Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Sep 10;18(1):50.
doi: 10.1186/s13036-024-00444-1.

Soil microbiome characterization and its future directions with biosensing

Lexi DeFord  1 , Jeong-Yeol Yoon  2   3
Affiliations

Soil microbiome characterization and its future directions with biosensing

Lexi DeFord et al. J Biol Eng. .

Abstract

Soil microbiome characterization is typically achieved with next-generation sequencing (NGS) techniques. Metabarcoding is very common, and meta-omics is growing in popularity. These techniques have been instrumental in microbiology, but they have limitations. They require extensive time, funding, expertise, and computing power to be effective. Moreover, these techniques are restricted to controlled laboratory conditions; they are not applicable in field settings, nor can they rapidly generate data. This hinders using NGS as an environmental monitoring tool or an in-situ checking device. Biosensing technology can be applied to soil microbiome characterization to overcome these limitations and to complement NGS techniques. Biosensing has been used in biomedical applications for decades, and many successful commercial products are on the market. Given its previous success, biosensing has much to offer soil microbiome characterization. There is a great variety of biosensors and biosensing techniques, and a few in particular are better suited for soil field studies. Aptamers are more stable than enzymes or antibodies and are more ready for field-use biosensors. Given that any microbiome is complex, a multiplex sensor will be needed, and with large, complicated datasets, machine learning might benefit these analyses. If the signals from the biosensors are optical, a smartphone can be used as a portable optical reader and potential data-analyzing device. Biosensing is a rich field that couples engineering and biology, and applying its toolset to help advance soil microbiome characterization would be a boon to microbiology more broadly.

Keywords: Aptamer; Biosensor; Machine learning; Soil health; Soil microbiome.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Overview of next-generation sequencing (NGS) process. Amplicon-based methods include the amplification step, while shotgun methods instead include DNA fragmentation. Figure created with BioRender.com
Fig. 2
Fig. 2
Overview of biosensing process as potentially applied to soil microbiome characterization. Figure created with BioRender.com
Fig. 3
Fig. 3
An example of analyzing bacterial species from environmental water samples. It utilizes a set of peptides extracted from bacterial biofilms, a multi-channel paper microfluidic chip, a smartphone camera as an optical reader and data processing unit, and machine learning to analyze the data. Reprinted with permission from [141]. Copyright 2021, Elsevier
Fig. 4
Fig. 4
Venn diagram showing a sample of machine learning algorithms applied to NGS and biosensor data. Most NGS algorithms found in the literature were supervised learning methods except for PCA. There were also several papers that detailed novel algorithms for NGS data. A great variety of algorithms are used with biosensor data, and many of them are also used with NGS data; there is significant overlap. ANN = artificial neural networks; kNN = k-nearest neighbors; SVM = support vector machine; XGBoost = extreme gradient boosting; LR = logistic regression; PCA = principal component analysis; LDA = linear discriminant analysis
Fig. 5
Fig. 5
An example of a gas sensor for assessing the microbiome. While this work was designed to assess the intestinal flora, e.g., gut microbiome, it can be adapted to the soil microbiome. Reprinted from [147] under Creative Commons Attribution License
Fig. 6
Fig. 6
An example of optical biosensor to soil microbiome characterization. A smartphone is modified with an acrylic film wheel (12 filters) and three LEDs to capture multiple autofluorescence images of various bacterial mixtures and soil samples. Learning databases are collected from various bacterial mixtures, and the field soil samples are used as a test set to predict the dominant bacterial species
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: The unseen majority. Proc Natl Acad Sci U S A. 1998;95:6578–83. 10.1073/pnas.95.12.6578. 10.1073/pnas.95.12.6578 - DOI - PMC - PubMed
    1. Mishra A, Singh L, Singh D. Unboxing the black box—one step forward to understand the soil microbiome: A systematic review. Microb Ecol. 2023;85:669–83. 10.1007/s00248-022-01962-5. 10.1007/s00248-022-01962-5 - DOI - PMC - PubMed
    1. Coyne MS. A cartoon history of soil microbiology. J Nat Res Life Sci Educ. 1996;25:30–6. 10.2134/jnrlse.1996.0030.10.2134/jnrlse.1996.0030 - DOI
    1. Cha JY, Han S, Hong HJ, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29. 10.1038/ismej.2015.95. 10.1038/ismej.2015.95 - DOI - PMC - PubMed
    1. Durán P, Jorquera M, Viscardi S, Carrion VJ, Mora M de la L, Pozo MJ. Screening and characterization of potentially suppressive soils against Gaeumannomyces graminis under extensive wheat cropping by Chilean indigenous communities. Front Microbiol. 2017;8:1552. 10.3389/fmicb.2017.01552. - PMC - PubMed

LinkOut - more resources