Computational and experimental framework for the integrated tissue analysis of spatial metabolomics and transcriptomics datasets

About This Grant

SUMMARY The cell type composition and cellular metabolism jointly shape a tissue’s microenvironment, and its function as a consequence. Over the past few years, spatial transcriptomics (ST) has become an important and commonly used method for mapping cellular states in tissues. As powerful as this approach is, however, transcriptomes constitute only one of many crucial biological modalities, including DNA, protein, and small molecules, which, upon integration, would provide a more comprehensive view of tissue architecture. Recently, spatial metabolomics (SM) by mass spectrometry imaging has become available and promises to enable the elucidation of entire metabolomes at high spatial resolution. At present though a robust approach is missing for the integration of spatial metabolomics with spatial transcriptomics. In this proposal we aim to develop novel algorithms for such an integration in the context of an important problem in cancer biology. When studying drug-treated tumors, ST and SM can reveal cellular states and the precise concentration of the drug that they are experiencing, respectively. We previously found that as cancer cells adapt to therapy, they undergo a set of cell state transitions that we have referred to as the ‘resistance continuum’. We also found in vivo evidence for the states along this continuum, however new insight requires an integration of spatial and temporal analysis. In our preliminary results, we showed the power of joint ST and SM analysis, but we were not able to track the clonal and drug treatment history of the cells over time. Thus an open question, with immense clinical relevance, is what is the effective concentration of a drug experienced by an adapting cell lineage. We propose to address this question here by deploying two independent frameworks. In Aim 1 we describe a method using an optimal transport framework to integrate data from a time-course comprising tumors adapting to a drug from different animals. Our computational framework will be designed to identify cellular transitions and propose specific hypotheses for testing. A second approach described in Aim 2 exploits novel lineaging technology and our established serial passaging approach for studying the same tumor over time. Analyzing this data will allow us to reveal the longitudinal history of a clone and reveal whether cells with lower or higher dose concentrations in their early adaptations were selected for higher drug resistance. Overall, the approaches developed in this proposal specifically address the challenges of spatial metabolomics and spatial transcriptomics data and we expect them to be of high value for many in the large community of researchers using spatial analyses.