Unveiling the cellular heterogeneity behind tuberculosis and nontuberculous mycobacterial diseases through cutting-edge genomic technologies
Imagine trying to understand a complex city by analyzing a blended smoothie of all its inhabitants—you'd get an average profile but miss the unique contributions of artists, engineers, teachers, and doctors. For decades, this was precisely how scientists studied the immune response to mycobacterial infections, including tuberculosis (TB)—the world's deadliest infectious disease until COVID-19 briefly surpassed it. When we homogenized tissue samples and analyzed them in bulk, we missed the critical conversations between individual cells that determine whether we successfully control these infections or succumb to active disease.
Single-cell genomics allows researchers to examine the genetic material of individual cells, revealing unprecedented complexity in immune responses.
This technology captures the precise interactions between cells that determine infection outcomes, moving beyond population averages.
Today, a revolutionary technology is changing everything. Single-cell genomics allows us to listen in on the activities of individual cells, revealing an astonishing complexity in how our bodies respond to mycobacterial threats. This isn't just incremental progress—it's fundamentally transforming our understanding of diseases that have plagued humanity for millennia. As we peer into the microscopic battlefields within infected lungs, we're discovering previously unrecognized cell types, understanding why treatments fail, and identifying entirely new therapeutic strategies to combat these formidable pathogens.
At its core, single-cell genomics is a suite of technologies that lets scientists examine the genetic material of individual cells rather than averaging signals across thousands or millions of cells. The most widely used approach—single-cell RNA sequencing (scRNA-seq)—captures the complete set of RNA molecules in each cell, revealing which genes are active and what functions the cell is performing at that exact moment.
Think of it this way: if a cell's DNA is its permanent instruction manual, then its RNA represents the specific pages being read at a given time. By cataloging which "pages" each cell is reading, researchers can determine not only what type of cell it is but also what it's doing—whether it's sounding alarm bells, repairing tissue, or even helping pathogens survive.
Figure 1: Single-cell RNA sequencing workflow from cell isolation to data analysis
Mycobacteria, including those that cause TB and nontuberculous mycobacterial (NTM) diseases, are masters of immune manipulation. They don't simply infect cells—they rewire our immune responses to create environments where they can persist for decades. The granulomas that form around these bacteria contain dozens of cell types in carefully organized microenvironments, and the fate of infection—whether the bacteria are controlled or whether disease progresses—depends on delicate balances between these cells.
Before single-cell genomics, our understanding of these granulomas was like trying to understand a political negotiation by listening to the crowd noise from outside the building. Now, we can place microphones on every participant and capture the precise conversations that lead to control or progression of disease. This isn't just academic—it has real implications for the 300,000 people diagnosed with NTM infections annually and the 10 million who develop TB each year worldwide 1 .
Single-cell RNA sequencing revealed Plin2-expressing macrophages as major sources of Ifnb1 and Cxcl1, specifically located at peripheral rim regions of necrotizing granulomas 1 .
These cells express both Il17a (anti-mycobacterial defense) and Pdcd1 (immune checkpoint), suggesting a dual nature—participating in defense while applying immune brakes 1 .
HIV preferentially depletes Mtb-specific Th1 and Th17 cells while enriching TCF7+ stem-like cells, dysregulating critical immune pathways 5 .
Figure 2: Distribution of major cell types identified in necrotizing granulomas through single-cell analysis
Researchers from the Research Institute of Tuberculosis in Japan designed a study to characterize the cellular landscape of necrotizing granulomatous lesions in the lungs of Mycobacterium tuberculosis-infected C3HeB/FeJ mice 1 .
Mice were infected with M. tuberculosis via aerosol exposure, developing necrotizing granulomas with human-like features over 12 weeks.
Lung lesions were collected and single-cell suspensions prepared using Ficoll-Paque density gradient centrifugation.
30,159 individual cells from granulomatous lesions were profiled using scRNA-seq.
Computational tools identified distinct cell populations, gene expression signatures, and potential functions.
Figure 3: Experimental workflow for single-cell analysis of necrotizing granulomas
The analysis revealed an astonishing diversity of cells within these granulomas. Researchers identified 11 major cell types, including various phagocytes, T cells, natural killer cells, B cells, dendritic cells, and structural cells 1 .
