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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Electrochemical Detection of SNP in Human Mitochondrial DNA Using Cyclic Primer Extension with Biotinylated Nucletides and Enzymatic Labeling at Disposable Pencil Graphite Electrodes

Abstract A novel method of SNP typing in human mitochondrial DNA utilizing enzymatic labeling and electrochemical detection at disposable pencil graphite electrodes is described. The procedure is based on amplification of DNA stretches by cyclic primer extension (PEx) of SNP‐specific diagnostic primers in a mixture of biotinylated and natural nucleotides. The diagnostic primers are designed to recognize, by its 3'‐terminal nucleotide, the SNP‐site in target template. Under optimized conditions of the PEx reaction, efficient polymerase synthesis of biotin‐labeled strands takes place only in the case of full complementarity between the diagnostic primer and the target SNP site. There is also benefit from introducing many biotin molecules per extended DNA strand, resulting in another level of signal amplification. After adsorption of biotinylated PEx products at the electrode surface, streptavidin‐alkaline phosphatase conjugate was bound to the biotin tags, 1‐naphthol was enzymatically produced and electrochemically detected. Several critical steps and parameters of the assay, including termination of 3’‐OH ends of residual amplification primers, temperature for annealing of diagnostic primers, relative amount of biotinylated deoxynucleoside triphosphate in the PEx mixture and number of PEx cycles were optimized in this study to attain best SNP resolution, and reduction of time needed for the analysis.

Plucnara, Medard↗
DOE OSTI2024

Statin Drug‐Drug Interactions: Pharmacokinetic Basis of FDA Labeling Recommendations and Comparison Across Common Tertiary Clinical Resources

Abstract Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)‐based drug–drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK‐based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding “Dose Adjustment” and “Avoid Coadministration” recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource.

Mease, James↗
DOE OSTI2019

A practical method for efficient and optimal production of Seleno‐methionine‐labeled recombinant protein complexes in the insect cells

Abstract The use of Seleno‐methionine (SeMet) incorporated protein crystals for single or multi‐wavelength anomalous diffraction (SAD or MAD) to facilitate phasing has become almost synonymous with modern X‐ray crystallography. The anomalous signals from SeMets can be used for phasing as well as sequence markers for subsequent model building. The production of large quantities of SeMet incorporated recombinant proteins is relatively straightforward when expressed in Escherichia coli . In contrast, production of SeMet substituted recombinant proteins expressed in the insect cells is not as robust due to the toxicity of SeMet in eukaryotic systems. Previous protocols for SeMet‐incorporation in the insect cells are laborious, and more suited for secreted proteins. In addition, these protocols have generally not addressed the SeMet toxicity issue, and typically result in low recovery of the labeled proteins. Here we report that SeMet toxicity can be circumvented by fully infecting insect cells with baculovirus. Quantitatively controlling infection levels using our Titer Estimation of Quality Control (TEQC) method allow for the incorporation of substantial amounts of SeMet, resulting in an efficient and optimal production of labeled recombinant protein complexes. With the method described here, we were able to consistently reach incorporation levels of about 75% and protein yield of 60–90% compared with native protein expression.

Wenzel, Sabine↗
DOE OSTI2016

Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS

Comprehensive, quantitative information on abundances of proteins and their post-translational modifications (PTMs) can potentially provide novel biological insights into diseases pathogenesis and therapeutic intervention. Herein, we introduce a quantitative strategy utilizing isobaric stable isotope-labelling techniques combined with two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) for large-scale, deep quantitative proteome profiling of biological samples or clinical specimens such as tumor tissues. The workflow includes isobaric labeling of tryptic peptides for multiplexed and accurate quantitative analysis, basic reversed-phase LC fractionation and concatenation for reduced sample complexity, and nano-LC coupled to high resolution and high mass accuracy MS analysis for high confidence identification and quantification of proteins. This proteomic analysis strategy has been successfully applied for in-depth quantitative proteomic analysis of tumor samples, and can also be used for integrated proteome and PTM characterization, as well as comprehensive quantitative proteomic analysis across samples from large clinical cohorts.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Non-invasive glucagon-like peptide-1 receptor imaging in pancreas with {sup 18}F-Al labeled Cys{sup 39}-exendin-4

