Investigation of the quasi‐simultaneous arrival (QSA) effect on a CAMECA IMS 7f‐GEO

Jones, Clive M.; Fike, David A.; Peres, P.

doi:10.1002/rcm.7828

Cited by 15 publications

(19 citation statements)

References 9 publications

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“…However, it is not possible to determine primary currents <50 pA very accurately on the 7f‐GEO instrument. Therefore, the ratio of the QSA coefficient (β) to the primary ion flux ( J ) was used to facilitate the correction . β/ J values were determined for each session, via data obtained from the internal standard grains, using the relationship:

R_{\exp} = R_{cor} + ((), normalβ / J) \times {}^{34}{normalS}_{\exp}

where R exp and 34 S exp are the dead‐time corrected 34 S/ 32 S ratio and 34 S count rate, respectively, and R cor is the QSA‐corrected 34 S/ 32 S ratio.…”

Section: Methodsmentioning

confidence: 97%

“…Raw isotope ratios for each grain were calculated by taking the mean 34 S − / 32 S − ion count ratio of a central area of the grain over the multiple analysis planes. Various corrections were applied to the data, including a dead‐time correction, an interpolation of 34 S − counts to align in time with those on 32 S − , and a quasi‐simultaneous arrival (QSA) effect correction . The magnitude of the QSA undercounting correction is proportional to the instrument transmission, i.e., the number of secondary ions reaching the detector per incident primary ion.…”

Section: Methodsmentioning

confidence: 98%

“…After using Raman microprobe analysis (1 mW laser power and 50× objective) 56 and optical microscopy (50× objective, plane-polarized light) to confirm the presence and mineralogy of analyte at the surface of the polished epoxy pucks, the pucks were coated with~50-nm-thick Au to ensure conductivity for SIMS analysis. 57 The magnitude of the QSA undercounting correction is proportional to the instrument transmission, i.e., the number of secondary ions reaching the detector per incident primary ion.…”

Section: Extraction Of Microcrystalline Pyrite From Geologic Samplesmentioning

confidence: 99%

“…Therefore, the ratio of the QSA coefficient (β) to the primary ion flux (J) was used to facilitate the correction. 57 β/J values were determined for each session, via data obtained from the internal standard grains, using the relationship:…”

Section: Bulk Sulfur Isotope Analysesmentioning

confidence: 99%

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Sulfur isotope analysis of microcrystalline iron sulfides using secondary ion mass spectrometry imaging: Extracting local paleo‐environmental information from modern and ancient sediments

Bryant

Jones

Raven

et al. 2019

Rapid Comm Mass Spectrometry

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Rationale Sulfur isotope ratio measurements of bulk sulfide from marine sediments have often been used to reconstruct environmental conditions associated with their formation. In situ microscale spot analyses by secondary ion mass spectrometry (SIMS) and laser ablation multiple‐collector inductively coupled plasma mass spectrometry (LA‐MC‐ICP‐MS) have been utilized for the same purpose. However, these techniques are often not suitable for studying small (≤10 μm) grains or for detecting intra‐grain variability. Methods Here, we present a method for the physical extraction (using lithium polytungstate heavy liquid) and subsequent sulfur isotope analysis (using SIMS; CAMECA IMS 7f‐GEO) of microcrystalline iron sulfides. SIMS sulfur isotope ratio measurements were made via Cs+ bombardment of raster squares with sides of 20–130 μm, using an electron multiplier (EM) detector to collect counts of 32S− and 34S− for each pixel (128 × 128 pixel grids) for between 20 and 960 cycles. Results The extraction procedure did not discernibly alter pyrite grain‐size distributions. The apparent inter‐grain variability in 34S/32S in 1–4 μm‐sized pyrite and marcasite fragments from isotopically homogeneous hydrothermal crystals was ~ ±2‰ (1σ), comparable with the standard error of the mean for individual measurements (≤ ±2‰, 1σ). In contrast, grain‐specific 34S/32S ratios in modern and ancient sedimentary pyrites and marcasites can have inter‐ and intra‐grain variability >60‰. The distributions of intra‐sample isotopic variability are consistent with bulk 34S/32S values. Conclusions SIMS analyses of isolated iron sulfide grains yielded distributions that are isotopically representative of bulk 34S/32S values. Populations of iron sulfide grains from sedimentary samples record the evolution of the S‐isotopic composition of pore water sulfide in their S‐isotopic compositions. These data allow past local environmental conditions to be inferred.

