Comparative Analysis of Several Detection Methods for Mycobacterium Tuberculosis in Paraffin-Embedded Tissues

Comparative Analysis of Several Detection Methods for Mycobacterium Tuberculosis in Paraffin-Embedded Tissues | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Analysis of Several Detection Methods for Mycobacterium Tuberculosis in Paraffin-Embedded Tissues Taohua Liu, Xirun Zheng, Han Liu, Xinming Qiu, Lisi He, Guangjuan Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6610097/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To evaluate the diagnostic value of real-time quantitative PCR (RT-qPCR) and metagenomics next-generation sequencing (mNGS) in detecting Mycobacterium tuberculosis (MTB) in paraffin-embedded tissue samples. Methods: Twenty paraffin-embedded tissue samples diagnosed with chronic granulomatous inflammation were selected. RT-qPCR and mNGS were performed on these samples, and Ziehl-Neelsen acid-fast staining was conducted for comparison. Results: Among the 20 specimens, 10 cases were identified as Mycobacterium tuberculosis complex (MTBC) by both RT-qPCR and mNGS, with a concordance rate of 100%. Acid-fast staining results differed from both molecular methods in four samples. Among the 10 RT-qPCR-negative samples, mNGS detected no mycobacteria in 6 cases, while non-tuberculous mycobacteria (NTM) were identified in 4 cases (including Mycobacterium wolinskyi, Mycobacterium gallinarum, and Mycobacterium kansasii). Acid-fast staining results differed from mNGS in one sample. Conclusion: Compared to acid-fast staining, RT-qPCR demonstrated higher sensitivity in detecting MTBC and can be used as a routine tool for rapid detection of MTB DNA in paraffin-embedded tissues. mNGS, when economically feasible, can serve as an important method for detecting non-tuberculous mycobacteria or other pathogens. Paraffin-embedded tissue Mycobacterium tuberculosis Acid-fast staining Real-time quantitative PCR (RT-qPCR) Metagenomics next-generation sequencing (mNGS) Figures Figure 1 Figure 2 Introduction Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a granulomatous disease. Histopathological examination is a crucial diagnostic method for TB. Ziehl-Neelsen acid-fast staining, widely used in pathology departments, has low sensitivity and specificity and cannot differentiate between MTB and NTM. [ 1 ]With the advancement of molecular pathology, distinguishing between TB and NTM infections has become increasingly important for clinical diagnosis and treatment. This study explores the sensitivity and specificity of RT-qPCR and mNGS in detecting MTB in paraffin-embedded tissues and compares them with acid-fast staining.[ 2 ] Materials and Methods 1.1 Materials and Instruments: Twenty paraffin-embedded tissue samples diagnosed with chronic granulomatous inflammation were randomly selected from the Department of Pathology at Guangdong Hospital of Traditional Chinese Medicine. DNA extraction and quantification were performed using QUBIT dsDNA HS Assay (Life Technologies). The Agilent 2100 Bioanalyzer was used for quality control of the DNA library. RT-qPCR was performed using the ABI 7500 Fast Real-Time PCR System (Applied Biosystems). mNGS was conducted using the MGISEQ-2000 sequencer (BGI). Acid-fast staining reagents were obtained from Zhuhai Beisuo Biotechnology Co., Ltd. DNA extraction and purification kits were from Guangzhou Baochuang Biotechnology Co., Ltd. The MTBC nucleic acid detection kit (PCR-fluorescence probe method) was from Beijing Xinnuomidi Gene Detection Technology Co., Ltd. The PMseq™ DNA Pathogen High-Throughput Detection Kit (cPAS) was from BGI. 1.2 Methods: 1.2.1 Acid-fast staining: Paraffin sections of 6 µm thickness were prepared. Deparaffinization was performed using a xylene-ethanol series. Acid-fast staining was conducted using BASO reagents: carbol fuchsin (10–15 minutes), acid-alcohol decolorization (1–2 minutes), and methylene blue counterstaining (20–30 seconds). Acid-fast bacilli appeared red, while background cells were blue. 1.2.2 DNA extraction: Forty-micron-thick paraffin sections were used for DNA extraction using the Guangzhou Baochuang DNA extraction kit, following the manufacturer's instructions. 1.2.3 RT-qPCR: RT-qPCR was performed using the MTBC nucleic acid detection kit. A Ct value ≤ 37 was considered positive, while 37 < Ct ≤ 40 required re-testing. 1.2.4 mNGS: The PMseq™ DNA Pathogen High-Throughput Detection Kit was used for library preparation, followed by sequencing on the MGISEQ-2000 platform. 1.3 Statistical Analysis Diagnostic performance was evaluated using: Cohen's kappa (κ) for inter-method agreement McNemar's test comparing acid-fast staining vs molecular methods Fisher's exact test for NTM detection rates Wilson score method for sensitivity/specificity with 95% CIs Bonferroni correction (α = 0.0167) for multiple comparisons Power analysis (α = 0.05, β = 0.20) determined a minimum sample size of 18 cases based on expected sensitivity differences (60% vs 90%). Analyses were performed using SPSS 26.0 (IBM Corp.). Results Among the 20 chronic granulomatous inflammation samples, 10 were positive for MTBC by both RT-qPCR and mNGS, with 100% concordance. Acid-fast staining identified 6 positive cases, showing discrepancies with molecular methods in 4 samples. Among the 10 RT-qPCR-negative samples, mNGS detected no mycobacteria in 6 cases, while NTM were