The study team, led by researchers at Zhejiang University and The Third Affiliated Hospital of Guangzhou Medical University, analyzed ten human cervical tissue samples spanning the full pathological continuum. Single-cell RNA sequencing allowed them to profile thousands of individual cells, resolving not only which cell types were present at each disease stage but also how their gene-expression programs shifted as lesions progressed. Whole-genome bisulfite sequencing added a crucial layer of epigenetic information, revealing how DNA methylation patterns change across cell types during carcinogenesis, while spatial transcriptomic data from a public cohort confirmed that the cellular relationships observed in dissociated single-cell data hold true within intact tissue architecture.

One of the most striking findings is that disease progression is accompanied by a marked increase in NK/T cell infiltration. As normal tissue advances through LSIL and HSIL toward invasive carcinoma, immune cells of the NK and T lineages crowd increasingly into the lesion environment. This might, at first glance, seem encouraging, since cytotoxic lymphocytes are the very cells capable of destroying tumor cells. Yet the infiltration coincides with upregulation of galectin-9, an immune checkpoint ligand encoded by the LGALS9 gene. Galectin-9 is known to bind Tim-3 on T cells, a interaction that dampens antitumor immunity and drives T-cell exhaustion. In other words, the tumor microenvironment appears to respond to escalating immune pressure by deploying an immunosuppressive ligand, a classic example of adaptive immune resistance.

The epigenetic data revealed something particularly interesting about how this deployment unfolds over time. The LGALS9 promoter underwent progressive demethylation beginning at the low-grade squamous intraepithelial lesion stage, indicating that the gene was being epigenetically primed for expression long before invasive cancer appeared. However, robust transcriptional induction of LGALS9 only became prominent at the high-grade lesion stage, coinciding with activation of interferon-gamma response programs. This temporal separation between demethylation and transcriptional activation suggests a two-step mechanism: early epigenetic poising followed by inflammatory triggering. The researchers found a remarkably tight correlation between interferon-gamma activity and LGALS9 expression at the sample level, with a Pearson correlation coefficient of 0.95, indicating that the very immune cells infiltrating the lesion may be inducing the ligand that ultimately silences them.

Beyond galectin-9, trajectory analysis of the single-cell data delineated the evolution of CD8-positive T cells across disease stages and identified TFCP2 as a transcriptional regulator whose activity is associated with patient prognosis. The study also documented enrichment of LAMP3-positive mature dendritic cells in tumor tissues compared with normal controls. These dendritic cells, which emerge along a maturation trajectory from conventional cDC1 and cDC2 subsets, carried a mixture of costimulatory molecules such as CD40 and CD80 and inhibitory checkpoint molecules including CD274, IDO1, and LGALS9 itself, suggesting that even the antigen-presenting arm of the immune response becomes entangled in the checkpoint machinery as cancer develops.

Multiplex immunohistochemistry provided protein-level confirmation of the story told by the sequencing data. Staining for exhausted CD8-positive T cells, marked by the co-expression of CD3, CD8, and Tim-3, alongside staining for the epithelial marker pan-cytokeratin and galectin-9, showed that galectin-9-positive epithelial cells increase in abundance as tissue progresses from normal cervix through LSIL and HSIL to cancer. The physical co-localization of Tim-3-expressing exhausted T cells with galectin-9-expressing epithelium within the same tissue sections strengthens the argument that this ligand-receptor pair represents a functional axis of immune evasion operating during precancerous progression, not merely a correlate of advanced disease.

Perhaps the most translational portion of the work came from animal experiments. The researchers established an ectopic subcutaneous syngeneic cervical cancer model in immunocompetent mice, an experimental system in which the immune system is fully intact and therefore capable of mounting genuine antitumor responses. Blocking galectin-9 in this model reduced tumor burden, demonstrating that the protein is not simply a passive biomarker but an active contributor to tumor growth. More strikingly, combining galectin-9 blockade with an agonist antibody against GITR, a costimulatory receptor on T cells, significantly enhanced the clonal expansion and cytotoxic activity of CD8-positive T cells. This combination strategy suggests that releasing one brake on the immune system while simultaneously pressing the accelerator may produce therapeutic effects greater than either intervention alone.