Purpose: Glucagon-like peptide-1 receptor (GLP-1R) is abundantly expressed on beta cells and may be an ideal target for the pancreas imaging. Monitoring the GLP-1R of pancreas could be benefit for understanding the pathophysiology of diabetes. In the present study, {sup 18}F-Al labeled exendin-4 analog, {sup 18}F-Al-NOTA-MAL-Cys{sup 39}-exendin-4, was evaluated for PET imaging GLP-1R in the pancreas. Methods: The targeting of {sup 18}F-Al labeled exendin-4 analog was examined in healthy and streptozotocin induced diabetic rats. Rats were injected with {sup 18}F-Al-NOTA-MAL-Cys{sup 39}-exendin-4 and microPET imaging was performed at 1 h postinjection, followed by ex vivo biodistribution. GLP-1R expression in pancreas was determined through post mortern examinations. Results: The pancreas of healthy rats was readily visualized after administration of {sup 18}F-Al-NOTA-MAL-Cys{sup 39}-exendin-4, whereas the pancreas of diabetic rats, as well as those from rats co-injected with excess of unlabeled peptides, was barely visible by microPET. At 60 min postinjection, the pancreatic uptakes were 1.02 ± 0.15%ID/g and 0.23 ± 0.05%ID/g in healthy and diabetic rats respectively. Under block, the pancreatic uptakes of non-diabetic rats reduced to 0.21 ± 0.07%ID/g at the same time point. Biodistribution data and IHC staining confirmed the findings of the microPET imaging. Conclusion: The favorable preclinical data indicated that {sup 18}F-Al-NOTA-MAL-Cys{sup 39}-exendin-4may be suitable for non-invasive monitoring functional pancreatic beta cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
DOE OSTI2022

Label-Free Profiling of up to 200 Single-Cell Proteomes per Day Using a Dual-Column Nanoflow Liquid Chromatography Platform

Single-cell proteomics (SCP) has great potential to advance biomedical research and personalized medicine. The sensitivity of such measurements increases with low-flow separations (<100 nL/min) due to improved ionization efficiency, but the time required for sample loading, column washing, and regeneration in these systems can lead to low measurement throughput and inefficient utilization of the mass spectrometer. Herein, we developed a two-column liquid chromatography (LC) system that dramatically increases the throughput of label-free SCP using two parallel subsystems to multiplex sample loading, online desalting, analysis, and column regeneration. The integration of MS1-based feature matching increased proteome coverage when short LC gradients were used. The high-throughput LC system was reproducible between the columns, with a 4% difference in median peptide abundance and a median CV of 18% across 100 replicate analyses of a single-cell-sized peptide standard. An average of 621, 774, 952, and 1622 protein groups were identified with total analysis times of 7, 10, 15, and 30 min, corresponding to a measurement throughput of 206, 144, 96, and 48 samples per day, respectively. When applied to single HeLa cells, we identified nearly 1000 protein groups per cell using 30 min cycles and 660 protein groups per cell for 15 min cycles. Finally, we explored the possibility of measuring cancer therapeutic targets with a pilot study comparing the K562 and Jurkat leukemia cell lines. This work demonstrates the feasibility of high-throughput label-free single-cell proteomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

High Initial Sputter Rate Found for Vaccinia Virions Using Isotopic Labeling, NanoSIMS, and AFM

High-lateral-resolution secondary ion mass spectrometry (SIMS) has the potential to provide functional and depth resolved information from small biological structures, such as viral particles (virions) and phage, but sputter rate and sensitivity are not characterized at shallow depths relevant to these structures. Here we combine stable isotope labeling of the DNA of vaccinia virions with correlated SIMS imaging depth profiling and atomic force microscopy (AFM) to develop a nonlinear, nonequilibrium sputter rate model for the virions and validate the model on the basis of reconstructing the location of the DNA within individual virions. Our experiments with a Cs+ beam show an unexpectedly high initial sputter rate (~100 um 2 ·nm·pA –1 ·s –1 ) with a rapid decline to an asymptotic rate of 0.7 um 2 ·nm·pA –1 ·s –1 at an approximate depth of 70 nm. Correlated experiments were also conducted with glutaraldehyde-fixed virions, as well as O – and Ga + beams, yielding similar results. Based on our Cs + sputter rate model, the labeled DNA in the virion was between 50 and 90 nm depth in the virion core, consistent with expectations, supporting our conclusions. Virion densification was found to be a secondary effect. In conclusion, accurate isotopic ratios were obtained from the initiation of sputtering, suggesting that isotopic tracers could be successfully used for smaller virions and phage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Boosting to Amplify Signal with Isobaric Labeling (BASIL) Strategy for Comprehensive Quantitative Phosphoproteomic Characterization of Small Populations of Cells

