Tuberculosis (TB) is one of the top 10 causes of death worldwide. It is challenging to find methods of diagnosis of active pulmonary TB that are sensitive enough to detect cases for proper treatment before unintentional transmission. Droplet digital PCR (ddPCR) is a highly sensitive method to detect genetic material of pathogens, but it has rarely been used for diagnosis of TB. This study compared the sensitivity of ddPCR with that of GeneXpert and AFB smear microscopy in 180 leftover sputum samples from patients suspected of having TB on the basis of clinical symptoms and radiography. Absolute quantification of copy numbers of MTB-specific genes was possible using ddPCR targeting the mpt64 gene. Among the 180 samples, 41.1% were diagnosed as having TB using ddPCR. The sensitivities of AFB smear microscopy, GeneXpert and ddPCR were 41.9%, 82.4% and 100%, respectively. AFB smear microscopy and GeneXpert both had a specificity of 100%, and the specificity of ddPCR was 95.3%. The accuracy of ddPCR (97.2%) is higher than that of GeneXpert (92.7%). This robust ddPCR system could potentially be used as a method for early diagnosis of TB.

Heteroresistance in MTB refers to the presence of distinct subpopulations of bacteria with varying levels of antibiotic susceptibility within a population. Multidrug-resistant and rifampicin-resistant TB are serious global health concerns. In this study, we aimed to determine the prevalence of heteroresistance in MTB from sputum samples of new TB cases using Droplet Digital PCR mutation detection assays for katG and rpoB genes, which are commonly associated with resistance to isoniazid and rifampicin, respectively. We found that out of 79 samples, 9 (11.4%) exhibited mutations in katG and rpoB genes. INH mono-resistant TB, RIF mono-resistant TB, and MDR-TB samples constituted 1.3%, 6.3%, and 3.8% of new TB cases, respectively. Heteroresistance in katG, rpoB, and both genes were found in 2.5%, 5%, and 2.5% of total cases, respectively. Our results suggest that these mutations may have arisen spontaneously, as the patients had not yet received anti-TB drugs. ddPCR is a valuable tool for the early detection and management of DR-TB, as it can detect both mutant and wild-type strains in a population, enabling the detection of heteroresistance and MDR-TB. Overall, our findings highlight the importance of early detection and management of DR-TB for effective TB control (in katG, rpoB, and katG/rpoB).

Single-cell RNA sequencing (scRNA-seq) provides an important means to reveal the heterogeneity and dynamic processes of tissues, organisms, and complex diseases, but technical capture loss (dropout) often obscures true biological expression, and existing imputation methods have difficulty distinguishing biological zeros (silent expression) from technical noise. To address this, we propose the imputation framework scZN. scZN assumes that the observed scRNA-seq data arise from a combination of RNA’s two-state transcription process and dropout, and formulates imputation as nonnegative factorization: decomposing the raw count matrix into two interpretable nonnegative factors, performing learning and optimization under constraints from prior knowledge and multiple regularizations, thereby reconstructing the cellular expression landscape. Experiments show that scZN can capture the true distributional characteristics at both the gene and cell levels and significantly suppress spurious activation of genes that should not be expressed. Across multiple real datasets, it outperforms dozens of state-of-the-art methods. Especially in complex experimental design scenarios, scZN markedly improves trajectory inference for embryonic stem cells and mouse dentate gyrus data. In Alzheimer’s disease data, scZN can also effectively recover pathways related to neuroinflammation, improving downstream scRNA-seq analysis. Overall, scZN provides a unified framework for missing-value imputation and expression reconstruction that combines accuracy and interpretability.