The findings arrive at a moment when immune checkpoint blockade has transformed the treatment of many cancers but has delivered comparatively modest results in cervical cancer. Understanding which checkpoint pathways are active at which stages of disease could allow clinicians to intervene earlier and more precisely. The observation that LGALS9 epigenetic poising begins at the LSIL stage is particularly provocative, since low-grade lesions are common, usually regress spontaneously, and are typically managed conservatively. If reliable markers of galectin-9 activation could be incorporated into screening algorithms, they might help distinguish the minority of low-grade lesions destined for progression from those that will resolve, sparing unnecessary procedures while directing attention to lesions that truly warrant close surveillance.

The study also illustrates the growing power of integrated multi-omics approaches in cancer biology. No single technology used here could have revealed the full sequence of events. Single-cell transcriptomics exposed the cellular composition and signaling programs of each lesion stage, but only DNA methylation profiling revealed that LGALS9 had been epigenetically prepared in advance of its expression, and only spatial transcriptomics could verify that the relevant cell populations occupy adjacent territories within intact tissue. Copy number variation inference, pseudotime trajectory modeling, and regulon analysis with tools such as pySCENIC and Monocle2 added further resolution, while methylation-based deconvolution using EpiSCORE extended the key NK/T cell infiltration trend across a larger cohort of twenty-one bulk tissue samples.

Caveats remain, as they do in any early-stage translational study. The human cohort comprised ten deeply profiled samples, and the syngeneic mouse model, while immunocompetent, does not fully recapitulate HPV-driven human cervical carcinogenesis. Clinical testing of galectin-9 blockade in cervical cancer patients would need to demonstrate safety and efficacy in the neoadjuvant, recurrent, or metastatic settings where immunotherapy is currently deployed. Nevertheless, by pinpointing a checkpoint ligand whose activation is detectable during precancerous progression and whose blockade shows antitumor efficacy in vivo, the study provides both a mechanistic framework for understanding immune evasion in cervical carcinogenesis and a concrete, testable therapeutic hypothesis. For a disease that remains a leading cause of cancer death among women globally, that combination of mechanistic insight and actionable target represents a meaningful step forward.

Subject of Research: Multi-omics analysis of immune evasion during cervical carcinogenesis and galectin-9 as a candidate immunotherapeutic target

Article Title: Multi-omics analysis of cervical carcinogenesis reveals galectin-9 driven immune evasion as a candidate immunotherapeutic target

Article References: Multi-omics analysis of cervical carcinogenesis reveals galectin-9 driven immune evasion as a candidate immunotherapeutic target. (n.d.). https://doi.org/10.1186/s12935-026-04458-1

Image Credits: AI Generated

DOI: 10.1186/s12935-026-04458-1

Keywords: cervical cancer, galectin-9, LGALS9, single-cell RNA sequencing, spatial transcriptomics, DNA methylation, CD8-positive T cells, immune evasion, immunotherapy, tumor microenvironment, interferon-gamma, cervical carcinogenesis

The clinical stakes are considerable. Sputum culture conversion, defined as two consecutive negative cultures collected at least 30 days apart, is a critical early indicator of treatment efficacy and a predictor of long-term cure. Patients whose cultures remain positive at the two-month mark face a higher risk of treatment failure, relapse, and continued transmission within their communities. The World Health Organization’s End TB Strategy, which aims to dramatically reduce tuberculosis incidence by 2035, depends on improving these unfavourable outcomes, yet the immunological mechanisms governing sputum conversion have remained poorly understood. Host-pathogen interactions in tuberculosis are notoriously heterogeneous, with significant variation between patients in radiographic presentation, symptoms, and disease trajectory, making it difficult to predict who will respond and who will not.

