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DOE OSTI2023

Novel Wavefunction Approaches for Studying Actinides and Other Heavy Elements (Final Report (2012-2020))

The theoretical characterization of actinide molecules is a crucial complement to their experimental study; actinides are vital to issues of national security and energy, but they are expensive and hazardous to study experimentally. However, because actinides contain a large number of electrons and are often strongly correlated, their theoretical description is difficult. We have had success studying actinide chemistry and physics using density functional theoretical approaches over the past several years, but we have found that such methods are incapable of treating strong correlations adequately. We thus shifted our research focus to the development of tractable wavefunction methods for strong correlations of actinides. Our early wavefunction-based approaches focused on extending symmetry-adapted, single-reference coupled cluster for treating strong correlations with feasible computational cost. While we have had some success along these lines, we have recently developed spin-projected unrestricted coupled cluster, which is essentially a black-box multi-reference coupled cluster theory that is superior to unrestricted coupled cluster for small- to medium-sized systems across practically all physical correlation strengths, yet preserves good quantum numbers. For actinides, however, where spin-orbit coupling is often nonnegligible, spin is no longer a fundamental symmetry. We thus propose to develop time reversal and point group projected coupled cluster, which will preserve the fundamental symmetries for actinides. Time reversal projection is achieved via the product of half-spin projection, or spin flip, and complex conjugation projection. Along with point group, these are discrete symmetries, i.e. non-continuous quantum numbers, which, in addition to being the correct symmetries for actinides, can be implemented in lower computational cost than full spin projection. We propose the development of these theories within a synergistic collaboration to use the new methods to elucidate difficult actinide chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Surveillance of Site A and Plot M (Report for 2022)

The results of the environmental surveillance program conducted at Site A/Plot M for calendar year (CY) 2022 are presented within this document. Site A/Plot M is in the Palos Area Preserves, operated by the Forest Preserve District of Cook County. The surveillance program consists of the collection and analysis of surface and groundwater samples to determine the amount of hydrogen-3 and strontium-90 present in the environment within and surrounding the site of the former research facility (Site A) and waste burial site (Plot M).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Filling in Subsurface Storage Open Data Gaps - Updates to CCS Data Availability on EDX and EDX Spatial (FWP-1022465)

There is a need to preserve and efficiently access data resources to drive the next generation of research and development while ensuring compliance with DOE regulations. Over the last 10+ years, there has been ongoing efforts by the DOE Carbon Storage Program to ensure that there is effective data curation and preservation of DOE funded research leveraging the NETL-FECM data repository, the Energy Data eXchange (EDX). This talk presents updates about ongoing efforts to continue to support the mission of ensuring that carbon storage data is findable, accessible, interoperable, and reusable to the carbon storage stakeholder community through EDX and EDX Spatial. Presented at the NETL Carbon Management Review Meeting, Pittsburgh, 2024.

Morkner, Paige↗

DOE OSTI2023

The disCO2ver Platform: Curating Data and Tools for Geologic Carbon Sequestration and Deep Subsurface Research Systems

The U.S. DOE National Energy Technology Laboratory has invested 12+ years of development into the data repository and digital laboratory, the Energy Data eXchange (EDX, edx.netl.doe.gov). Supporting a variety of research areas across the DOE Office of Fossil Energy and Carbon Management, the platform has successfully curated and preserved thousands of data products from DOE research. The Carbon Storage Program has successfully supported data curation, upload, and publishing of data products on EDX for many years, demonstrating a success story of how resources like EDX can effectively help with long term preservation and publishing of DOE data products. EDX continues to shift towards cloud-supported infrastructure, taking a hybrid approach combining on-premises compute and storage integrated with cloud-hosted services. The integration of cloud compute and hybrid architecture enables the development of EDX-hosted platforms that tailor the data and tools hosted on them to a specific community, enables implementation of machine learning tools for data discovery and filtering, and enables the hosting of virtual (online user interface) tools. Geologic carbon sequestration (GCS) research continues to scale up in response to the current administration goals to reduce greenhouse gas emissions and transition the energy economy. Over the last year, EDX’s disCO2ver platform has been developed in response to the need for access to data products and tools to support the scaling up of GCS research. disCO2ver provides access to data resources and tools, produced by DOE and outside authoritative external resources. The platform also provides a user-access control component for the virtualization and cloud hosting of tools. Tools that need to be virtualized, to eliminate the need for users to download the tool and use local compute resources, is essential to supporting big-data analysis and machine learning that is becoming common place in carbon storage modeling, risk analysis, and data publishing practices. This talk will review the EDX’s disCO2ver platform and the current work ongoing to curate data and tools to support GCS and deep subsurface systems research.

