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At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Analysis of twelve genomes of the bacterium Kerstersia gyiorum from brown-throated sloths ( Bradypus variegatus ), the first from a non-human host

Kerstersia gyiorum is a Gram-negative bacterium found in various animals, including humans, where it has been associated with various infections. Knowledge of the basic biology of K. gyiorum is essential to understand the evolutionary strategies of niche adaptation and how this organism contributes to infectious diseases; however, genomic data about K. gyiorum is very limited, especially from non-human hosts. In this work, we sequenced 12 K. gyiorum genomes isolated from healthy free-living brown-throated sloths (Bradypus variegatus) in the Parque Estadual das Fontes do Ipiranga (São Paulo, Brazil), and compared them with genomes from isolates of human origin, in order to gain insights into genomic diversity, phylogeny, and host specialization of this species. Phylogenetic analysis revealed that these K. gyiorum strains are structured according to host. Despite the fact that sloth isolates were sampled from a single geographic location, the intra-sloth K. gyiorum diversity was divided into three clusters, with differences of more than 1,000 single nucleotide polymorphisms between them, suggesting the circulation of various K. gyiorum lineages in sloths. Genes involved in mobilome and defense mechanisms against mobile genetic elements were the main source of gene content variation between isolates from different hosts. Sloth-specific K. gyiorum genome features include an IncN2 plasmid, a phage sequence, and a CRISPR-Cas system. The broad diversity of defense elements in K. gyiorum (14 systems) may prevent further mobile element flow and explain the low amount of mobile genetic elements in K. gyiorum genomes. Gene content variation may be important for the adaptation of K. gyiorum to different host niches. This study furthers our understanding of diversity, host adaptation, and evolution of K. gyiorum, by presenting and analyzing the first genomes of non-human isolates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Adaptive variation and plasticity in non‐structural carbohydrate storage in a temperate tree species

Abstract Trees' total amount of non‐structural carbohydrate (NSC) stores and the proportion of these stores residing as insoluble starch are vital traits for individuals living in variable environments. However, our understanding of how stores vary in response to environmental stress is poorly understood as the genetic component of storage is rarely accounted for in studies. Here, we quantified variation in NSC traits in branch samples taken from over 600 clonally transplanted black cottonwood ( Populus trichocarpa) trees grown in two common gardens. We found heritable variation in both total NSC stores and the proportion of stores in starch (H 2 TNC = 0.19, H 2 PropStarch = 0.31), indicating a substantial genetic component of variation. In addition, we found high amounts of plasticity in both traits in response to cold temperatures and significant genotype‐by‐environment (GxE) interactions in the total amount of NSC stored (54% of P is GxE). This finding of high GxE indicates extensive variation across trees in their response to environment, which may explain why previous studies of carbohydrate stores' responses to stress have failed to converge on a consistent pattern. Overall, we found high amounts of environmental and genetic variation in NSC storage concentrations, which may bolster species against future climate change.

Blumstein, Meghan↗

DOE OSTI2024

Single-nuclei transcriptome analysis of channel catfish spleen provides insight into the immunome of an aquaculture-relevant species

The catfish industry is the largest sector of U.S. aquaculture production. Given its role in food production, the catfish immune response to industry-relevant pathogens has been extensively studied and has provided crucial information on innate and adaptive immune function during disease progression. To further examine the channel catfish immune system, we performed single-cell RNA sequencing on nuclei isolated from whole spleens, a major lymphoid organ in teleost fish. Libraries were prepared using the 10X Genomics Chromium X with the Next GEM Single Cell 3’ reagents and sequenced on an Illumina sequencer. Each demultiplexed sample was aligned to the Coco_2.0 channel catfish reference assembly, filtered, and counted to generate feature-barcode matrices. From whole spleen samples, outputs were analyzed both individually and as an integrated dataset. The three splenic transcriptome libraries generated an average of 278,717,872 reads from a mean 8,157 cells. The integrated data included 19,613 cells, counts for 20,121 genes, with a median 665 genes/cell. Cluster analysis of all cells identified 17 clusters which were classified as erythroid, hematopoietic stem cells, B cells, T cells, myeloid cells, and endothelial cells. Subcluster analysis was carried out on the immune cell populations. Here, distinct subclusters such as immature B cells, mature B cells, plasma cells, γδ T cells, dendritic cells, and macrophages were further identified. Differential gene expression analyses allowed for the identification of the most highly expressed genes for each cluster and subcluster. This dataset is a rich cellular gene expression resource for investigation of the channel catfish and teleost splenic immunome.

