The ICPMS signal as a Poisson process: a review of basic concepts

Ulianov, Alexey; Müntener, Othmar; Schaltegger, Urs

doi:10.1039/c4ja00319e

Cited by 22 publications

(19 citation statements)

References 114 publications

(263 reference statements)

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“…Phansalkar adapted the Sauvola method to identify low contrast and high contrast positive cells 29 . The large variance in Poisson flicker noise from the ICP-MS 46 results in pixels which will be incorrectly identified as positive signal. This was observed in Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin

Bishop

Westerhausen

Barthélémy

et al. 2021

Sci Rep

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Emerging and promising therapeutic interventions for Duchenne muscular dystrophy (DMD) are confounded by the challenges of quantifying dystrophin. Current approaches have poor precision, require large amounts of tissue, and are difficult to standardize. This paper presents an immuno-mass spectrometry imaging method using gadolinium (Gd)-labeled anti-dystrophin antibodies and laser ablation-inductively coupled plasma-mass spectrometry to simultaneously quantify and localize dystrophin in muscle sections. Gd is quantified as a proxy for the relative expression of dystrophin and was validated in murine and human skeletal muscle sections following k-means clustering segmentation, before application to DMD patients with different gene mutations where dystrophin expression was measured up to 100 µg kg−1 Gd. These results demonstrate that immuno-mass spectrometry imaging is a viable approach for pre-clinical to clinical research in DMD. It rapidly quantified relative dystrophin in single tissue sections, efficiently used valuable patient resources, and may provide information on drug efficacy for clinical translation.

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Section: Discussionmentioning

confidence: 99%

Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin

Bishop

Westerhausen

Barthélémy

et al. 2021

Sci Rep

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“…These technologies each detect a random fraction of the total molecular content of every individual cell. This relationship between the measurement signal and cellular analyte abundance has been frequently modeled by a binomial distribution Bi ( X , p ) whose success probability p corresponds to the capture efficiency for the analyte present at amount X [42, 43]. We have devised an estimator to subtract the misleading measurement noise component to provide the reactionet lasso with the appropriate noise-correct empirical moment estimates for structure learning.…”

Section: Methodsmentioning

confidence: 99%

“…We assume that the capture efficiency p of the single cell instrument is known [42, 43] and estimates the empirical moments of X on the basis of the empirical moments of X obs by solving the above equations for the respective moment of X . The resulting moment estimates are then used in the regression procedure described above to perform structure learning.…”

Section: Methodsmentioning

confidence: 99%

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

2016

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Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

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“…Potential sources of overdispersion for LA‐ICPMS measurements are discussed in more detail in Ulianov et al . [].…”

Section: Principles Of La‐icpms Data Handlingmentioning

confidence: 99%

“…The solution used in the fission track [Galbraith, 2005] and (U-Th)/He [Vermeesch, 2010] communities is to include an extra source of uncertainty called an overdispersion, a variance term added to each measurement to account for the excess scatter in the data. Potential sources of overdispersion for LA-ICPMS measurements are discussed in more detail in Ulianov et al [2015].…”

Section: Weighted Least Squares and Overdispersionmentioning

confidence: 99%

Algorithms and software for U‐Pb geochronology by LA‐ICPMS

McLean

Bowring

Gehrels

2016

Geochem Geophys Geosyst

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The past 15 years have produced numerous innovations in geochronology, including experimental methods, instrumentation, and software that are revolutionizing the acquisition and application of geochronological data. For example, exciting advances are being driven by Laser-Ablation ICP Mass Spectrometry (LA-ICPMS), which allows for rapid determination of U-Th-Pb ages with 10s of micrometer-scale spatial resolution. This method has become the most commonly applied tool for dating zircons, constraining a host of geological problems. The LA-ICPMS community is now faced with archiving these data with associated analytical results and, more importantly, ensuring that data meet the highest standards for precision and accuracy and that interlaboratory biases are minimized. However, there is little consensus with regard to analytical strategies and data reduction protocols for LA-ICPMS geochronology. The result is systematic interlaboratory bias and both underestimation and overestimation of uncertainties on calculated dates that, in turn, decrease the value of data in repositories such as EarthChem, which archives data and analytical results from participating laboratories. We present free open-source software that implements new algorithms for evaluating and resolving many of these discrepancies. This solution is the result of a collaborative effort to extend the U-Pb_Redux software for the ID-TIMS community to the LA-ICPMS community. Now named ET_Redux, our new software automates the analytical and scientific workflows of data acquisition, statistical filtering, data analysis and interpretation, publication, community-based archiving, and the compilation and comparison of data from different laboratories to support collaborative science.

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The ICPMS signal as a Poisson process: a review of basic concepts

Cited by 22 publications

References 114 publications

Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin

Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

Algorithms and software for U‐Pb geochronology by LA‐ICPMS

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