Zhe Liu , Shangdong Yang , Shumei Xie , Depan Cao , Wen Xi , Yang Xiao , Xin Xu , Zhonglin Wang , Lifeng Li , Jian Hu , Xiaoqin Wang
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引用次数: 0
Abstract
Objectives
Lower respiratory tract infections pose significant clinical challenges due to their high morbidity and mortality rates. While metagenomic next-generation sequencing (mNGS) has emerged as a promising diagnostic tool, its sensitivity is often compromised by host DNA contamination that overwhelms microbial signals. Selective host DNA depletion through cell lysis effectively reduces host DNA; however, it has an impact on microorganisms with relatively thin cell walls, and samples with low host content may introduce more environment or reagent-derived microbial contamination, interfering the detection results. Methods for determining host DNA depletion based on sample type, sample characteristics or using spike-in controls to monitor sensitivity do not fully consider the potential limitations of host depletion technology on microbial detection, nor do they evaluate the possible significant impact on detection efficiency. This study aimed to develop a pre-analytical method for accurate host DNA content assessment.
Methods
We established a cell-counting-based method for precise cellular content measurement in clinical bronchoalveolar lavage fluid (BALF) samples. The protocol involved: (1) evaluating the linearity and robustness of cell-counting dyes in BALF samples with varying characteristics, (2) assessing the correlation between cell counts and extracted nucleic acid mass, (3) investigating cellular counting thresholds for host depletion in clinical BALF analysis, and (4) implementing the optimized cell-counting method in clinical mNGS testing to guide selective-lysis treatment.
Results
Acridine orange/propidium iodide (AO/PI) staining demonstrated superior performance compared to trypan blue and 4',6-diamidino-2-phenylindole (DAPI), particularly in turbid and bloody BALF samples. Implementing a host depletion threshold at 1 × 106 cell counts significantly improves pathogen detection rates in high host background samples, while effectively preserving the detection sensitivity for pathogens in moderate and low host background samples.
Conclusions
Our findings demonstrate that cell counting serves as a reliable pre-analytical tool for determining optimal selective-lysis treatment in BALF mNGS testing, enhancing diagnostic accuracy while preserving pathogen integrity.
期刊介绍:
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.