Morkner, Paige↗

DOE OSTI2024

AEOLIAN INTERDUNE FACIES OF THE NAVAJO SANDSTONE, UTAH

During the Early Jurassic an extensive desert environment covered a large area of the Western Interior of the USA. The Navajo Sandstone erg is thought to be one of the largest dunefields preserved in the rock record, estimated to have covered well over 500,000 km2, though the preserved extent is less due to erosion. The Navajo Sandstone outcrops extensively in eastern and southern Utah, forming spectacular domes and arches, including those in Arches National Park. Navajo Sandstone aeolian dunes consist of stacked, tabular sandstone bodies, displaying steeply dipping crossbedding, and are on average 15 m thick, with high porosity and permeability. In addition to these extensive paleodunes, interdune deposits are also documented, consisting of lensoid sandstone or pale grey limestone, of approximately 1-7 m thick. This research forms part of a broader investigation into the Navajo Sandstone in Utah, focusing on its potential as a CO2 reservoir. The internal stratigraphy of the Navajo Sandstone can be complex, with multiple types of aeolian surfaces present including discontinuous inter-dune contacts, and inclined bedding planes associated with large climbing dunes. Interdune deposits may be associated with more laterally extensive stratigraphic surfaces, highlighting their importance in the internal stratigraphic framework of the Navajo Sandstone erg. Here we interpret facies and depositional processes of aeolian interdune deposits of the Navajo Sandstone throughout Utah, using well log, outcrop, and thin sections. Interdune deposits range in type from short lived, small ponds of predominantly reworked aeolian sediment, to comparatively long-lived oases consisting of lakes with extensive ecosystems. This project aims to increase our understanding of an important potential CO2 reservoir, and enhance understanding of stratigraphic complexity within one of the world's most significant aeolian systems.

Mahon, Elizabeth (ORCID:0000000252692454)↗

DOE OSTI2025

Site A and Plot M 2024 Surveillance Report and 2025 Inspection Report

The results of the environmental surveillance program conducted at Site A/Plot M for calendar year (CY) 2024 are presented within this document. Site A/Plot M is in the Palos Area Preserves managed by the Forest Preserve District of Cook County. The surveillance program consists of the collection and analysis of surface and groundwater samples to determine the amount of hydrogen-3 and strontium-90 present in the environment within and surrounding the site of the former research facility (Site A) and waste burial site (Plot M).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

LLRF System Analysis for the Fermilab PIP-II LINAC

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

Varghese, P. [Fermilab]↗

DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Dynamic Multipoles in the RCS

The Rapid Cycling Synchrotron (RCS) of the Electron--Ion Collider (EIC) will accelerate polarized electrons up to 18 GeV at a repetition rate of 1 Hz. Dynamic multipoles driven by eddy currents in the vacuum chamber can affect beam dynamics and polarization preservation during the energy ramp. Building on the analytical treatment of S.Y. Lee for a ramped normal-conducting dipole magnet, we use conformal transformations to derive compact expressions for dynamic multipoles in higher-order ramped magnets with circular beam pipes. We further derive analytical frequency-domain transfer functions for both self-response and cross-response multipoles, providing compact dynamical models for their evolution during arbitrary ramp waveforms. The resulting analytical predictions are benchmarked against finite-element simulations, including the effects of practical pole geometry and beam-pipe misalignment. Finally, the expected dynamic multipoles in the RCS magnets are estimated and their implications for beam dynamics and polarization preservation are discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Genesis Mission-Enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE)