Science & Technology - Other Topics↗

DOE OSTI2025

Reward based optimization of resonance-enhanced piezoresponse spectroscopy

Dynamic spectroscopies in scanning probe microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, electrochemical reactions, and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity and on the high end by the time needed to acquire high-resolution spectra or the propensity for sample degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like dual amplitude resonance tracking in piezoresponse force microscopy that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. Furthermore, this approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Industrial deposition of wavelength-shifting films for liquid argon photon detection systems

The Deep Underground Neutrino Experiment (DUNE) Phase-II Far Detector is considering an approximately 2000 m 2 photon detection system to achieve a target mean light yield of 180 PE/MeV. Meeting this requirement demands scalable, cost-effective, and high-quality wavelength-shifter (WLS) coatings capable of converting 127 nm liquid-argon scintillation light into visible photons with controlled and reproducible optical performance. We report on the successful realization of an industrial physical vapor deposition (PVD) process forp-terphenyl (pTP) coatings, adapted from vacuum deposition techniques developed for OLED display manufacturing, to produce uniform WLS layers on large-area inorganic substrates, a task traditionally challenged by adhesion and uniformity issues at organic-inorganic interfaces. Surface characterization by profilometry and spectroscopic measurements demonstrates edge-region thickness variation below 10% and emission spectra consistent with high-quality pTP reference samples. The industrial process demonstrates reproducibility, scalability, and significantly reduced production time compared to laboratory-based methods, while maintaining optical characteristics consistent with established pTP reference samples. These results establish a viable pathway for mass production of high-performance pTP coatings for DUNE FD3 and future neutrino experiments, from a coating manufacturing and process standpoint. Detector-level performance validation, including quantitative VUV conversion efficiency measurements at 127 nm, is identified as future work.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

ADAPTIVE GROUP TESTING WITH MISMATCHED MODELS

Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups, instead of testing each individual in the population, can be more efficient. In this work, we consider noisy adaptive group testing design with specific test sensitivity and specificity that select the optimal group given previous test results based on pre-selected utility function. As in prior studies on group testing, we model this problem as a sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the groups for each test. We analyze the required number of group tests when using the updated posterior on the infection status and the corresponding Mutual Information (MI) as our utility function for selecting new groups. More importantly, we study how the potential bias on the ground-truth noise of group tests may affect the group testing sample complexity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Comparison of fluence-rate determination techniques for the Advanced Test Reactor-Critical Facility

A technique using small, enriched uranium-aluminum alloy monitors to measure fission rates in fuel plate assembly reactors was developed at the Idaho National Laboratory Radiation Measurements Laboratory in support of Advanced Test Reactor Critical (ATR-C) Facility irradiations. Previous measurements were performed to determine the correlation between the beta-particle activity and the fission rate in the U-Al monitors following irradiation. The American Society for Testing and Materials (ASTM) has established a standard practice E704 for determining neutron fluence rate by radioactivation of depleted uranium by measuring the concentration of fission-products in irradiated uranium monitors. Rather than measuring the beta-particle decay from fission-products, the decay rate of fission-products is determined by gamma-ray spectroscopy. The feasibility of using a method adapted from the ASTM standard practice E704 was investigated for ATR-C irradiations and compared to the present method by analyzing a subset of 21 monitor wires using both methods. The resulting calculated fission rates agreed with an average standard deviation of 7.45%, within the average combined measurement error of 9.05%. Practical considerations such as expected fluence-rates, sample activity, and counting-time were compared between the two methods and the sources of error for each method were investigated. Quality assurance measurements utilizing the modified ASTM standard method are recommended for future test irradiation, but gross-beta-particle counting remains the most practical solution for ATR-C fluence-rate determination.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Tropical lacustrine sediment microbial community response to an extreme El Niño event

