Understanding an ecosystem requires maps of where the organisms present live and act. For example, sea otters live on the surface of kelp beds and feed on sea urchins that consume kelp from the ocean floor. The location of an organism in an ecosystem reflects its physiology and function. This is especially true of microbes, which live in and respond to highly dynamic and diverse habitats. For example, micrometer scale gradients of nutrients or pH are a typical feature of microbial habitats that stimulate bacterial spatial organization and behavior1,2. But despite the incredible array of functions that different bacteria perform independently and in conjunction with other living systems3,4, most microbes are similar in shape and indistinguishable under the microscope. Register in Nature, Shi et al.5 present a method to meet the great challenge of distinguishing between the hundreds to thousands of bacterial species that exist in the natural habitats of microbes.
An important tool used in biogeographic studies to assess the spatial location of relevant components is called fluorescence in situ hybridization (FISH). This technique is based on the use of fluorescently labeled nucleic acid sequences called probes to locate corresponding specific sequences of DNA or RNA in a sample immobilized by a process called fixation. When applied to samples containing bacteria, a DNA probe can be used to identify a target species in the context of its native environment, and a fluorescent molecule (a fluorophore) attached to the probe allows the location of the species under the microscope6.
Designing the DNA component of FISH probes is now relatively easy7. However, the maximum number of bacterial species that can be identified by FISH in a single sample is limited by the limited number of fluorophores available for simultaneous visualization. Methods that go beyond the limits of traditional fluorescence microscopy, using spectral imaging and combinations of fluorophores, can distinguish between 15 and 120 different types of microbes captured in the same image8,9. However, a disadvantage of these techniques is the cost of the large number of fluorescently labeled probes required.
Shi and colleagues introduce a method that surpasses previous FISH benchmarks by combining a new type of probe design with custom image analysis. Their technique (Fig. 1), called high phylogenetic resolution microbiome mapping by means of fluorescence in situ hybridization (HiPR-FISH), builds on a combinatorial strategy in which bacteria are targeted and labeled in two steps10.
Figure 1 | A method to identify more than 1,000 bacterial species with high spatial resolution. Shi et al.5 present their technique called microbiome mapping with high phylogenetic resolution by means of fluorescence in situ hybridization (HiPR-FISH). In this approach, DNA sequences, called coding probes, are designed to match a species-specific RNA sequence (16S ribosomal RNA is the target sequence for each species). Each encoding probe is flanked by two sequences, called read sequences, each selected from ten possible read sequences. A variety of coding probes targeting the same species-specific sequence are designed to generate a unique combination of readout sequences corresponding to a particular species. Ten types of probes are added that are attached to a fluorescent molecule called a fluorophore, and each probe binds the one read sequence to which it corresponds. The sample is imaged to record which of the fluorophores (up to ten in total) are bound to RNA in each cell. This information allows the genetic identification of individual bacteria.
First, a DNA probe, described as a coding probe, is designed to match a species-specific sequence (16S ribosomal RNA) for the target bacteria. This coding probe is flanked on either side by integral parts of the probe described as read sequences. Each of these two read out sequences in a coding probe can be one of ten possible read out sequences. Then, each read sequence is targeted by a different DNA probe fused to a fluorophore specific for the particular read sequence.
Bacterial cells contain hundreds of copies of 16S rRNA, so each bacterial species can be targeted by a series of coding probes: each targeting the same RNA sequence, but flanked by different pairs of readout sequences, allowing a variety of readout sequences to be associated with a particular species. . By choosing the readout sequences for each target bacterial species, a unique combination of readout sequences (and thus fluorophores) can be assigned to correspond to each species. Depending on the coding probes used, each bacterial species can bind up to ten of the possible fluorophores. This system can generate 1,023 unique visual ‘barcodes’ for the identification of individual bacterial species. Bacterial identities are simultaneously assigned through spectral imaging monitoring and classification of microbes in the images using a machine learning algorithm. HiPR-FISH overcomes the previous financial limitations by simplifying the coding sequence into cheaply synthesized DNA sequences requiring only ten different types of fluorophore.
Using an automated program to delineate a large number of single cells in a dense crowd, Shi and colleagues used HiPR-FISH to locate and determine the species identity of individual bacteria in mouse gut samples and in samples of oral microbes in human plaque. These different microbial ecosystems are both examples of bacterial communities that can contain hundreds of different species.
This demonstration of a previously impossible level of analysis of complex communities using single cell level mapping allows for the quantitative study of bacterial spatial organization, such as the determination of the distance between specific microbial species residing in a host. Such high-resolution data is important to answer important questions about microbial community behavior, such as who interacts with whom and where those interactions take place. Interactions are theoretically possible between microbes in spatial proximity, so HiPR-FISH opens a new era in the study of microbial ecology by micrometer-scale mapping of the spatial distance between hundreds of microbial species in complex communities.
Shi et al. assessed the distance between different bacterial species normally residing in the mouse gut and measured how these distances changed after antibiotic treatment. Such therapy is known to alter the range and abundance of bacterial species in the gut11. The greatest distance changes due to antibiotic treatment observed by Shi and colleagues were between Oscillibacter and Veillonella, these are microbes that are both separately associated with health benefits in the human gut12,13. Whether and how these bacteria interact functionally remains to be discovered. However, quadrupling the spatial distance between these bacteria after antibiotic use increases the possibility that the antibiotic treatment may interfere with an interaction that aids the host. Identifying such interactions and deciphering the underlying mechanisms will increase our understanding of how microbial communities respond to and recover from environmental disturbances.
Shedding light on microbial biogeography, this work maps new avenues for exploring microbial interactions in complex ecosystems. Exciting next steps to anticipate include the elucidation of mechanisms by which environmental disturbances alter bacterial spatial organization, and how altered organization affects community functioning. For example, how does exposure to antibiotics lead to a greater distance between Oscillibacter and Veillonella? Is the spatial proximity between specific bacteria important for community regeneration after disturbances such as antibiotic exposure?
Finally, extending this technology to access the spatial organization of transcriptional responses would allow the generation of maps that reveal the spatial gradients of bacterial species and their functions. These future applications will truly revolutionize our understanding of complex microbial communities and the spatial diversity that is so fundamental to life.
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