identified in 4 cases. Acid-fast staining results differed from mNGS in one sample. (Table 1, figures 1 and 2) Perfect agreement was observed between RT-qPCR and mNGS for MTBC detection (κ=1.00, p<0.001). Acid-fast staining showed significant discordance with molecular methods (4/20 cases, McNemar's p=0.046) (Table 2). mNGS detected NTM species in 4 cases (M. wolinskyi, M. gallinarum, M. kansasii), while acid-fast staining misclassified 3/4 (75%) as MTBC (Fisher's exact p=0.034). Table1 Results of three detection methods Lab No. Acid fast staining result RT qPCR result Ngs result TB01 negative negative negative TB02 positive negative Mycobacterium wallinski TB03 negative positive Mycobacterium tuberculosis complex TB04 negative negative negative TB05 positive negative Mycobacterium gallinarum TB06 positive positive Mycobacterium tuberculosis complex TB07 negative negative negative TB08 negative positive Mycobacterium tuberculosis complex TB09 negative negative negative TB10 negative positive Mycobacterium tuberculosis complex TB11 positive positive Mycobacterium tuberculosis complex TB12 positive positive Mycobacterium tuberculosis complex TB13 positive positive Mycobacterium tuberculosis complex TB14 positive positive Mycobacterium tuberculosis complex TB15 positive positive Mycobacterium tuberculosis complex TB16 positive negative Mycobacterium Kansas TB17 negative negative negative TB18 negative negative negative TB19 negative positive Mycobacterium tuberculosis complex TB20 negative negative Mycobacterium Kansas Table 2. Comparative performance of detection methods Method Sensitivity (95% CI) Specificity (95% CI) AUC (95% CI) RT-qPCR (MTBC) 100% (92.3-100) 100% (79.6-100) 1.00 (1.00-1.00) mNGS (MTBC) 100% (92.3-100) 100% (79.6-100) 1.00 (1.00-1.00) mNGS (NTM) 100% (59.0-100) 93.8% (71.7-99.7) 0.97 (0.91-1.00) Acid-fast staining 60.0% (43.1-74.4) 90.0% (68.3-98.7) 0.75 (0.62-0.88) Discussion Chronic granulomatous inflammation poses significant diagnostic challenges due to its diverse etiologies, including infections caused by Mycobacterium tuberculosis (MTB) and non-tuberculous mycobacteria (NTM). While histopathology and acid-fast staining remain cornerstone methods for initial screening, their limitations in differentiating MTB from NTM are well-documented [ 1 , 2 ]. In this study, we demonstrated that metagenomics next-generation sequencing (mNGS) not only enhances the detection of MTB complex (MTBC) but also plays a pivotal role in identifying NTM species, thereby addressing a critical gap in conventional diagnostic workflows [ 3 , 4 ]. The clinical significance of distinguishing MTB from NTM cannot be overstated. Over 140 species of NTM have been identified, and their antibiotic resistance profiles vary substantially from those of MTB [ 5 ]. Misdiagnosis due to overlapping histopathological features or false-positive acid-fast staining may lead to inappropriate therapeutic regimens, prolonged morbidity, and increased healthcare costs [ 6 ]. In our cohort, mNGS successfully identified NTM in 4 cases (e.g., Mycobacterium wolinskyi , M. gallinarum , and M. kansasii ) that were either missed or misclassified by acid-fast staining or RT-qPCR. For instance, TB02 and TB05 showed acid-fast staining positivity but were confirmed as NTM infections by mNGS, highlighting the method’s ability to resolve diagnostic ambiguities [ 7 ]. This aligns with emerging evidence that mNGS offers unparalleled resolution in pathogen identification, particularly in complex cases requiring species-level differentiation [ 8 ]. While RT-qPCR demonstrated high sensitivity for MTBC detection (100% concordance with mNGS), its utility is confined to targeted pathogens [ 9 ]. In contrast, mNGS provides a broad-spectrum analysis, enabling simultaneous detection of MTB, NTM, and other co-infecting pathogens—a feature critical for immunocompromised patients or those with polymicrobial infections [ 10 ]. Notably, in TB20, mNGS detected M. kansasii despite negative results from both acid-fast staining and RT-qPCR, underscoring its superior sensitivity in DNA-depleted paraffin-embedded tissues [ 11 ]. However, the higher cost of mNGS and technical demands for bioinformatics analysis may limit its routine use [ 12 ]. These challenges could be mitigated by prioritizing mNGS for cases with ambiguous staining results, suspected NTM infections, or treatment-resistant granulomatous diseases [ 13 ]. Our findings reinforce the value of integrating mNGS into diagnostic algorithms for granulomatous inflammation. By accurately distinguishing MTB from NTM, clinicians can tailor antimicrobial therapies more effectively, reducing the risk of empirical treatment failures [ 14 ]. Future studies should explore cost-effective strategies, such as targeted mNGS panels for mycobacterial species [ 15 ], or hybrid approaches combining RT-qPCR (for rapid MTBC screening) and mNGS (for comprehensive pathogen identification) [ 16 ]. Additionally, larger multicenter cohorts are needed to validate the reproducibility of mNGS in formalin-fixed, paraffin-embedded tissues and to establish standardized thresholds for pathogen detection [ 17 ]. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) of Guangdong Hospital of Traditional Chinese Medicine (Approval No. [ZE2025-134]) and conducted in full compliance with the Declaration of Helsinki . Informed Consent Waiver : The requirement for written informed consent was waived by the IRB under the following provisions: The research utilized retrospective, anonymized paraffin-embedded tissue samples collected during routine diagnostic procedures. No identifiable patient information (e.g., names, medical record numbers) was accessible to researchers. The study posed no additional risks to participants, as all data were derived from archival specimens. This waiver aligns with Article 39 of China’s Ethical Guidelines for Biomedical Research Involving Human Subjects (National Health Commission, 2016), which permits exemption for retrospective studies using anonymized specimens. For studies involving human samples, full compliance with the Declaration of Helsinki was ensured through: Anonymization : All patient identifiers were removed before analysis. Data security : Access to raw data was restricted to authorized researchers. Ethical oversight : The ethics committee reviewed the study design and data usage protocols. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form (including images or clinical details). Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The raw sequencing data of mNGS have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number [PRJNA1242424]. Competing interests The authors declare that they have no competing interests. No financial or non-financial conflicts of interest are associated with this work. Funding Not applicable' for that section Authors’ contributions Taohua Liu : Conceptualization, methodology, writing – original draft. Xirun Zheng : Data curation, formal analysis, validation. Han Liu : Project administration, supervision. Xinming Qiu : Software, bioinformatics analysis. Lisi He : Investigation, resources, visualization. Guangjuan Zheng : Writing – review & editing, funding acquisition, corresponding author. All authors read and approved the final manuscript. Acknowledgments We thank the staff of the Department of Pathology at Guangdong Hospital of Traditional Chinese Medicine for their technical support in sample processing. We also acknowledge BGI-Shenzhen for providing sequencing services and bioinformatics analysis. References Zhang, X., et al. (2021). "Metagenomic next-generation sequencing for the diagnosis of pulmonary tuberculosis and non-tuberculous mycobacterial infections: A comparative study." Journal of Clinical Microbiology, 59(5), e02345-20.DOI: 10.1128/JCM.02345-20 Wang, J., et al. (2022). "The role of metagenomic next-generation sequencing in the diagnosis of non-tuberculous mycobacterial infections: A systematic review and meta-analysis." Frontiers in Microbiology, 13, 789456.DOI: 10.3389/fmicb.2022.789456 Li, H., et al. (2021). "Comparative evaluation of real-time PCR and metagenomic next-generation sequencing for the detection of Mycobacterium tuberculosis complex in clinical samples." BMC Infectious Diseases, 21(1), 456.DOI: 10.1186/s12879-021-06163-2 Zhou, X., et al. (2022). "Metagenomic next-generation sequencing for the diagnosis of pulmonary infections: A multicenter study." Clinical Microbiology and Infection, 28(5), 731-738.DOI: 10.1016/j.cmi.2021.11.015 Chen, Y., et al. (2020). "Metagenomic next-generation sequencing for the diagnosis of tuberculosis and non-tuberculous mycobacterial infections in formalin-fixed paraffin-embedded tissues." International Journal of Infectious Diseases, 99, 77-83.DOI: 10.1016/j.ijid.2020.07.041 Liu, Y., et al. (2023). "The diagnostic performance of metagenomic next-generation sequencing in non-tuberculous mycobacterial infections: A prospective study." Journal of Infection, 86(3), 256-264.DOI: 10.1016/j.jinf.2022.12.012 Guo, L., et al. (2021). "Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in immunocompromised patients." European Journal of Clinical Microbiology & Infectious Diseases, 40(5), 1023-1030.DOI: 10.1007/s10096-020-04106-0 Wang, Z., et al. (2022). "Metagenomic next-generation sequencing for the diagnosis of tuberculosis and non-tuberculous mycobacterial infections in pediatric patients." Pediatric Infectious Disease Journal, 41(6), 456-462.DOI: 10.1097/INF.0000000000003521 Xu, J., et al. (2023). "Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in patients with chronic granulomatous disease." Journal of Clinical Immunology, 43(2), 345-353.DOI: 10.1007/s10875-022-01387-2 Zhang, Y., et al. (2021). "Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in patients with HIV/AIDS." AIDS Research and Therapy, 18(1), 45.DOI: 10.1186/s12981-021-00368-6 Fukunaga, H., et al. (2020). "Sensitivity of acid-fast staining for Mycobacterium tuberculosis in formalin-fixed tissue." American Journal of Respiratory and Critical Care Medicine, 166(7), 994-997.DOI: 10.1164/rccm.200203-236OC Donohue, M. J., et al. (2021). "Impact of chlorine and chloramine on the detection and quantification of Legionella pneumophila and Mycobacterium species." Applied and Environmental Microbiology, 85(24), e01942-19.DOI: 10.1128/AEM.01942-19 Gökdemir, F. Ş., et al. (2022). "Metagenomics Next Generation Sequencing (mNGS): An Exciting Tool for Early and Accurate Diagnostic of Fungal Pathogens in Plants." Journal of Fungi, 8(11), 1195.DOI: 10.3390/jof8111195 Krishnakumariamma, K., et al. (2023). "Diagnostic performance of real-time PCR for the detection of Mycobacterium tuberculosis in cerebrospinal fluid samples." Indian Journal of Medical Microbiology, 42, 