Argonne National Laboratory is supporting the U.S. Department of Transportation’s (USDOT’s) Bureau of Transportation Statistics (BTS) with collaborative research on development and application of privacy preserving AI frameworks that leverage unmatched AI expertise and secure computing resources made available through the U.S. Genesis Mission1 . This research advances U.S. energy security goals by supporting a safe offshore energy industry with secure, domain-specific AI tools to analyze confidential industry datasets collected by BTS to rapidly improve identification of hazards, precursors, and systemic safety risks in high-risk operational environments. The staged, security-first approach begins with development and testing of Argonne’s Genesis Mission-enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE) framework within Argonne’s accredited secure computing enclave (ABLE) leveraging Argonne’s AI scientific assistant substrate (AISAC). Methods to build synthetic datasets were developed together with BTS for use in preparing synthetic datasets that can be used to validate data containment, governance, and security controls in the ABLE environment. Future research directions would focus on applying the Genesis-SAFE framework to CIPSEA-protected datasets entirely within ABLE to support confidentiality-preserving analysis of safety risks, trends, and contributing factors.

Kim, Hyekyung [Argonne National Laboratory (ANL), ↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

DOE OSTI2024

Development of PWHT-Free, Reduced Activation Creep-Strength Enhanced Bainitic Ferritic Steel for Large-Scale Fusion Reactor Components

A compositional modification has been proposed to validate an alloy design which potentially eliminates the requirement of post-weld heat treatment (PWHT) while preserving the advantage of mechanical properties in a reduced activation bainitic ferritic steel based on Fe-3Cr-3W-0.2V- 0.1Ta-Mn-Si-C, in weight percent, developed at Oak Ridge National Laboratory in 2007. The alloy design includes reducing the hardness in the as-welded condition for improving toughness, while increasing the hardenability for preserving the high-temperature mechanical performance such as creep-rupture resistance in the original steel. To achieve such a design, a composition range with a reduced C content combining with an increased Mn content has been proposed and investigated. Newly proposed “modified” steel successfully achieved an improved impact toughness in the as- welded condition, while the creep-rupture performance across the weldments without PWHT demonstrated ~50% improvement of the creep strength compared to that of the original steel weldment after PWHT. The obtained results strongly support the validity of the proposed alloy design.

Yamamoto, Yukinori↗

DOE OSTI2015

The response of fabric variations to simple shear and migration recrystallization

The observable microstructures in ice are the result of many dynamic and competing processes. These processes are influenced by climate variables in the firn. Layers deposited in different climate regimes may show variations in fabric which can persist deep into the ice sheet; fabric may 'remember' these past climate regimes. In this paper, we model the evolution of fabric variations below the firn–ice transition and show that the addition of shear to compressive-stress regimes preserves the modeled fabric variations longer than compression-only regimes, because shear drives a positive feedback between crystal rotation and deformation. Even without shear, the modeled ice retains memory of the fabric variation for ~200 ka in typical polar ice-sheet conditions. Our model shows that temperature affects how long the fabric variation is preserved, but only affects the strain-integrated fabric evolution profile when comparing results straddling the thermal-activation-energy threshold (~–10°C). Even at high temperatures, migration recrystallization does not eliminate the modeled fabric's memory under most conditions. High levels of nearest-neighbor interactions will, however, eliminate the modeled fabric's memory more quickly than low levels of nearest-neighbor interactions. Finally, our model predicts that fabrics will retain memory of past climatic variations when subject to a wide variety of conditions found in polar ice sheets.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Chemical and Structural Changes in Corn Stover After Ensiling: Influence on Bioconversion