Comprehensive phosphoproteomic analysis of small populations of cells remains a daunting task due primarily to the insufficient MS signal intensity from low concentrations of enriched phosphopeptides. Isobaric labeling has a unique multiplexing feature where the “total” peptide signal from all channels (or samples) triggers MS/MS fragmentation for peptide identification while the reporter ions provide quantitative information. In light of this feature, we tested the concept of using a “boosting” sample (e.g., a biological sample mimicking the study samples but available in much larger quantity) in multiplexed analysis to enable sensitive and comprehensive quantitative phosphoproteomic measurements with <100,000 cells. This simple Boosting to Amplify Signal with Isobaric Labeling (BASIL) strategy increased the overall number of quantifiable phosphorylation sites more than 4-fold. Good reproducibility in quantification was demonstrated with a median CV of 15.3% and Pearson correlation coefficient of 0.95 from biological replicates. A proof-of-concept experiment demonstrated the ability of BASIL to distinguish acute myeloid leukemia cells based on the phosphoproteome data. Moreover, in a pilot application, this strategy enabled quantitative analysis of over 20,000 phosphorylation sites from human pancreatic islets treated with interleukin-1ß and interferon-gamma. Together, this signal boosting strategy provides an attractive solution to comprehensive and quantitative phosphoproteome profiling of relatively small populations of cells where traditional phosphoproteomic workflows provide insufficient sensitivity.

phosphorylation, phosphoproteomics, pancreatic isl↗
DOE OSTI2019

Tandem Mass Tag Labeling Facilitates Reversed-Phase Liquid Chromatography-Mass Spectrometry Analysis of Hydrophilic Phosphopeptides

Protein phosphorylation is a critical post-translational modification (PTM). Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides with enriched regions of serines, threonines, and tyrosines that often orchestrate critical biological functions. It is known that G protein coupled receptors (GPCRs) are regulators of cellular signaling events and rely on phosphorylation of serines and threonines within the GPCR C-terminus for appropriate signaling. However, the low abundance of GPCRs, combined with the hydrophilicity of C-terminal phosphopeptides, has prevented robust RPLC-MS analysis of potentially crucial regions. To address this issue, we developed a simple, easily implemented method to introduce a commonly used tandem mass tag (TMT) to increase peptide hydrophobicity, effectively enhancing RPLC-MS analysis of hydrophilic peptides. This method was applied to study the peptide QPSSSR, a tryptic peptide located on the C-terminus of the GPCR CXCR3 not been reported, and validated by comparative RPLC-MS analyses of both a hydrophilic QPSSSR peptide library as well as common phosphopeptides before and after TMT labeling. We further confirmed the utility of this method by quantifying QPSSSR phosphorylation abundances in HEK 293 cells under different treatement conditions predicted to alter QPSSSR phosphorylation. We anticipate that this simple TMT-labeling method can be broadly used not only for decoding GPCR phosphoproteomes, but also for effective RPLC-MS analysis of other highly hydrophilic analytes.

Tsai, Chia-Feng↗
DOE OSTI2019

High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labelling in a Nanodroplet Sample Preparation Platform

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ~1,600 proteins with median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2,300 proteins identified. Principal component analysis grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. Our platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.

Dou, Maowei↗
DOE OSTI2017

Determination of the Complete Spin Density Distribution in 13 C-Labeled Protein-Bound Radical Intermediates Using Advanced 2D Electron Paramagnetic Resonance Spectroscopy and Density Functional Theory