To probe this heterogeneity at unprecedented resolution, the research team enrolled eight patients with confirmed drug-sensitive Mycobacterium tuberculosis infection, dividing them into four treatment responders and four non-responders based on their two-month sputum culture conversion status. The researchers isolated peripheral blood mononuclear cells and subjected them to single-cell RNA sequencing using the 10x Genomics Chromium X platform, sequencing on an Illumina NovaSeq X Plus instrument. After rigorous quality control, 83,999 cells were retained for analysis, with an average of 1,723 genes detected per cell. Unsupervised clustering resolved the data into 17 transcriptional clusters, which the team annotated into six major immune cell types: T cells, NK cells, B cells, myeloid cells, mast cells, and platelets.

The most striking finding emerged from the myeloid compartment. Non-classical monocytes, a subset of monocytes characterized by their patrolling behaviour in the vasculature, were relatively enriched in the non-responder group and carried elevated scores on a previously established tuberculosis progression risk signature. The total monocyte fraction showed a large standardized between-group difference, with a Cohen’s d of 1.04, although the authors caution that with only four patients per group this effect size is descriptive rather than definitive. Trajectory analyses using PAGA and Monocle, combined with CytoTRACE differentiation scoring, indicated that these non-classical monocytes were terminally differentiated, and SCENIC regulatory network analysis highlighted activation of transcription factors including ZNF177, FOXP3, and HES5 within the subset.

The transcriptional programs of these cells diverged sharply between groups. Non-classical monocytes from non-responders showed upregulation of TLR6, TLR1, IFI44, and PARP9, and were enriched for pathways involving Toll-like receptor 2 signalling, type II interferon-mediated signalling, and negative regulation of cytokine signalling through I-kappa-B kinase and NF-kappa-B pathways. In contrast, the corresponding cells from responders expressed higher levels of LAPTM5, CXCL8, and PTGS2 and were associated with cell adhesion and positive regulation of cytokine-mediated signalling. Monocytes from non-responders also displayed elevated expression of GBP2, STAT1, and TAP1, genes previously proposed as candidate biomarkers of tuberculosis infection. Taken together, these signatures suggest that non-classical monocytes in non-responders are locked into a terminally differentiated, senescence-like state biased towards innate inflammatory activation, echoing earlier reports that linked the pro-inflammatory phenotype of this subset to cellular senescence driven by NF-kappa-B and IL-1 alpha signalling.

Perhaps the most conceptually intriguing result came from CellChat-based intercellular communication analysis, which models how cell populations signal to one another through known ligand-receptor pairs. In non-responders, non-classical monocytes received more incoming signals but emitted fewer outgoing signals than their counterparts in responders, effectively behaving as signalling sinks rather than active communicators. The analysis identified the IL16 and TRAIL, encoded by TNFSF10, pathways among the upregulated inferred interactions involving non-responder non-classical monocytes, classical monocyte subsets, and CD4-positive T cells. The authors are careful to frame these findings as transcriptomic ligand-receptor hypotheses rather than demonstrations of protein secretion or causal signalling, but the pattern resembles the behaviour of exhausted or senescent cells that accumulate inhibitory inputs while failing to respond effectively, a phenomenon previously described in severe tuberculosis where CD14-positive CD16-positive monocytes correlate with T-cell inhibition and immune paralysis.

The study also documented profound changes in the lymphocyte compartment. Overall T cell abundance was reduced in non-responders, yet subclustering revealed a significant expansion of CD8-positive cytotoxic T cells alongside CD4-positive regulatory T cells with stronger inhibitory profiles. Exhaustion-associated markers told a consistent story: HAVCR2, which encodes the TIM-3 checkpoint receptor, was broadly elevated in non-responders, particularly in cytotoxic T cells and regulatory T cells, while TIGIT showed more restricted expression in the same subsets. Critically, LGALS9, the ligand for TIM-3, was significantly upregulated in regulatory T cells from non-responders, and CellChat inferred LGALS9-dependent interactions, including LGALS9-P4HB, LGALS9-CD45, and LGALS9-CD44, primarily targeting CD8-positive cytotoxic T cells. At the single-cell level, LGALS9 expression correlated positively with HAVCR2 in non-responders, with a correlation coefficient of 0.471, whereas the corresponding association in responders was weaker and statistically non-significant, and a similar group-specific pattern held for TIGIT.