Salinity can influence microbial communities and related functional groups in lacustrine sediments, but few studies have examined temporal variability in salinity and associated changes in lacustrine microbial communities and functional groups. To better understand how microbial communities and functional groups respond to salinity, we examined geochemistry and functional gene amplicon sequence data collected from 13 lakes located in Kiritimati, Republic of Kiribati (2° N, 157° W) in July 2014 and June 2019, dates which bracket the very large El Niño event of 2015–2016 and a period of extremely high precipitation rates. Lake water salinity values in 2019 were significantly reduced and covaried with ecological distances between microbial samples. Specifically, phylum- and family-level results indicate that more halophilic microorganisms occurred in 2014 samples, whereas more mesohaline, marine, or halotolerant microorganisms were detected in 2019 samples. Functional Annotation of Prokaryotic Taxa (FAPROTAX) and functional gene results (nifH, nrfA, aprA) suggest that salinity influences the relative abundance of key functional groups (chemoheterotrophs, phototrophs, nitrogen fixers, denitrifiers, sulfate reducers), as well as the microbial diversity within functional groups. Accordingly, we conclude that microbial community and functional gene groups in the lacustrine sediments of Kiritimati show dynamic changes and adaptations to the fluctuations in salinity driven by the El Niño-Southern Oscillation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Microdiverse bacterial clades prevail across Antarctic wetlands

Antarctica's extreme environmental conditions impose selection pressures on microbial communities. Indeed, a previous study revealed that bacterial assemblages at the Cierva Point Wetland Complex (CPWC) are shaped by strong homogeneous selection. Yet which bacterial phylogenetic clades are shaped by selection processes and their ecological strategies to thrive in such extreme conditions remain unknown. Here, we applied the phyloscore and feature-level βNTI indexes coupled with phylofactorization to successfully detect bacterial monophyletic clades subjected to homogeneous (HoS) and heterogenous (HeS) selection. Remarkably, only the HoS clades showed high relative abundance across all samples and signs of putative microdiversity. The majority of the amplicon sequence variants (ASVs) within each HoS clade clustered into a unique 97% sequence similarity operational taxonomic unit (OTU) and inhabited a specific environment (lotic, lentic or terrestrial). Our findings suggest the existence of microdiversification leading to sub-taxa niche differentiation, with putative distinct ecotypes (consisting of groups of ASVs) adapted to a specific environment. We hypothesize that HoS clades thriving in the CPWC have phylogenetically conserved traits that accelerate their rate of evolution, enabling them to adapt to strong spatio-temporally variable selection pressures. Variable selection appears to operate within clades to cause very rapid microdiversification without losing key traits that lead to high abundance. Variable and homogeneous selection, therefore, operate simultaneously but on different aspects of organismal ecology. The result is an overall signal of homogeneous selection due to rapid within-clade microdiversification caused by variable selection. It is unknown whether other systems experience this dynamic, and we encourage future work evaluating the transferability of our results.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Development and validation of a next-generation sequencing-based multigene assay to predict the prognosis of estrogen receptor-positive, HER2-negative breast cancer

Purpose: Multigene assays provide useful prognostic information regarding hormone receptor (HR)-positive breast cancer. Next-generation sequencing (NGS)-based platforms have numerous advantages including reproducibility and adaptability in local laboratories. Here we aimed to develop and validate an NGS-based multigene assay to predict the distant recurrence risk. Experimental Design: In total, 179 genes including 30 reference genes highly correlated with the 21-gene recurrence score (RS) algorithm were selected from public databases. Targeted RNA-sequencing was performed using 250 and 93 archived breast cancer samples with a known RS in the training and verification sets, respectively, to develop the algorithm and NGS–Prognostic Score (NGS-PS). The assay was validated in 413 independent samples with long-term follow-up data on distant metastasis. Results: In the verification set, the NGS-PS and 21-gene RS displayed 91.4% concurrence (85/93 samples). In the validation cohort of 413 samples, area under the receiver operating characteristic curve plotted using NGS-PS values classified for distant recurrence was 0.76. The best NGS-PS cut-off value predicting distant metastasis was 20. Furthermore, 269 and 144 patients were classified as low- and high-risk patients in accordance with the cut-off. Five- and 10-year estimates of distant metastasis–free survival (DMFS) for low- versus high-risk groups were 97.0% versus 77.8% and 93.2% versus 64.4%, respectively. The age-related HR for distant recurrence without chemotherapy was 9.73 (95% CI, 3.59–26.40) and 3.19 (95% CI, 1.40–7.29) for patients aged ≤50 and >50 years, respectively. Conclusions: The newly developed and validated NGS-based multigene assay can predict the distant recurrence risk in ER-positive, HER2-negative breast cancer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Simulation based inference with domain adaptation for strong gravitational lensing