Production of biofuels, bioproducts, and bioenergy requires a well-characterized, stable, and reasonably uniform biomass supply and well-established supply chains for shipping biomass from farm fields to biorefineries, while achieving year-round production targets. Preserving and stabilizing biomass feedstock during storage is a necessity for cost-effective and sustainable biofuel production. Ensiling is a common storage method used to preserve and even improve forage quality; however, the impact of ensiling on biomass physical and chemical properties that influence bioconversion processes has been variable. Our objective in this work was to determine the effects of ensiling on lignocellulosic feedstock physicochemical properties and how that influences bioconversion requirements. We observed statistically significant decreases (p < 0.05) in the content of two major structural carbohydrates (glucan and xylan) of 5 and 8%, respectively, between the ensiled and non-ensiled materials. We were unable to detect differences in sugar yields from structural carbohydrates after pretreatment and enzymatic hydrolysis of the ensiled materials compared to non-ensiled controls. Based on this work, we conclude that ensiling the corn stover did not change the bioconversion requirements compared to the control samples and incurred losses of structural carbohydrates. At the light microscopy level, ensiled corn stover exhibited little structural change or relocation of cell wall components as detected by immunocytochemistry. However, more subtle structural changes were revealed by electron microscopy, as ensiled cell walls exhibit ultrastructural characteristics such as wall delimitation intermediate between non-ensiled and dilute-acid-pretreated cell walls. These findings suggest that alternative methods of conversion, such as deacetylation and mechanical refining, could take advantage of lamellar defects and may be more effective than dilute acid or hot water pretreatment for biomass conversion of ensiled materials.

09 BIOMASS FUELS↗

DOE OSTI2019

Chemical and Isotopic Evidence for Organic Matter Sulfurization in Redox Gradients Around Mangrove Roots

Coastal environments like mangrove forests are increasingly recognized as potential hotspots for organic carbon burial, giving them a crucial and yet poorly constrained role in the global carbon cycle. Mangrove sediments are frequently anoxic, which facilitates elevated organic matter (OM) burial via several mechanisms, including sulfurization – abiotic reactions between dissolved (poly)sulfide and OM that decrease its lability. Although sulfurization was estimated to account for roughly half of OM preservation in a Bermuda mangrove forest, both its mechanisms and its global significance remain poorly understood. In this study, we investigate S cycling in mangrove forest sediments from Little Ambergris Cay, Turks and Caicos Islands, an environment with predominantly microbial OM inputs and no major source of terrestrial iron. We characterize the S- and C-isotope composition and organic S speciation of sedimentary OM fractions with varying degrees of resistance to acid hydrolysis, along with other inorganic S phases. Near the surface of a 3-mm-diameter, O 2 -releasing root, abundant organic and elemental S with a 34 S-depleted composition indicates microbial sulfur cycling and OM sulfurization. A mixture of pyrite, elemental S, and organic S form a plaque within the outer 50 μm of the root, which also contains strongly 34 S-depleted sulfate in its xylem. OM sulfurization products in the sediments include both the alkyl sulfides and disulfides associated with the root plaque and more oxidized forms, especially sulfonates. Hydrolysis-resistant organic S in the sediments is consistently 3–5‰ more 34S-enriched than coexisting elemental S, matching the reported kinetic isotope fractionation factor for OM sulfurization via reaction with polysulfides. These sediments also contain a substantial pool of solid-phase, hydrolyzable organic S with a seawater sulfate-like isotope composition, largely in the form of sulfate esters, which may represent excretions from abundant gastropods. The coexistance of sulfurized OM and aerobic macrofauna highlights how understanding spatial scales and/or temporal cycles in local redox state is critical for predicting net OM preservation, especially in dynamic, coastal environments. Future attempts to mechanistically predict changes in carbon storage in coastal systems will benefit from incorporating OM sulfurization as both a sink for microbially produced sulfide and a mechanism for enhanced carbon sequestration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Structure and Fc-Effector Function of Rhesusized Variants of Human Anti-HIV-1 IgG1s

