of LFAs, challenges, and prospects for more development in this field in sensing pathogenic bacteria have been summarized. Subsequently, visually-read LFAs improvement to further progressive platforms have been explored by considering the prospects of this very flexible method for ultrasensitive detection of pathogenic bacteria. In addition, novel labeling methodologies, electrochemical and optical transducers are described. Also, recent developments in these detection methods elements in combination with other considered approaches have been highlighted.

Application of 2H stable isotope labelling methodology and ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry for the metabolite identification of dehydroandrographolide in rats.

Dehydroandrographolide (DA), one of the crucial diterpenoids of Andrographis paniculata (Burm.F.) Nees, which has been widely used clinically due to its excellent biological activities and pharmacological safety. Until now, various investigations about the biological activities, pharmacokinetic profiles, and in vitro metabolism of DA have been conducted. However, information about the in vivo biotransformation of DA was still not available. In this study, a rapid and reliable approach based on stable isotope labeling and UPLC-Q/TOF-MS was developed and applied for the first systematic research about the in vivo metabolism of DA. As a result, a total of 35 metabolites were identified in rat urine, bile, plasma, and feces samples after DA was orally administered at the dose of 95mg/kg, and 33 of them were further verified based on stable isotope labeling. The major metabolic pathways for DA were hydroxylation, hydration, sulfonation, sulfate conjugation, and glucuronidation. Meanwhile, sulfonation, sulfate conjugation, and amino acids conjugation of DA were reported for the first time. This is the first systematic investigation of the in vivo metabolism of DA in rats, and the identification of these metabolites might provide scientific and reliable support for a full understanding of the metabolism of DA.

Tag Thy Neighbour: Nanometre-Scale Insights Into Kinetoplastid Parasites With Proximity Dependent Biotinylation.

Proximity labelling is a powerful and rapidly developing technology for exploring the interaction space and molecular environment of a protein of interest at the nanometre scale. In proximity labelling, a promiscuous biotinylating enzyme is genetically fused to the protein of interest, initiation of labelling then results in the biotinylating enzyme generating reactive biotin which covalently ‘tags’ nearby molecules. Importantly, this labelling takes place in vivo whilst the protein of interest continues to perform its normal functions in the cell. Due to its unique advantageous characteristics, proximity labelling is driving discoveries in an ever increasing range of organisms. Here, we highlight the applications of proximity labelling to the study of kinetoplastids, a group of eukaryotic protozoa that includes trypanosomes and Leishmania which can cause serious disease in humans and livestock. We first provide a general overview of the proximity labelling experimental workflow including key labelling enzymes used, proper experimental design with appropriate controls and robust statistical analysis to maximise the amount of reliable spatial information that is generated. We discuss studies employing proximity labelling in kinetoplastid parasites to illustrate how these key principles of experimental design are applied. Finally, we highlight emerging trends in the development of proximity labelling methodology.

A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia

Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden. We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL. Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349). Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

Robust Chemical Strategy for Stably Labeling Polyester-Based Nanoparticles with BODIPY Fluorophores.

Aliphatic polyesters are among materials most extensively used for producing biodegradable polymeric nanoparticles currently in development as delivery carriers and imaging agents for a range of biomedical applications. Their clinical translation requires robust particle labeling methodologies that allow reliably monitoring the fate of these formulations in complex biological environments. In the present study, a practical and versatile synthetic strategy providing conjugates of poly(D,L-lactide) representative of this class of polymers with BODIPY fluorophores varying in functional groups and excitation/emission maxima was investigated as a tool for making traceable nanoparticles. Polymer-probe conjugation was accomplished by carbodiimide-induced and 4-(dimethylamino)pyridinium 4-toluenesulfonate-catalyzed esterification of the polymer's terminal hydroxyl group, either directly with a carboxy-functionalized fluorophore or with amine-protected amino acids (Boc-glycine or Boc-6-aminohexanoic acid). In the latter case, the amino acid-derivatized polymeric precursors were reacted with amine-reactive BODIPY dyes after the removal of the protective group. Unlike nanoparticles encapsulating a strongly hydrophobic BODIPY505/515 (logPo/w = 4.3), nanoparticles labeled covalently with its carboxy-functionalized analogue (BODIPY FL) demonstrated stable particle-tracer association under perfect sink conditions. Furthermore, in contrast to the encapsulated dye rapidly partitioning from particles onto cell membranes but not stably retained by cultured cells, the internalization of the covalently attached probe was an irreversible process requiring the presence of serum, consistent with active nanoparticle uptake by endocytosis. In conclusion, the conjugation of particle-forming polymers with BODIPY fluorophores offers an effective and accessible labeling strategy for making traceable polyester-based biodegradable nanoparticles and is expected to facilitate their development and optimization as therapeutic carriers and diagnostic agents.

Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation

Identifying the location of the ice-bedrock interface of glaciers and ice sheets is crucial for a wide range of geophysical applications, such as searching for liquid water in basal regions and computing ice thickness to quantify ice sheet and glacier mass balance. Simple, record-by-record, approaches to detecting the bottom of the ice echo may be affected by spurious off-nadir noise that requires significant manual interaction to correct. In this paper, we propose a deep learning model based on convolutional neural networks (CNNs) and continuous conditional random fields (CCRFs) to automate ice bed identification and better capture fine-grained basal detail. We deploy this approach on HiCARS radargrams, the first time deep learning methods have been applied to this dataset. Intuitively, our CNN captures the global geometry of the ice bed, while the CCRF adjusts initial CNN outputs to better incorporate fine-scale spatial information into the final prediction. We also develop a coherent geophysical framework using three echo characters (along-track continuity, relative delay, and signal coherency) to compare our model’s outputs with those of a manually targeted approach. Our analysis suggests that our CNN + CCRF model is as suitable as the manual approach for radiometric applications, and it outperforms the manual technique in identifying the first continuous return, which is most often the near-nadir reflection. Thus, our approach is more universal than the current manual labeling methodology in the range of geophysical applications where it can be used, and it provides better confidence regarding the source of the basal return detected.