Simulation based inference leverages machine learning to carry out Bayesian inference in systems with intractable likelihoods. However, transitioning a network trained on simulated data to real data runs the risk of encountering domain shift, leading to performance losses. We attempt to implement domain adaptation into the sbi neural posterior estimation framework using the Maximum Mean Discrepancy as an additional network loss, using masked autoregressive fow (MAF) as our density estimator. We test the network on a set of 400,000 simulated strong gravitational lensing images generated using deeplenstronomy. The source domain is defined as low noise whereas the target domain has a noise profile sampled from experimentally derived DES survey conditions. We find that SBI appears robust against small changes in the data with similar performance on source and target. Moreover, while DA does lead to performance improvements, they are marginal at 6% less inference error.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. Here, we also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

42 ENGINEERING↗

DOE OSTI2020

Surface and bulk characterization of reservoir and cap-rocks: Past, present, and future

Geologic formations provide potentially some of the largest volume capacities for CO 2 storage or sequestration. Potential storage sites can be deep saline aquifers, depleted oil reservoirs, and coal seams, surrounded by sealing layers to prevent CO 2 from leaking. It is therefore critical to understand mechanisms contributing to CO 2 trapping and CO 2 leaks. Both phenomena are governed by reactions at the interfaces of the reservoir and cap-rocks and are controlled by the complex chemistry and pore structures of rocks. Mechanisms at the macroscale are affected by the processes occurring at the nanoscale. This review highlights the necessity of multitechnique, multiscale characterization of rocks and points to the importance of surface analysis and surface science studies. Two shale rocks (seals) from Niobrara and Agardhfjellet formations with complex surface chemistry are used as examples throughout the paper. Typically, evaluation of rocks with x-ray diffraction, thermogravimetric analysis, Rock-Eval pyrolysis, gas adsorption, and electron microscopy combined with energy dispersive x-ray spectroscopy is conducted to provide valuable information about the bulk mineralogy, elemental composition, pore volume, and adsorbed species on the sample surface. These studies are necessary prior to designing surface sensitive experiments with x-ray photoelectron spectroscopy (XPS), guiding both sample preparation and sample analysis. XPS has been widely used to study the surface composition of rocks during the investigations of their fine-scale wettability, and the main findings are highlighted here. This paper also reviews the existing literature on ambient-pressure XPS, which provides new opportunities to study in situ chemical alteration due to interactions with CO 2 and offers recommendations for adapting this technique to study rock-fluid interactions, allowing for the identification of fundamental interactions during CO 2 sequestration and guide selection of formation sites for improved storage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗

DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Effective point-charge analysis of crystal fields: Application to rare-earth pyrochlores and tripod kagome magnets 𝑅 ⁢3⁢ Mg⁢ 2 ⁢Sb⁢ 3⁢ O⁢ 14

An indispensable step to understand collective magnetic phenomena in rare-earth compounds is the determination of spatially anisotropic single-ion properties resulting from spin-orbit coupling and crystal field (CF). The CF Hamiltonian has a discrete energy spectrum—accessible to spectroscopic probes such as neutron scattering—controlled by a number of independent parameters reflecting the point symmetry of the magnetic sites. Determining these parameters in low-symmetry systems is often challenging. Here, we describe a general method to analyze CF excitation spectra using adjustable effective point-charges. We benchmark our method to existing neutron-scattering measurements on pyrochlore rare-earth oxides and obtain a universal point-charge model that describes a large family of related materials. We adapt this model to the newly discovered tripod kagome magnets (𝑅 3 ⁢Mg 2 ⁢Sb 3 ⁢O 14 , 𝑅 = Tb, Ho, Er, Yb) for which we report broadband inelastic neutron-scattering spectra. Analysis of these data using adjustable point-charges yields the CF wave functions for each compound. From this, we calculate thermomagnetic properties that accurately reflect our measurements on powder samples and predict the effective gyromagnetic tensor for pseudospin degrees of freedom—a crucial step to understand the exotic collective properties of these kagome magnets at low temperature. We present further applications of our method to other tripod kagome materials and triangular rare-earth compounds 𝑅⁢MgGaO 4 (𝑅 =Yb, Tm). Overall, this study establishes a widely applicable methodology to predict CF and single-ion properties of rare-earth compounds based on interpretable and adjustable models of effective point charges.

Crystal-field theory↗

DOE OSTI2024

Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures

ABSTRACT The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri . In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used. IMPORTANCE Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum , C. ljungdahlii , and Clostridium kluyveri . We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.

Hill, John D.↗