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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution

With advances in DNA sequencing and miniaturized molecular biology workflows, rapid and affordable sequencing of single-cell genomes has become a reality. Compared to 16S rRNA gene surveys and shotgun metagenomics, large-scale application of single-cell genomics to whole microbial communities provides an integrated snapshot of community composition and function, directly links mobile elements to their hosts, and enables analysis of population heterogeneity of the dominant community members. To that end, we sequenced nearly 500 single-cell genomes from a low diversity hot spring sediment sample from Dewar Creek, British Columbia, and compared this approach to 16S rRNA gene amplicon and shotgun metagenomics applied to the same sample. We found that the broad taxonomic profiles were similar across the three sequencing approaches, though several lineages were missing from the 16S rRNA gene amplicon dataset, likely the result of primer mismatches. At the functional level, we detected a large array of mobile genetic elements present in the single-cell genomes but absent from the corresponding same species metagenome-assembled genomes. Moreover, we performed a single-cell population genomic analysis of the three most abundant community members, revealing differences in population structure based on mutation and recombination profiles. While the average pairwise nucleotide identities were similar across the dominant species-level lineages, we observed differences in the extent of recombination between these dominant populations. Most intriguingly, the creek's Hydrogenobacter sp. population appeared to be so recombinogenic that it more closely resembled a sexual species than a clonally evolving microbe. Together, this work demonstrates that a randomized single-cell approach can be useful for the exploration of previously uncultivated microbes from community composition to population structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Single-cell RNA sequencing of batch Chlamydomonas cultures reveals heterogeneity in their diurnal cycle phase

The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of its ease of culture in the laboratory. Genomic and systems biology approaches have previously described transcriptome responses to environmental changes using bulk data, thus representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing by the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample natural algal communities. Second, scRNA-seq successfully separated single cells into nonoverlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest photosynthesis and cell division to economize resources. Notably, these groups of cells not only recapitulated known patterns observed with bulk RNA-seq but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We document experimentally that bulk RNA-seq data represent an average of typically hidden heterogeneity in the population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improved Single-Cell Proteome Coverage Using Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry

Single-cell proteomics can provide unique insights into biological processes by resolving heterogeneity that is obscured by bulk measurements. Gains in the overall sensitivity and proteome coverage through improvements in sample processing and analysis increase the information content obtained from each cell, particularly for less abundant proteins. Here we report on improved single-cell proteome coverage through the combination of the previously developed Nanodroplet Processing in One Pot for Trace Samples (nanoPOTS) platform with further miniaturization of liquid chromatography (LC) separations and implementation of an ultrasensitive latest-generation mass spectrometry (MS) instrument. Following nanoPOTS sample preparation, protein digest from single cells were separated using a 20-µm-i.d. in-house-packed nanoLC column. Separated peptides were ionized using an etched fused silica emitter capable of stable ionization at the ~20 nL/min flow rate provided by the LC separation. Ultrasensitive LC-MS analysis was achieved using the Orbitrap Eclipse Tribrid mass spectrometer. An average of 362 protein groups were identified by MS/MS from single HeLa cells, and 874 protein groups were identified using the Match Between Runs (MBR) feature of MaxQuant. This represents a >70% increase in label-free proteome coverage for single cells relative to previous efforts using larger-bore (30-µm-i.d.) LC columns coupled to a previous-generation MS (Orbitrap Fusion Lumos).

Cong, Yongzheng↗
DOE OSTI2021

A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus

Single-cell transcriptome analysis has been extensively applied in humans and animal models to uncover gene expression heterogeneity between the different cell types of a tissue or an organ. It demonstrated its capability to discover key regulatory elements that determine cell fate during developmental programs. Single-cell analysis requires the isolation and labeling of the messenger RNA (mRNA) derived from each cell. These challenges were primarily addressed in mammals by developing microfluidic-based approaches. For plant species whose cells contain cell walls, these approaches have generally required the generation of isolated protoplasts. Many plant tissues’ secondary cell wall hinders enzymatic digestion required for individual protoplast isolation, resulting in an unequal representation of cell types in a protoplast population. This limitation is especially critical for cell types located in the inner layers of a tissue or the inner tissues of an organ. Consequently, single-cell RNA sequencing (scRNA-seq) studies using microfluidic approaches in plants have mainly been restricted to Arabidopsis roots, for which well-established procedures of protoplast isolation are available. Here we present a simple alternative approach to generating high-quality protoplasts from plant tissue by characterizing the mRNA extracted from individual nuclei instead of whole cells. We developed the protocol using two different plant materials with varying cellular complexity levels and cell wall structure, Populus shoot apices, and more lignified stems. Using the 10× Genomics Chromium technology, we show that this procedure results in intact mRNA isolation and limited leakage, with a broad representation of individual cell transcriptomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Laminarin stimulates single cell rates of sulfate reduction whereas oxygen inhibits transcriptomic activity in coastal marine sediment

