FMT, the practice of transferring stool from a healthy donor to a patient’s intestinal tract, has gained widespread recognition for its remarkable effectiveness in combatting Clostridioides difficile infections. Beyond this, it holds promise for treating a host of conditions including inflammatory bowel disease (IBD) and certain forms of cancer. Yet a significant knowledge gap has persisted: which bacterial strains are the true champions of colonization and therapeutic benefit, and how they evolve within their new hosts has remained elusive.

The new method pioneered by Mount Sinai researchers cleverly combines state-of-the-art long-read DNA sequencing with an innovative computational algorithm termed LongTrack. Unlike traditional short-read sequencing that processes fragmented pieces of microbial genomes, long-read sequencing decodes extended DNA sequences, providing a continuous genetic narrative of each bacterium. When paired with LongTrack, this enables strain-level resolution — distinguishing even the most closely related bacterial strains by their unique genetic fingerprints. The synergy of these technologies allows scientists to monitor individual donor bacteria from the instant of transplant and track their genetic adaptation for up to five years post-treatment.

Professor Gang Fang, the senior researcher and corresponding author of the study, emphasizes the transformative potential of this method. He explains, “Our ability to reliably follow donor bacterial strains over long durations was previously limited by the short-read sequencing technology. With LongTrack and long-read metagenomics, we achieve unprecedented scale and specificity, revealing the complex evolutionary pathways bacteria undertake to survive and thrive in diverse patients’ gastrointestinal environments.” This level of detail lays the foundation for designing microbiome therapies that are precise, reproducible, and personalized.

Collaborating closely with co-author Dr. Jeremiah Faith, specialists in immunology and immunotherapy at the Icahn School of Medicine, the research team applied this approach to analyze stool samples from both FMT donors and recipients. Their cohort included individuals treated for recurring C. difficile infections and those battling IBD. Samples were collected across multiple time points, with some extending up to five years after transplantation. The results were illuminating: numerous donor-derived bacterial strains successfully colonized the recipients’ guts, maintaining their presence long term. Intriguingly, genetic mutations observed in certain strains strongly suggested active adaptation to the recipient’s unique gut ecosystem, highlighting the dynamic interplay between host environment and microbial evolution.

This study delivers a blueprint for systematically identifying and selecting beneficial bacterial mixtures, streamlining the development of novel microbiome-based interventions that could serve as safer, more controllable alternatives compared to whole-stool FMT. By pinpointing which microbial strains persist and adapt effectively, therapeutic formulations can be tailored with greater predictability and safety, potentially minimizing risks associated with donor variability and pathogen transmission currently observed in conventional FMT.

In practical terms, the implications for precision medicine are profound. Dr. Fang states, “This technology does not merely enable us to track bacterial survival; it sheds light on the exact genetic shifts that facilitate their long-term adaptation. Such insights are critical for engineering microbial therapeutics that consistently deliver beneficial outcomes without unintended consequences.” This capability opens doors to next-generation, strain-specific treatments for microbiome-related diseases that transcend the one-size-fits-all model.

Looking ahead, the research team plans to harness this technology in larger clinical studies involving diverse patient populations and a broader array of diseases where the gut microbiome influences health outcomes. Their goal is to leverage LongTrack’s capacity to identify functionally advantageous bacterial strains that can be developed into bespoke microbial therapeutics, ushering in a new era of microbiome science where treatments are personalized at the genetic level.

Published in the October 22, 2025 issue of Nature Microbiology, this landmark study, titled “Long-read metagenomics for strain tracking after faecal microbiota transplant,” was authored by Yu Fan, Mi Ni, Varun Aggarwala, Edward A. Mead, Magdalena Ksiezarek, Lei Cao, Michael A. Kamm, Thomas J. Borody, Sudarshan Paramsothy, Nadeem O. Kaakoush, Ari Grinspan, Jeremiah J. Faith, and Gang Fang. Their work was funded by the National Institutes of Health under grant number R35 GM139655.

This development marks a significant stride toward unlocking the full therapeutic potential of the human microbiome. By enabling direct observation of beneficial microbes in action over extended periods, this research not only advances scientific understanding but also promises to accelerate the translation of microbiome science into precision interventions that could transform medicine for patients worldwide.

Subject of Research: People

Article Title: Long-read metagenomics for strain tracking after faecal microbiota transplant

News Publication Date: October 22, 2025

Web References:
https://doi.org/10.1038/s41564-025-02164-8

References:
Fan Y, Ni M, Aggarwala V, Mead EA, Ksiezarek M, Cao L, Kamm MA, Borody TJ, Paramsothy S, Kaakoush NO, Grinspan A, Faith JJ, Fang G. Long-read metagenomics for strain tracking after faecal microbiota transplant. Nature Microbiology. 2025 Oct 22. doi:10.1038/s41564-025-02164-8.