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R_{\exp} = R_{cor} + ((), normalβ / J) \times {}^{34}{normalS}_{\exp}

where R exp and 34 S exp are the dead‐time corrected 34 S/ 32 S ratio and 34 S count rate, respectively, and R cor is the QSA‐corrected 34 S/ 32 S ratio.…”

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 98%

Section: Extraction Of Microcrystalline Pyrite From Geologic Samplesmentioning

confidence: 99%

Section: Bulk Sulfur Isotope Analysesmentioning

confidence: 99%

See 2 more Smart Citations

Sulfur isotope analysis of microcrystalline iron sulfides using secondary ion mass spectrometry imaging: Extracting local paleo‐environmental information from modern and ancient sediments

Bryant

Jones

Raven

et al. 2019

Rapid Comm Mass Spectrometry

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“…Each region of interest (ROI; i.e., one individual cell) was selected using WinImage software, and all count rates were exported for all ROIs for all cycles. Electron multiplier dead time and quasi-simultaneous arrival corrections (40) were applied as necessary. For each field of view, isotope ratios for all ROIs were plotted against cycle number.…”

Section: Single-cell Metabolism During Diauxic Growthmentioning

confidence: 99%

Direct Observation of the Dynamics of Single-Cell Metabolic Activity during Microbial Diauxic Growth

McClelland

Jones²,

Chubiz

et al. 2020

mBio

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Population-level analyses are rapidly becoming inadequate to answer many of biomedical science and microbial ecology’s most pressing questions. The role of microbial populations within ecosystems and the evolutionary selective pressure on individuals depend fundamentally on the metabolic activity of single cells. Yet, many existing single-cell technologies provide only indirect evidence of metabolic specialization because they rely on correlations between transcription and phenotype established at the level of the population to infer activity. In this study, we take a top-down approach using isotope labels and secondary ion mass spectrometry to track the uptake of carbon and nitrogen atoms from different sources into biomass and directly observe dynamic changes in anabolic specialization at the level of single cells. We investigate the classic microbiological phenomenon of diauxic growth at the single-cell level in the model methylotroph Methylobacterium extorquens. In nature, this organism inhabits the phyllosphere, where it experiences diurnal changes in the available carbon substrates, necessitating an overhaul of central carbon metabolism. We show that the population exhibits a unimodal response to the changing availability of viable substrates, a conclusion that supports the canonical model but has thus far been supported by only indirect evidence. We anticipate that the ability to monitor the dynamics of anabolism in individual cells directly will have important applications across the fields of ecology, medicine, and biogeochemistry, especially where regulation downstream of transcription has the potential to manifest as heterogeneity that would be undetectable with other existing single-cell approaches. IMPORTANCE Understanding how genetic information is realized as the behavior of individual cells is a long-term goal of biology but represents a significant technological challenge. In clonal microbial populations, variation in gene regulation is often interpreted as metabolic heterogeneity. This follows the central dogma of biology, in which information flows from DNA to RNA to protein and ultimately manifests as activity. At present, DNA and RNA can be characterized in single cells, but the abundance and activity of proteins cannot. Inferences about metabolic activity usually therefore rely on the assumption that transcription reflects activity. By tracking the atoms from which they build their biomass, we make direct observations of growth rate and substrate specialization in individual cells throughout a period of growth in a changing environment. This approach allows the flow of information from DNA to be constrained from the distal end of the regulatory cascade and will become an essential tool in the rapidly advancing field of single-cell metabolism.

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A statistical model of secondary ion emission and attenuation clarifies disparities in quasi‐simultaneous arrival coefficients measured with secondary ion mass spectrometry

Jones

Fike

2020

Rapid Comm Mass Spectrometry

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Rationale Secondary ion mass spectrometry data collected using electron multiplier detectors are subject to a correction for the quasi‐simultaneous arrival (QSA) effect. Published Poisson statistical models indicate that the QSA coefficients, β, should have an invariant value of 0.5, whereas, with one exception, published experimental determinations vary between 0.6 and 1.0, with a mean value of 0.75. Methods We developed a more complex model, combining both ion emission and attenuation, that predicts the observed range in measured β and elucidates the mechanism of secondary ion formation. For a given aperture setting, any secondary ion has an equal probability of successful transit to the electron multiplier. Binomial statistics can model pass–fail aperture attenuation but require probability distributions of the quasi‐simultaneously emitted (QSE) ion tally, per primary ion, as input. Assuming (a) that each primary ion impact results in 0, 1, 2,… secondary ion emissions, randomly, with an average Ks and (b) that there is finite probability (P2) of a further emission process dependent on Ks, the required QSE probability distributions were generated via a combined Poisson–binomial statistical model. Results The value of β was output as a function of Ks and P2. For values of P2 > 0 and any value of Ks, β always exceeds 0.5. As P2 → 0, β → 0.5; for values of increasing P2 > 0.5 and decreasing Ks < 0.5, β → >1. Conclusions Were the emission of one ion not to influence the probability of the formation of a second (i.e. model output for P2 = 0), β should always be 0.5. Yet measurements have never reported this value. Consequently, assuming that published β values are correct, emissions of QSE secondary ions do not occur independently, and it may be inferred that there are linked mechanisms of secondary ion formation as shown here.

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Investigation of the quasi‐simultaneous arrival (QSA) effect on a CAMECA IMS 7f‐GEO

Cited by 15 publications

References 9 publications

Sulfur isotope analysis of microcrystalline iron sulfides using secondary ion mass spectrometry imaging: Extracting local paleo‐environmental information from modern and ancient sediments

Sulfur isotope analysis of microcrystalline iron sulfides using secondary ion mass spectrometry imaging: Extracting local paleo‐environmental information from modern and ancient sediments

Direct Observation of the Dynamics of Single-Cell Metabolic Activity during Microbial Diauxic Growth

A statistical model of secondary ion emission and attenuation clarifies disparities in quasi‐simultaneous arrival coefficients measured with secondary ion mass spectrometry

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