Bloodstream infection (BSI), a serious systemic infectious disease, often leads to sepsis and multiple organ dysfunction syndrome (MODS) (Mervyn et al., 2016; Kristina E et al., 2020; Laura et al., 2021). Despite consuming significant medical resources, the mortality rate for BSI remains high, posing a substantial medical burden. It has become one of the major global public health challenges, making early diagnosis essential for improving patient prognosis and reducing mortality (Mical et al., 2019; Jean-François et al., 2020). However, traditional diagnostic methods such as blood culture, PCR technology, and single biomarker detection have notable limitations: blood culture is time-consuming (typically 24–72 h) and its sensitivity is compromised by antibiotic use. PCR technology, although faster, relies on the genetic information of known pathogens, limiting its effectiveness in complex infection scenarios. Single biomarker-based detection strategies fail to account for the complex molecular network regulation of the disease and are easily influenced by factors such as patient age, underlying conditions, and infection type, leading to significant limitations in sensitivity and specificity (Christophe et al., 2013; Ramon et al., 2017; Fei et al., 2024). For instance, procalcitonin (PCT) levels can be abnormally elevated in noninfectious inflammation, resulting in a false-positive rate of up to 20%–30% (Philipp et al., 2017). The complexity of biomarker interpretation is further highlighted in drug-induced conditions like carfilzomib-induced thrombotic microangiopathy, where markers such as LDH and ADAMTS13 activity must be carefully contextualized (Weilun et al., 2024). Hence, the development of new diagnostic technologies with rapid, high sensitivity, and high specificity has become an urgent clinical need. Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has shown unique advantages in pathogen identification and metabolomic analysis (Andrew E et al., 2013; Verhoeven et al., 2015). Its high-throughput and high-resolution capabilities enable the simultaneous capture of thousands of metabolite profiles, making it a crucial tool for metabolomics research (Michelle S S et al., 2019). For example, quasi-targeted metabolomics using such platforms has successfully identified predictive metabolites and potential drug candidates in β-thalassemia mouse models (Xianfeng et al., 2024). As a two-dimensional transition metal carbide/nitride material, MXene has garnered significant attention in the field of mass spectrometry due to its ultra-high specific surface area, excellent laser absorption capabilities, and chemical tunability (Shijie et al., 2025; Vivek et al., 2021). The abundant functional groups on the surface of MXene (such as -O, -F) enhance the adsorption and desorption efficiency of analytes and optimize the ionization process through interface engineering, significantly improving detection sensitivity and reproducibility. This principle of material-enhanced detection sensitivity parallels strategies used in nanomedicine, where the precise design of prodrug nanoassemblies has been shown to drastically improve pharmacokinetic profiles and therapeutic outcomes (Chengcheng et al., 2025).

In this study, the MXene matrix was utilized with MALDI-TOF MS technology to screen for bloodstream infections. As depicted in Figure 1A, the MXene solution was applied to air-dried serum on a steel plate and subsequently analyzed using MALDI-TOF MS. The mechanism of MALDI-TOF MS is illustrated in Figure 1B. Various metabolites in serum samples are dispersed within the matrix material. Upon laser irradiation, the matrix absorbs the laser energy, facilitating the ionization of metabolite molecules. These charged ions are then detected, and the metabolic fingerprint of the sample is determined based on the mass-to-charge ratio of the ions. A typical spectrum is shown in Figure 1C. Through comprehensive and in-depth analysis of metabolic fingerprints, effective diagnosis of blood infections was achieved.

Scanning electron microscopy (SEM) characterization revealed the morphology and structure of MXene nanomaterials (Figures 2A,B), which exhibited distinct two-dimensional layered structures. Additionally, EDS elemental mapping displayed the element distribution within MXene, indicating a uniform distribution of Ti, C, F, and O (Figure 2C; Supplementary Figure S1). The unique two-dimensional layered structure and surface chemical properties of MXene enhance the adsorption and desorption efficiency of metabolites. XPS spectrum (Figure 2D), FTIR spectrum (Figure 2E) and the Nitrogen Physisorption (BET Surface Area) detection (Figure 2F) of MXene also was tested.

