A major challenge to microbiome research is the lack of access researchers have to human microbiome samples. The Harvard Chan School is building the Biobank for Microbiome Research in Massachusetts (BIOM-Mass) to fill this critical gap.
Harvard T.H. Chan Microbiome Analysis Core
The Harvard T.H. Chan Microbiome Analysis Core
, established to advance the rapidly emerging field of microbiome science, aids in human microbiome study design and interpretation, helping researchers develop and apply quantitative methods to investigate the role of microbial community function in health. The Core provides a critical foundation for advancing the frontiers of human microbiome research, providing cutting-edge analytical tools and technologies needed to help scientists mine the extraordinarily rich and complex data that is central to microbiome discovery, research and development.
Harvard T.H. Chan Microbiome Collection Core
The Microbiome Collection Core at the Harvard T.H. Chan School of Public Health (HCMCC)
was established in response to a strong demand among the research community for validated microbiome sample collection kit configurations and easy usability for in-home sampling. Under the umbrella of HCMPH, HCMCC aims to support population-scale microbiome sample collection and expand our understanding of the microbiome to improve population health.
The Harvard Chan Gnotobiotic Facility houses mice that harbor defined microbial communities through highly controlled husbandry and monitoring procedures. Such animals can lack microbes (germ-free), contain microbes from a human donor sample (microbially ‘humanized’ mice), or be constructed with highly defined microbial communities ranging from one to hundreds of microbes. The study and use of such mice enables investigators to grow and expand microbes that will not grow in vitro and also enable the screening of bioactivity of microbes and microbial communities. The Harvard Chan facility provides cutting-edge capabilities to generate and maintain gnotobiotic mice from a variety of genetic backgrounds, under varied environmental and dietary conditions, and for molecular and physiological readouts ranging from microbiome sequencing to histology and immunology.
Flagship Projects are research and development projects that are strategically and scientifically defined and are of substantial size with regard to their scientific and financial volume, the number of project partners and the running time. Flagship Projects aim at the horizontal and/or vertical integration of the value chain and thus at the technological feasibility of systems solutions with long-term potential for growth.
The MICRObiome Among Nurses(MICRO-N)
The MICRObiome Among Nurses (MICRO-N)
Study will enable research on the connection between diet and lifestyle, microbiome composition, and the risk of developing chronic diseases such as cancer, diabetes, and heart disease. The following are highlights about the study; if you are interested in learning more or in participating, please contact us at NHS2MicroN@bwh.harvard.edu
or visit our page.
The Human Microbiome Bioactives Resources (HMBR)
The Human Microbial Bioactives Resource (HMBR)
thus aims to provide a comprehensive platform for discovery, validation, and early-stage translation of novel therapeutics derived from the microbiome.
Inflammatory Bowel Disease Multi'omics Database (IBDMDB)
will provide an integrated resource for analyzing the gut microbial ecosystem in the context of IBD, improving our ability to understand, diagnose, and treat IBD. It will use several existing, well-described patient cohorts to provide many different types of longitudinal data.
Enter OPTIMISTICC (Opportunity To Investigate the Microbiome’s Impact on Science and Treatment In Colorectal Cancer)
– an international team led by Matthew Meyerson, MD, PhD, and Wendy Garrett, MD, PhD. Their goal is to pinpoint the mechanisms by which the microbiome impacts the initiation and progression of colorectal cancer and to apply this understanding for therapeutic benefit.The team comprises geneticists, immunologists, oncologists, microbiologists and patient advocates, each of whom are pioneers in their respective fields.In an ambitious plan that spans the translational pipeline, the aim is to integrate these diverse yet complementary perspectives to provide a 360° view of the role of the microbiota in colorectal cancer.
Education and Training
Different Harvard courses are available through the following links.
This course will provide a thorough introduction to microbial community data analysis (metagenomics, metatranscriptomics, and other culture-independent molecular data) through a balanced approach of lectures and hands-on lab sessions. Course participants will learn how to process data from raw meta’omic sequencing files through appropriate bioinformatic methods and approaches for subsequent integrative statistical analyses. Participants are invited to bring their own data to the practical session on the final day or can use publicly available data from the Integrative Human Microbiome Project (HMP2).
This course is designed for established investigators, postdoctoral fellows and advanced graduate students from diverse biological fields. Topics to be covered include but are not limited to acquisition and organization of next generation sequence data; principles of quality control of sequence data and data management; methods of taxonomic assignment and clustering of targeted gene data; assembly, functional classification and characterization of shotgun metagenomic data; statistical models for estimating microbial diversity; and microbial community comparison methodology and metrics.
This online course will cover the different omics areas, including their appropriate applications and experimental challenges to understand the scope of omics research and methods in: genomics, epigenomics, transcriptomics, proteomics, and metabolomics, understand omics in terms of investigation for your biological questions (disease etiology, diagnosis, and treatment), learn about the importance of experimental design in omics research and understand the challenges and limitations of big data analysis, including integration of data, batching, computational resources, and working with team members across all fields.