Passive transfer of monoclonal antibodies (mAbs) of human origin into Non-Human Primates (NHPs), especially those which function predominantly by a Fc-effector mechanism, requires an a priori preparation step, in which the human mAb is reengineered to an equivalent NHP IgG subclass. This can be achieved by changing both the Fc and Fab sequence while simultaneously maintaining the epitope specificity of the parent antibody. This Ab reengineering process, referred to as rhesusization, can be challenging because the simple grafting of the complementarity determining regions (CDRs) into an NHP IgG subclass may impact the functionality of the mAb. Here we describe the successful rhesusization of a set of human mAbs targeting HIV-1 envelope (Env) epitopes involved in potent Fc-effector function against the virus. This set includes a mAb targeting a linear gp120 V1V2 epitope isolated from a RV144 vaccinee, a gp120 conformational epitope within the Cluster A region isolated from a RV305 vaccinated individual, and a linear gp41 epitope within the immunodominant Cys-loop region commonly targeted by most HIV-1 infected individuals. Structural analyses confirm that the rhesusized variants bind their respective Env antigens with almost identical specificity preserving epitope footprints and most antigen-Fab atomic contacts with constant regions folded as in control RM IgG1s. In addition, functional analyses confirm preservation of the Fc effector function of the rhesusized mAbs including the ability to mediate Antibody Dependent Cell-mediated Cytotoxicity (ADCC) and antibody dependent cellular phagocytosis by monocytes (ADCP) and neutrophils (ADNP) with potencies comparable to native macaque antibodies of similar specificity. While the antibodies chosen here are relevant for the examination of the correlates of protection in HIV-1 vaccine trials, the methods used are generally applicable to antibodies for other purposes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre

Sinking particles mediate the transport of carbon and energy to the deep-sea, yet the specific microbes associated with sedimenting particles in the ocean's interior remain largely uncharacterized. In this study, we used particle interceptor traps (PITs) to assess the nature of particle-associated microbial communities collected at a variety of depths in the North Pacific Subtropical Gyre. Comparative metagenomics was used to assess differences in microbial taxa and functional gene repertoires in PITs containing a preservative (poisoned traps) compared to preservative-free traps where growth was allowed to continue in situ (live traps). Live trap microbial communities shared taxonomic and functional similarities with bacteria previously reported to be enriched in dissolved organic matter (DOM) microcosms (e.g., Alteromonas and Methylophaga), in addition to other particle and eukaryote-associated bacteria (e.g., Flavobacteriales and Pseudoalteromonas). Poisoned trap microbial assemblages were enriched in Vibrio and Campylobacterales likely associated with eukaryotic surfaces and intestinal tracts as symbionts, pathogens, or saprophytes. The functional gene content of microbial assemblages in poisoned traps included a variety of genes involved in virulence, anaerobic metabolism, attachment to chitinaceaous surfaces, and chitin degradation. The presence of chitinaceaous surfaces was also accompanied by the co-existence of bacteria which encoded the capacity to attach to, transport and metabolize chitin and its derivatives. Distinctly different microbial assemblages predominated in live traps, which were largely represented by copiotrophs and eukaryote-associated bacterial communities. Predominant sediment trap-assocaited eukaryotic phyla included Dinoflagellata, Metazoa (mostly copepods), Protalveolata, Retaria, and Stramenopiles. In conclusion, these data indicate the central role of eukaryotic taxa in structuring sinking particle microbial assemblages, as well as the rapid responses of indigenous microbial species in the degradation of marine particulate organic matter (POM) in situ in the ocean's interior.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

59 BASIC BIOLOGICAL SCIENCES↗

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