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Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA quantities, measured by reverse transcription quantitative PCR (RT-qPCR), have been proposed to stratify clinical risk or determine analytical performance targets. We investigated reproducibility and how setting diagnostic cutoffs altered the clinical sensitivity of coronavirus disease 2019 (COVID-19) testing. Methods Quantitative SARS-CoV-2 RNA distributions [quantification cycle (Cq) and copies/mL] from more than 6000 patients from 3 clinical laboratories in United Kingdom, Belgium, and the Republic of Korea were analyzed. Impact of Cq cutoffs on clinical sensitivity was assessed. The June/July 2020 INSTAND external quality assessment scheme SARS-CoV-2 materials were used to estimate laboratory reported copies/mL and to estimate the variation in copies/mL for a given Cq. Results When the WHO-suggested Cq cutoff of 25 was applied, the clinical sensitivity dropped to about 16%. Clinical sensitivity also dropped to about 27% when a simulated limit of detection of 106 copies/mL was applied. The interlaboratory variation for a given Cq value was >1000 fold in copies/mL (99% CI). Conclusion While RT-qPCR has been instrumental in the response to COVID-19, we recommend Cq (cycle threshold or crossing point) values not be used to set clinical cutoffs or diagnostic performance targets due to poor interlaboratory reproducibility; calibrated copy-based units (used elsewhere in virology) offer more reproducible alternatives. We also report a phenomenon where diagnostic performance may change relative to the effective reproduction number. Our findings indicate that the disparities between patient populations across time are an important consideration when evaluating or deploying diagnostic tests. This is especially relevant to the emergency situation of an evolving pandemic.

Morphological cell analysis offers a means of identification and classification of key morphological measurement parameters linked to cell bioactivity and cell health and, as such, it is of great interest to academic and industrial research sectors. Widespread adoption of this approach has yet to occur, partially due to the lack of alignment in analysis methodologies and output metrics, limiting data comparability. Work within the cell metrology and wider multidisciplinary community aims to reduce data variability through the improved alignment of image acquisition and analysis methodologies. Furthermore, to improve data comparability, research has also focused on the identification of a minimal set of morphological measurands, often termed critical quality attributes (CQAs), which are traceable to standardised (SI) units of measurement. Whilst efforts in defining CQAs have progressed significantly for healthcare applications, there are still numerous measurement challenges associated with image analysis of cultured cells due, in part, to their complex heterogenous nature. This review evaluates the various automated image analysis tools developed for morphological analysis of four commonly considered cell morphological features: the nucleus, actin cytoskeleton, mitochondria, and the cell membrane. The measurement methodologies and outputs from each tool have been evaluated and coinciding outputs have been highlighted as potential CQAs.

Viral vectors are agents enabling gene transfer and genome editing and have widespread utility across the healthcare and biotechnology sectors. In January 2023, the International Bureau for Weights and Measures’ Consultative Committee for Amount of Substance (CCQM) held a workshop on Metrology for Viral systems as molecular tools. The workshop brought together international leaders from across regulatory, industry, government science, and metrology sectors to better understand key challenges for the community: Exploring current limitations in the measurement of virus-derived, virus-based, and virus-like systems in terms of quantification and characterisation; surveying the state-of-the-art in analytical methods and reference material provision for these entities; and initiating a dialog for the strategic development and implementation of suitable standardisation approaches for this sector. This article presents the workshop background and rationale, presentation summaries, conclusions, and recommendations.

Background In response to the global COVID-19 pandemic, many in vitro diagnostic (IVD) tests for SARS-CoV-2 have been developed. Given the urgent clinical demand, researchers must balance the desire for precise estimates of sensitivity and specificity against the need for rapid implementation. To complement estimates of precision used for sample size calculations, we aimed to estimate the probability that an IVD will fail to perform to expected standards after implementation, following clinical studies with varying sample sizes. Methods We assumed that clinical validation study estimates met the ‘desirable’ performance (sensitivity 97%, specificity 99%) in the target product profile (TPP) published by the Medicines and Healthcare products Regulatory Agency (MHRA). To estimate the real-world impact of imprecision imposed by sample size we used Bayesian posterior calculations along with Monte Carlo simulations with 10,000 independent iterations of 5,000 participants. We varied the prevalence between 1 and 15% and the sample size between 30 and 2,000. For each sample size, we estimated the probability that diagnostic accuracy would fail to meet the TPP criteria after implementation. Results For a validation study that demonstrates ‘desirable’ sensitivity within a sample of 30 participants who test positive for COVID-19 using the reference standard, the probability that real-world performance will fail to meet the ‘desirable’ criteria is 10.7–13.5%, depending on prevalence. Theoretically, demonstrating the 'desirable' performance in 90 positive participants would reduce that probability to below 5%. A marked reduction in the probability of failure to hit ‘desirable’ specificity occurred between samples of 100 (19.1–21.5%) and 160 (4.3–4.8%) negative participants. There was little further improvement above sample sizes of 160 negative participants. Conclusion Based on imprecision alone, small evaluation studies can lead to the acceptance of diagnostic tests which are likely to fail to meet performance targets when deployed. There is diminished return on uncertainty surrounding an accuracy estimate above a total sample size of 250 (90 positive and 160 negative).

Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

International audience

Fractionation data for cadmium in tobacco products, as obtained by sequential leaching of cadmium species with ICP-MS/MS analysis, and separately by X-ray absorption near edge structure (XANES) are presented here for the first time. The total amount of cadmium found in 3R4F cigarette cut tobacco was 1526 ± 42 μg kg−1, of which 5% was found in the smoke under ISO smoking conditions. XANES analysis showed that Cd in tobacco, cigarette smoke and ash was present in the + 2 oxidation state. Examination of the gas-particle partitioning of smoke cadmium suggests that Cd in mainstream smoke is best viewed as semi-volatile, existing in both particulate and gas phases. Sequential extraction of trapped tobacco smoke was carried out to get a deeper insight into the chemistry of cigarette smoke cadmium compounds. Consecutive extractions with ultrapure water, dilute (1%) nitric acid and 10% nitric acid led to extraction of a total amount of Cd which agreed with that obtained after microwave digestion of the whole sample, suggesting that cadmium was quantitatively leachable into aqueous/acidic solutions. Most Cd (~ 90% of the total Cd in the smoke condensate) was extracted into dilute nitric acid (likely as CdO, Cd(OH)2 and CdCO3) with a minor percentage (3%) extracted into water (likely as CdCl2) and in 10% nitric acid (likely as CdS). Extraction of trapped mainstream smoke with pentane, followed by ICP-MS/MS analysis, to examine the possible presence of organocadmium in 3R4F tobacco smoke, did not show the presence of organocadmium compounds above the method LOQ (2 μg kg−1), possibly due to their reactivity under the experimental conditions. The high selectivity with sufficient sensitivity achieved by ICP-MS/MS was invaluable to quantify Cd (at low μg kg−1levels) simultaneously with sulphur and chlorine in the tobacco smoke fractions of complex matrix. The cadmium chemistry in the smoke, identified in this study, is consistent with both relatively high lung absorption and DNA binding; both potentially important factors for disease progression in smokers.