PQG Working Group

Abstract: Visual cell sorting (VCS) is a single-cell co-assay that combines microscopy and high-throughput sequencing.  The microscopy measures cell morphology ​​and marks cells with phenotypes of interest, which enables sorting of cells based on visual phenotype. The subsequent sequencing step can be used to measure any one of a variety of cell characteristics, such as gene expression, chromatin accessibility, or chromatin 3D architecture. In the current VCS analysis pipeline, the imaging data is used primarily to generate discrete morphology labels. This approach does not fully exploit the rich information from images. VCS can associate single-cell profiles with their associated morphological phenotypes, but the images and the single-cell profiles do not have a direct correspondence. To attempt to recover this correspondence information, we developed a supervised version of the MMD-MA manifold alignment algorithm, with the goal of embedding the single-cell sequencing measurements and microscopy images into a shared manifold in such a way that two observations derived from the same cell are nearby in the embedded space. Clearly, successfully creating such an embedding would be valuable because it would allow us to explicitly describe how changes in gene expression relate to specific changes in cell morphology. We utilized partial phenotype labels of cells to guide the embedding, and we used the labels of the remaining cells for evaluation. Our approach sheds light on how gene expression profiles interact with cell morphology.