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At least 181 records · Page 10

Robust surfactant-assisted one-pot sample preparation for label-free single-cell and nanoscale proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in recent 5 years there are a surge of small sample preparation methods developed for robust effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: specific device- and standard PCR tube- or multi-well plate-based methods. Herein we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of a MS-compatible surfactant, DDM (n-Dodecyl-ß-D-maltoside), and standard low-bind PCR tube or multi-well plate for ‘all-in-one’ one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in the biological and biomedical research.

Single-cell proteomics, nanoscale proteomics, SOP-↗

ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.

97 MATHEMATICS AND COMPUTING↗

Preliminary investigation of 48 V-labeled VO(acac) 2 for cancer imaging: An initial proof-of-concept study

In this preliminary study, a procedure for synthesizing novel PET radiotracer vanadium-48-labeled-vanadyl acetylacetonate was developed, including radioisotope production via cyclotron, separation of 48 V, chelation as 48 VO(acac) 2 , and assessment through in vitro cellular studies. We employed the beam-stop setup in a cyclotron as the target holder to irradiate titanium foils in the reaction of nat Ti (p,n) 48 V. The radioisotope production rate was 4.84 ± 0.67 μCi/μA-h. Overall radiochemical yield was 12.86 ± 0.51% with gamma-ray spectroscopy showing no detectable contaminant peaks. HPLC of 48 VO(acac) 2 showed a retention time (1:48) corresponding closely to that (1:50) of commercial VO(acac) 2 , verifying the successful synthesis of 8VO(acac) 2 . In vitro cellular studies demonstrated radiotracer uptake and saturation around 0.48 nM. These studies pave the way for improving methodologies and in vivo experiments, including imaging studies, in future investigations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Constructing Self-Labeled Materials Imaging Datasets from Open Access Scientific Journals with EXSCLAIM!

Due to recent improvements in image resolution and acquisition speeds, materials microscopy is experiencing an explosion in imaging data. Yet, despite the volume of images generated, the overall accessibility landscape is highly fragmented, as researchers who do release images to the public, often only do so as snapshots of their larger private dataset in context of scientific journal publications. The effort to automatically consolidate images and descriptive information from web-based platforms has garnered broad attention from the computer vision, language technologies, and chemistry/materials informatics communities. However, these methods are problematic for scientific figures because over 30% of figures are compound in nature, and it is the individual images themselves, paired with relevant context, that are necessary for construction a proper labeled dataset. To this end, we outline in this paper the design of a software pipeline for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific figures (EXSCLAIM!). Successful consolidation of materials imaging across literature sources will enhance navigation and searchability of materials microscopy images for both novice and experienced researchers, as well as establish the framework necessary for users to search by images, text, or some combination of both.

36 MATERIALS SCIENCE↗

Label-Free Anti-Brownian Trapping of Single Nanoparticles in Solution

Today, biomolecular nanoparticles are prevalent as diagnostic tools and molecular delivery carriers, and it is particularly useful to examine individuals within a sample population to quantify the variations between objects and directly observe the molecular dynamics involving these objects. Using interferometric scattering as a highly sensitive label-free detection scheme, we recently developed the interferometric scattering anti-Brownian electrokinetic (ISABEL) trap to hold a single nanoparticle in solution for extended optical observation. In this perspective, we describe how we implemented this trap, how it extends the capabilities of previous ABEL traps, and how we have begun to study individual carboxysomes, a fascinating biological carbon fixation nanocompartment. By monitoring single nanocompartments for seconds to minutes in the ISABEL trap using simultaneous interferometric scattering and fluorescence spectroscopy, we have demonstrated single-compartment mass measurements, cargo-loading trends, and redox sensing inside individual particles. These experiments benefit from rich multiplexed correlative measurements utilizing both scattering and fluorescence with many exciting future capabilities within reach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Graphene Electric Field Sensor Enables Single Shot Label-Free Imaging of Bioelectric Potentials

