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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Data Validation Package - April and July 2015 Groundwater and Surface Water Sampling at the Gunnison, Colorado, Processing Site

This event included annual sampling of groundwater and surface water locations at the Gunnison, Colorado, Processing Site. Sampling and analyses were conducted as specified in Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites. Samples were collected from 28 monitoring wells, three domestic wells, and six surface locations in April at the processing site as specified in the 2010 Ground Water Compliance Action Plan for the Gunnison, Colorado, Processing Site. Domestic wells 0476 and 0477 were sampled in July because the homes were unoccupied in April, and the wells were not in use. Duplicate samples were collected from locations 0113, 0248, and 0477. One equipment blank was collected during this sampling event. Water levels were measured at all monitoring wells that were sampled. No issues were identified during the data validation process that requires additional action or follow-up.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Data Validation Package May 2016 Groundwater Sampling at the Lakeview, Oregon, Processing Site August 2016

This biennial event includes sampling five groundwater locations (four monitoring wells and one domestic well) at the Lakeview, Oregon, Processing Site. For this event, the domestic well (location 0543) could not be sampled because no one was in residence during the sampling event (Note: notification was provided to the resident prior to the event). Per Appendix A of the Groundwater Compliance Action Plan, sampling is conducted to monitor groundwater quality on a voluntary basis. Sampling and analyses were conducted as specified in the Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated). One duplicate sample was collected from location 0505. Water levels were measured at each sampled monitoring well. The constituents monitored at the Lakeview site are manganese and sulfate. Monitoring locations that exceeded the U.S. Environmental Protection Agency (EPA) Secondary Maximum Contaminant Levels for these constituents are listed in Table 1. Review of time-concentration graphs included in this report indicate that manganese and sulfate concentrations are consistent with historical measurements.

2016 Groundwater Sampling Lakeview, Oregon↗
DOE OSTI2016

Data Validation Package December 2015 Groundwater Sampling at the Ambrosia Lake, New Mexico, Disposal Site March 2016

The Long-Term Surveillance Plan for the Ambrosia Lake, New Mexico, Disposal Site does not require groundwater monitoring because groundwater in the uppermost aquifer is of limited use, and supplemental standards have been applied to the aquifer. However, at the request of the New Mexico Environment Department, the U.S. Department of Energy conducts annual monitoring at three locations: monitoring wells 0409, 0675, and 0678. Sampling and analyses were conducted as specified in the Sampling and Analysis Plan for US. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated). Monitoring Well 0409 was not sampled during this event because it was dry. Water levels were measured at each sampled well. One duplicate sample was collected from location 0675. Groundwater samples from the two sampled wells were analyzed for the constituents listed in Table 1. Time-concentration graphs for selected analytes are included in this report. At well 0675, the duplicate results for total dissolved solids and for most metals (magnesium, molybdenum, potassium, selenium, sodium, and uranium) were outside acceptance criteria, which may indicate non-homogeneous conditions at this location. November 2014 results for molybdenum and uranium at well 0675 also were outside acceptance criteria. The well condition will be evaluated prior to the next sampling event.

2015 Sampling Ambrosia Lake West, New Mexico↗
DOE OSTI2017

Graph Sampling for Visual Analytics

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, the size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Microfluidic-Based Robotic Sampling System for Radioactive Solutions

A novel microfluidic based robotic sampling system has been developed for sampling and analysis of liquid solutions in nuclear processes. This system couples the use of a microfluidic sample chip with a robotic system designed to allow remote, automated sampling of process solutions in-cell and facilitates direct coupling of the microfluidic sample chip with analytical instrumentation. This system provides the capability for near real time analysis, reduces analytical waste, and minimizes the potential for personnel exposure associated with traditional sampling methods. A prototype sampling system was designed, built and tested. System testing demonstrated operability of the microfluidic based sample system and identified system modifications to optimize performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Expediting Groundwater Sampling at Hanford and Making It Safer - 13158

