Application value and challenges associated with plasma cell-free DNA metagenomic sequencing technology in the diagnosis of infections in patients with hematological disorders

Abstract

In patients with hematological disorders, the high risk of complex infections caused by immune dysfunction and intensive therapies poses a major challenge to the use of conventional microbiological tests (CMTs). Plasma cell-free DNA (cfDNA) metagenomic next-generation sequencing (mNGS) has emerged as a revolutionary noninvasive tool that enables unbiased, broad-spectrum, and rapid pathogen identification directly from blood samples. This review summarizes the core applications of plasma cfDNA mNGS in patients with hematological disorders, including the diagnosis of febrile neutropenia, bloodstream infections, focal infections, and infections caused by uncommon/fastidious pathogens. It highlights the advantages of this technology in overcoming antibiotic interference, enabling early detection, and providing diagnostic value in cases without clear infection foci or when invasive sampling is not feasible. This review further discusses how China has facilitated the widespread adoption of this technology through a localized application model, cost reduction, and the development of clinically relevant interpretation models. Nevertheless, challenges remain, such as lower sensitivity than site-specific specimens in focal infections, and the difficulty in predicting antimicrobial resistance (AMR) on the basis of cfDNA mNGS. Future developmental directions should focus on technical optimization (eg, combined plasma cell-fraction testing), quality assurance and quality control management, multidimensional data integration (eg, host immune response analysis), artificial intelligence (AI)–assisted interpretation, and cost reduction through technology popularization and insurance coverage. These efforts will advance cfDNA mNGS from a pathogen detection tool toward an intelligent clinical decision-support platform, ultimately improving the diagnostic accuracy and clinical outcomes of hematological patients with infections.

1. INTRODUCTION

Patients with hematological disorders, such as leukemia, lymphoma, and aplastic anemia, have a considerably higher risk of infections due to underlying immune dysfunction, chemotherapy, hematopoietic stem cell transplantation (HSCT), and associated immunosuppressive therapy.^1–4 Infections in this population exhibit distinct clinical characteristics, including atypical symptoms, occult infection foci, and a high incidence of mixed infections or infections caused by fastidious pathogens.^5–8 These features pose challenges to timely and accurate diagnosis, and delayed or inappropriate antimicrobial therapy can lead to a marked increase in mortality rates.

Conventional microbiological tests (CMTs), such as culture and smear microscopy, show substantial limitations in diagnosing infections in patients with hematological disorders. Their prolonged turnaround time (TAT), low sensitivity, especially after empiric antibiotic therapy, and narrow, hypothesis-driven detection spectrum often delay critical therapeutic decisions.^9–14 In contrast, metagenomic next-generation sequencing (mNGS) offers a revolutionary, hypothesis-free approach that can enable unbiased detection of a broad range of pathogens from a single test.^15,16 For patients with hematological conditions, plasma is the most widely used specimen for mNGS because of its high accessibility. Because microbial nucleic acids in blood exist predominantly in a cell-free form, cell-free DNA (cfDNA) mNGS is preferred. Its rapid TAT, as well as its advantages in detecting rare pathogens and mixed infections, have led to its rapid adoption for the diagnosis of infection in this vulnerable population.^5,17–19 Despite these promising results, cfDNA mNGS still faces challenges such as insufficient specificity, high costs, and difficulties in result interpretation.^20,21 However, ongoing technological optimizations and deeper integration of clinical information have progressively alleviated these limitations, contributing to the increasing maturity of its practical application. This review aimed to systematically discuss the clinical utility, limitations, and optimization strategies of plasma cfDNA mNGS in the diagnosis of infections in patients with hematological disorders, providing a reference for its rational clinical use.

2. PRIMARY APPLICATION SCENARIOS

Noninvasive plasma cfDNA mNGS has been widely applied in the diagnosis of various infections in patients with hematological disorders, including those with bacterial, viral, fungal, and parasitic infections. This is particularly valuable in the following scenarios (Fig. 1A).

Figure 1.