Determining the complete electron spin density distribution for protein-bound radicals, even with advanced pulsed electron paramagnetic resonance (EPR) methods, is a formidable task. Here we present a strategy to overcome this problem combining multifrequency HYSCORE and ENDOR measurements on site-specifically 13 C-labeled samples with DFT calculations on model systems. As a demonstration of this approach, pulsed EPR experiments are performed on the primary Q A and secondary Q B ubisemiquinones of the photosynthetic reaction center from Rhodobacter sphaeroides 13C-labeled at the ring and tail positions. Despite the large number of nuclei interacting with the unpaired electron in these samples, two-dimensional X- and Q-band HYSCORE and orientation selective Q-band ENDOR resolve and allow for a characterization of the eight expected 13 C resonances from significantly different hyperfine tensors for both semiquinones. From these results we construct, for the first time, the most complete experimentally determined maps of the s- and p π -orbital spin density distributions for any protein organic cofactor radical to date. This work lays a foundation for understanding the relationship between the electronic structure of semiquinones and their functional properties, and introduces new techniques for mapping out the spin density distribution that are readily applicable to other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2018

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), is a data acquisition technique used in targeted analysis of molecules, including targeted proteomic studies. It exploits the unique capability of triple-quadrupole (QQQ) mass spectrometers to monitor the predefined precursor and fragment ion pairs of specific molecules of interest throughout a liquid chromatography (LC) elution profile. Compared to shotgun proteomics, targeted proteomics using SRM has high selectivity, high sensitivity, and a wide linear dynamic range,(1-3) which makes it especially useful in the accurate and reproducible quantification of low-abundance proteins in highly complex biological samples. SRM has been widely used in the fields of biomarker discovery,(4-7) analysis of protein post-translational modifications(8) and characterization of biological protein networks.(4,9) In the recent years, multiple technical advances have greatly improved the throughput of SRM analyses, allowing for the quantification of hundreds of peptides in a single analysis.(6,7,10) For example, a single 800-plex SRM assay (e.g., 400 unlabeled and heavy-labeled peptide pairs and 2400 transitions with retention time scheduling) using ultrahigh-performance liquid chromatography (UHPLC) has been developed to quantify proteins in plasma.(11) Advanced labeling techniques utilizing in vitro proteome-assisted MRM for protein absolute quantification (iMPAQT) demonstrated the capability of SRM in genome-wide protein quantification of over 18?000 human proteins.(12) Moreover, the scan speed of QQQ instrumentation has been greatly improved in recent releases of commercial instrumentation. The newly developed TSQ Altis (released in 2017) can scan more than 600 transitions per second, which is 6 times more than a traditional QQQ scan speed of 100 transitions per second. The breadth of measurement enabled by these technological improvements to QQQ mass spectrometry has increased the feasibility and popularity of large scale targeted proteomics studies,(13) a major application of which will be in clinical studies in which up to hundreds of protein candidates need to be quantified in hundreds of clinical specimens.(14) Quantitative accuracy is a primary motivating factor for utilizing a targeted proteomics protocol. The precise and reproducible absolute quantification produced by SRM assays is essential to many clinical and laboratory experiments.(15,16) Because the abundance of an endogenous peptide is calculated from the measurement of the spiked-in reference standard, it is essential to assess the data quality of these references.(17) In early applications of targeted proteomics, when instrument speed greatly limited the number of transitions that could be monitored, much of this quality assessment was done manually. However, recent improvements in instrument performance and experimental design have enabled a dramatic increase in the number of target peptides and associated SRM transitions, which makes manual quality assessment an untenable and laborious task. A variety of computational tools assist in SRM experiment design and data analysis. The first task in creating an SRM experiment is the choice of proteins and representative peptides to monitor. Achieving a reliable protein abundance requires appropriately choosing peptides that have a strong signal and are free from interferences in the biological matrix. Numerous computational tools exist to facilitate assay design by identifying peptides and refining SRM transitions.(18-21) To help share these assays and eliminate time spent designing the same transitions at multiple institutions, community portals have begun to host well-designed and vetted assays.(22-24) Analyzing the experimental data requires significant computational effort to align files across replicates and experimental conditions, pick peaks and produce quantitative values, normalize data and perform statistical tests, etc.(25-27)

Gibbons, Bryson C.↗
DOE OSTI2024

Label-Free In Situ Chemical Characterization of Amyloid Plaques in Human Brain Tissues