Keywords: Microbial infections, Fecal microbiota transplant, Long-read sequencing, Gut microbiome, Clostridioides difficile, Inflammatory bowel disease, Microbial therapeutics, Genetic adaptation, Precision medicine, Metagenomics, Microbiome therapy, Strain tracking

The announcement of MARQO, which stands for Multi-Analytical Robust Quantitative Observation, emerges from research recently published in the esteemed journal Nature Biomedical Engineering. The study outlines MARQO’s capabilities in extracting detailed cellular and spatial information from whole-slide images of tumor tissues. This development signals a leap forward in the integration of computational power with traditional pathology, enabling more nuanced examinations of cancerous samples than ever before.

Historically, the process of analyzing stained tissue sections is labor-intensive and often limited in scope. Pathologists typically examine small areas under a microscope, making it challenging to see the broader picture of tumor composition and organization. MARQO addresses this limitation by permitting the analysis of entire slides without the need for manual segmentation into smaller patches. This innovation not only saves time but also improves the accuracy of analyses, which is critical for ensuring precise interpretations and diagnoses.

One of the remarkable features of MARQO is its adaptability to multiple staining techniques. It supports common immunohistochemistry (IHC) and immunofluorescence (IF) staining methods, which are staples in cancer research for identifying and localizing markers in tissues. The ability to unify analyses across different staining protocols enhances reproducibility, making it easier for researchers to draw comparisons across studies and improve the reliability of their findings.

In addition to speed and versatility, MARQO incorporates sophisticated algorithms that automatically detect likely positive cells within the tissue sample. By flagging these cells and recording their precise coordinates and marker intensities, MARQO creates a structured dataset that pathologists can then validate. This combination of automated processing and human expertise fosters a more efficient workflow, allowing medical professionals to focus on interpreting the data and uncovering insights rather than getting bogged down by the tedious aspects of analysis.

Dr. Sacha Gnjatic, the lead researcher and a prominent figure in immunology and immunotherapy at Mount Sinai, emphasized the importance of MARQO in filling a crucial gap in current pathology practices. He stated that the tool was specifically designed to streamline the conversion of complex whole-slide images into actionable, structured datasets swiftly and consistently. By automating the more demanding aspects of slide analysis, MARQO empowers experts to concentrate on what truly matters—their interpretative insights and the advancement of cancer research.

Despite its impressive capabilities, MARQO is still in the research phase and has not yet been validated for clinical diagnostics. However, its compatibility with widely accepted clinical staining methods suggests a promising future where MARQO could enhance routine pathology work. The research team behind MARQO has plans to continue developing the tool, aiming to improve its user interface and introduce advanced analytical capabilities that will facilitate large-scale studies involving immense volumes of digitized tissue slides.

The potential applications of MARQO extend beyond mere analysis. As a platform for biomarker discovery, it could revolutionize how researchers identify and evaluate potential targets for cancer therapies. With accurate data on cellular composition and spatial organization, oncologists and researchers would be better equipped to predict which patients are likely to benefit from specific treatments, thereby supporting the development of personalized medicine approaches.

The implications of MARQO are far-reaching, with the potential to enhance cancer diagnostics significantly. By resolving issues related to speed, accuracy, and user-friendliness in the analysis of whole-slide images, MARQO could change the standard operating procedures within pathology labs and, ultimately, the outcomes for cancer patients. The research team views this tool as a critical step toward more precise and efficient diagnostic processes in the ever-evolving landscape of cancer treatment.

As more focus is placed on technology’s role in healthcare, tools like MARQO come to the forefront, showcasing how artificial intelligence can augment human capabilities in medical settings. The careful integration of such technologies into pathology not only supports current research efforts but also lays the groundwork for future innovations in disease detection and management.

With continued development and validation, MARQO stands to become an indispensable asset in the fight against cancer, bridging the gap between traditional analytical methods and the advancing digital landscape of medicine. As it progresses toward larger studies and clinical applications, the scientific community remains optimistic about the possibilities that MARQO opens up for enhanced cancer tissue analysis.

In conclusion, the introduction of MARQO marks a pivotal moment in how pathologists can examine and interpret cancer tissues. By harnessing the power of AI, this new tool promises to deliver unprecedented speeds and accuracy in slide analysis, encouraging a new era of research that prioritizes rapid and refined insights into cancer biology. The pursuit of precision in cancer diagnostics takes a significant leap forward with innovations like MARQO, ultimately aiming to improve patient outcomes and advance treatment methodologies.

Subject of Research: Cells
Article Title: Multiparametric cellular and spatial organization in cancer tissue lesions with a streamlined pipeline
News Publication Date: 25-Aug-2025
Web References: Nature Biomedical Engineering
References: doi:10.1038/s41551-025-01475-9
Image Credits: Credit: Mount Sinai Health System

Keywords

• Imaging
• Cell pathology