The measurement of electrical activity across systems of excitable cells underlies current progress in neuroscience, cardiac pharmacology, and neurotechnology. However, bioelectricity spans orders of magnitude in intensity, space, and time, posing substantial technological challenges. The development of methods permitting network-scale recordings with high spatial resolution remains key to studies of electrogenic cells, emergent networks, and bioelectric computation. Here, for this work, we demonstrate single-shot and label-free imaging of extracellular potentials with high resolution across a wide field-of-view. The critically coupled waveguide-amplified graphene electric field (CAGE) sensor leverages the field-sensitive optical transitions in graphene to convert electric potentials into the optical regime. As a proof-of-concept, we use the CAGE sensor to detect native electrical activity from cardiac action potentials with tens-of-microns resolution, simultaneously map the propagation of these potentials at tissue-scale, and monitor their modification by pharmacological agents. This platform is robust, scalable, and compatible with existing microscopy techniques for multimodal correlative imaging.

47 OTHER INSTRUMENTATION↗

Coordination-Induced N–H Bond Weakening in a Molybdenum Pyrrolidine Complex: Isotopic Labeling Provides Insight into the Pathway for H 2 Evolution

The synthesis and characterization of a cationic molybdenum pyrrolidine complex is described that exhibits significant coordination-induced N–H bond weakening. Here, the N–H bond dissociation free energy (BDFE) of the coordinated pyrrolidine in [( Ph Tpy)(PPh 2 Me) 2 Mo(NH(pyrr))][BArF 24 ] ([1-NH(pyrr)] + ; PhTpy = 4'-Ph-2,2',6',2''-terpyridine, NH(pyrr) = pyrrolidine, ArF 24 = [C 6 H 3 -3,5-(CF 3 ) 2 ] 4 ) was determined to be between 41–51 kcal mol -1 by thermochemical analysis and supported by a density functional theory (DFT) computed value of 48 kcal mol -1 . The complex [1-NH(pyrr)] + underwent proton-coupled electron transfer (PCET) to 2,4,6-tri-tert-butylphenoxyl radical, as well as spontaneous H 2 evolution upon gentle heating to furnish the corresponding molybdenum pyrrolidide complex, [( Ph Tpy)(PPh 2 Me) 2 Mo(N(pyrr))][BArF 24 ] ([1-N(pyrr)] + ). Thermolysis of the deuterated isotopolog, [1-ND(pyrr)] + still produced H 2 with concomitant incorporation of the isotopic label into the pyrrolidide ligand in the product [(1-N(pyrr-d n )] + (n = 0–2), consistent with an H 2 evolution pathway involving intramolecular H–H bond formation followed by an intermolecular product-forming PCET step. These observations provide context for understanding H 2 evolution in the nonclassical ammine complex [( Ph Tpy)(PPh 2 Me) 2 Mo(NH 3 )][BArF 24 ] ([1-NH 3 ] + ) and are supported by DFT-computed reaction thermochemistry. Overall, these studies offer rare insight into the H 2 formation pathway in nonclassical amine complexes with N–H BDFEs below the thermodynamic threshold for H 2 evolution and inform the development of well-defined, thermody-namically potent PCET reagents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rapid, Sensitive, Label-Free Electrical Detection of SARS-CoV-2 in Nasal Swab Samples

