The development of single-cell genomics technologies has been a driving force in biomedical research over the past decade because of the throughput, sensitivity, and precision with which such techniques can dissect complex biological systems. A primary example of the impact of these tools is the rapid translation of single-cell RNA sequencing (scRNAseq) for comprehensively cataloging cellular states for the Human Cell Atlas project. Technology that combine microfluidic platforms with DNA barcoding strategies have drastically increased the throughput and accessibility of scRNAseq so that to-date, the Human Cell Atlas consortium has logged over 67 million cells, enabling unprecedented insight into the cellular diversity in healthy and diseased organs and tissues. However, while the transcriptome provides a comprehensive and quantitative proxy for cellular state, proteomic measurements provide a more direct understanding of cellular function, and epigenetic measurements that profile methylation, histone modifications, and protein-DNA interactions, provide a more complete picture of the regulatory mechanisms that maintain cell state or drive cellular transitions. Furthermore, cell morphology and the spatial distribution of proteins and chemicals within the cell can reveal important cellular phenotypes that can only be characterized by microscopy. Finally, the relative positions of cells within tissues and organs are necessary for a more complete understanding of the cellular interactions that lead to functional tissues and organs. This research program focuses on the development of technology to facilitate multimodal precision measurements in single cells and tissues. We use molecular biology tools and DNA sequencing platforms to measure the proteome, transcriptome, and epigenome of single cells and nonlinear optical imaging to characterize chemical composition and morphology of cells. We leverage microfluidic technology to integrate molecular and optical measurements to enable multimodal single-cell measurement. Additionally, this research program aims to develop novel computational approaches for integrated analysis of multimodal single-cell measurements. Our ultimate goal is to develop a tool to make all of these measurements in situ, in order to retain single-cell spatial information and cellular context in a developing tissue or whole organism.