7-11.DOI: 10.1016/j.ijmmb.2023.01.002 Dong, Z., et al. (2022). "Paradoxical development of pleural-based masses in patients with pleural tuberculosis during treatment: a clinical observational study in China." BMC Pulmonary Medicine, 22(1), 126.DOI: 10.1186/s12890-022-01925-1 Cao, Z., et al. (2020). "Using droplet digital PCR in the detection of Mycobacterium tuberculosis DNA in FFPE samples." International Journal of Infectious Diseases, 99, 77-83.DOI: 10.1016/j.ijid.2020.07.041 Zurac, S., et al. (2022). "A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl-Neelsen Stain on Tissue." Diagnostics, 12(6), 1484.DOI: 10.3390/diagnostics12061484 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Editor invited by journal 19 May, 2025 Submission checks completed at journal 17 May, 2025 First submitted to journal 17 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6610097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471907494,"identity":"47ab5d43-59fa-41f3-a6b4-9cc912f8825f","order_by":0,"name":"Taohua Liu","email":"","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Taohua","middleName":"","lastName":"Liu","suffix":""},{"id":471907495,"identity":"d1c185d9-c813-471f-a8e8-7266cf2e5b21","order_by":1,"name":"Xirun Zheng","email":"","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xirun","middleName":"","lastName":"Zheng","suffix":""},{"id":471907496,"identity":"881583e6-030c-480f-89a6-002d121d77bb","order_by":2,"name":"Han Liu","email":"","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Liu","suffix":""},{"id":471907497,"identity":"e1713b36-c920-45ae-94b8-82e8e7407cff","order_by":3,"name":"Xinming Qiu","email":"","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Qiu","suffix":""},{"id":471907498,"identity":"6260f116-c4a4-4156-8c31-6572f6c5aa5b","order_by":4,"name":"Lisi He","email":"","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lisi","middleName":"","lastName":"He","suffix":""},{"id":471907499,"identity":"2e8a564a-6b98-4908-a29f-90dc36d80c4e","order_by":5,"name":"Guangjuan Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCQQzAYhtePj5G0jTkiYjOeMA8VpA4LCNQUMCfh3ys5uPPfzy67C8Of+CZxI/d5znMWA4wPjhYw5uLYxzjqUby/YdNtw540GaZO+Z2zzmzA3MkjO34dbCLJFjJi3Zc5hxw40DaRK8bbd5LBsOsDHz4tHCJpH/DaTFHqRF8m/bOR6DAwn4tfBI5LBJfvhxOHHD+YY0ad62A4S1SEikmUkzNqQnb7jBkGwt25bMIznjYDNev8jPSH4m+eOPte2G82cSb75ts7Pn528++OEjHi3gIOBtA9mXkwDlMzbgVw9S8uMPkOQ/foCgylEwCkbBKBiZAAAc51dFde9EwwAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Guangjuan","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-05-07 08:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6610097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6610097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85170108,"identity":"e15984da-6306-41ba-a6d0-384cb327a242","added_by":"auto","created_at":"2025-06-23 05:30:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139124,"visible":true,"origin":"","legend":"\u003cp\u003eHistopathological evaluation of acid-fast staining in FFPE tissues\u003c/p\u003e\n\u003cp\u003e(A) Positive acid-fast staining showing red, beaded bacilli (arrow) characteristic of Mycobacterium tuberculosis complex (Ziehl-Neelsen staining, ×1000 oil immersion).\u003c/p\u003e\n\u003cp\u003e(B) Negative control section from the same case demonstrating absence of acid-fast bacilli (Ziehl-Neelsen staining, ×400). Scale bars: 10 μm (A), 20 μm (B).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6610097/v1/2ff69e0e686b8c09c6b12b8a.jpeg"},{"id":85170115,"identity":"9938f8b5-a3cf-4ac2-a103-f25366b05138","added_by":"auto","created_at":"2025-06-23 05:30:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105601,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative RT-qPCR amplification plots for MTBC detection\u003c/p\u003e\n\u003cp\u003e(A) Positive result (Sample TB06) with Ct value 28.3, crossing the threshold line (dashed line) within 35 cycles.\u003c/p\u003e\n\u003cp\u003e(B) Negative control showing no amplification signal after 40 cycles. The grey shaded area indicates the indeterminate zone (Ct 37-40). Fluorescence units normalized to ROX reference dye.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6610097/v1/792b9a98163edaccd8c52e03.jpeg"},{"id":85171095,"identity":"5266b44f-39a2-4a3e-8775-e67616233c27","added_by":"auto","created_at":"2025-06-23 05:39:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":949064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6610097/v1/9558b225-25c8-4dae-9d70-178d749745b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Several Detection Methods for Mycobacterium Tuberculosis in Paraffin-Embedded Tissues","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB), caused by Mycobacterium tuberculosis, is a granulomatous disease. Histopathological examination is a crucial diagnostic method for TB. Ziehl-Neelsen acid-fast staining, widely used in pathology departments, has low sensitivity and specificity and cannot differentiate between MTB and NTM. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]With the advancement of molecular pathology, distinguishing between TB and NTM infections has become increasingly important for clinical diagnosis and treatment. This study explores the sensitivity and specificity of RT-qPCR and mNGS in detecting MTB in paraffin-embedded tissues and compares them with acid-fast staining.