Computational methods for the fast evaluation of the quality of microbiota using untargeted chemical analysis

The study of the microbiome is transforming medicine, offering insights into conditions from psychiatric disorders, such as depression and anxiety, to the efficacy of immunotherapy in advanced cancer treatment. Central to this are the immunomodulatory functions of the gut microbiota, often considered a functional organ. Short-chain fatty acids (SCFAs), key metabolites, maintain intestinal health and modulate immune responses, profoundly influencing therapeutic outcomes.

Microbiota transplantation (FMT) has shown great promise in restoring healthy microbiota, with evidence supporting its role in enhancing responses to immunotherapy for certain cancers. However, the success of FMT hinges on the quality of donor samples, where high SCFA levels serve as a critical indicator. Current SCFA measurement methods are resource-intensive and unsuited for large-scale donor screening, limiting their utility in microbiota biobanks.

This proposal aims to address this gap by integrating ion mobility spectrometry (IMS), Fourier Transform Infrared (FTIR) spectroscopy, and advanced computational methods to enable rapid, untargeted analysis of microbiota samples. Signal processing techniques, such as noise reduction and feature extraction, will enhance spectral data quality, while machine learning (ML) models will identify patterns correlating spectral signatures with SCFA levels. Together, these approaches will provide an efficient and scalable solution for ranking and selecting high-quality microbiota samples.

By establishing this spectrometry-based screening platform, the Catalan Microbiota Bank can streamline the identification of suitable samples for advanced clinical applications, such as FMT for immunotherapy. This innovative integration of IMS, FTIR, and computational analysis will accelerate microbiota-based therapies, expanding their clinical relevance and impact.

We are seeking a highly motivated and independent individual with experience in chemometrics, machine learning, and spectrometry. The ideal candidate will hold a PhD in Physics, Chemistry, Biomedical Engineering, Data Science, or a related field, with a focus on instrumental data analysis. Previous experience in metabolomics is a valuable asset. A strong publication record in high-impact, peer-reviewed journals is essential, as is the ability to work independently and collaborate effectively within a multidisciplinary team.

Candidate must comply with the MSCA Postdoctoral Fellowships 2025 (HORIZON-MSCA-2025-PF-01) eligibility criteria as described by the call. If interested, please send a CV and cover letter to smarco@ibecbarcelona.eu before July 31st  indicating MSCA-2025-PF in the title. The selected candidate will be in charge of the writing of the MSCA-PF proposal with our help, and its incorporation into the laboratory will depend on the success of the proposal