Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “single cell”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Enabling reactive microscopy with MicroMator

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator’s potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluating the effect of ionomer chemical composition in silver-ionomer catalyst inks toward the oxygen evolution reaction by half-cell measurements and water electrolysis

The effect of anion exchange ionomer (AEI) chemistry on the kinetics of the oxygen evolution reaction (OER) was systematically studied in both half-cell and single-cell electrolysis experiments. OER was studied in 1 M K 2 CO 3 at 50°C using an array of ionomer-silver catalyst inks deposited on Ni foam. Different ionomer modifications were investigated to optimize the OER performance. The AEI used in this work features a block copolymer backbone of polychloromethylstyrene-b-polycyclooctene-b-polychloromethylstyrene which was functionalized with either benzyltrimethylammonium (TMA) or benzylmethylpiperidinium (MPRD) quaternary ammonium cations. Here, using an MPRD quaternary ammonium cation shifted the rate determining step at high overpotentials, ultimately providing enhanced performance. Increasing the catalyst ink dilution was also found to substantially improve mass activity and catalyst utilization (increase from 35 to 88 A g –1 Ag), likely by reducing the ionomer thickness and decreasing transport resistances. In the final ionomer modification, the Ni foam substrate was leveraged to partially hydrogenate the polycyclooctene midblock to be polyethylene-like at the Ni interface. A significant increase in the electrochemical surface area (Cdl increased from 8.8 to 20.8 mF cm –2 ) and performance (by 47 mA cm –2 or 29 A g –1 Ag) was observed with the incorporation of polyethylene. Kinetic results from half-cell experiments were validated via single-cell anion exchange membrane electrolysis experiments, where the optimized electrode displayed enhanced performance in both 1 M K 2 CO 3 and DI H 2 O.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Lithium-Ion Battery Design for Grid-Scale Energy Storage App

A software that delivers parameters from energy storage system (ESS) to container, rack, module and single cell design, as well as data analysis on arbitrage energy and frequency regulation of ESS in different regions, has been developed. The Lithium-ion Battery Design for Grid-scale Energy Storage App V1.0 has the capability to output the system, module and cell design with the energy, power, capacity, group method, cost of single cell, and single cell test protocol which break down from input energy storage system data in different regions. The default chemistry of the battery is LiFePO4 and graphite. The energy density of the graphite/LiFePO 4 pouch cell ranges from 100 Wh/kg to 200 Wh/Kg in the software. Graphite/LiFePO 4 pouch cell (up to 1Ah in lab) manufacturing line is also built and can be used to evaluate the test protocol, moreover, for electrolyte evaluation in other ESMI seedling projects. The software enables rapid prototyping to accelerate energy storage research, development, and manufacturing.

Liu, Dianying↗
DOE OSTI2020

In-Situ Characterization of Dynamic Morphological and Phase Changes of Selenium-doped Germanium Using a Single Particle Cell and Synchrotron Transmission X-ray Microscopy

The dynamic information of lithium-ion battery active materials obtained from coin cell-based in-situ characterizations might not represent the properties of the active material itself because many other factors in the cell could have impacts on the cell performance. To address this problem, a single particle cell was developed to perform the in-situ characterization without the interference of inactive materials in the battery electrode as well as the X-ray-induced damage. In this study, the dynamic morphological and phase changes of selenium-doped germanium (Ge 0.9 Se 0.1 ) at the single particle level were investigated via synchrotron-based in-situ transmission X-ray microscopy. The results demonstrate the good reversibility of Ge 0.9 Se 0.1 at high cycling rate that helps understand its good cycling performance and rate capability. This in-situ and operando technique based on a single particle battery cell provides an approach to understanding the dynamic electrochemical processes of battery materials during charging and discharging at the particle level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

Studies of silicon p-n junction solar cells

Single crystal silicon p-n junction solar cells made with low resistivity substrates show poorer solar energy conversion efficiency than traditional theory predicts. The physical mechanisms responsible for this discrepancy are identified and characterized. The open circuit voltage in shallow junction cells of about 0.1 ohm/cm substrate resistivity is investigated under AMO (one sun) conditions.

Lindholm, F. A.↗
DOE OSTI2025

Quantitative multiplexed analysis of gene and protein expression patterns in Yarrowia lipolytica

In this report, we present coordinated observations of protein and mRNA transcript counts at the single-cell level in the oleaginous yeast model Yarrowia lipolytica. The transcription factor Xbp1p regulates entry into a quiescent state, representing a shift of resources to sequestration of nutrients rather than cell division. We observed the responses of wild-type and Δxbp1 cells to protein (by fluorescence) and transcript quantification and localization at both single-cell and population-averaged levels. Data were collected via single-molecule fluorescence in situ hybridization (smFISH) and qPCR under nitrogen depletion, a condition that drives lipid accumulation. These techniques reveal a complex and heterogeneous population of Xbp1p dynamics and downstream regulation. Our findings highlight the need for single-cell resolution analyses to describe cellular dynamics and regulatory processes.

Yarrowia lipolytica↗
DOE OSTI2020

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. In this study, we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
NASA NTRS1990

Cell separation and electrofusion in space

In microgravity, free-fluid electrophoretic methods for separating living cells and proteins are improved significantly by the absence of gravity-driven phenomena. Cell fusion, culture, and other bioprocessing steps are being investigated to understand the limits of earth-based processing. A multistep space bioprocess is described that includes electrophoretic separation of human target cells, single-cell manipulations using receptor-specific antibodies, electrofusion to produce immortal hybridomas, gentle suspension culture, and monoclonal antibody recovery using continuous-flow electrophoresis or recirculating isoelectric focusing. Improvements in several key steps already have been demonstrated by space experiments, and others will be studied on Space Station Freedom.

