Recommendations for using artificial intelligence in clinical flow cytometry.

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Rapid switching and durable on-chip spark-cavitation-bubble cell sorter.

Precise and high-speed sorting of individual target cells from heterogeneous populations plays an imperative role in cell research. Although the conventional fluorescence-activated cell sorter (FACS) is capable of rapid and accurate cell sorting, it occupies a large volume of the instrument and inherently brings in aerosol generation as well as cross-contamination among samples. The sorting completed in a fully enclosed and disposable microfluidic chip has the potential to eliminate the above concerns. However, current microfluidic cell sorters are hindered by the high complexities of the fabrication procedure and the off-chip setup. In this paper, a spark-cavitation-bubble-based fluorescence-activated cell sorter is developed to perform fast and accurate sorting in a microfluidic chip. It features a simple structure and an easy operation. This microfluidic sorter comprises a positive electrode of platinum and a negative electrode of tungsten, which are placed on the side of the main channel. By applying a high-voltage discharge on the pair of electrodes, a single spark cavitation bubble is created to deflect the target particle into the downstream collection channel. The sorter has a short switching time of 150 µs and a long lifespan of more than 100 million workable actions. In addition, a novel control strategy is proposed to dynamically adjust the discharge time to stabilize the size of the cavitation bubble for continuous sorting. The dynamic control of continuously triggering the sorter, the optimal delay time between fluorescence detection and cell sorting, and a theoretical model to predict the ideal sorting recovery and purity are studied to improve and evaluate the sorter performance. The experiments demonstrate that the sorting rate of target particles achieves 1200 eps, the total analysis throughput is up to 10,000 eps, the particles sorted at 4000 eps exhibit a purity greater than 80% and a recovery rate greater than 90%, and the sorting effect on the viability of HeLa cells is negligible.

Monocyte subtype counts are associated with 10-year cardiovascular disease risk as determined by the Framingham Risk Score among subjects of the LIFE-Adult study.

Coronary heart disease, an inflammatory disease, is the leading cause of death globally. White blood cell counts (including monocytes) are easily available biomarkers of systemic inflammation. Monocyte subtypes can be measured by flow cytometry and classified into classical (CD14high, CD16neg), intermediate (CD14high, CD16+) and non-classical (CD14+, CD16high) with distinct functional properties. The goal of this study was to investigate the association of monocyte total count and its subtypes with cardiovascular risk groups defined by the Framingham Risk Score, which is used to estimate the 10-year risk of developing myocardial infarction or predict mortality following coronary heart disease. We also aimed to investigate whether monocyte counts are associated with relevant cardiovascular risk factors not included in the Framingham Risk Score, such as carotid atherosclerotic plaque and intima-media thickness. Our data came from the LIFE-Adult study, a population-based cohort study of 10,000 randomly selected participants in Leipzig, Germany. Data was gathered using self-administered questionnaires and physical examinations. Carotid plaques and intima-media thickness were measured using carotid artery sonography. Monocyte subtypes in blood were determined by 10-color flow cytometry for a total of 690 individuals. In a multivariate regression analysis adjusting for the risk factors BMI, intima-media thickness, presence of carotid plaques and diabetes mellitus, monocyte subtypes and total count were found to be significantly associated with the dichotomized Framingham Risk Score (≥10% versus <10%): Odds ratios [95% confidence interval] for monocyte subtypes: classical: 11.19 [3.79-34.26]; intermediate: 2.27 [1.11-4.71]; non-classical: 4.18 [1.75-10.20]; total: 14.59 [4.61-47.95]. In absence of prospective data, the FRS was used as a surrogate for CHD. Our results indicate that monocyte counts could provide useful predictive value for cardiovascular disease risk.