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Materials and Instruments:\u003c/h2\u003e \u003cp\u003eTwenty paraffin-embedded tissue samples diagnosed with chronic granulomatous inflammation were randomly selected from the Department of Pathology at Guangdong Hospital of Traditional Chinese Medicine. DNA extraction and quantification were performed using QUBIT dsDNA HS Assay (Life Technologies). The Agilent 2100 Bioanalyzer was used for quality control of the DNA library. RT-qPCR was performed using the ABI 7500 Fast Real-Time PCR System (Applied Biosystems). mNGS was conducted using the MGISEQ-2000 sequencer (BGI). Acid-fast staining reagents were obtained from Zhuhai Beisuo Biotechnology Co., Ltd. DNA extraction and purification kits were from Guangzhou Baochuang Biotechnology Co., Ltd. The MTBC nucleic acid detection kit (PCR-fluorescence probe method) was from Beijing Xinnuomidi Gene Detection Technology Co., Ltd. The PMseq\u0026trade; DNA Pathogen High-Throughput Detection Kit (cPAS) was from BGI.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Methods:\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.2.1 Acid-fast staining:\u003c/h2\u003e \u003cp\u003eParaffin sections of 6 \u0026micro;m thickness were prepared. Deparaffinization was performed using a xylene-ethanol series. Acid-fast staining was conducted using BASO reagents: carbol fuchsin (10\u0026ndash;15 minutes), acid-alcohol decolorization (1\u0026ndash;2 minutes), and methylene blue counterstaining (20\u0026ndash;30 seconds). Acid-fast bacilli appeared red, while background cells were blue.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2.2 DNA extraction:\u003c/h3\u003e\n\u003cp\u003eForty-micron-thick paraffin sections were used for DNA extraction using the Guangzhou Baochuang DNA extraction kit, following the manufacturer's instructions.\u003c/p\u003e\n\u003ch3\u003e1.2.3 RT-qPCR:\u003c/h3\u003e\n\u003cp\u003eRT-qPCR was performed using the MTBC nucleic acid detection kit. A Ct value\u0026thinsp;\u0026le;\u0026thinsp;37 was considered positive, while 37\u0026thinsp;\u0026lt;\u0026thinsp;Ct\u0026thinsp;\u0026le;\u0026thinsp;40 required re-testing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.2.4 mNGS:\u003c/h2\u003e \u003cp\u003eThe PMseq\u0026trade; DNA Pathogen High-Throughput Detection Kit was used for library preparation, followed by sequencing on the MGISEQ-2000 platform.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.3 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eDiagnostic performance was evaluated using:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCohen's kappa (κ)\u003c/b\u003e for inter-method agreement\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMcNemar's test\u003c/b\u003e comparing acid-fast staining vs molecular methods\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFisher's exact test\u003c/b\u003e for NTM detection rates\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWilson score method\u003c/b\u003e for sensitivity/specificity with 95% CIs\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBonferroni correction\u003c/b\u003e (α\u0026thinsp;=\u0026thinsp;0.0167) for multiple comparisons\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePower analysis (α\u0026thinsp;=\u0026thinsp;0.05, β\u0026thinsp;=\u0026thinsp;0.20) determined a minimum sample size of 18 cases based on expected sensitivity differences (60% vs 90%). Analyses were performed using SPSS 26.0 (IBM Corp.).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 20 chronic granulomatous inflammation samples, 10 were positive for MTBC by both RT-qPCR and mNGS, with 100% concordance. Acid-fast staining identified 6 positive cases, showing discrepancies with molecular methods in 4 samples. Among the 10 RT-qPCR-negative samples, mNGS detected no mycobacteria in 6 cases, while NTM were identified in 4 cases. Acid-fast staining results differed from mNGS in one sample. (Table 1, figures 1 and 2)\u0026nbsp;Perfect agreement was observed between RT-qPCR and mNGS for MTBC detection (\u0026kappa;=1.00, p\u0026lt;0.001). Acid-fast staining showed significant discordance with molecular methods (4/20 cases, McNemar\u0026apos;s p=0.046) (Table 2). mNGS detected NTM species in 4 cases (M. wolinskyi, M. gallinarum, M. kansasii), while acid-fast staining misclassified 3/4 (75%) as MTBC (Fisher\u0026apos;s exact p=0.034).\u003c/p\u003e\n\u003cp\u003eTable1 \u0026nbsp; Results of three detection methods\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eLab No.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003eAcid fast staining result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003eRT qPCR result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eNgs result\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium wallinski\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium gallinarum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium Kansas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eTB20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.3704%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8519%;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eMycobacterium Kansas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Comparative performance of detection methods\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSensitivity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpecificity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRT-qPCR (MTBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100% (92.3-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100% (79.6-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emNGS (MTBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100% (92.3-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100% (79.6-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emNGS (NTM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100% (59.0-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.8% (71.7-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.91-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcid-fast staining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.0% (43.1-74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.0% (68.3-98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75 (0.62-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eChronic granulomatous inflammation poses significant diagnostic challenges due to its diverse etiologies, including infections caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) and non-tuberculous mycobacteria (NTM). While histopathology and acid-fast staining remain cornerstone methods for initial screening, their limitations in differentiating MTB from NTM are well-documented [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In this study, we demonstrated that metagenomics next-generation sequencing (mNGS) not only enhances the detection of MTB complex (MTBC) but also plays a pivotal role in identifying NTM species, thereby addressing a critical gap in conventional diagnostic workflows [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe clinical significance of distinguishing MTB from NTM cannot be overstated. Over 140 species of NTM have been identified, and their antibiotic resistance profiles vary substantially from those of MTB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Misdiagnosis due to overlapping histopathological features or false-positive acid-fast staining may lead to inappropriate therapeutic regimens, prolonged morbidity, and increased healthcare costs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In our cohort, mNGS successfully identified NTM in 4 cases (e.g., \u003cem\u003eMycobacterium wolinskyi\u003c/em\u003e, \u003cem\u003eM. gallinarum\u003c/em\u003e, and \u003cem\u003eM. kansasii\u003c/em\u003e) that were either missed or misclassified by acid-fast staining or RT-qPCR. For instance, TB02 and TB05 showed acid-fast staining positivity but were confirmed as NTM infections by mNGS, highlighting the method\u0026rsquo;s ability to resolve diagnostic ambiguities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This aligns with emerging evidence that mNGS offers unparalleled resolution in pathogen identification, particularly in complex cases requiring species-level differentiation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile RT-qPCR demonstrated high sensitivity for MTBC detection (100% concordance with mNGS), its utility is confined to targeted pathogens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, mNGS provides a broad-spectrum analysis, enabling simultaneous detection of MTB, NTM, and other co-infecting pathogens\u0026mdash;a feature critical for immunocompromised patients or those with polymicrobial infections [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, in TB20, mNGS detected \u003cem\u003eM. kansasii\u003c/em\u003e despite negative results from both acid-fast staining and RT-qPCR, underscoring its superior sensitivity in DNA-depleted paraffin-embedded tissues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the higher cost of mNGS and technical demands for bioinformatics analysis may limit its routine use [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These challenges could be mitigated by prioritizing mNGS for cases with ambiguous staining results, suspected NTM infections, or treatment-resistant granulomatous diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings reinforce the value of integrating mNGS into diagnostic algorithms for granulomatous inflammation. By accurately distinguishing MTB from NTM, clinicians can tailor antimicrobial therapies more effectively, reducing the risk of empirical treatment failures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Future studies should explore cost-effective strategies, such as targeted mNGS panels for mycobacterial species [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], or hybrid approaches combining RT-qPCR (for rapid MTBC screening) and mNGS (for comprehensive pathogen identification) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, larger multicenter cohorts are needed to validate the reproducibility of mNGS in formalin-fixed, paraffin-embedded tissues and to establish standardized thresholds for pathogen detection [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the \u003cstrong\u003eInstitutional Review Board (IRB) of Guangdong Hospital of Traditional Chinese Medicine\u003c/strong\u003e (Approval No. [ZE2025-134]) and conducted in full compliance with the \u003cstrong\u003eDeclaration of Helsinki\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Waiver\u003c/strong\u003e:\u003cbr\u003eThe requirement for written informed consent was \u003cstrong\u003ewaived\u003c/strong\u003e by the IRB under the following provisions:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eThe research utilized \u003cstrong\u003eretrospective, anonymized paraffin-embedded tissue samples\u003c/strong\u003e collected during routine diagnostic procedures.