Plasma cfDNA mNGS in patients with hematological disorders: applications, implementation, and report interpretation. BSI = bloodstream infections, cfDNA = cell-free DNA, mNGS = metagenomic next-generation sequencing, NTM = nontuberculous mycobacteria, TAT = turnaround time.

2.1. Neutropenic fever

Over 80% of patients may develop febrile neutropenia due to underlying diseases, chemotherapy, immunotherapy, and transplantation.⁶ A defining characteristic of this condition is the frequent absence of a localized infection focus, since the inflammatory response is blunted by severe neutropenia. Consequently, the findings of clinical evaluation and imaging assessments in such cases are often non-specific.^5,22 Furthermore, polymicrobial infections are common, involving complex combinations of bacteria (including multidrug-resistant strains), fungi, and viruses.²² The pathogen spectrum in these cases is exceptionally broad, encompassing not only common bacteria but also fastidious organisms such as Mycobacterium tuberculosis, Legionella spp., and invasive fungi such as Aspergillus spp. and Mucorales.^5,23,24 Furthermore, the widespread use of empirical broad-spectrum antibiotics further reduces the sensitivity of culture-based tests, representing a major barrier to timely and accurate identification of pathogens that directly influences patient outcomes.²⁵

The core value of plasma cfDNA mNGS in managing febrile neutropenia can be summarized by 3 key advantages: Comprehensive and rapid pathogen detection: cfDNA mNGS provides an unbiased approach that can increase the pathogen detection rate (positivity rate, 67.6%–84.9%) in comparison with CMT,^7,25–27 especially for fungal and polymicrobial infections often missed by CMT.^27–32 In regions like China with widespread access, the short TAT of cfDNA mNGS (24–48 hours) facilitates faster treatment decisions in comparison with culture-based methods.^24,25,29 Lower influence of antibiotics: Unlike blood cultures, the sensitivity of cfDNA mNGS is not drastically reduced by prior antibiotic administration.²⁷ This was demonstrated in our previous study, which reported a cfDNA mNGS positivity rate of 81.5% without and 88.9% with antimicrobial use within 24 hours, while blood culture positivity declined from 23.8% to 13.5%.²⁴ Diagnosis of non-localized infections: Patients with febrile neutropenia frequently lack clear infection foci. cfDNA mNGS acts as a “liquid biopsy” by detecting microbial cfDNA in the bloodstream from deep-seated infections, enabling diagnosis where CMT is inconclusive (eg, abdominal abscesses, pneumonia).⁵

2.2. Bloodstream infections

Patients with hematological disorders constitute a high-risk population for bloodstream infections (BSI). The incidence of BSI during neutropenia can be as high as 20% to 30%.²² The pathogen distribution in BSI is predominated by Gram-negative bacteria, and the prevalence of carbapenem-resistant strains has intensified therapeutic challenges.^11,33–35 Furthermore, fungal BSI is also observed, with Candida spp. being the most frequently reported.^36–39 Infections caused by Fusarium spp. and Trichosporon spp. have also been documented.^31,40

For BSI in patients with hematological disorders, the key advantages of plasma cfDNA mNGS, beyond its higher detection rate and shorter TAT,²⁴ are as follows: Prolonged detectable window with prognostic value: In comparison with blood culture, cfDNA mNGS can detect microbial nucleic acids for a longer duration, and this sustained detection correlates with the severity of infection, providing an extended testing window for diagnosis.^5,41,42 A study by Xu et al¹⁸ revealed that the median duration of pathogen detection by cfDNA mNGS was significantly longer than that by blood culture (4 vs 1 day, p < .001). Persistent cfDNA mNGS positivity was independently associated with an increased risk of septic shock, prolonged fever, and a higher 28-day mortality. Moreover, a positivity duration of ≥3 days was identified as an independent risk factor for septic shock.¹⁸ These findings indicate that serial cfDNA mNGS monitoring can effectively reflect microbial clearance, predict adverse infection outcomes, and help assess the effectiveness of anti-infective treatment. Early prediction of BSI: In some infected patients, nucleic acids of BSI-causing pathogens can be detected using cfDNA mNGS prior to blood culture, enabling more efficient early diagnosis.⁵

