Organoids, as three-dimensional, self-organizing structures derived from stem cells, have emerged as powerful tools for modeling human development and disease and screening drug candidates. These miniaturized organ-like systems recapitulate key aspects of tissue architecture and function, offering insights into developmental processes that are often unattainable using traditional two-dimensional cultures or animal models. By closely mimicking organ development, organoids provide researchers with a unique platform to study how tissues and organs form, function, and respond to various stimuli. And as techniques for generating and manipulating organoids evolve, researchers are increasingly able to study complex biological processes, drug responses, and disease mechanisms in a context that closely resembles the human body.

The significance of advanced organoid methods lies in their ability to bridge the gap between basic research and clinical applications. Recent advances have demonstrated their utility in areas such as cancer research, regenerative medicine, and infectious diseases, providing a platform for personalized medicine approaches. Moreover, organoids can be employed in high-throughput drug screening and toxicity testing, paving the way for more effective therapeutic strategies.

In light of these developments, BMC Methods is opening a collection on “Advanced organoid methods and protocols: from development to disease modeling.” We invite submissions on a wide range of method-based topics and approaches, including but not limited to:

Methods for organoid development and optimization

Organoid applications in personalized medicine

Approaches for utilizing organoids for disease models

High-throughput screening with organoid systems

Organoids as models for human and animals development and tissue formation

Comparative studies using human and animal organoids

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Microbial communities, which include bacteria, fungi, archaea, and viruses, play a fundamental role in various ecosystems, influencing processes such as nutrient cycling, disease dynamics, and environmental stability, and it is necessary to study them, as they have crucial applications in agriculture, medicine, and environmental science.

Traditional methods, such as microbial isolation and culture, have long been used to study these communities, though they often fail to capture the full diversity due to their reliance on culturable microorganisms. Advancements in technologies such as high-throughput sequencing, bioinformatics, metagenomics, the integration of machine learning techniques and the development of advanced statistical framework, have transformed how we analyze microbial communities, enabling researchers to explore their diversity, composition, and functional potential in unprecedented detail. However, there remain significant challenges in harmonizing methods, optimizing protocols, and interpreting data across diverse environments and research contexts.

As the field evolves, continued research into novel analytical methods will empower scientists to address pressing challenges, from antibiotic resistance to climate change mitigation, by leveraging the potential of microbial communities.

Topics of interest for this Collection include:

High-throughput sequencing techniques for microbial community profiling

Metagenomic, metatranscriptomic, and metaproteomic analyses

Single-cell sequencing and its application in microbiome research

Isolation and culturing protocols for microbial community analysis

Novel sampling techniques for diverse habitats (soil, water, human microbiomes, etc.)

Novel bioinformatics approaches for microbial community analysis

Standardization of methodologies in microbial ecology

Integrating machine learning with microbial data interpretation

Functional analysis of microbial communities

New methodologies for studying microbial interactions and networks

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.