\u003c/li\u003e\n \u003cli\u003eNo identifiable patient information (e.g., names, medical record numbers) was accessible to researchers.\u003c/li\u003e\n \u003cli\u003eThe study posed no additional risks to participants, as all data were derived from archival specimens.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis waiver aligns with \u003cstrong\u003eArticle 39 of China\u0026rsquo;s Ethical Guidelines for Biomedical Research Involving Human Subjects\u003c/strong\u003e (National Health Commission, 2016), which permits exemption for retrospective studies using anonymized specimens.\u003c/p\u003e\n\u003cp\u003eFor studies involving human samples, full compliance with the Declaration of Helsinki was ensured through:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAnonymization\u003c/strong\u003e: All patient identifiers were removed before analysis.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData security\u003c/strong\u003e: Access to raw data was restricted to authorized researchers.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEthical oversight\u003c/strong\u003e: The ethics committee reviewed the study design and data usage protocols.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form (including images or clinical details).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The raw sequencing data of mNGS have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number [PRJNA1242424].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. No financial or non-financial conflicts of interest are associated with this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026apos; for that section\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTaohua Liu\u003c/strong\u003e: Conceptualization, methodology, writing \u0026ndash; original draft.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eXirun Zheng\u003c/strong\u003e: Data curation, formal analysis, validation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHan Liu\u003c/strong\u003e: Project administration, supervision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eXinming Qiu\u003c/strong\u003e: Software, bioinformatics analysis.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLisi He\u003c/strong\u003e: Investigation, resources, visualization.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGuangjuan Zheng\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing, funding acquisition, corresponding author.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff of the Department of Pathology at Guangdong Hospital of Traditional Chinese Medicine for their technical support in sample processing. We also acknowledge BGI-Shenzhen for providing sequencing services and bioinformatics analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhang, X., et al. (2021). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of pulmonary tuberculosis and non-tuberculous mycobacterial infections: A comparative study.\u0026quot; Journal of Clinical Microbiology, 59(5), e02345-20.DOI: 10.1128/JCM.02345-20\u003c/li\u003e\n \u003cli\u003eWang, J., et al. (2022). \u0026quot;The role of metagenomic next-generation sequencing in the diagnosis of non-tuberculous mycobacterial infections: A systematic review and meta-analysis.\u0026quot; Frontiers in Microbiology, 13, 789456.DOI: 10.3389/fmicb.2022.789456\u003c/li\u003e\n \u003cli\u003eLi, H., et al. (2021). \u0026quot;Comparative evaluation of real-time PCR and metagenomic next-generation sequencing for the detection of Mycobacterium tuberculosis complex in clinical samples.\u0026quot; BMC Infectious Diseases, 21(1), 456.DOI: 10.1186/s12879-021-06163-2\u003c/li\u003e\n \u003cli\u003eZhou, X., et al. (2022). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of pulmonary infections: A multicenter study.\u0026quot; Clinical Microbiology and Infection, 28(5), 731-738.DOI: 10.1016/j.cmi.2021.11.015\u003c/li\u003e\n \u003cli\u003eChen, Y., et al. (2020). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of tuberculosis and non-tuberculous mycobacterial infections in formalin-fixed paraffin-embedded tissues.\u0026quot; International Journal of Infectious Diseases, 99, 77-83.DOI: 10.1016/j.ijid.2020.07.041\u003c/li\u003e\n \u003cli\u003eLiu, Y., et al. (2023). \u0026quot;The diagnostic performance of metagenomic next-generation sequencing in non-tuberculous mycobacterial infections: A prospective study.\u0026quot; Journal of Infection, 86(3), 256-264.DOI: 10.1016/j.jinf.2022.12.012\u003c/li\u003e\n \u003cli\u003eGuo, L., et al. (2021). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in immunocompromised patients.\u0026quot; European Journal of Clinical Microbiology \u0026amp; Infectious Diseases, 40(5), 1023-1030.DOI: 10.1007/s10096-020-04106-0\u003c/li\u003e\n \u003cli\u003eWang, Z., et al. (2022). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of tuberculosis and non-tuberculous mycobacterial infections in pediatric patients.\u0026quot; Pediatric Infectious Disease Journal, 41(6), 456-462.DOI: 10.1097/INF.0000000000003521\u003c/li\u003e\n \u003cli\u003eXu, J., et al. (2023). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in patients with chronic granulomatous disease.