2.3. Focal infections: a vital diagnostic alternative

In patients with hematological disorders, focal infections are characterized by an insidious onset, atypical symptoms, and a propensity for deep-seated involvement.^19,43 Profound neutropenia, functional immune deficits, and T-cell immunosuppression render these individuals highly vulnerable to infections by invasive fungi (eg, Aspergillus spp. and Candida spp.), viruses (eg, cytomegalovirus [CMV], respiratory viruses), and fastidious pathogens. The lungs are the most common site, and infections of the upper respiratory tract, gastrointestinal tract, oral mucosa, perianal region, and the skin and soft tissues are also frequently observed.^5,44–46

Plasma cfDNA mNGS is a vital noninvasive tool for diagnosing focal infections, especially when direct sampling from the infection site is impractical. Its utility is particularly pronounced in immunocompromised patients with hematological disorders, and it offers the following key advantages for detecting invasive fungal infections (IFIs)^47,48: Safety and noninvasiveness: In patients with coagulopathies, invasive procedures like the collection of bronchoalveolar lavage fluid (BALF) carry considerable risks. cfDNA mNGS, which only requires a blood sample, is a safer alternative. Early diagnosis: cfDNA mNGS often detects pathogenic nucleic acids during the early stages of infection. Our study on mucormycosis demonstrated that in comparison with CMT, cfDNA mNGS can advance the diagnosis of infection by 12 days.⁴⁹ Advantages for the detection of specific fungal species: For deep-seated mold infection, cfDNA mNGS can noninvasively identify the causative pathogen from circulating cfDNA,⁵⁰ with the sensitivity for non-Aspergillus invasive mold infections being as high as 79.2% (compared to 31.4% for Aspergillus spp.).⁵¹ For invasive yeast infections, invasive candidiasis may present as abscesses in organs (such as the liver, spleen, kidneys, and brain).⁵² Existing studies have shown that the positivity rate of blood cultures is lower than that of molecular assays.⁵² Although large-cohort studies of cfDNA mNGS for invasive candidiasis in patients with hematological disorders are still lacking, detection of Candida spp. by cfDNA mNGS in patients with suspected deep-seated candidiasis can support the diagnosis of invasive candidiasis.⁵³

2.4. Infections with uncommon pathogens

The core value of plasma cfDNA mNGS in managing infections with uncommon pathogens is demonstrated by the following features: Overcoming the cultivation barrier for fastidious organisms: For bacterial pathogens that are difficult or impossible to isolate by routine culture (eg, Nocardia spp., Legionella spp., and nontuberculous mycobacteria), mNGS provides a rapid route to definitive diagnosis by direct detection of nucleic acid fragments, shortening the diagnostic TAT.^5,54,55 Unbiased detection and broad coverage: mNGS simultaneously detects fungi, viruses, and parasites in addition to bacteria.^21,56 This enables direct identification of uncommon pathogens that are typically not considered or detectable in standard diagnostic workflows, such as Toxoplasma gondii, adenoviruses, and Pneumocystis jirovecii.^57–60 Guiding targeted therapy: Accurate identification of the uncommon pathogen to the species or genus level directly informs the selection of effective narrow-spectrum antibiotics or targeted antimicrobials, preventing the overuse of broad-spectrum agents and treatment failures associated with misdiagnosis and thereby improving patient outcomes.

3. FROM GLOBAL CHALLENGES TO LOCAL SOLUTIONS: HOW CHINA FACILITATED WIDESPREAD ADOPTION OF mNGS

A recent commentary highlighted that although plasma cfDNA mNGS demonstrates considerable diagnostic potential in transplant and hematological patients, its global adoption in clinical practice still faces multiple barriers.⁶¹ The study pointed out that high testing costs (approximately 2000 USD per test), prolonged TAT due to the shipment of samples to centralized laboratories, complexity in interpreting results, and a lack of standardized management guidelines all hinder the integration of mNGS into routine clinical workflows.⁶¹ However, by establishing a localized testing model, reducing costs, and integrating clinical information for result interpretation, China has progressively achieved widespread implementation of mNGS (Fig. 1B).

