Dual spatially resolved transcriptomics for human host–pathogen colocalization studies in FFPE tissue sections

  1. Hailey Sounart
  2. Enikő Lázár
  3. Yuvarani Masarapu
  4. Jian Wu
  5. Tibor Várkonyi
  6. Tibor Glasz
  7. András Kiss
  8. Erik Borgström
  9. Andrew Hill
  10. Sefanit Rezene
  11. Soham Gupta
  12. Aleksandra Jurek
  13. Anezka Niesnerová
  14. Henrik Druid
  15. Olaf Bergmann
  16. Stefania Giacomello

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Abstract

Technologies to study localized host–pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host–pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

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  1. Version published to 10.1186/s13059-023-03080-y
  2. ScreenIT

    SciScore for 10.1101/2022.03.14.484288: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsField Sample Permit: The use of tissue specimens collected at Semmelweis University in this study was approved by the Hungarian Scientific Research Ethics Committee (ETT TUKEB IV/3961-2/2020/EKU).
    IRB: The use of tissue specimens collected at Semmelweis University in this study was approved by the Hungarian Scientific Research Ethics Committee (ETT TUKEB IV/3961-2/2020/EKU).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Spatial Transcriptomics Hematoxylin & Eosin Imaging: Hematoxylin & Eosin brightfield images were acquired with a Zeiss Axiolmager.Z2 VSlide Microscope using the Metasystems VSlide scanning system with Metafer 5 v3.14.179 and VSlide software.
    Metafer
    suggested: (Metafer, RRID:SCR_016306)
    The DE genes distinguishing SARS-CoV-2+ spots from SARS-CoV-2- spots were obtained as described in the Methods section “Differential Gene Expression” with an additional filter of average logFC +/-1.0.
    Gene Expression”
    suggested: None
    Validation by RNAScope: RNAScope and ST images were manually aligned with Adobe Photoshop 2022.
    Adobe Photoshop
    suggested: (Adobe Photoshop, RRID:SCR_014199)
    The computational validation was performed as follows: the RNAScope signal was detected with an ad hoc Matlab (version R2021b) algorithm, which is specified in the next section “Automatic detection of RNAScope signal”; then both the binary ST and RNAScope signal images were aligned and binned into 200×200 μm2 blocks (Extended Data Figures 3-4).
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In terms of the general outlook on spatially resolved host-pathogen interactions, limitations of our proposed approach include the requirement of previous knowledge of the pathogen transcriptome of interest to develop targeted probes, the inability to detect different human RNA splice variants, the lack of capturing human non-coding RNA groups that may have important regulatory functions, and the inability to detect new viral variants since the viral RNA is not directly sequenced. However, probes targeting specific host RNAs of interest could be developed to overcome some of these shortcomings. In conclusion, the proposed method enables insights into highly localized host response to pathogen infection within the spatial context of the tissue microenvironment at the whole-transcriptome level in an unbiased and high-throughput manner. The method has the potential to be applied to other human pathogens with the development of targeted probes and thus examine the interplay between host and pathogen across the multitude of human infectious diseases. Overall, our approach opens the door to new possibilities of studying infectious disease at a large scale by exploring multiple transcriptomes in a single experiment.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2022.03.14.484288 on bioRxiv