To validate the clinical performance of the MXene nanomatrix applied to the MALDI-TOF MS platform for screening bloodstream infections, we tested 50 positive samples of bloodstream infection (BSI) and 50 samples from a control group (Non-BSI). The sample information is shown in Figure 3A and Supplementary Table S1. To distinguish BSI from Non-BSI, we conducted classical unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The PCA analysis, shown in Figure 3B, indicates a partial overlap between Non-BSI and BSI, suggesting that PCA cannot fully extract sufficient differential information to distinguish between disease and non-disease states in such complex data. However, using the OPLS-DA method, the data were directionally processed and analyzed. The OPLS-DA analysis, shown in Figure 3C, effectively distinguished all samples, revealing significant differences in metabolites between the groups. OPLS-DA successfully achieved the effective identification of BSI and Non-BSI. A heatmap can intuitively show the differences in metabolite expression patterns. The heatmap analysis of clinical samples, shown in Figure 3D, reveals clear metabolic differences in composition and intensity between the two groups. The horizontal axis of the volcano plot represents the fold change in metabolite concentration between the two groups (log2 FC), while the vertical axis represents the significance of metabolite differences (−log10 p-value). This plot visualizes statistical significance and fold change, aiding in the rapid screening of metabolites with significant differences between the groups (Gary J et al., 2012; Jasmine et al., 2018). The volcano plot shown in Figure 3E displays the upregulated and downregulated MZ panels, with these highly significant metabolites and low p-values serving as potential biomarkers.

The serum metabolic fingerprint successfully distinguished between BSI and Non-BSI samples, and the area under the curve (AUC) was further used to evaluate diagnostic performance. As shown in Figure 4A, the AUC of the MALDI-TOF MS platform is 0.981, indicating its superior diagnostic ability in bloodstream infections. Additionally, BSI and Non-BSI clinical groups exhibited significant metabolic differences in MZ values. The MZ values shown in Figures 4B–E are 203.64, 206.75, 218.67, and 220.70, respectively, with corresponding AUC values of 0.969, 0.972, 0.974, and 0.977. These findings suggest that these metabolites may be associated with the occurrence or progression of diseases, and these MZ values can serve as potential biomarker combinations for screening bloodstream infections.

Further evaluate the diagnostic performance of the MALDI-TOF MS platform by comparing its detection effectiveness with that of individual biomarkers. Previous studies have demonstrated that human epididymis protein 4 (HE4), procalcitonin (PCT), C-reactive protein (CRP), and the neutrophil-to-lymphocyte ratio (NLR) are valuable in diagnosing bloodstream infections (Vincent et al., 2021; Schuetz et al., 2009; Póvoa et al., 2005). Figures 5A,B present the AUC values for HE4, PCT, CRP, NLR, and the MALDI-TOF MS platform for clinical samples. The AUC values for HE4, PCT, CRP, and NLR were 0.862, 0.913, 0.802, and 0.816, respectively, all of which were lower than the 0.981 achieved by the MALDI-TOF MS platform. Figures 5C,D illustrate the differences in sensitivity and specificity among the various detection methods. The MALDI-TOF MS platform exhibited a diagnostic sensitivity of 92% and a specificity of 96%, outperforming the best single biomarker, PCT, which had a sensitivity of 82% and a specificity of 94%.

Early diagnosis of bloodstream infections (BSIs) remains a significant challenge in clinical practice. Conventional blood cultures are time-consuming (24–72 h) and susceptible to prior antibiotic exposure. Although PCR reduces turnaround time, it requires prior knowledge of pathogen genetic information and struggles with complex infections (Shymma et al., 2025). Single-biomarker strategies (e.g., PCT, CRP) often lack sensitivity and specificity because they overlook the disease’s complex molecular network regulation, which is influenced by patient age, comorbidities, and infection type (Divya et al., 2025; Yosuke et al., 2018; Won-Ho et al., 2017). Therefore, there is an urgent need to develop new diagnostic technologies that are rapid, highly sensitive, and specific.

In recent years, research on nanomaterials for medical testing and drug delivery has advanced substantially. In this study, we successfully developed a functionalized MALDI-TOF MS platform based on MXene nanomaterials and applied it to early metabolomics screening of BSIs. By leveraging MXene’s unique two-dimensional layered structure, ultrahigh specific surface area, and abundant, tunable surface functional groups, the platform enables efficient enrichment and soft ionization of low-abundance serum metabolites, significantly improving analytical sensitivity and reproducibility. Experimental results show a diagnostic sensitivity of 92%, specificity of 96%, and an AUC of 0.981 for BSI, clearly outperforming traditional single-biomarker methods. Comprehensive characterization by XPS, FT-IR, and BET elucidated MXene’s surface chemistry and structural features.