3.1. Establishment of a localized application model

Unlike many countries where dependence on centralized reference laboratories results in lengthy reporting cycles—often amounting to several days due to intercity sample shipment^61–63—China has overcome this logistical barrier by establishing in-hospital or local laboratories. A distinctive “company-developed, hospital-utilized” operational model has emerged in this regard. In this model, all processes—from sample collection and experimental procedures to bioinformatics analysis and report delivery—are completed in-house at the hospital. The mNGS assays and bioinformatics analysis pipelines used in the hospital are developed and built by a third-party laboratory. During routine use, the hospital continues to optimize the workflow and improve the bioinformatics database. For example, if a culture identifies a microorganism not included in the database, this finding can prompt an update. This local, on-site model reduces turnaround delays and minimizes reporting inaccuracies caused by limited clinical information while enabling mNGS to be incorporated into routine clinical workflows.^64–66

To ensure consistent and reliable performance under this decentralized “company-developed, hospital-utilized” model, robust quality assurance and quality control (QA/QC) systems are strictly implemented in full accordance with the 2026 Guidelines for the Standardized Application of Metagenomic High-Throughput Sequencing in Clinical Laboratories.⁶⁷ The guidelines mandate standardized laboratory zoning, unified standard operating procedures (SOPs) for wet and dry laboratory processes, mandatory internal and external quality assessments, regular database validation, and uniform cutoff value interpretation across all hospital-based laboratories. These mandatory QA/QC measures effectively control pre-analytical, analytical, and post-analytical variations, minimize cross-laboratory discrepancies, and guarantee the accuracy and comparability of the mNGS results in decentralized hospital settings.

3.2. Cost analysis and economic benefits

In China, the cost of a plasma cfDNA mNGS test is approximately 500 USD, which is substantially lower than that of international commercial testing services (approximately 2000 USD).⁶¹ This results in differing economic impacts and varying accessibility levels of mNGS across countries. An international study indicated that although plasma cfDNA mNGS provides greater diagnostic value than CMT and helps patients receive targeted treatment earlier, it does not lead to savings in total healthcare costs and may even increase expenditures.⁶⁸ However, in China, the cost-effectiveness of mNGS is likely to be more favorable. A recent clinical study showed that performing mNGS within 48 hours of symptom onset yielded economic benefits. In comparison with delayed testing, early mNGS reduced the average cost of anti-infective treatment from approximately 5824 to 4099 USD and the average total hospitalization cost from approximately 18,756 to 14,194 USD.⁶⁹ The precise pathogen identification enabled by mNGS minimized unnecessary empirical broad-spectrum therapy—particularly costly antifungal agents and novel drugs—when clear diagnostic evidence was lacking, thereby optimizing overall healthcare expenditure.^69,70

3.3. Clinically relevant interpretation model

The cfDNA mNGS workflow, from blood collection to final report interpretation, is completed at the hospital. As shown in Figure 1C, this streamlined process achieved a TAT of 24 to 48 hours, with most results available within 24 hours. Report interpretation required a comprehensive assessment that integrates the patient’s immune status, clinical symptoms, imaging, and other laboratory data. For hematological disorders, the same microorganism has vastly different clinical implications depending on immune function; a finding in an immunocompromised patient may indicate active infection requiring urgent treatment, while the same result in a stable patient could reflect colonization or background noise. Thus, patient-specific interpretation is critical to avoid overdiagnosis and guide management. For example, the detection of certain viruses (such as CMV or Epstein–Barr virus [EBV]) in blood does not necessarily indicate viremia.^5,46 A Chinese expert consensus statement on the application of mNGS for the etiological diagnosis of infections in hematological disorders that was published in 2025 has attempted to classify the clinical significance of different types of microorganisms from different samples, which can facilitate the evaluation of mNGS results.¹⁹ Furthermore, our team has begun providing an “interpretive report” for positive mNGS findings.²³ This report primarily helped clinicians determine whether the detected microorganisms were pathogenic, based on the integration of clinical medical records. Our research showed that before the availability of these interpretive reports, 82.6% of clinicians experienced difficulties in interpreting mNGS reports, and the provision of interpretive reports can effectively alleviate this issue. Our data also showed that while mNGS identified multiple microorganisms in up to 72.2% of cases, only 37.4% were confirmed as true co-infections after clinical evaluation, highlighting the necessity and effectiveness of correlating pathogen findings with clinical information.²³

