Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

ALL-tRNAseq enables robust tRNA profiling in tissue samples.

Transfer RNAs (tRNAs) are small adaptor RNAs essential for mRNA translation. Alterations in the cellular tRNA population can directly affect mRNA decoding rates and translational efficiency during cancer development and progression. To evaluate changes in the composition of the tRNA pool, multiple sequencing approaches have been developed to overcome reverse transcription blocks caused by the stable structures of these molecules and their numerous base modifications. However, it remains unclear whether current sequencing protocols faithfully capture tRNAs existing in cells or tissues. This is specifically challenging for clinical tissue samples that often present variable RNA qualities. For this reason, we developed ALL-tRNAseq, which combines the highly processive MarathonRT and RNA demethylation for the robust assessment of tRNA expression, together with a randomized adapter ligation strategy prior to reverse transcription to assess tRNA fragmentation levels in both cell lines and tissues. Incorporation of tRNA fragments not only informed on sample integrity but also significantly improved tRNA profiling of tissue samples. Our data showed that our profiling strategy effectively improves classification of oncogenic signatures in glioblastoma and diffuse large B-cell lymphoma tissues, particularly for samples presenting higher levels of RNA fragmentation, further highlighting the utility of ALL-tRNAseq for translational research.

Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories.

Bayesian coalescent skyline plot models are widely used to infer demographic histories. The first (non-Bayesian) coalescent skyline plot model assumed a known genealogy as data, while subsequent models and implementations jointly inferred the genealogy and demographic history from sequence data, including heterochronous samples. Overall, there exist multiple different Bayesian coalescent skyline plot models which mainly differ in two key aspects: (i) how changes in population size are modeled through independent or autocorrelated prior distributions, and (ii) how many change-points in the demographic history are used, where they occur and if the number is pre-specified or inferred. The specific impact of each of these choices on the inferred demographic history is not known because of two reasons: first, not all models are implemented in the same software, and second, each model implementation makes specific choices that the biologist cannot influence. To facilitate a detailed evaluation of Bayesian coalescent skyline plot models, we implemented all currently described models in a flexible design into the software RevBayes. Furthermore, we evaluated models and choices on an empirical dataset of horses supplemented by a small simulation study. We find that estimated demographic histories can be grouped broadly into two groups depending on how change-points in the demographic history are specified (either independent of or at coalescent events). Our simulations suggest that models using change-points at coalescent events produce spurious variation near the present, while most models using independent change-points tend to over-smooth the inferred demographic history.

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus.

Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10-5) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.

A comprehensive assessment of cell type-specific differential expression methods in bulk data.

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network.

Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

A ratiometric fluorescence probe for bisulfite detection in live cells and meat samples.

The development of reliable probe technology for the detection of bisulfite (HSO3-) in situ in food and biological samples is contributing significantly to food quality and safety assurance as well as community health. In this work, a responsive probe, EHDI, is developed for ratiometric fluorescence detection of HSO3- in aqueous solution, meat samples, and living cells. The probe is designed based on the HSO3- triggered 1,4-addition of electron deficit C = C bond of EHDI. As a result of this specific 1,4-addition, the π-conjugation system was destructed, resulting in blue shifts of the emission from 687 to 440 nm and absorption from 577 to 355 nm. The probe has good water solubility, high sensitivity and selectivity, allowing it to be used for imaging of HSO3- internalization and production endogenously. The capability of probe EHDI for HSO3- was then validated by traditional HPLC technology, enabling accurately detect HSO3- in beef samples. The successful development of this probe thus offers a new tool for investigating HSO3- in situ in food and biological conditions.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices With Zeno SWATH Data-Independent Acquisition.

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nanoflow and microflow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized, and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH) data-independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness, and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3300 proteins were identified in tissues at 2 µg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive, and robust proteomic workflows using analytical flow and is amenable to large-scale studies.

Unrevealing the connection between real sample analysis and analytical method. The case of cytokines.