This study demonstrates the clinical utility and diagnostic performance of the MXene-functionalized MALDI-TOF MS platform for early BSI screening. Our platform exploits MXene’s unique physicochemical properties—particularly its high surface area for metabolite enrichment and efficient laser energy absorption/transfer—in synergy with the high throughput of MALDI-TOF MS to achieve rapid, label-free metabolic fingerprinting. Compared with conventional MALDI matrices (e.g., CHCA, SA), the MXene nano-matrix provides a cleaner background and higher ion yields for small metabolites (Fan et al., 2023). Additionally, relative to more complex LC-MS/MS workflows that require lengthy separations and specialized expertise, our platform offers a simplified and rapid workflow (<30 min per sample), suitable for potential point-of-care translation. Future comparative studies will quantitatively benchmark sensitivity, reproducibility, and coverage against gold-standard LC-MS/MS and other emerging nanosheet-assisted MS platforms to fully establish its application in clinical metabolomics testing (Hui et al., 2022; Sen et al., 2023).

Various nanomaterial-assisted LDI-MS platforms have shown promise for disease metabolite analysis in recent years. Although advanced LDI-MS platforms based on diverse nanostructured matrices have been reported—such as graphene oxide derivatives for structural and photochemical studies (Seung-Woo et al., 2022), iron particle–assisted systems for metabolic fingerprinting of diabetic retinopathy (Yu et al., 2023), and ZrMOF/Au hybrids for high-throughput urinary metabolomics (Junyu et al., 2024)—comparative analyses with these recent methods further highlight the advantages of our MXene-based MALDI-TOF MS platform. Our platform differs in several key respects. First, unlike graphene oxide systems that primarily focus on material characterization and photothermal properties, our MXene-functionalized platform is purpose-built for clinical metabolite screening in complex biological samples, delivering an AUC of 0.981 with high sensitivity (92%) and specificity (96%) for BSI diagnosis. Second, relative to iron particle–assisted LDI-MS, our MXene matrix provides enhanced laser absorption and surface functionalization capacity, enabling superior metabolite enrichment and ionization efficiency. Third, while MOF-based hybrids exhibit excellent salt tolerance and reproducibility, our MXene platform offers a unique combination of ultrahigh specific surface area, tunable surface chemistry, and mechanical stability, facilitating reproducible and sensitive detection of low-abundance metabolites without complex sample pretreatment.

Recently, nanomaterial-assisted LDI-MS platforms have demonstrated significant potential in disease metabolite analysis. For instance, graphene oxide and its derivatives have been employed to study photochemical properties, iron particle-assisted platforms have shown clinical utility in metabolic fingerprinting of diabetic retinopathy, and ZrMOF/Au hybrid materials have enabled high-throughput urinary metabolic subtyping of thyroid nodules. The MXene-based platform proposed in this study differs from graphene oxide systems, which primarily focus on material characterization and photothermal properties. Our MXene-functionalized platform is specifically designed for clinical metabolite screening in complex biological samples, achieving an AUC of 0.981 with high sensitivity (92%) and specificity (96%) for bloodstream infection diagnosis.

Compared to iron particle-assisted LDI-MS, our MXene matrix offers enhanced laser absorption and surface functionalization, resulting in superior metabolite enrichment and ionization efficiency. Although MOF-based hybrids exhibit excellent salt tolerance and reproducibility, the MXene platform described here provides a unique combination of ultrahigh specific surface area, tunable surface chemistry, and mechanical stability. This enables reproducible and sensitive detection of low-abundance metabolites without complex sample pretreatment. MXenes possess higher laser energy absorption and photothermal conversion efficiency, which improves ionization efficiency and reduces fragmentation interferences. Their surface functional groups can be chemically modified to target the enrichment of specific metabolites. Additionally, MXenes maintain a high specific surface area while offering better mechanical stability and reusability potential, making them more suitable for high-throughput screening of clinical samples.

In conclusion, this study not only verified the potential application of MXene in mass spectrometry but also provided an efficient and high-throughput metabolomic analysis platform for the early diagnosis of infectious diseases. The further development of this platform is expected to facilitate the transition of bloodstream infection diagnostics from reliance on single indicators to multidimensional metabolic fingerprint analysis, thereby offering a new technological pathway for the accurate diagnosis and treatment of infectious diseases.