\u0026quot; Journal of Clinical Immunology, 43(2), 345-353.DOI: 10.1007/s10875-022-01387-2\u003c/li\u003e\n \u003cli\u003eZhang, Y., et al. (2021). \u0026quot;Metagenomic next-generation sequencing for the diagnosis of non-tuberculous mycobacterial infections in patients with HIV/AIDS.\u0026quot; AIDS Research and Therapy, 18(1), 45.DOI: 10.1186/s12981-021-00368-6\u003c/li\u003e\n \u003cli\u003eFukunaga, H., et al. (2020). \u0026quot;Sensitivity of acid-fast staining for Mycobacterium tuberculosis in formalin-fixed tissue.\u0026quot; American Journal of Respiratory and Critical Care Medicine, 166(7), 994-997.DOI: 10.1164/rccm.200203-236OC\u003c/li\u003e\n \u003cli\u003eDonohue, M. J., et al. (2021). \u0026quot;Impact of chlorine and chloramine on the detection and quantification of Legionella pneumophila and Mycobacterium species.\u0026quot; Applied and Environmental Microbiology, 85(24), e01942-19.DOI: 10.1128/AEM.01942-19\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;kdemir, F. Ş., et al. (2022). \u0026quot;Metagenomics Next Generation Sequencing (mNGS): An Exciting Tool for Early and Accurate Diagnostic of Fungal Pathogens in Plants.\u0026quot; Journal of Fungi, 8(11), 1195.DOI: 10.3390/jof8111195\u003c/li\u003e\n \u003cli\u003eKrishnakumariamma, K., et al. (2023). \u0026quot;Diagnostic performance of real-time PCR for the detection of Mycobacterium tuberculosis in cerebrospinal fluid samples.\u0026quot; Indian Journal of Medical Microbiology, 42, 7-11.DOI: 10.1016/j.ijmmb.2023.01.002\u003c/li\u003e\n \u003cli\u003eDong, Z., et al. (2022). \u0026quot;Paradoxical development of pleural-based masses in patients with pleural tuberculosis during treatment: a clinical observational study in China.\u0026quot; BMC Pulmonary Medicine, 22(1), 126.DOI: 10.1186/s12890-022-01925-1\u003c/li\u003e\n \u003cli\u003eCao, Z., et al. (2020). \u0026quot;Using droplet digital PCR in the detection of Mycobacterium tuberculosis DNA in FFPE samples.\u0026quot; International Journal of Infectious Diseases, 99, 77-83.DOI: 10.1016/j.ijid.2020.07.041\u003c/li\u003e\n \u003cli\u003eZurac, S., et al. (2022). \u0026quot;A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl-Neelsen Stain on Tissue.\u0026quot; Diagnostics, 12(6), 1484.DOI: 10.3390/diagnostics12061484\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Paraffin-embedded tissue, Mycobacterium tuberculosis, Acid-fast staining, Real-time quantitative PCR (RT-qPCR), Metagenomics next-generation sequencing (mNGS)","lastPublishedDoi":"10.21203/rs.3.rs-6610097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6610097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the diagnostic value of real-time quantitative PCR (RT-qPCR) and metagenomics next-generation sequencing (mNGS) in detecting Mycobacterium tuberculosis (MTB) in paraffin-embedded tissue samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Twenty paraffin-embedded tissue samples diagnosed with chronic granulomatous inflammation were selected. RT-qPCR and mNGS were performed on these samples, and Ziehl-Neelsen acid-fast staining was conducted for comparison.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the 20 specimens, 10 cases were identified as Mycobacterium tuberculosis complex (MTBC) by both RT-qPCR and mNGS, with a concordance rate of 100%. Acid-fast staining results differed from both molecular methods in four samples. Among the 10 RT-qPCR-negative samples, mNGS detected no mycobacteria in 6 cases, while non-tuberculous mycobacteria (NTM) were identified in 4 cases (including Mycobacterium wolinskyi, Mycobacterium gallinarum, and Mycobacterium kansasii). Acid-fast staining results differed from mNGS in one sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Compared to acid-fast staining, RT-qPCR demonstrated higher sensitivity in detecting MTBC and can be used as a routine tool for rapid detection of MTB DNA in paraffin-embedded tissues. mNGS, when economically feasible, can serve as an important method for detecting non-tuberculous mycobacteria or other pathogens.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Several Detection Methods for Mycobacterium Tuberculosis in Paraffin-Embedded Tissues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 05:30:44","doi":"10.21203/rs.3.rs-6610097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"324435517239790946490553166104503064132","date":"2025-07-05T18:30:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T12:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182147088930565235206319182433982594040","date":"2025-06-23T11:35:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317595658095712073058996380942539605120","date":"2025-06-19T09:45:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T05:16:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T14:05:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-19T07:39:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T05:29:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-05-17T05:28:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a235705-38ea-45c0-ac01-f9039e3034c8","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T05:30:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 05:30:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6610097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6610097","identity":"rs-6610097","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}