The CH2M HILL Plateau Remediation Company (CHPRC) manages the groundwater monitoring programs at the Department of Energy's 586-square-mile Hanford site in southeastern Washington state. These programs are regulated by the Resource Conservation and Recovery Act (RCRA), the Comprehensive Environmental Response Compensation and Liability Act (CERCLA), and the Atomic Energy Act (AEA). The purpose of monitoring is to track existing groundwater contamination from past practices, as well as other potential contamination that might originate from RCRA treatment, storage, and disposal (TSD) facilities. An integral part of the groundwater-monitoring program involves taking samples of the groundwater and measuring the water levels in wells scattered across the site. Each year, more than 1,500 wells are accessed for a variety of reasons. Historically, the monitoring activities have been very 'people intensive'. Field personnel or 'samplers' have been issued pre-printed forms showing information about the well(s) for a particular sampling evolution. This information is taken from two official electronic databases: the Hanford Well Information System (HWIS) and the Hanford Environmental Information System (HEIS). The samplers traditionally used these hardcopy forms to document the groundwater samples and well water-levels. After recording the entries in the field, the samplers turned the forms in at the end of the day and other personnel posted the collected information. In Automating Groundwater Sampling at Hanford (HNF-38542-FP Revision 0, Presented at Waste Management 2009 Conference, March 1 - March 5, 2009, Phoenix, AZ), we described the methods, tools, and techniques that would be used in automating the activities associated with measuring water levels. The Field Logging and Electronic Data Gathering (FLEDG) application/database that automates collecting the water-level measurement data has now been implemented at Hanford. In addition to eliminating the need to print out documents, the system saves three-to-four man days each month for the field personnel taking the measurements and the scientists and administrators managing the data and the documentation. After the information has received technical review, FLEDG automatically updates the database for water-level measurements and loads the document management system with the completed sampling report. Due to safety considerations, access to wells is conditional. A spreadsheet with appropriate data not only lists the wells that are cleared for work, but also the safety personnel who must be present before work can start. This spreadsheet is used in planning daily activities. Daily plans are structured to ensure that the wells to be sampled are cleared for work and the appropriate safety personnel have been assigned and are present before the work starts. Historically, the spreadsheets have been prepared manually, and as a result, are potentially subject to human error. However, a companion database application has been developed to work with FLEDG - making the entire sampling process more efficient and safer for personnel. The Well Access List - Electronic, WAL-E, is a database that contains much the same information that was previously manually loaded into the spread sheet. In addition, WAL-E contains a managed work-flow application that shows the access requirements and allows for appropriate reviews of the compiled well. Various CHPRC organizations, including Industrial Hygiene, RADCON, and Well Maintenance and Sample Administration are able to enter and review the wells added or deleted from the WAL-E database. The FLEDG system then accesses this database information to identify appropriate support personnel and provide safety requirements to field personnel. In addition, WAL-E offers the assurance that wells have appropriate locks and are correctly labeled and electrically grounded as required, before well activities begin. This feature is an extremely important aspect of the FLEDG/WAL-E system because it adds another safety check to the work evolution and reduces the potential for unsafe conditions to exist that would lead to a stop-work situation. This paper will further describe the technical aspects of both electronic programs/databases and their successful application at Hanford. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Small Scale Mixing Demonstration Batch Transfer and Sampling Performance of Simulated HLW - 12307

