Highly resolved spatial transcriptomics for detection of rare events in cells

  1. Silvia Groiss
  2. Daniela Pabst
  3. Cynthia Faber
  4. Andreas Meier
  5. Annette Bogdoll
  6. Conny Unger
  7. Benedikt Nilges
  8. Sascha Strauss
  9. Esther Föderl-Höbenreich
  10. Melina Hardt
  11. Andreas Geipel
  12. Frank Reinecke
  13. Christian Korfhage
  14. Kurt Zatloukal

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Abstract

Single-cell spatial transcriptomics technologies leveraged the potential to transcriptionally landscape sophisticated reactions in cells. Current methods to delineate such complex interplay lack the flexibility in rapid target adaptation and are particularly restricted in detecting rare transcripts. We developed a multiplex single-cell RNA In-situ hybridization technique, called ‘Molecular Cartography’ (MC) that can be easily tailored to specific applications and, by providing unprecedented sensitivity, specificity and resolution, is particularly suitable in tracing rare events at a subcellular level. Using a SARS-CoV-2 infection model, MC allows the discernment of single events in host-pathogen interactions, dissects primary from secondary responses, and illustrates differences in antiviral signaling pathways affected by SARS-CoV-2, simultaneously in various cell types.

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    SciScore for 10.1101/2021.10.11.463936: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    The infectivity of the virus stock was further verified by immunohistochemistry (IHC) using a primary rabbit monoclonal antibody against the SARS-CoV-2 (2019-nCoV) nucleocapsid protein (SinoBiological, 40143-R019) and commercial staining reagents (Agilent Technologies, K346430-2, K406189-2) following the manufacturer’s instructions.
    2019-nCoV ) nucleocapsid protein ( SinoBiological , 40143-R019 )
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Cell culture: HeLa (DSMZ, Cat. No. ACC 57) and NIH-3T3 cells (DSMZ, Cat. No. ACC59) were cultured in DMEM supplemented with 10 % FCS, 1 % penicillin-streptomycin (P/S, 10,000 U/mL), 200 mM L-Glutamine and 1 % MEM Non-Essential Amino Acids Solution (100X, all media and components from Gibco, ThermoFisher Scientific).
    NIH-3T3
    suggested: None
    Human lung adenocarcinoma cell line Calu3 and human colon adenocarcinoma cell line Caco2 were maintained in MEM with 10 % FCS, 1 % P/S and 200 mM L-Glutamine.
    Calu3
    suggested: RRID:CVCL_EQ19)
    For MC, probe sets for 19 human genes were hybridized to fixed HeLa cells and transcripts were detected using the standard MC protocol.
    HeLa
    suggested: CLS Cat# 300194/p772_HeLa, RRID:CVCL_0030)
    SARS-CoV-2 infection for MC: For SARS-CoV-2 infection experiments, 7.000 Huh7 cells, 15.000 PLC5 cells, 15.000 Caco2 cells and 20.000 Calu3 cells were seeded into 8x glass bottom slides (Ibidi) at a confluence of 30-50 % and infected with SARS-CoV-2 at an MOI of 0.4 (if applied to VeroE6 cells, titer maintained for comparison across cell lines) for 60 min at 37 °C at 5 % CO2.
    Huh7
    suggested: RRID:CVCL_YU20)
    Caco2
    suggested: None
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    Briefly, the probe-design was performed at the gene-level using all full-length protein-coding transcript sequences from the ENSEMBL database tagged as ‘basic’52,53.
    ENSEMBL
    suggested: (Ensembl, RRID:SCR_002344)
    The fully automated imaging process (including water immersion generation and precise relocation of regions to image in all three dimensions) was realized by a custom Python script using the scripting API of the Zeiss ZEN software (open application development).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Zeiss ZEN
    suggested: None
    Hierarchical clustering and covariation analysis: Expression data was normalized to the total counts per cell (with exception of SARS-CoV-2 Np transcripts to avoid skewing of data) or ROI (Fig. 4) and scaled using Excel (Microsoft)
    Excel
    suggested: None
    Final figures were prepared using GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 16 and 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

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

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