Abstract The chemical cycles carried out by bacteria and archaea living in coastal sediments are vital aspects of benthic ecology. These ecosystems are subject to physical disruption, which may allow for increased respiration and complex carbon consumption—impacting chemical cycling in this environment often thought to be a terminal place of deposition. We use the redox-enzyme sensitive probe RedoxSensor Green to measure rates of electron transfer physiology in individual sulfate reducer cells residing in anoxic sediment, subjected to transient exposure of oxygen and laminarin. We use index fluorescence activated cell sorting and single cell genomics sequencing to link those measurements to genomes of respiring cells. We measure per-cell sulfate reduction rates in marine sediments (0.01–4.7 fmol SO42− cell−1 h−1) and determine that cells within the Chloroflexota phylum are the most active in respiration. Chloroflexota respiration activity is also stimulated with the addition of laminarin, even in marine sediments already rich in organic matter. Evaluating metatranscriptomic data alongside this respiration-based technique, Chloroflexota genomes encode laminarinases indicating a likely ability to degrade laminarin. We also provide evidence that abundant Patescibacteria cells do not use electron transport pathways for energy, and instead likely carry out fermentation of polysaccharides. There is a decoupling of respiration-related activity rates from transcription, as respiration rates increase while transcription decreases with oxygen exposure. Overall, we reveal an active community of respiring Chloroflexota that cycles sulfate at potential rates of 23–40 nmol h−1 per cm3 sediment in incubation settings, and non-respiratory Patescibacteria that can cycle complex polysaccharides.

Lindsay, Melody R.↗
DOE OSTI2021

Plant single-cell solutions for energy and the environment

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species’ cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Investigating biological nitrogen fixation via single-cell transcriptomics

The extensive use of nitrogen fertilizers has detrimental environmental consequences, and it is essential for society to explore sustainable alternatives. One promising avenue is engineering root nodule symbiosis, a naturally occurring process in certain plant species within the nitrogen-fixing clade, into non-leguminous crops. Advancements in single-cell transcriptomics provide unprecedented opportunities to dissect the molecular mechanisms underlying root nodule symbiosis at the cellular level. This review summarizes key findings from single-cell studies in Medicago truncatula, Lotus japonicus, and Glycine max. We highlight how these studies address fundamental questions about the development of root nodule symbiosis, including the following findings: (i) single-cell transcriptomics has revealed a conserved transcriptional program in root hair and cortical cells during rhizobial infection, suggesting a common infection pathway across legume species; (ii) characterization of determinate and indeterminate nodules using single-cell technologies supports the compartmentalization of nitrogen fixation, assimilation, and transport into distinct cell populations; (iii) single-cell transcriptomics data have enabled the identification of novel root nodule symbiosis genes and provided new approaches for prioritizing candidate genes for functional characterization; and (iv) trajectory inference and RNA velocity analyses of single-cell transcriptomics data have allowed the reconstruction of cellular lineages and dynamic transcriptional states during root nodule symbiosis.

Lotus japonicus↗
DOE OSTI2024

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. Here, the cell size detected by single-cell omics ranges from ~2 µm (e.g., Bacillus subtilis) to ~120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in MALDI-MSI and micro/nanoPOTS for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Single cell heterogeneity in influenza A virus gene expression shapes the innate antiviral response to infection

Viral infection outcomes are governed by the complex and dynamic interplay between the infecting virus population and the host response. It is increasingly clear that both viral and host cell populations are highly heterogeneous, but little is known about how this heterogeneity influences infection dynamics or viral pathogenicity. To dissect the interactions between influenza A virus (IAV) and host cell heterogeneity, we examined the combined host and viral transcriptomes of thousands of individual cells, each infected with a single IAV virion. We observed complex patterns of viral gene expression and the existence of multiple distinct host transcriptional responses to infection at the single cell level. We show that human H1N1 and H3N2 strains differ significantly in patterns of both viral and host anti-viral gene transcriptional heterogeneity at the single cell level. Our analyses also reveal that semi-infectious particles that fail to express the viral NS can play a dominant role in triggering the innate anti-viral response to infection. Altogether, these data reveal how patterns of viral population heterogeneity can serve as a major determinant of antiviral gene activation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods

Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐ Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploring Plant Cis –Regulatory Elements at Single–Cell Resolution: Overcoming Biological and Computational Challenges to Advance Plant Research