Rapid diagnosis of coronavirus disease 2019 (COVID-19) is key for the long-term control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amid renewed threats of mutated SARS-CoV-2 around the world. Here, we report on an electrical label-free detection of SARS-CoV-2 in nasopharyngeal swab samples directly collected from outpatients or in saliva-relevant conditions by using a remote floating-gate field-effect transistor (RFGFET) with a 2-dimensional reduced graphene oxide (rGO) sensing membrane. RFGFET sensors demonstrate rapid detection (<5 min), a 90.6% accuracy from 8 nasal swab samples measured by 4 different devices for each sample, and a coefficient of variation (CV) < 6%. Also, RFGFET sensors display a limit of detection (LOD) of pseudo-SARS-CoV-2 that is 10 000-fold lower than enzyme-linked immunosorbent assays, with a comparable LOD to that of reverse transcriptionpolymerase chain reaction (RT-PCR) for patient samples. To achieve this, comprehensive systematic studies were performed regarding interactions between SARS-CoV-2 and spike proteins, neutralizing antibodies, and angiotensin-converting enzyme 2, as either a biomarker (detection target) or a sensing probe (receptor) functionalized on the rGO sensing membrane. Finally, taken together, this work may have an immense effect on positioning FET bioelectronics for rapid SARS-CoV-2 diagnostics.

60 APPLIED LIFE SCIENCES↗

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗

Learning protein fitness models from evolutionary and assay-labeled data

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.

59 BASIC BIOLOGICAL SCIENCES↗

Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering

Nanoscopic imaging of cell metabolism is hindered by the incompatibility of small metabolites with fluorescent dyes and the limited resolution of imaging mass spectrometry. We present ultrasensitive reweighted visible stimulated Raman scattering (URV-SRS), a label-free vibrational nanoscopy technique that enables multiplexed detection of metabolic nanostructures within cells. We developed an extensively chirped spectral focusing visible SRS microscope that achieves a detection limit of 4,000 molecules and introduced a self-supervised learning-based denoiser to robustly suppress non-independent SRS noise by over 7.2 dB. The instrumentation-based signal enhancement and computation-based noise suppression synergistically improved the detection sensitivity by 50 times over near-infrared SRS. Leveraging this enhanced sensitivity, we further pushed the resolution to nanoscopic levels by introducing Fourier reweighting to amplify sub-100 nm spatial frequencies previously overwhelmed by noise. Validated by Fourier ring correlation, URV-SRS achieves a lateral resolution of 86 nm in cellular imaging. Here, we applied URV-SRS to elucidate the reprogramming of metabolic nanostructures associated with virus replication in Vero E6 host cells and to compositionally delineate subcellular fatty acid synthesis in engineered Escherichia coli, demonstrating its capability towards nanoscopic spatial metabolomics.

59 BASIC BIOLOGICAL SCIENCES↗

DiB-splits: nature-guided design of a novel fluorescent labeling split system

Fluorogen-activating proteins (FAPs) are innovative fluorescent probes combining advantages of genetically-encoded proteins such as green fluorescent protein and externally added fluorogens that allow for highly tunable and on demand fluorescent signaling. Previously, a panel of green- and red-emitting FAPs has been created from bacterial lipocalin Blc (named DiBs). Here we present a rational design as well as functional and structural characterization of the first self-assembling FAP split system, DiB-splits. This new system decreases the size of the FAP label to ~8–12 kDa while preserving DiBs’ unique properties: strong increase in fluorescence intensity of the chromophore upon binding, binding affinities to the chromophore in nanomolar to low micromolar range, and high photostability of the protein-ligand complex. These properties allow for use of DiB-splits for wide-field, confocal, and super-resolution fluorescence microscopy. DiB-splits also represent an attractive starting point for further design of a protein-protein interaction detection system as well as novel FAP-based sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Label-free X-ray estimation of brain amyloid burden

Amyloid plaque deposits in the brain are indicative of Alzheimer’s and other diseases. Measurements of brain amyloid burden in small animals require laborious post-mortem histological analysis or resource-intensive, contrast-enhanced imaging techniques. We describe a label-free method based on spectral small-angle X-ray scattering with a polychromatic beam for in vivo estimation of brain amyloid burden. Our findings comparing 5XFAD versus wild-type mice correlate well with histology, showing promise for a fast and practical in vivo technique.