| Cell Type | Abbreviation | Key Functions | Noteworthy Features |
|---|---|---|---|
| Neutrophils | Neu | Early defense, inflammation | Abundant in necrotizing lesions |
| Macrophages | Mac | Bacterial phagocytosis, antigen presentation | Contain foamy macrophage subset |
| Conventional Dendritic Cells | cDC | Antigen presentation, T cell activation | Bridge innate and adaptive immunity |
| αβ T Cells | abT | Adaptive immunity | Include CD4+ and CD8+ subsets |
| γδ T Cells | gdT | Border patrol, rapid response | Express both IL-17 and PD-1 |
Table 1: Major cell types identified in necrotizing granulomas
| T Cell Subset | Key Marker Genes |
|---|---|
| Effector CD4+ T cells | Cd4, Il2ra, Icos |
| Cytotoxic CD8+ T cells | Cd8a, Gzmb, Prf1 |
| Exhausted CD8+ T cells | Cd8a, Pdcd1, Havcr2 |
| γδ T cells | Trdc, Trgc, Il17a |
Table 2: T cell subsets and their marker genes
| Macrophage Subset | Signature Genes |
|---|---|
| Plin2-expressing | Plin2, Flrt2, Hyal1, Mmp13 |
| Cxcl10-expressing | Cxcl10, Cxcl9 |
| Clec4e-expressing | Clec4e, Il1b |
| C1qc-expressing | C1qc, Fcna |
Table 3: Macrophage subpopulations and signature genes
The revolution in single-cell genomics depends on both cutting-edge technologies and specialized reagents. Here are some of the key tools enabling these discoveries:
| Tool Category | Specific Examples | Function and Application |
|---|---|---|
| Cell Isolation Technologies | FACS, LCM, Micromanipulators, Microfluidics 3 | Isolate individual cells or populations for sequencing |
| Amplification Reagents | WTA (Whole Transcriptome Amplification), WGA (Whole Genome Amplification) 3 | Amplify minute amounts of genetic material from single cells |
| Single-Cell Sequencing Kits | 10x Genomics Xenium, Scale Biosciences ScalePlex 3 | Prepare barcoded libraries for high-throughput sequencing |
| Bioinformatics Tools | Scanpy, Seurat, Biostate AI, scDeepCluster 6 | Analyze and interpret complex single-cell datasets |
| AI-Enhanced Analysis Platforms | OmicsWeb AI, Biostate AI, scFMs (single-cell foundation models) 2 6 | Leverage artificial intelligence for pattern recognition and prediction |
Table 4: Essential research tools for single-cell genomics of mycobacterial infections
Perhaps the most exciting development in the field is the emergence of single-cell foundation models (scFMs). Inspired by the same technology behind ChatGPT, these artificial intelligence systems are trained on massive datasets containing millions of single-cell profiles from diverse tissues and conditions 2 .
In these models, individual cells are treated like sentences, and genes or genomic features become words or tokens. By learning the "language" of cells from enormous training corpora, scFMs can identify subtle patterns that might escape human researchers or conventional statistical methods. They're particularly valuable for integrating data across different studies, predicting cellular behaviors, and identifying rare cell populations that might be important in mycobacterial control or pathogenesis 2 .
Preserving spatial context while sequencing RNA to understand precise cell arrangements in granulomas, where location determines function.
Analyzing RNA, DNA accessibility, protein levels, and other molecular features from the same single cells for comprehensive cellular views.
Developing new diagnostics and therapies based on single-cell discoveries, such as measuring ATG7 levels to identify severe NTM risk .
Figure 4: Projected growth of the single-cell genomics market, reflecting increasing adoption in research institutions worldwide
Despite the remarkable progress, significant challenges remain. The computational analysis of single-cell data requires sophisticated expertise, and integrating information across multiple studies is complicated by technical variations between laboratories. There's also an urgent need to make these technologies more accessible to researchers in countries where mycobacterial diseases are most prevalent.
Nevertheless, the potential impact is enormous. The single-cell genomics market is projected to reach $18.68 billion by 2034, reflecting the growing adoption of these technologies across research institutions worldwide 6 . As methods continue to improve and costs decline, we can expect even deeper insights into the complex interplay between mycobacteria and their human hosts.
Single-cell genomics has transformed our understanding of mycobacterial infections from a simplified story of "good guys versus bad guys" into a rich narrative of cellular diversity, specialisation, and complex communication. We now appreciate that the outcome of infection depends not just on which cells are present, but on their precise functional states, their spatial relationships, and their dynamic interactions.
The discoveries of specialized foamy macrophages, dual-function γδ T cells, and HIV-induced reprogramming of TB-specific immunity represent more than academic achievements—they offer concrete paths to better diagnostics, treatments, and prevention strategies for diseases that affect millions worldwide. As these technologies continue to evolve and reveal even finer details of the immune response, we move closer to a future where we can precisely modulate host defenses to control these ancient pathogens on their own terms.
The invisible battle within infected lungs remains complex, but thanks to single-cell genomics, we're no longer listening to the crowd noise from outside—we have front-row seats to the cellular conversations that determine health and disease.