Cis-regulatory elements (CREs) are important sequences for gene expression and for plant biological processes such as development, evolution, domestication, and stress response. However, studying CREs in plant genomes has been challenging. The totipotent nature of plant cells, coupled with inability to maintain plant cell types in culture and the inherent technical challenges posed by the cell wall have limited our understanding of how plant cell types acquire and maintain their identities and respond to the environment via CRE usage. Furthermore, advances in single cell epigenomics have revolutionized the field identifying cell-type-specific CREs. These new technologies have the potential to significantly advance our understanding of plant CRE biology, and shed light on how the regulatory genome gives rise to diverse plant phenomena. However, there are significant biological and computational challenges associated with analyzing single cell epigenomic datasets. In this review, we discuss the historical and foundational underpinnings of plant single-cell research, challenges and common pitfalls in analysis of plant single-cell epigenomic data, and highlight biological challenges unique to plants. Additionally, we discuss how the application of single-cell epigenomic data in various contexts stands to transform our understanding of the importance of CREs in plant genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Single-Cell Metabolomics with Rapid Determination of Chemical Formulas from Isotopic Fine Structures

Metabolomic measurements can provide functional readouts of cellular states and phenotypes. Here, we present a protocol for single-cell metabolomics that permits direct untargeted detection of a broad number of metabolites under ambient conditions, without the need for sample processing, and with high confidence in the discovery and identification of the molecular formulas for detected metabolites. This protocol describes combining fiber-based laser ablation electrospray ionization (f-LAESI) with a 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer (21T-FTICR-MS) to obtain high confidence molecular formula information about detected metabolites. The f-LAESI source utilizes mid-infrared laser ablation through a sharp optical fiber tip, affording direct ambient analysis of cells without the need for sample processing. Using the 21T-FTICR-MS as a mass analyzer enabled measurement of the isotopic fine structure (IFS) for numerous metabolites simultaneously from single cells, and the IFSs were in turn computationally processed to rapidly determine the corresponding elemental compositions. This metabolomics technique complements other single cell omics measurement methods, helping to resolve complex molecular interactions that take place within cells unattainable from single cell transcriptomic and proteomics methods.

mass spectrometry, FTICR, FTMS, ultrahigh mass res↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2024

Device and Method for Parallel Measurement of Phosphoproteome and Proteome from Single Cells

We present the development of an immobilized metal affinity chromatography (IMAC) chip designed to enable nanoscale phosphopeptide enrichment within microfabricated nanowells. This novel platform leverages surface chemistry to immobilize high-density Nickel-Nitrilotriacetic Acid (Ni-NTA) molecules on nanowells, followed by applying Fe 3+ . The nanowell surface serves as a capture media to enrich phosphopeptides based on IMAC. The system's efficiency was validated using ß-casein as a model protein, demonstrating the chip’s capability to significantly enrich phosphopeptides. Future applications of this technology are anticipated to enable the detection of over 100 phosphopeptides from individual cells and more than 500 phosphopeptides from pools of 100 cells, offering exciting potential for single-cell phosphoproteomics. We will next apply an integrated proteomics workflow to perform multi-omics measurements, including single-cell isolation, protein digestion, and phosphopeptide enrichment, followed by LC-MS analysis of both the global proteome and phosphoproteome. Future research will explore the use of this technology to study phosphorylation dynamics in cancer cells, enhancing our understanding of cellular signaling and disease mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering

Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Near-Single-Cell Proteomics Profiling of the Proximal Tubular and Glomerulus of the Normal Human Kidney

Molecular assessments at the single-cell level can accelerate biological research by providing detailed assessments of cellular organization and tissue heterogeneity in both disease and health. The human kidney has complex multi-cellular states with varying functionality, much of which can now be completely harnessed with recent technological advances in tissue proteomics at a near single-cell level. We discuss the foundational steps in the first application of this mass spectrometry (MS) based proteomics method for analysis of sub-sections of the normal human kidney, as part of the Kidney Precision Medicine Project (KPMP). Using approximately 30-40 laser captured micro-dissected kidney cells, we identified more than 2500 human proteins, with specificity to the proximal tubular (PT; n=25 proteins) and glomerular (Glom; n=67 proteins) regions of the kidney and their unique metabolic functions. This pilot study provides the roadmap for application of our near-single-cell proteomics workflow for the analysis of other renal micro-compartments, on a larger scale, to unravel perturbations of renal sub-cellular function in the normal kidney as well as different etiologies of acute and chronic kidney disease.

60 APPLIED LIFE SCIENCES↗