A position is open for a full-time research assistant (RA) in the Department of Pathology at Brigham and Women’s Hospital. Under the supervision of Dr. David Walt and Dr. Travis Gibson, the selected candidate will provide technical assistance for research pertaining to the human gut microbiome and its interactions with the host immune system. This position will provide the opportunity to work with histologic, immunohistochemical, molecular cytogenetic, and in situ protocols in a state-of-the-art lab environment.
Among other responsibilities, the selected candidate will perform multiplexed error-robust fluorescence in situ hybridization (MERFISH). The RA will perform all stages of the MERFISH protocol, including tissue fixation, permeabilization, hybridization, embedding, and clearing, in addition to imaging using a custom-built semi-automated robotic system built by a team at BWH. The selected candidate will primarily work independently to prepare and process tissue samples while optimizing the respective protocols, and will work closely with the surrounding research team to interpret the resulting data. Previous experience with protocols involving RNA is considered a strong asset. Other responsibilities of the selected candidate include assistance with the execution of the ultrasensitive single molecule array (Simoa) assay. Research conducted by the candidate in collaboration with the research team will provide new insights into the dynamics of the gut microbiome and will address underlying biological questions.
The responsibilities of the selected candidate may include:
- Independently performing routine and non-routine experimental protocols of moderate to high complexity. Experimental work will include preparing buffers and reagents without RNase contamination and evaluating the reagents for viability.
- Performing literature searches relevant to the execution of the required protocols.
- In collaboration with the PIs or Research Manager, modifying existing research techniques and potentially establishing new techniques.
- Communicating progress professionally with collaborators and with the scientific community.
- Coordinating and scheduling tests and procedures, and documenting experimental work accurately and in detail.
- Ordering laboratory supplies related to their assigned tasks.
- Following all lab safety protocols.
BA/BS in biological/physical science required. Some prior research preferred but not essential.
Skills/Abilities/Competencies Required:
- Sound analytical and organizational skills.
- Requires good oral and written communication skills.
- Must be able to logically and effectively structure tasks and set priorities.
- Ability to identify potential problems and troubleshoot solutions.
About the lab environment
The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis, and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.
Applications Process
Submit: (1) cover letter; (2) curriculum vitae to: Utkarsh Sharma, usharma1@bwh.harvard.edu
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Cell identity drives cell-cell communication and tissue architecture and is in return regulated by cell-extrinsic cues. Cell identity is determined by the combination of intrinsic developmentally established transcription factor use (TF) and constitutive as well as cell communication-dependent TF activities. Presented work shows two probabilistic models that we developed to advance the understanding of these processes using single-cell and spatial genomic data.
The human gut microbiome, and its metabolic activity in particular, have been implicated in a wide range of disease states, including metabolic disorders, inflammatory bowel diseases, and colorectal cancer. This growing appreciation for the impact of the gut microbiome’s metabolism on human health has given rise to studies that generate both microbiome and metabolome high-throughput data from human gut microbiome samples. Truly integrated analysis of both omic datasets, however, remains a challenging task. My research aims to develop new frameworks for analyzing and integrating these datasets using a combination of machine learning, metabolic modeling, and network analysis. I’ll specifically present two projects: the first evaluates the robustness of microbiome-metabolome associations using machine learning and meta-analysis models; the second aims to identify “multi-omic modules” that capture both cross-omic associations and associations with disease simultaneously. Taken together, these frameworks can enhance our understanding of microbiome-metabolome connections and equip the microbiome research community with novel methods for such integrated data analysis.
Fibrosis is a pathology of excessive scarring which causes morbidity and mortality worldwide. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. In this talk, I will introduce a mathematical model we recently developed for a cell circuit between myofibroblasts and macrophages – the two cell types that produce and remodel the scar. The mathematical framework predicts two types of fibrosis – hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. Moreover, we use the model to predict that the autocrine signal for myofibroblast division is a potential therapeutic target to reduce fibrosis. Finally, I will discuss how we use myocardial infarction (MI), a widely studied in-vivo injury model for cardiac fibrosis, to test these theoretical concepts and intervention strategies experimentally.