The accumulation of amyloid plaques and increased brain redox burdens are neuropathological hallmarks of Alzheimer’s disease. Altered metabolism of essential biometals is another feature of Alzheimer’s, with amyloid plaques representing sites of disturbed metal homeostasis. Despite these observations, metal-targeting disease treatments have not been therapeutically effective to date. A better understanding of amyloid plaque composition and the role of the metals associated with them is critical. To establish this knowledge, the ability to resolve chemical variations at nanometer length scales relevant to biology is essential. Here, we present a methodology for the label-free, nanoscale chemical characterization of amyloid plaques within human Alzheimer’s disease tissue using synchrotron X-ray spectromicroscopy. Our approach exploits a C–H carbon absorption feature, consistent with the presence of lipids, to visualize amyloid plaques selectively against the tissue background, allowing chemical analysis to be performed without the addition of amyloid dyes that alter the native sample chemistry. Using this approach, we show that amyloid plaques contain elevated levels of calcium, carbonates, and iron compared to the surrounding brain tissue. Chemical analysis of iron within plaques revealed the presence of chemically reduced, low-oxidation-state phases, including ferromagnetic metallic iron. The zero-oxidation state of ferromagnetic iron determines its high chemical reactivity and so may contribute to the redox burden in the Alzheimer’s brain and thus drive neurodegeneration. Ferromagnetic metallic iron has no established physiological function in the brain and may represent a target for therapies designed to lower redox burdens in Alzheimer’s disease. Additionally, ferromagnetic metallic iron has magnetic properties that are distinct from the iron oxide forms predominant in tissue, which might be exploitable for the in vivo detection of amyloid pathologies using magnetically sensitive imaging. We anticipate that this label-free X-ray imaging approach will provide further insights into the chemical composition of amyloid plaques, facilitating better understanding of how plaques influence the course of Alzheimer’s disease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Substrate-Directed Underlayer Growth of Bilayer MoS 2 Revealed by Mo Isotope Labeling

Direct control over the vertical formation sequence and stacking registry in van der Waals (vdW) bilayers is essential for device performance and moiré engineering yet difficult to resolve unambiguously with conventional probes. Here, we use Mo isotope labeling in a two-step chemical vapor deposition process to synthesize bilayer MoS 2 and trace its vertical formation on common substrates. By combining site-selective laser thinning, Raman spectroscopy, time-of-flight secondary ion mass spectrometry, and atomic-resolution scanning transimission electron microscopy (STEM), we find a clear substrate dependence: on SiO 2 /Si, the second layer nucleates and grows beneath the first (underlayer), whereas on sapphire, it forms on top (overlayer). Density functional theory indicates that a larger equilibrium interfacial separation and weaker MoS 2 –substrate interactions on amorphous SiO 2 permit confined interfacial diffusion and underlayer nucleation, whereas stronger interactions and smaller separations on sapphire favor overlayer growth. On SiO 2 , confined epitaxy templates commensurate 2H, 3R, and mixed bilayers, as confirmed by second harmonic generation spectroscopy and STEM. During underlayer coalescence, embedded mirror-twin grain boundaries stitch atomically sharp 2H|3R junctions via alternating 4|8 ring motifs. Molecular-dynamics simulations reveal that these alternating 4|8 motifs accommodate interlayer vdW coupling and locally modulate the stacking registry. These results provide mechanistic insight into confined epitaxial growth and establish isotope labeling as a powerful probe of two-dimensional materials synthesis.

MoS2↗
DOE OSTI2016

Antigen Binding and Site-Directed Labeling of Biosilica-Immobilized Fusion Proteins Expressed in Diatoms

The diatom Thalassiosira pseudonana was genetically modified to express biosilica-targeted fusion proteins incorporating a tetracysteine tag for site-directed labeling with biarsenical affinity probes and either EGFP or single chain antibody to test colocalization of probes with the EGFP-tagged recombinant protein or binding of biosilica-immobilized antibodies to large and small molecule antigens, respectively. Site-directed labeling with the biarsenical probes demonstrated colocalization with EGFP-encoded proteins in nascent and mature biosilica, supporting their use in studying biosilica maturation. Isolated biosilica transformed with a single chain antibody against either the Bacillus anthracis surface layer protein EA1 or small molecule explosive trinitrotoluene (TNT) effectively bound the respective antigens. A marked increase in fluorescence lifetime of the TNT surrogate Alexa Fluor 555-trinitrobenzene reflected the high binding specificity of the transformed isolated biosilica. These results demonstrated the potential use of biosilica-immobilized single chain antibodies as binders for large and small molecule antigens in sensing and therapeutics.

anthrax↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