Quantitative whole-tissue 3D imaging reveals bacteria in close association with mouse jejunum mucosa
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1.
California Institute of Technology
Abstract
Because the small intestine (SI) epithelium lacks a thick protective mucus layer, microbes that colonize the thin SI mucosa may exert a substantial effect on the host. For example, bacterial colonization of the human SI may contribute to environmental enteropathy dysfunction (EED) in malnourished children. Thus far, potential bacterial colonization of the mucosal surface of the SI has only been documented in disease states, suggesting mucosal colonization is rare, likely requiring multiple perturbations. Furthermore, conclusive proof of bacterial colonization of the SI mucosal surface is challenging, and the three-dimensional (3D) spatial structure of mucosal colonies remains unknown. Here, we tested whether we could induce dense bacterial association with jejunum mucosa by subjecting mice to a combination of malnutrition and oral co-gavage with a bacterial cocktail (E. coli and Bacteroides spp.) known to induce EED. To visualize these events, we optimized our previously developed whole-tissue 3D imaging tools with third-generation hybridization chain reaction (HCR v3.0) probes. Only in mice that were malnourished and gavaged with the bacterial cocktail did we detect dense bacterial clusters surrounding intestinal villi suggestive of colonization. Furthermore, in these mice we detected villus loss, which may represent one possible consequence that bacterial colonization of the SI mucosa has on the host. Our results suggest that dense bacterial colonization of jejunum mucosa is possible in the presence of multiple perturbations and that whole-tissue 3D imaging tools can enable the study of these rare events.
Copyright and License
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Acknowledgement
We thank the Caltech Office of Laboratory Animal Resources as well as the veterinary technicians at the animal facilities for animal care, personnel training, and resources. We thank Biological Imaging Facility at Caltech (including Andres Collazo, Giada Spigolon, and Steven Wilbert) for resources, training, and technical support. We thank Brett Finley (University of British Columbia) and Prof. Emma Allen Vercoe (University of Guelph) for providing bacterial isolates. We thank Prof. Jared Leadbetter and Prof. Sarkis Mazmanian for providing feedback on study design. We thank Justin Bois for introduction to data analysis in Python. We thank Emily Savela, Mary Arrastia, and Eugenia Khorosheva for reviewing and filing Institutional Biosafety Committee paperwork. We thank Jacob T. Barlow for processing sequencing data. We thank Joanne Lau for maintaining anaerobic chambers for bacterial culture. We also thank Natasha Shelby for contributions to writing and editing this manuscript. This work was supported in part by the Kenneth Rainin Foundation (2018-1207), Army Research Office Multidisciplinary University Research Initiative (W911NF-17-1-0402), Defense Advanced Research Projects Agency (HR0011-17-2-0037), and the Jacobs Institute for Molecular Engineering for Medicine. OMP was supported by a Burroughs Welcome Fund Career Award at the Scientific Interface (ID# 106969). The funders had no role in the design of the study, the collection, analysis, and interpretation of data, nor in writing the manuscript.
Contributions
RP Conception, animal study execution, sample collection, sample processing for imaging, imaging, data analysis, figure generation, manuscript preparation. SRB Tail cup study implementation. sample collection. AER RT-qPCR, dPCR, and sequencing data acquisition. AHD dPCR data acquisition. OMP Preliminary imaging and sequencing. HT Preliminary image analysis. OJ Management of histopathology data acquisition. RFI Project supervision and administration, acquisition of funding, manuscript review and editing. All authors read and approved the final manuscript. See Supplementary Information for detailed author contributions.
Data Availability
Supplementary Data 1–4, all raw data, data analysis scripts, and the new HCR v3.0 rRNA probe sequences from this publication are available at Caltech DATA at https://doi.org/10.22002/zg1d3-k3b49.
Conflict of Interest
RFI is an inventor on a series of patents licensed by the University of Chicago to Bio-Rad Laboratories Inc. in the context of dPCR.
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Additional details
- PMCID
- PMC10485000
- 2018-1207
- Kenneth Rainin Foundation
- W911NF-17-1-0402
- United States Army Research Office
- HR0011-17-2-0037
- Defense Advanced Research Projects Agency
- 106969
- Burroughs Wellcome Fund
- Caltech groups
- Jacobs Institute for Molecular Engineering for Medicine, Division of Biology and Biological Engineering