Cytokines are compounds that belong to a special class of signaling biomolecules that are responsible for several functions in the human body, being involved in cell growth, inflammatory, and neoplastic processes. Thus, they represent valuable biomarkers for diagnosing and drug therapy monitoring certain medical conditions. Because cytokines are secreted in the human body, they can be detected in both conventional samples, such as blood or urine, but also in samples less used in medical practice such as sweat or saliva. As the importance of cytokines was identified, various analytical methods for their determination in biological fluids were reported. The gold standard in cytokine detection is considered the enzyme-linked immunosorbent assay method and the most recent ones have been considered and compared in this study. It is known that the conventional methods are accompanied by a few disadvantages that new methods of analysis, especially electrochemical sensors, are trying to overcome. Electrochemical sensors proved to be suited for the elaboration of integrated, portable, and wearable sensing devices, which could also facilitate cytokines determination in medical practice.

Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.

Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.

Meta-learning shows great potential in plant disease recognition under few available samples.

Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta-learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

DNAJB6 is expressed in neurons and oligodendrocytes of the human brain.

DNAJB6 is a suppressor of α-synuclein aggregation in vivo and in vitro. DNAJB6 is strongly expressed in the brain, and its overall protein expression is altered in neurodegenerative conditions such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA). These two diseases are characterized by accumulation of aggregated α-synuclein in neurons and oligodendrocytes, respectively. To further explore this, we employed post-mortem normal human brain material to investigate the regional and cell type specific protein expression of DNAJB6. We found that the DNAJB6 protein is ubiquitously expressed across various regions of the brain. Notably, we demonstrate for the first time that DNAJB6 is present in nearly half (41%-53%) of the oligodendrocyte population and in the majority (68%-80%) of neurons. However, DNAJB6 was only sparsely present in other cell types such as astrocytes and microglia. Given that α-synuclein aggregation in oligodendrocytes is a hallmark of MSA, we investigated DNAJB6 presence in MSA brains compared to control brains. We found no significant difference in the percentage of oligodendrocytes where DNAJB6 was present in MSA brains relative to control brains. In conclusion, our results reveal an expression of the DNAJB6 protein across various regions of the human brain, and that DNAJB6 is almost exclusively present in neurons and oligodendrocytes. Since prior studies have shown that PD and MSA brains have altered levels of DNAJB6 relative to control brains, DNAJB6 may be an interesting target for drug development.

Ascites-derived hsa-miR-181a-5p serves as a prognostic marker for gastric cancer-associated malignant ascites.

BACKGROUND: Peritoneal carcinomatosis was the main reason leading to gastric cancer (GC)-related death. We aimed to explore the roles of dysregulated microRNAs (miRNAs) and related immune regulation activities in GC-associated malignant ascites. METHODS: GSE126399 were downloaded from GEO database. Differentially expressed miRNAs in GC ascites samples was firstly screened, and critical miRNAs were further investigated by LASSO (least absolute shrinkage and selection operator) logistic regression and random forest (RF) algorithm. Receiver operating characteristic of critical miRNAs was also constructed. Moreover, functional analysis, immune cell infiltration associated with differentially expressed mRNAs were further analyzed. After selecting key modules by weighted gene co-expression network analysis, mRNAs related with survival performance and transcription factor (TF)-miRNA-mRNA network were constructed. RESULTS: Hsa-miR-181b-5p was confirmed as critical differentially expressed miRNAs in GC ascites. Then, the tumor samples were divided into high- and low- expression groups divided by mean expression levels of hsa-miR-181b-5p, and subjects with high hsa-miR-181b-5p levels had better survival outcomes. In total, 197 differentially expressed mRNAs associated with hsa-miR-181b-5p levels were obtained, and these mRNAs were mainly enriched in muscle activity and vascular smooth muscle contraction. Hsa-miR-181b-5 was positively related with activated CD4 T cells and negatively related with eosinophil. 17 mRNAs were selected as mRNAs significantly related with prognosis of GC, such as PDK4 and RAMP1. Finally, 75 TF-miRNA-mRNA relationships were obtained, including 15 TFs, hsa-miR-181b-5p, and five mRNAs. CONCLUSION: Our data suggest that the differentially expressed hsa-miR-181b-5p in ascites samples of GC patients may be a valuable prognostic marker and a potential target for therapeutic intervention, which should be validated in the near future.

