PMID- 33268897 OWN - NLM STAT- MEDLINE VI - 588 IP - 7839 TI - Highly multiplexed spatial mapping of microbial communities. PG - 676-681 LA - eng PT - Journal Article PT - Research Support, N.I.H., Extramural PL - England TA - Nature JT - Nature JID - 0410462 IS - 1476-4687 (Electronic) LID - 10.1038/s41586-020-2983-4 [doi] FAU - Shi, Hao AU - Shi H AUID- ORCID: http://orcid.org/0000-0002-9658-2847 AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. hs673@cornell.edu. FAU - Shi, Qiaojuan AU - Shi Q AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. FAU - Grodner, Benjamin AU - Grodner B AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. FAU - Lenz, Joan Sesing AU - Lenz JS AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. FAU - Zipfel, Warren R AU - Zipfel WR AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. FAU - Brito, Ilana Lauren AU - Brito IL AUID- ORCID: http://orcid.org/0000-0002-2250-3480 AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. FAU - De Vlaminck, Iwijn AU - De Vlaminck I AUID- ORCID: http://orcid.org/0000-0001-6085-7311 AD - Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA. vlaminck@cornell.edu. IS - 0028-0836 (Linking) RN - 0 (Anti-Bacterial Agents) SB - IM MH - Algorithms MH - Animals MH - Anti-Bacterial Agents/pharmacology MH - Biofilms MH - Escherichia coli/classification/cytology/genetics/isolation & purification MH - Gastrointestinal Microbiome/drug effects MH - Humans MH - In Situ Hybridization, Fluorescence/*methods MH - Mice MH - *Microbiota/drug effects MH - Mouth/drug effects/microbiology MH - Ribosomes/metabolism MH - Single-Cell Analysis PMC - PMC8050837 DCOM- 20211116 LR - 20221130 DP - 202012 DEP - 20201202 AB - Mapping the complex biogeography of microbial communities in situ with high taxonomic and spatial resolution poses a major challenge because of the high density1 and rich diversity2 of species in environmental microbiomes and the limitations of optical imaging technology3-6. Here we introduce high-phylogenetic-resolution microbiome mapping by fluorescence in situ hybridization (HiPR-FISH), a versatile technology that uses binary encoding, spectral imaging and decoding based on machine learning to create micrometre-scale maps of the locations and identities of hundreds of microbial species in complex communities. We show that 10-bit HiPR-FISH can distinguish between 1,023 isolates of Escherichia coli, each fluorescently labelled with a unique binary barcode. HiPR-FISH, in conjunction with custom algorithms for automated probe design and analysis of single-cell images, reveals the disruption of spatial networks in the mouse gut microbiome in response to treatment with antibiotics, and the longitudinal stability of spatial architectures in the human oral plaque microbiome. Combined with super-resolution imaging, HiPR-FISH shows the diverse strategies of ribosome organization that are exhibited by taxa in the human oral microbiome. HiPR-FISH provides a framework for analysing the spatial ecology of environmental microbial communities at single-cell resolution.