Morrison, D. R.↗
NASA NTRS2004

Two-Photon Flow Cytometry

Flow cytometry is a powerful technique for obtaining quantitative information from fluorescence in cells. Quantitation is achieved by assuring a high degree of uniformity in the optical excitation and detection, generally by using a highly controlled flow such as is obtained via hydrodynamic focusing. In this work, we demonstrate a two-beam, two- channel detection and two-photon excitation flow cytometry (T(sup 3)FC) system that enables multi-dye analysis to be performed very simply, with greatly relaxed requirements on the fluid flow. Two-photon excitation using a femtosecond near-infrared (NIR) laser has the advantages that it enables simultaneous excitation of multiple dyes and achieves very high signal-to-noise ratio through simplified filtering and fluorescence background reduction. By matching the excitation volume to the size of a cell, single-cell detection is ensured. Labeling of cells by targeted nanoparticles with multiple fluorophores enables normalization of the fluorescence signal and thus ratiometric measurements under nonuniform excitation. Quantitative size measurements can also be done even under conditions of nonuniform flow via a two-beam layout. This innovative detection scheme not only considerably simplifies the fluid flow system and the excitation and collection optics, it opens the way to quantitative cytometry in simple and compact microfluidics systems, or in vivo. Real-time detection of fluorescent microbeads in the vasculature of mouse ear demonstrates the ability to do flow cytometry in vivo. The conditions required to perform quantitative in vivo cytometry on labeled cells will be presented.

Zhog, Cheng Frank↗
NASA NTRS2019

Deciphering the Effects of Microgravity Cell by Cell

Forces generated by gravity have a profound impact on the behavior of cells in tissues affecting the course of the cell cycle and differentiation fate of progenitors in mammalian tissues. These cells are contributing to normal tissue regenerative health and defence against disease. In Human space exploration context, it is extremely important to determine spaceflight provoked changes in tissue's regenerational capabilities. Microgravity experienced during spaceflight causes unloading and mechanical disuse on all orthostatic support tissues, therefore impacting stem cell fate and lineage commitment decisions. Investigating how ESCs respond to mechanical stimulation is a platform for fundamental developmental and regeneration research applicable for spaceflight. However, the gene expression programs associatiated with early committment stem cell pathways in response to physical stimulation are not readily known. Single-cell RNA-seq technologies have recently revolutionized the world of molecular biology by providing the capability to assess gene expression pattern within a single cell. Our method isolates and separately barcodes mRNAs from thousands of single cells and sequences their expressomes. Understanding regenerative processes on a molecular level would not only help reduce long-term spaceflight impact on health, but also may enable the development of novel tissue regenerative approaches to tissue degeneration on Earth.

Single cell sequencing↗
DOE OSTI2026

Learning genetic perturbation effects with variational causal inference

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1996

Performance features of 22-cell, 19Ah single pressure vessel nickel hydrogen battery

Two 22-cells 19Ah Nickel-Hydrogen (Ni-H2) Single Pressure Vessel (SPV) Qual batteries, one each from EPI/Joplin and EPI/Butler, were designed and procured. The two batteries differ in the cell encapsulation technology, stack preload, and activation procedure. Both the Butler and Joplin batteries met the specified requirements when subjected to qualification testing and completed 2100 and 1300 LEO cycles respectively, with nominal performance. This paper discusses advantages, design features, testing procedures, and results of the two single pressure vessel Ni-H2 batteries.

Rao, Gopalakrishna M.↗
DOE OSTI2020

Insights into the dynamics between viruses and their hosts in a hot spring microbial mat

Abstract Our current knowledge of host–virus interactions in biofilms is limited to computational predictions based on laboratory experiments with a small number of cultured bacteria. However, natural biofilms are diverse and chiefly composed of uncultured bacteria and archaea with no viral infection patterns and lifestyle predictions described to date. Herein, we predict the first DNA sequence-based host–virus interactions in a natural biofilm. Using single-cell genomics and metagenomics applied to a hot spring mat of the Cone Pool in Mono County, California, we provide insights into virus–host range, lifestyle and distribution across different mat layers. Thirty-four out of 130 single cells contained at least one viral contig (26%), which, together with the metagenome-assembled genomes, resulted in detection of 59 viruses linked to 34 host species. Analysis of single-cell amplification kinetics revealed a lack of active viral replication on the single-cell level. These findings were further supported by mapping metagenomic reads from different mat layers to the obtained host–virus pairs, which indicated a low copy number of viral genomes compared to their hosts. Lastly, the metagenomic data revealed high layer specificity of viruses, suggesting limited diffusion to other mat layers. Taken together, these observations indicate that in low mobility environments with high microbial abundance, lysogeny is the predominant viral lifestyle, in line with the previously proposed “Piggyback-the-Winner” theory.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

Lipid extraction from isolated single nerve cells

A method of extracting lipids from single neurons isolated from lyophilized tissue is described. The method permits the simultaneous extraction of lipids from 30-40 nerve cells and for each cell provides equal conditions of solvent removal at the conclusion of extraction.

Krasnov, I. V.↗
DOE OSTI2022

LungMAP Portal Ecosystem: Systems-level Exploration of the Lung

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). Finally, as this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.

59 BASIC BIOLOGICAL SCIENCES↗