4. CHALLENGES AND CONSIDERATIONS IN CLINICAL APPLICATION

4.1. Limitations of plasma cfDNA mNGS in pathogen diagnosis for infections

Although plasma cfDNA mNGS is capable of detecting pathogens in localized infections, its performance remains inferior to that of samples obtained from the infection site.^15,19,71 Therefore, cfDNA mNGS cannot fully replace the testing of samples obtained from the site of infection, such as BALF, cerebrospinal fluid, or tissue biopsies. Studies have shown that for invasive pulmonary aspergillosis, the overall sensitivity of plasma cfDNA mNGS is 31.4% to 55.0%, while BALF mNGS generally performs better, with reported sensitivities between 59.6% and 82.6%.^17,51,72 In addition, for bacterial infections, blood samples often fail to detect the true pathogen. In studies by Chen et al⁷³ and Sun et al,⁷⁴ plasma cfDNA mNGS was significantly less sensitive than BALF samples for pulmonary bacterial infections. Similarly, in patients with suspected central nervous system (CNS) infections, pathogens are often detectable in the cerebrospinal fluid while plasma cfDNA mNGS results remain negative; therefore, plasma cfDNA mNGS alone should not be recommended to diagnose or exclude CNS infections.^19,75 If the clinical suspicion of a deep-seated infection remains high despite a negative plasma cfDNA mNGS result, efforts should still be made to obtain local samples for testing.

In comparison with other nucleic acid detection methods, including polymerase chain reaction (PCR) and targeted next-generation sequencing (tNGS), plasma cfDNA mNGS has distinct application characteristics and limitations. For plasma-based testing, cfDNA mNGS is more suitable in situations without predictable pathogens, whereas PCR covers only a limited panel of targets. For established PCR assays, especially for viruses such as CMV and EBV, quantitative PCR remains the basis of clinical management. Regarding specific pathogens, our previous studies showed that plasma cfDNA mNGS has higher sensitivity than Aspergillus-PCR in neutropenic patients but not in non-neutropenic patients.¹⁷ For tNGS, studies in plasma are still limited in patients with hematological disorders; due to its targeted design, tNGS may miss rare pathogens such as Coprinopsis cinerea.⁷⁶

4.2. Challenges in predicting bacterial resistance

Although mNGS has demonstrated robust capabilities in pathogen detection, its usefulness in predicting bacterial antimicrobial resistance (AMR) remains challenging, particularly in plasma samples. Most current mNGS-based AMR prediction studies have focused on high microbial load specimens (eg, BALF), whereas research on blood samples is limited. The main challenges in predicting AMR using cfDNA stem from 3 aspects: Microbial cfDNA accounts for an extremely low proportion of total cfDNA, and resistance genes constitute only approximately 0.01% to 0.05% of the entire microbial genome, making resistance gene detection in cfDNA highly challenging. Resistance gene detection typically requires 10 to 100 times higher data volume than bacterial identification.⁴⁸ cfDNA is highly fragmented with short sequences, and the resultant difficulties in assembly and identification further hinder the determination of the species origin of resistance genes. Some resistance genotypes in existing databases have low matching rates with phenotypic resistance, and the databases of gene sequences that enable the accurate prediction of resistance phenotypes require improvement.⁷⁷