The ability to effectively mix, sample, certify, and deliver consistent batches of High Level Waste (HLW) feed from the Hanford Double Shell Tanks (DST) to the Waste treatment Plant (WTP) has been recognized as a significant mission risk with potential to impact mission length and the quantity of HLW glass produced. At the end of 2009 DOE's Tank Operations Contractor, Washington River Protection Solutions (WRPS), awarded a contract to EnergySolutions to design, fabricate and operate a demonstration platform called the Small Scale Mixing Demonstration (SSMD) to establish pre-transfer sampling capacity, and batch transfer performance data at two different scales. This data will be used to examine the baseline capacity for a tank mixed via rotational jet mixers to transfer consistent or bounding batches, and provide scale up information to predict full scale operational performance. This information will then in turn be used to define the baseline capacity of such a system to transfer and sample batches sent to WTP. The Small Scale Mixing Demonstration (SSMD) platform consists of 43'' and 120'' diameter clear acrylic test vessels, each equipped with two scaled jet mixer pump assemblies, and all supporting vessels, controls, services, and simulant make up facilities. All tank internals have been modeled including the air lift circulators (ALCs), the steam heating coil, and the radius between the wall and floor. The test vessels are set up to simulate the transfer of HLW out of a mixed tank, and collect a pre-transfer sample in a manner similar to the proposed baseline configuration. The collected material is submitted to an NQA-1 laboratory for chemical analysis. Previous work has been done to assess tank mixing performance at both scales. This work involved a combination of unique instruments to understand the three dimensional distribution of solids using a combination of Coriolis meter measurements, in situ chord length distribution measurements, and electro-resistive tomography. This current work utilized the same instruments to monitor simulated waste transfers. This paper will discuss some of the scaling compromises when it came to the scaled sampling system design, handling of large quantities of material for sampling, and present data for the discuss of likely behavior of the full scale DST based on scaling correlations using a scale ratio exponent (SRE) from 0.25 to 0.45 and the behavior observed in the SSMD platform. This does not establish a scaling factor for DST mixing using paired jet mixers but is an attempt to envelope the likely performance ranges in terms of certification sampling bias, certification sample root-mean-square-deviation, and bath to batch relative standard deviation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Delayed-Gamma Energy Biasing with Exact Energy Sampling in MCNP 6.2.0

Characterization of delayed-particle signatures is of the utmost importance for detecting, identifying, and quantifying special nuclear material (SNM). Various non-destructive assay (NDA) techniques used in nuclear material safeguards rely on characteristic gamma-ray or neutron emissions to infer properties of a material. The all-particle, all-energy Monte-Carlo radiation transport code MCNP6 is capable of simulating delayed-particle signatures from SNM in addition to modeling the transport of emitted particles to a detector or other instrument. In some cases, NDA techniques require the measurement of low probability emissions from a given material. In physical scenarios, this problem can be bypassed by longer count-times or may be simply irrelevant due to the mass, and therefore the total activity, of the material being measured. However, when applying the Monte-Carlo method to such scenarios, sampling of low probability emissions requires a large number of histories to converge to a statistically acceptable solution. To address this issue, the delayed-neutron and gamma energy biasing feature was introduced in MCNP6 by H. Armstrong et al. in 2013 which allows the user to set up energy windows in which delayed-particle emission sampling can be adjusted. For delayed-gamma sampling, this biasing method was based on the bin-wise structure of gamma emission data for both the multi-bin gamma and line-emission sampling modes. Recent improvements have been made to the line-emission sampling algorithm which upgrade the previous mini-bin sampling treatment to an exact energy-line treatment. This upgrade was not compatible with the biasing method implemented by H. Armstrong et al. due to the sampled data no longer having a bin-wise structure in line-emission mode. For this reason the delayed-gamma energy biasing feature has been rewritten to support the exact line-sampling treatment. This paper provides a description of the delayed-gamma energy biasing (DGEB) feature in MCNP6 as well as results and discussion for a gamma-ray spectroscopy example used to demonstrate the biasing utility. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Evaluating Manual Sampling Locations for Regulatory and Emergency Response

Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system. Results showed that sampling locations could be effective in confirming incidents for which they were not designed. When evaluating sampling locations optimized for emergency response against regulatory scenarios, the average performance was reduced by 3%–4%, while using optimized regulatory sampling locations for emergency response reduced performance by 7%–10%. Secondary constraints were also included in the formulation to ensure geographical and water age diversity with minimal impact on the performance. This work highlighted that regulatory sampling locations provide value in responding to an emergency for these networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Internet of Samples

Abstract Material samples are indispensable data sources in many natural science, social science, and humanity disciplines. More and more researchers recognize that samples collected in one discipline can be of great value for another. This has motivated organizations that manage a large number of samples to make their holdings accessible to the world. Currently, multiple projects are working to connect natural history and other samples managed by individual institutions or individuals into a universe of samples that follow FAIR principles. This poster reports the progress of the US NSF‐funded iSamples project, in the context of other efforts initiated by US DOE, DiSCCo, BCoN, and GBIF. By October 2021, we will also be able to present an iSamples prototype. We encourage individual organizations that hold material samples to get to know these projects and help shape these projects to realize the goal of a global linked sample cloud that connects all material samples and is accessible to all.