A single workflow for multi-species blood transcriptomics.

BACKGROUND: Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. RESULTS: Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. CONCLUSIONS: Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Association of opium use and tobacco smoking with α-, ß-, and γ-human papillomavirus oral infection.

Head and neck squamous cell carcinomas (HNSCCs) are linked to tobacco smoking, opium use, and human papillomavirus (HPV) infection. However, little is known about the association of HPV infection with risk factors of HNSCCs, including opium and tobacco use. This cross-sectional analysis of a national multi-center case-control study in Iran included 498 HNSCC cases and 242 controls. We investigated the association of opium and tobacco use with α- (n = 21), ß- (n = 46), and γ-HPV (n = 52) types in saliva samples using type-specific bead-based multiplex genotyping assays (TS-MPG). We found that α-HPV positivity was significantly associated with tobacco smoking (OR = 10.35; 95% CI = 1.15, 93; p = .03), but not with opium use (OR = 1.06; 95% CI = 0.41, 2.76; p = .89). Additionally, tobacco smoking correlated with an elevated risk of ß-species 2 HPV infection (OR = 1.28; 95% CI = 1.04, 1.58; p = .020). Conversely, opium use showed a positive association with γ-species 12 HPV infection (OR = 5.67; 95% CI = 1.43, 22.44; p = .013). These findings indicate that tobacco and opium use may influence the risk of HPV infection in different ways depending on the HPV genus and species. Further studies are needed to replicate these findings in other populations.

Polyhexamethylene Biguanide Reduces High-Risk Human Papilloma Virus Viral Load in Cervical Cell Samples Derived from ThinPrep Pap Test.

Human papilloma virus (HPV) infection and its progression still represent a great medical challenge worldwide. Clinical evidence has demonstrated the beneficial effects of polyhexamethylene biguanide (PHMB) on HPV clinical manifestations; however, evidence of the effect of this molecule on HPV viral load is still lacking. In this in vitro study, 13 ThinPrep Papanicolaou (Pap) tests were treated with a PHMB solution (0.10 g/100 mL) for 2 h. We observed no cytological changes but a significant reduction in the viral load of high-risk (HR) HPV after PHMB treatment, also revealing a dose-dependent antiviral effect. In addition, by stratifying the obtained results according to HR-HPV genotype, we observed a significant reduction in the viral load of HPV 16, P2 (56, 59, 66), 31, and P3 (35, 39, 68) and a strong decrease in the viral load of HPV 45, 52, and P1 (33, 58). Overall, 85% of the analyzed cervical cell samples exhibited an improvement in HPV viral load after PHMB exposure, while only 15% remain unchanged. For the first time, the data from this pilot study support the activity of PHMB on a specific phase of the HPV viral lifecycle, the one regarding the newly generated virions, reducing viral load and thus blocking the infection of other cervical cells.

Microbiome composition recovery after liver transplantation correlates with initial liver disease severity and antibiotics treatment.