However, substantial progress has been made in AMR detection and resistance prediction in recent years. For resistance gene databases and species origin determination of resistance genes, Rao et al successfully identified drug-resistance genes that show high consistency with phenotypes by integrating machine learning and whole-genome sequencing data. Based on these findings, they established a self-built proprietary database of drug-resistance genes and proposed a technique to determine the origin of resistance genes in a species. These advancements have markedly improved the accuracy of mNGS-based prediction of drug-susceptibility phenotypes.^78–83 In addition, to address the low sensitivity of AMR gene detection in blood tests, Christians et al reported an innovative application of plasma cfDNA mNGS.⁸⁴ This study used targeted enrichment before sequencing to focus on only 7 specific AMR markers (SCCmec, mecA, mecC, vanA, vanB, bla_CTX-M, and bla_KPC) covering 18 clinically important pathogens, with AMR detection results provided as a supplement to the main pathogen identification report with a 1-day delay. This targeted sequencing method is not equivalent to comprehensive mNGS but serves as a targeted supplement to initial mNGS findings. Analytical validation showed excellent performance (100% reproducibility, 54%–100% inclusivity, and 100% exclusivity).

5. SCOPE FOR OPTIMIZATION AND FUTURE DEVELOPMENT DIRECTIONS

5.1. Depth and multidimensional applications of cfDNA mNGS data

The future value of cfDNA mNGS extends beyond pathogen identification toward the integration of multidimensional information. First, the interpretation of host–pathogen interactions is crucial. The detection of certain viruses, such as Torque teno virus (TTV), although not directly pathogenic, has been shown to correlate with the degree of overall host immunosuppression.^85–87 One recent study indicated that TTV is commonly identified in immunocompromised individuals, with its presence frequently linked to elevated viral loads, enhanced microbial diversity in blood samples, and a heightened burden of co-infecting pathogens.⁸⁵ Our previous studies have identified differences in virus detection at various stages after allogeneic HSCT (allo-HSCT), and they have revealed that co-detection of multiple viruses is common.⁴⁶ Exploring how to utilize viral data to establish immune-assessment methods represents a promising direction worthy of further investigation.

The expansion and intelligent analysis of resistance gene databases represent another critical direction. In addition to common bacterial resistance genes, future databases should incorporate resistance-mutation information for viruses (eg, CMV), which is essential for managing refractory post-transplant CMV infections.^88,89 By developing more comprehensive resistance databases and predictive algorithms, cfDNA mNGS can provide holistic resistance risk assessments alongside pathogen identification, thereby guiding precise antimicrobial therapy.

5.2. Technical improvements to enhance detection performance

Several technical approaches can be used to improve the sensitivity and specificity of plasma cfDNA mNGS. One promising strategy involves the combined detection of plasma and blood cells. Plasma is rich in cell-free pathogen DNA, whereas blood cells contain intracellular pathogens or pathogen nucleic acids that are phagocytosed by immune cells. By analyzing both fractions simultaneously, the pathogen detection rate can be improved. Preliminary studies have shown that combining plasma and blood cell mNGS can improve diagnostic sensitivity.^90–92

Advancements in bioinformatics algorithms are also crucial for optimizing the performance of mNGS. Improved database curation, including the addition of more comprehensive pathogen genetic information and the removal of redundant or erroneous sequences, can enhance the accuracy of pathogen identification. The development of advanced filtering algorithms to distinguish between true pathogens and background contaminants or host sequences can improve specificity.^48,93 Machine learning and artificial intelligence (AI) technologies show great potential in this regard since they can learn from large datasets of clinical and mNGS data to develop more accurate pathogen classification and interpretation models.^94–96

Technological improvements have also enabled the accurate quantification of plasma microbial cfDNA using mNGS. Karius Inc. has achieved reliable quantification using a synthetic whole-assay internal normalization control (WINC) for workflow normalization, with microbial loads quantified as microbe-specific cfDNA molecules per microliter of plasma (MPM).⁹⁷ However, in China, although similar studies have been conducted, most mNGS tests are laboratory-developed assays without universal internal standards or unified protocols.⁹⁸ In addition, variations in experimental procedures and bioinformatics analysis also contribute to the current difficulty in achieving reliable quantification.