Innate lymphocytes were not spared. NK cell subclustering identified immature and mature subsets, with NKT cells more abundant in non-responders and a mature NK subcluster showing clear spatial separation between groups. CellChat inferred stronger GALECTIN signalling, including LGALS9-HAVCR2 interactions, from regulatory T cells to this mature NK subset in non-responders, and the affected cells displayed higher HAVCR2 and TIGIT expression alongside reduced CCL5 and CXCR4, with pseudotime analysis placing them towards the terminal end of the inferred differentiation trajectory. To contextualize these cross-sectional findings temporally, the team reanalyzed the public microarray dataset GSE40553 using CIBERSORT deconvolution, which suggested that estimated non-classical monocyte proportions decline after two months of anti-tuberculosis therapy and remain low through twelve months. The authors emphasize that this bulk-transcriptome analysis provides descriptive temporal context only and cannot validate the single-cell-defined functional states or test individual conversion outcomes.

An exploratory in silico layer added further hypothesis-generating depth. Virtual knockout analysis using scTenifoldKnk predicted that perturbing IL16 would disturb 24 genes and TNFSF10 would disturb 34 genes at false discovery rate significance, with representative affected genes including TCF7, IL7R, and CCR7 for IL16, and NKG7, PRF1, and GNLY for TNFSF10, pointing towards consequences for T cell and cytotoxic lymphocyte function. Ninety perturbation-associated genes were shared between the two analyses and were enriched for cellular defence response and response to molecules of bacterial origin. The authors stress that these computational results prioritize candidates for experimental study but do not establish gene function, protein effects, or therapeutic benefit.

The authors are admirably forthright about the limitations of their work. With eight patients in total, four per group, the study lacks the statistical power for definitive conclusions, and no protein measurements of IL16, TNFSF10, or LGALS9 were performed, nor were experimental gene perturbations conducted. Trajectory inference methods are sensitive to sampling density and modelling assumptions, making the terminal differentiation interpretation provisional, and observational cross-sectional data cannot determine whether the observed immune changes are causes or consequences of persistent infection. Nevertheless, the study nominates non-classical monocyte composition and IL16 and TNFSF10-related transcripts as candidate biomarkers worthy of prospective validation, and proposes a testable network model in which dysregulated monocyte signalling coexists with regulatory T cell-mediated galectin signalling and exhaustion features in cytotoxic lymphocytes. If larger cohorts with protein-level measurements and functional assays confirm these associations, the findings could open the door to host-directed therapies or early immunological monitoring that identifies at-risk patients before treatment failure becomes entrenched, a development that would materially advance the global effort to bring tuberculosis under control.

Subject of Research: Single-cell immune profiling of tuberculosis patients to identify immunological determinants of sputum culture conversion and treatment response

Article Title: Single‐Cell Immune Profiling Suggests Non‐Classical Monocyte Is Associated Immunodepression in Tuberculosis Treatment Non‐Responders

Article References: Single‐Cell Immune Profiling Suggests Non‐Classical Monocyte Is Associated Immunodepression in Tuberculosis Treatment Non‐Responders. (n.d.). https://doi.org/10.1111/jcmm.71345

Image Credits: AI Generated

DOI: 10.1111/jcmm.71345

Keywords: tuberculosis, single-cell RNA sequencing, non-classical monocytes, sputum culture conversion, immune suppression, IL16, TNFSF10, LGALS9, regulatory T cells, treatment non-responders, immunodepression, host-directed therapy