Liver transplantation (LT) is crucial for end-stage liver disease, but it is linked to infection risks. Pathobionts, microorganisms potentially harmful under specific conditions, can cause complications posttransplant. Monitoring such pathogens in fecal samples can be challenging and therefore remains underexplored post-LT. This study aimed to analyze the gut microbiome before and after LT, tracking pathobionts and correlating clinical data. The study involved 17 liver transplant recipients, 17 healthy relatives (spouses), and 13 donors. Gut samples collected pretranplantation and posttransplantation underwent bacterial and fungal profiling through DNA sequencing. Quantitative polymerase chain reaction was used to assess microbial load. Statistical analyses included alpha and beta diversity measures, differential abundance analysis, and correlation tests between microbiome and clinical parameters. Microbiome analysis revealed dynamic changes in diversity posttransplant. Notably, high-severity patients showed persistent and greater dysbiosis during the first months post-LT compared with low-severity patients, partly due to an antibiotic treatment pre-LT. The analysis identified a higher proportion of pathogens such as Escherichia coli/Shigella flexneri in high-severity cases posttransplant. Furthermore, butyrate producers including Roseburia intestinalis, Anaerostipes hadrus, and Eubacterium coprostanoligenes were positively correlated with levels of albumin. This study offers valuable insights into post-LT microbiome changes, shedding light on the need for tailored prophylactic treatment post-LT.

Evidence of the Monopolar-Dipolar Crossover Regime: A Multiscale Study of Ferroelastic Domains by In Situ Microscopy Techniques.

The elastic interaction between kinks (and antikinks) within domain walls plays a pivotal role in shaping the domain structure, and their dynamics. In bulk materials, kinks interact as elastic monopoles, dependent on the distance between walls (d-1) and typically characterized by a rigid and straight domain configuration. In this work the evolution of the domain structure is investigated, as the sample size decreases, by the means of in situ heating microscopy techniques on free-standing samples. As the sample size decreases, a significant transformation is observed: domain walls exhibit pronounced curvature, accompanied by an increase in both domain wall and junction density. This transformation is attributed to the pronounced influence of kinks, inducing sample warping, where "dipole-dipole" interactions are dominant (d-2). Moreover, a critical thickness range that delineates a crossover between the monopolar and dipolar regimens is experimentally identified and corroborated by atomic simulations. These findings are relevant for in situ TEM studies and for the development of novel devices based on free-standing ferroic thin films and nanomaterials.

Nanopore-based targeted sequencing test for direct tuberculosis identification, genotyping, and detection of drug resistance mutations: a side-by-side comparison of targeted next-generation sequencing technologies.

We investigated the performance of the targeted next-generation sequencing (tNGS)-based Oxford Nanopore Diagnostics AmPORE TB assay, recently approved by the World Health Organization (WHO) as tuberculosis (TB) diagnostic test for the detection of drug resistance on respiratory specimens. A total of 104 DNA samples from Xpert MTB/RIF-positive TB sputum specimens were tested using the AmPORE TB kit, with the GenoScreen Deeplex Myc-TB as a comparative tNGS assay. For AmPORE TB, DNA samples were divided into five sequencing runs on the MinION device. Data analysis was performed using proprietary software. The WHO catalog of mutations was used for drug resistance interpretation. The assay achieved a high validity rate of 98% (102/104 DNA samples), homogeneous mean reads coverage across TB-positive specimens, and 100% positive and negative agreements for detecting mutations associated with resistance to rifampicin, pyrazinamide, fluoroquinolones, ethambutol, and capreomycin compared with Deeplex Myc-TB. The main discrepancies for the remaining drugs were attributable to the different assay panel designs. The AmPORE TB turnaround time was approximately 5-6 hours from extracted DNA to tNGS reporting for batches of 22 DNA samples. The AmPORE TB assay drastically reduced the time to tNGS reporting from days to hours and showed good performance for drug-resistant TB profiling compared with Deeplex Myc-TB. IMPORTANCE: Targeted next-generation sequencing (tNGS) of Mycobacterium tuberculosis provides comprehensive resistance predictions matched to new multidrug-resistant/rifampicin-resistant tuberculosis regimens and received World Health Organization approval for clinical use in respiratory samples in 2024. The advanced version of the Oxford Nanopore Diagnostics AmPORE TB tNGS kit was evaluated in this study for the first time and demonstrated good performance, flexibility, and faster turnaround time compared with the existing solutions.