Abstract Many human diseases ? including autoimmunity, cancer, and aging ? are linked to a breakdown in the ability of different cell types to coordinate their behaviors and decisions across a tissue. Coordination occurs through a variety of mechanisms, including molecular, mechanical, and electrical signals that are exchanged among cells. These signals are integrated through signaling pathways, and ultimately, impact the transcription of numerous genes. The multicellular regulatory networks that coordinate transcription among each cell type of a tissue can be deduced by perturbing the transcriptional state of individual genes or cell types, then measuring how each cell type relaxes to a new steady state, both dynamically and spatially. However, the systematic application of this approach will require new experimental tools. We will therefore build on a new method developed in our lab ? MULTIseq ? that allows the simultaneous transcriptional analysis of numerous samples using existing single cell RNAseq pipelines (e.g. DropSeq, 10X, SeqWell, etc.). MULTIseq enables the implementation of entirely new classes of single cell experiments. The method has the additional benefits of dramatically increasing throughput and decreasing artifacts such as doublets and batch effects. As a consequence, MULTIseq allows researchers to gather richer single cell information at a 5- to 100-fold reduction in sample preparation costs. We present three aims that will enable application of MULTIseq to analyze dynamic biological processes in time; heterogeneous biological processes in space; and the response of complex tissues to hundreds or thousands of genetic, chemical, or microenvironmental perturbations. Finally, we propose to combine MULTIseq with methods to reduce sequencing costs by 10-fold while simultaneously increasing the information content from low-abundance transcripts. Completion of our goals will provide a powerful new tool to the scientific community that can be applied to any cell type using a simple and economical protocol.