Richard, Stephen M.↗
DOE OSTI2018

Role of Ti Content on the Occurrence of the 3309-cm{sup –1} Peak in FTIR Absorption Spectra of Ruby Samples

The study is based on the determination of trace elements in ruby samples, Ti in particular, using laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS). The samples were then divided into three groups: high (≥ 200 ppma), moderate (100–200 ppma), and low (≤ 50 ppma) Ti content. In addition, X-ray absorption spectroscopy (XAS) was employed to confirm that the Ti oxidation state was Ti{sup 4+}. The heat experiment conditions were set at 800, 1200, and 1650°C to investigate the transformation of the –OH vibration in the FTIR spectra of ruby samples. The FTIR spectra showed that samples containing a low Ti concentration did not show the 3309-cm{sup –1} absorption peak either before or after heating at any of the designed temperatures. An obvious 3309-cm{sup –1} peak appeared only in samples with a moderate to high Ti content after heating at ≥ 1200°C. The results also suggest that, in addition of Ti{sup 4+}, the occurrence of the 3309-cm{sup –1} peak in the FTIR spectra is strongly affected by the charge compensation of –OH in Al sites, i.e., the structural –OH; thus, the structural –Ti–OH stretching in the ruby samples. Hence, for gemological identification, the 3309-cm{sup –1} peak can be applied to indicate whether a ruby has undergone heat treatment if those samples contain enough Ti ions in their structure, i.e., Ti ≥ 100 ppma. This condition is usually found in ruby samples from major deposits such as Mong Hsu and Mogok, Myanmar; Luc Yen, Vietnam; and Montopuez, Mozambique.

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

Production Procedure and Characterization of Zn-Doped Y-123 Superconducting Samples Prepared by Sol-Gel Method

The superconducting YBa{sub 2}Cu{sub 3−x}Zn{sub x}O{sub 7} (Y-123) bulk materials have been synthesized by using the sol-gel method. Samples are produced as undoped Y-123 and transition metal (Zn)-doped Y-123. Before the final heat treatment, the samples are calcined at 850 °C for 24 h. This process is repeated three times. Then, samples are sintered at 950 °C for 24 h in an air environment and at 500 °C for 5 h in an oxygen atmosphere. The synthesized products are characterized by XRD, R-T, and Vickers microhardness tester. The XRD investigation revealed that the prepared sample has an orthorhombic structure. According to XRD measurements, an orthorhombic structure has not changed with Zn doping. It was observed that undoped and Zn-doped samples have superconductivity properties by electrical measurements. T{sub c}{sup onset} is 89 K for undoped Y-123 sample, and the T{sub c}{sup onset} value decreases monotically with Zn addition. All samples show metallic behavior above T{sub c}{sup onset} temperature. As a result of Vickers microhardness measurements, it is observed that all samples have reverse indentation size effect (RISE) behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Rapid determination of radiostrontium in seawater samples

