Methodological approaches for identifying rhizosphere bacteria and fungi: A systematic review of comparative insights and best practices

Advances in microbiome research, especially high-throughput molecular, omics, and next-generation sequencing (NGS) technologies, have transformed our ability to characterize rhizosphere microbial communities, which are essential to plant health, soil function, and ecosystem resilience. This systematic review synthesizes literature from 2014 to 2025, following PRISMA 2020 guidelines, to evaluate methodological approaches for identifying rhizosphere bacteria and fungi. We compare the performance, limitations, applicability, and functional characterization capabilities of culture-based and culture-independent techniques—including imaging approaches across diverse agro-ecological contexts. Culture-based methods remain accessible and effective for isolating cultivable taxa (microorganisms that can be isolated and grown on artificial medium in the laboratory) but typically capture less than 1% of microbial diversity. Early molecular tools such as Polymerase Chain Reaction – Denaturing Gradient Gel Electrophoresis (PCR-DGGE) and Terminal Restriction Fragment Length Polymorphism (T-RFLP) allow detection beyond cultivable species but suffer from PCR bias and limited functional resolution. Functional and phylogenetic gene arrays provide insight into community structure and function but rely on prior sequence knowledge and therefore cannot be used for discovering novel species or functions. High-throughput molecular, omics, and next-generation sequencing (NGS) platforms, including 16S ribosomal ribonucleic acid (16S rRNA) or Internal Transcribed Spacer (ITS) target gene sequencing, have revolutionized taxonomic discovery but offer minimal direct functional insight and remain cost- and expertise-intensive. Emerging omics-based platforms such as metagenomics, metatranscriptomics, metabolomics, and metaproteomics enable integrated analyses of community structure and function yet pose challenges in data interpretation and resource demands. Imaging techniques, further complement multi-omics by providing spatial context. We advocate for methodological standardization, bias reduction, and enhanced integration of multi-omics and imaging approaches. Optimizing microbial identification strategies and improving functional characterization will be critical for advancing rhizosphere research and designing sustainable agricultural systems.