{"title":"Single-Cell Measurement Revolution: A New Paradigm in Tuberculosis Diagnosis","authors":"Dianmo Ni, Gang Liu","doi":"10.1021/acsmeasuresciau.6c00245","DOIUrl":null,"url":null,"abstract":"<p>Despite a century of advances in tuberculosis (TB) diagnostics,\r\na deceptively simple question remains unanswered: Can we reliably\r\ndetect a single viable <em>Mycobacterium tuberculosis</em> (<em>Mtb</em>) bacillus in a clinical sample? In this perspective,\r\nwe examine three complementary single-cell measurement platforms that\r\nface significant challenges in addressing this seemingly simple yet\r\nexceedingly difficult question. Single-cell RNA sequencing (scRNA-seq)\r\ncan resolve host immune heterogeneity by identifying novel cell subsets\r\nand diagnostic biomarkers, such as <em>GBP5</em>, that are\r\notherwise obscured in bulk measurements. Direct <em>Mtb</em> scRNA-seq offers unprecedented insights into four critical biological\r\nmechanisms: infectivity, drug resistance, persister formation, and\r\nhost–pathogen interactions; however, its practical clinical\r\napplication remains limited. Complementing these genomic approaches,\r\nsingle-cell surface-enhanced Raman spectroscopy serves as a spectroscopic\r\nplatform to capture molecular heterogeneity among individual bacilli,\r\nrevealing drug-resistance signatures that are typically masked by\r\npopulation-averaged spectra. Furthermore, single-cell fluorescent\r\nprobes, arguably the most promising approach for ultimately overcoming\r\nthis challenge, act as chemical measurement platforms. They enable\r\nthe direct visualization of viable <em>Mtb</em> by leveraging\r\npathogen-specific metabolic, enzymatic, or transport-based fluorescence\r\nactivations. We critically evaluate existing translational barriers,\r\nincluding inadequate sensitivity across diverse clinical specimens\r\n(e.g., sputum, swabs, body fluids, tissues, and breath aerosols) as\r\nwell as high costs and incompatibility with routine clinical workflows.\r\nLooking ahead, we underscore future direction in TB diagnostic measurement\r\nscience, emphasizing specimen-driven probe design and the critical\r\nchallenge of distinguishing slow replication-competent bacilli from\r\nmetabolically active but nonreplicating populations. Collectively,\r\nthese emerging technologies chart a roadmap toward the ultimate goal\r\nof detecting a single viable <em>Mtb</em> bacterium in clinical\r\nsamples.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 5","pages":"1384–1407"},"PeriodicalIF":14.7000,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Measurement Science Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsmeasuresciau.6c00245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/9/15 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Despite a century of advances in tuberculosis (TB) diagnostics,
a deceptively simple question remains unanswered: Can we reliably
detect a single viable Mycobacterium tuberculosis (Mtb) bacillus in a clinical sample? In this perspective,
we examine three complementary single-cell measurement platforms that
face significant challenges in addressing this seemingly simple yet
exceedingly difficult question. Single-cell RNA sequencing (scRNA-seq)
can resolve host immune heterogeneity by identifying novel cell subsets
and diagnostic biomarkers, such as GBP5, that are
otherwise obscured in bulk measurements. Direct Mtb scRNA-seq offers unprecedented insights into four critical biological
mechanisms: infectivity, drug resistance, persister formation, and
host–pathogen interactions; however, its practical clinical
application remains limited. Complementing these genomic approaches,
single-cell surface-enhanced Raman spectroscopy serves as a spectroscopic
platform to capture molecular heterogeneity among individual bacilli,
revealing drug-resistance signatures that are typically masked by
population-averaged spectra. Furthermore, single-cell fluorescent
probes, arguably the most promising approach for ultimately overcoming
this challenge, act as chemical measurement platforms. They enable
the direct visualization of viable Mtb by leveraging
pathogen-specific metabolic, enzymatic, or transport-based fluorescence
activations. We critically evaluate existing translational barriers,
including inadequate sensitivity across diverse clinical specimens
(e.g., sputum, swabs, body fluids, tissues, and breath aerosols) as
well as high costs and incompatibility with routine clinical workflows.
Looking ahead, we underscore future direction in TB diagnostic measurement
science, emphasizing specimen-driven probe design and the critical
challenge of distinguishing slow replication-competent bacilli from
metabolically active but nonreplicating populations. Collectively,
these emerging technologies chart a roadmap toward the ultimate goal
of detecting a single viable Mtb bacterium in clinical
samples.
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.