59 BASIC BIOLOGICAL SCIENCES↗

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a label-free imaging approach that provides detailed chemical information at high spatial resolution in a sample through nonlinear, coherent excitation of molecular vibrations and detection of Raman spectra. While its utility for biological imaging has been demonstrated, many aspects of this technique must mature before it can be widely adopted. One of the areas of required improvement is imaging speed—most BCARS implementations involve sample rastering, which limits imaging speed. Beam scanning can provide faster BCARS imaging but presents some unique challenges. Here, we describe a beam-scanning BCARS microscopy system that improves spatial resolution twofold and imaging speed by fivefold over a previous beam-scanning implementation. These enhancements were enabled by an improvement in supercontinuum power and the use of a sCMOS camera for its high data transfer rate and low read noise. Implementation of the sCMOS camera required correction for the significant pixel-to-pixel background and photon response nonuniformity. Here, we report on the method that we implemented for calibrating and correcting the pixel-to-pixel differences in sCMOS camera noise.

Dixon, Jessica Z. [Georgia Institute of Technology↗

Label-free quantum super-resolution imaging using entangled multi-mode squeezed light

Abstract In this study, we explore the theoretical application of entangled multi-mode squeezed light for label-free optical super-resolution imaging. By generating massively entangled multi-mode squeezed light through an array of balanced beam splitters, using a single-mode squeezed light input, we create a multi-mode quantum light state with exceptional entanglement and noise suppression below the shot noise level. This significantly reduces imaging measurement errors compared to classical coherent state light imaging when the same number of photons are used on the imaging sample. We demonstrate how to optimize the imaging system’s parameters to achieve the Heisenberg imaging error limit, taking into account the number of entangled modes and photons used. We also examine the effects of optical losses in the imaging system, necessitating adjustments to the optimized parameters based on the degree of optical loss. In practical applications, this new quantum imaging approach reduces the number of photons needed to achieve the same image quality by two orders of magnitude compared to classical imaging methods that use non-entangled, non-squeezed coherent state light.

Soh, Daniel (ORCID:0000000325625675)↗

ULTRA-effective labeling of tandem repeats in genomic sequence

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read-sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally repetitive sequence, ULTRA Locates Tandemly Repetitive Areas (ULTRA). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions.

59 BASIC BIOLOGICAL SCIENCES↗

13C-labeling reveals how membrane lipid components contribute to triacylglycerol accumulation in Chlamydomonas

Abstract Lipid metabolism in microalgae has attracted much interest due to potential utilization of lipids as feedstocks for biofuels, nutraceuticals, and other high-value compounds. Chlamydomonas reinhardtii is a model organism for characterizing the synthesis of the neutral lipid triacylglycerol (TAG), from which biodiesel is made. While much of TAG accumulation under N-deprivation is the result of de novo fatty acid (FA) synthesis, recent work has revealed that approximately one-third of FAs, especially polyunsaturated FAs (PUFAs), come from preexisting membrane lipids. Here, we used 13C-isotopic labeling and mass spectrometry to analyze the turnover of glycerol backbones, headgroups, FAs, whole molecules, and molecular fragments of individual lipids. About one-third of the glyceryl backbones in TAG are derived from preexisting membrane lipids, as are approximately one-third of FAs. The different moieties of the major galactolipids turn over synchronously, while the FAs of diacylglyceryltrimethylhomoserine (DGTS), the most abundant extraplastidial lipid, turn over independently of the rest of the molecule. The major plastidic lipid monogalactosyldiacylglycerol (MGDG), whose predominant species is 18:3α/16:4, was previously shown to be a major source of PUFAs for TAG synthesis. This study reveals that MGDG turns over as whole molecules, the 18:3α/16:4 species is present in both DAG and TAG, and the positional distribution of these PUFAs is identical in MGDG, DAG, and TAG. We conclude that headgroup removal with subsequent acylation is the mechanism by which the major MGDG species is converted to TAG during N-deprivation. This has noteworthy implications for engineering the composition of microalgal TAG for food, fuel, and other applications.

Plant Sciences↗