Structured PREreview of The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Does the introduction explain the objective of the research presented in the preprint?

Yes

Are the methods well-suited for this research?

Highly appropriate

A multimodal approach was used; including highly multiplexed MIBI-TOF and spatial transcriptomics allowing for profiling of the spatial organization of the granuloma.

Are the conclusions supported by the data?

Highly supported

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

The data presentations and visualizations in this study are well-suited to represent the complex spatial and cellular data from tuberculosis granulomas. Here are some key strengths of the data presentation: Multimodal approach: The study combines multiple imaging and sequencing techniques (MIBI-TOF, scRNA-seq, spatial transcriptomics) to provide a comprehensive view of granuloma structure and function. This is effectively illustrated in Figure 1A, which gives an overview of the experimental approach. Spatial visualization: The use of multiplexed imaging allows for clear visualization of the spatial organization of granulomas. Figure 1B-D effectively demonstrates the newly identified metabolic subzones within the granuloma structure. Quantitative analysis: Figures 2 and 3 present quantitative analyses of cellular composition and gene expression, using appropriate statistical visualizations like volcano plots and heatmaps. Comparative analysis: The study effectively compares granulomas with high and low bacterial burdens, as shown in Figure 2C-E, which helps identify cellular signatures associated with bacterial control. Innovative visualization techniques: The study introduces a novel "immunotopography" approach (Figure 4) to analyze the radial distribution of cells and proteins within granulomas. This provides a unique and informative way to visualize spatial patterns. Network analysis: Figure 5 uses network diagrams to represent cellular interactions in high and low bacterial burden granulomas, effectively summarizing complex spatial relationships. Integration of multiple data types: Figure 3 integrates scRNA-seq and spatial transcriptomics data, providing a comprehensive view of gene expression patterns in different granuloma zones. Clear presentation of methodology: Supplementary figures provide detailed information on the image analysis pipeline, cell segmentation, and phenotyping approaches, ensuring transparency and reproducibility. Consistent color scheme: The study uses a consistent color scheme throughout (e.g., blue for IDO zone, green for hypoxic zone), which aids in interpretation across different figures. Appropriate use of statistical visualizations: The study uses appropriate statistical visualizations such as boxplots, violin plots, and scatter plots with regression lines to represent data distributions and relationships. Overall, the data presentations and visualizations in this study effectively communicate the complex spatial and functional relationships within TB granulomas, supporting the main conclusions of the research.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The authors discuss, explain, and interpret their findings and potential next steps very clearly. They systematically present their key findings, starting from the identification of distinct metabolic subzones in the granuloma myeloid core to detailed analyses of cellular composition, spatial organization, and functional states across these zones. The authors provide thorough interpretations of their observations, linking the hypoxic environment to pathologic immune cell states, dysfunctional cellular organization, and impaired T-cell infiltration. They effectively integrate and interpret data from multiple techniques to build a comprehensive understanding of granuloma structure and function. The authors clearly explain how hypoxia-associated features correlate with worsened bacterial burden, synthesizing their findings into a clear model of how hypoxia influences granuloma organization and function in TB. They discuss the implications of their findings for understanding TB pathogenesis and potential therapeutic approaches, while also acknowledging limitations and suggesting future research directions. The clarity and thoroughness of their discussion demonstrate a very clear presentation of their findings, interpretations, and future directions for the research.

Is the preprint likely to advance academic knowledge?

Highly likely

The study's comprehensive spatial and functional mapping of granulomas provides a new framework for understanding TB pathogenesis and identifies hypoxia as a key driver of immune dysfunction. The findings have significant implications for developing new therapeutic approaches targeting granuloma hypoxia. Overall, this study represents a major advance in our understanding of TB immunology and granuloma biology.

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, it’s of high quality

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

Authors must analyze multiple timepoints: Current analysis limited to 9-12 weeks Authors should track the evolution of hypoxic conditions from early infection stages because this would determine whether hypoxia precedes or follows bacterial expansion Authors need to establish temporal sequence of hypoxia vs. bacterial growth because this is essential for determining causality in the disease process Authors should follow Lin PL et al. (2013) approach showing granuloma trajectories over time because temporal dynamics reveal critical infection control mechanisms Authors must establish causality beyond correlation: Authors should conduct interventional experiments manipulating oxygen levels because this would test whether hypoxia directly impairs bacterial control Authors must use oxygen modulation techniques as in Barsoum et al. (2014) because this would provide mechanistic evidence for their hypoxia hypothesis Authors need to revise interpretations to avoid implying causality without evidence because misattribution of cause and effect could misdirect therapeutic development Authors should investigate factors beyond hypoxia: Authors must quantify contribution of necrosis and caseum to bacterial persistence because these may be independently driving bacterial survival Authors should examine immune suppression in normoxic regions as control because this would isolate hypoxia-specific effects from general immunosuppressive mechanisms Authors need to integrate their findings with Gideon HP et al. (2022) multi-factor model because a comprehensive framework better explains granuloma heterogeneity

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.