A new method for the determination of radiostrontium in seawater samples has been developed at the Savannah River National Laboratory (SRNL) that allows rapid preconcentration and separation of strontium and yttrium isotopes in seawater samples for measurement. The new SRNL method employs a novel and effective pre-concentration step that utilizes a blend of calcium phosphate with iron hydroxide to collect both strontium and yttrium rapidly from the seawater matrix with enhanced chemical yields. The pre-concentration steps, in combination with rapid Sr Resin and DGA Resin cartridge separation options using vacuum box technology, allow seawater samples up to 10 liters to be analyzed. The total 89 Sr + 90 Sr activity may be determined by gas flow proportional counting and recounted after ingrowth of 90 Y to differentiate 89 Sr from 90 Sr. Gas flow proportional counting provides a lower method detection limit than liquid scintillation or Cerenkov counting and allows simultaneous counting of samples. Simultaneous counting allows for longer count times and lower method detection limits without handling very large aliquots of seawater. Seawater samples up to 6 liters may be analyzed using Sr Resin for 89 Sr and 90 Sr with a Minimum Detectable Activity (MDA) of 1-10 mBq/L, depending on count times. Seawater samples up to 10 liters may be analyzed for 90 Sr using a DGA Resin method via collection and purification of 90 Y only. If 89 Sr and other fission products are present, then 91 Y (beta energy 1.55 MeV, 58.5 day half-life) is also likely to be present. 91 Y interferes with attempts to collect 90 Y directly from the seawater sample without initial purification of Sr isotopes first and 90 Y ingrowth. The DGA Resin option can be used to determine 90 Sr, and if 91 Y is also present, an ingrowth option with using DGA Resin again to collect 90 Y can be performed. An MDA for 90 Sr of <1 mBq/L for an 8 hour count may be obtained using 10 liter seawater sample aliquots.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions

This study compares eight surrogate-model construction approaches using computational experiments. Here, the construction approaches considered include: Artificial Neural Networks (ANNs), Automated Learning of Algebraic Models using Optimization (ALAMO), Radial Basis Networks (RBNs), Extreme Learning Machines (ELMs), Gaussian Progress Regression (GPR), Random Forests (RFs), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines. Each approach is used to construct surrogate models for predicting the outputs of thirty-four test functions. The data used to construct surrogate models were generated using Latin Hypercube Sampling (LHS), Halton and Sobol sampling methods. We studied the impact of sampling method, sample size, and challenge function characteristics on the accuracy of surrogate model predictions. Our results revealed that for smaller sample sizes, on average, surrogate models trained using the data points generated by Sobol sequences provided the best estimation for all surrogate model construction approaches. As the sample size increased, the impact of sampling methods diminished. The comparison of surrogate-modeling construction approaches at large sample sizes revealed that surrogate models trained using ANN, ALAMO, and ELM yielded smaller root mean squared error and higher adjusted R-squared values than the models trained using the rest of the approaches.

42 ENGINEERING↗
DOE OSTI2025

Comparison of measurement techniques and sorption of radium-226 in low and high salinity aqueous samples

Human activities have the potential to redistribute radium (Ra) in the marine environment in a manner that may necessitate monitoring or management of subsequent human or environmental exposures. There is therefore a need to identify accurate and accessible techniques for Ra measurement in high salinity samples and to describe the distribution of Ra in estuarine and marine environments, but most efforts in these areas have focused on low salinity matrices. In addition, rapid and reliable measurements are crucial for time-sensitive samples such as short-lived isotopes or emergency situations. The objective of this study is to describe the limits of detection, cost, and relative ease for measurement of Ra in both low and high salinity aqueous samples via three analytical methods: liquid scintillation counting (LSC), high purity germanium (HPGe) gamma spectrometry, and inductively coupled plasma mass spectrometry (ICP-MS). To contextualize these measurements for real-world scenarios, the partitioning of 226 Ra to substrates relevant to the marine environment was also characterized. Although HPGe detection with solid phase extraction had the lowest limit of detection for low salinity samples (0.27 Bq L −1 ), poor 226 Ra recovery for high salinity samples and high materials costs make this method prohibitive for many users. Limits of detection for high salinity samples were lower for LSC (1.28 Bq L −1 ) than for ICP-MS without dilution (11.4 Bq L −1 ), but significant and unexpected degradation of the high salinity LSC standards was observed after six months. Furthermore, our preferred measurement method for high salinity Ra samples is ICP-MS with sample dilution as necessary to reduce matrix effects.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