5.3. From pathogen list to clinical insight

The next phase in the evolution of mNGS is moving beyond simple pathogen listings to establish a data-driven intelligent clinical management and interpretation framework. The core concept has shifted from providing data to delivering support for decisions. Currently, the interpretation of cfDNA mNGS reports relies heavily on physician expertise, which introduces subjectivity and a lack of standardization. Therefore, one future direction for optimization could be the deep integration of AI to create a multidimensional data integration and analysis platform.^96,99 Utilizing technologies like natural language processing, this platform would automatically extract and structure patient clinical information (eg, fever, duration of neutropenia), imaging features (eg, “halo sign” in chest CT), conventional lab markers (eg, C-reactive protein, procalcitonin), and the raw data/report from mNGS.^99,100

This integrated approach may achieve 2 key clinical advancements: accurately distinguishing infections from non-infectious conditions and intelligently identifying true pathogens among all detected microorganisms while filtering out colonizers and contaminants. Ultimately, this transformation will shift the clinical focus from struggling to interpret complex reports to efficiently identifying the cause of infection with AI assistance, thereby systematically enhancing the precision of diagnosis and treatment.

5.4. Cost reduction and popularization

Reducing the cost of plasma cfDNA mNGS is critical for its widespread adoption. The development of domesticated and large-scale production of sequencing reagents and equipment in countries such as China can substantially lower the cost of mNGS testing. The implementation of automated sample processing and data analysis systems can reduce labor costs and improve efficiency.

Increasing the insurance coverage for mNGS testing is another important step. In China, the expenses for mNGS testing are not covered by public medical insurance, which places the financial burden on patients. Demonstrating the cost-effectiveness of mNGS through clinical studies, such as its ability to reduce hospital stays, antimicrobial use, and mortality, can help secure insurance coverage.^69,70,96

Capacity-building in resource-limited settings is essential. This includes training laboratory technicians and clinicians in mNGS technology, result interpretation, and clinical applications. The establishment of regional mNGS reference laboratories can centralize testing services, reducing the need for individual institutions to invest in expensive equipment and personnel.

6. CONCLUSIONS

Noninvasive plasma cfDNA mNGS, which allows unbiased detection, broad coverage, and rapid turnaround, has become a key tool for diagnosing infections in patients with hematological disorders. It has shown irreplaceable value, especially for febrile neutropenia, BSI, deep-seated focal infections, and infections caused by uncommon pathogens. By establishing in-hospital localized testing models, reducing costs, and developing clinically integrated interpretation systems, China has effectively addressed the core barriers to global mNGS popularization, thereby confirming its clinical utility and cost-effectiveness. However, this technology still faces challenges, such as lower sensitivity than site-specific specimens for focal infections, limited capacity for AMR prediction, and reliance on professional expertise for result interpretation. Future efforts, including experimental workflow optimization, AI-assisted multidimensional data integration, and in-depth data mining, are expected to transform mNGS from a “pathogen list generator” into a “clinical decision-support platform.” Meanwhile, the development of QA/QC systems, sustained cost reduction, expanded insurance coverage, and enhanced capacity-building in resource-limited regions will further unlock the potential of this technique in precision medicine, ultimately improving the diagnosis and treatment outcomes of patients with infections.

ACKNOWLEDGMENTS

This work was supported by the Tianjin Municipal Science and Technology Commission Grant (25ZXRKSY00040), National Natural Sciences Foundation of China (82470208), and National Key R&D Program of China (2024YFC2510500).

AUTHOR CONTRIBUTIONS

C.X. and T.L. contributed equally to this work and were responsible for the conception and design of the review and writing of the original draft. X.Z. and S.F. supervised the study and critically revised the manuscript.

All authors read and approved the final version of the manuscript.