Livia Eberlin Lab

A key strength of our research team is in the development and refinement of mass spectrometry imaging techniques for spatial profiling of metabolism in cancer tissues. We have developed several methods using desorption electrospray ionization (DESI) MS imaging to improve specificity in the detection and imaging of hundreds of metabolites and lipids directly from human tissue sections. We then implemented these methods to discover novel metabolic signatures and biomarkers that are diagnostic, prognostic, and related to treatment response across multiple diseases.

One example is a study in which we employed MS imaging to characterize the metabolic composition of oncocytic thyroid and kidney tumors. Using DESI-MS imaging, we obtained metabolic information from specific tumor regions within the complex tumor microenvironment and discovered that oncocytic tumors have abnormal cardiolipin signatures, including previously unreported oxidized cardiolipins that may serve as targets for treatment. We have also implemented DESI-MS imaging to characterize the metabolic profiles of cancerous and normal cancer tissues and identified predictive markers of cancer aggressiveness and response to chemotherapies. Ongoing funded projects include imaging of pediatric cancer tissues for disease stratification, identification of metabolic markers of response to immunotherapies in human tissues, imaging of molecular markers of endometriosis, among others.

Our laboratory is constantly pushing the limits in analytical performance of direct MS techniques to improve disease detection from tissues and challenging clinical samples such as minimally invasive biopsies. Using DESI-MS imaging and other direct MS techniques, we are optimizing experimental protocols and data analytics to maximize molecular coverage and sensitivity to detect diagnostic molecules from patient samples that have complex cellular and molecular composition. 

We are working on several important projects within this area. For example, we have an ongoing NIH-funded prospective clinical study to collect thyroid fine needle aspiration biopsies from patients to evaluate if we can pre-operative identify cancer within the samples. We are also using DESI-MS imaging technology to analyze platelets and liquid biopsies from cancer patients for diagnostic purposes.

Our work in this field has paved the way for innumerable new applications for using MS to diagnose disease based on metabolic information acquired from minimally invasive biopsies. 

Our team has made significant contributions to the field of in vivo MS analysis by developing and translating MS systems to the operating room, thereby enabling in vivo molecular measurements of the dynamic metabolic information that can help stratify disease and guide treatment decisions for patients in real time. A major development in tissue analysis by MS that was pioneered by our group is the MSPen technology. The MSPen employs solid-liquid chemical extraction to directly analyze and identify unmodified tissue samples within seconds. The resulting mass spectra display rich molecular profiles capturing hundreds of naturally occurring biomolecules extracted from the tissue’s cells. Thus, the MSPen is widely applicable for molecular analysis of various tissues and diseases.

To interpret the complex data, we have also developed a suite of advanced statistical tools for prediction of an output, e.g., tissue type or disease state, and achieved high accuracies for cancer diagnosis based on novel metabolic biomarkers. Importantly, the gentle solvent-based extraction process enables ex vivo and in vivo analyses without tissue damage, thus opening a new field in chemical sensing of tissues that can be broadly explored and expand various research studies in biomedical sciences. Since its inception, the MSPen technology has generated immense interest from scientific and medical communities and stimulated new research applications. We have continued to advance the technology and extend its application through multiple collaborations and ongoing funded research projects, including cancer diagnosis and subtyping, surgical margin evaluation, surgical robotics, COVID-19 and other infectious disease identification, and in vivo natural products analysis.
 

Our laboratory is very interested in using our innovative technologies to provide new methods for rapid identification of infectious diseases. We are particularly interested in using the MasSpec Pen technology to look at molecular signatures of clinically relevant bacteria and identify markers to antibiotic resistant. We have a particular interest in clinically relevant projects in this area that have clear impact in patient care. For example, we have an ongoing NIH-funded project in which we are applying the MasSpec pen to identify pathogens and resistance in osteoarticular infection samples form pediatric patients. Our work in this field has the potential to allow rapid identification of bacteria and their resistance to improve patient care. 

Our lab has developed many data analysis workflows adapting and integrating sophisticated statistical methods to evaluate complex molecular datasets that we obtain with MS technologies. We are particularly interested in using logistic regression and machine learning techniques such as the least absolute shrinkage and selector operator (Lasso) to develop statistical classifiers and identify predictive molecules within our data. The use of the Lasso technique provides key advantages in selecting molecular features that are indicative of disease state, and in building sparse classification models that are predictive of cancer and can thus be used for tissue diagnosis. We have also adapted significant analysis of microarrays (SAM) statistical techniques to identify significant molecular markers using MS imaging datasets. Using SAM, we can identify specific markers that are significantly altering in biological tissues. These approaches have been broadly employed by the scientific community to evaluate MS imaging data, and we continue to explore and develop new methods to improve rigor and robustness in our data analysis.