  High-content cellular imaging, transcriptomics, and proteomics data provide
rich and complementary views on the molecular layers of biology that influence
cellular states and function. However, the biological determinants through
which changes in multi-omics measurements influence cellular morphology have
not yet been systematically explored, and the degree to which cell imaging
could potentially enable the prediction of multi-omics directly from cell
imaging data is therefore currently unclear. Here, we address the question of
whether it is possible to predict bulk multi-omics measurements directly from
cell images using Image2Omics - a deep learning approach that predicts
multi-omics in a cell population directly from high-content images of cells
stained with multiplexed fluorescent dyes. We perform an experimental
evaluation in gene-edited macrophages derived from human induced pluripotent
stem cells (hiPSC) under multiple stimulation conditions and demonstrate that
Image2Omics achieves significantly better performance in predicting
transcriptomics and proteomics measurements directly from cell images than
predictions based on the mean observed training set abundance. We observed
significant predictability of abundances for 4927 (18.72%; 95% CI: 6.52%,
35.52%) and 3521 (13.38%; 95% CI: 4.10%, 32.21%) transcripts out of 26137 in M1
and M2-stimulated macrophages respectively and for 422 (8.46%; 95% CI: 0.58%,
25.83%) and 697 (13.98%; 95% CI: 2.41%, 32.83%) proteins out of 4986 in M1 and
M2-stimulated macrophages respectively. Our results show that some transcript
and protein abundances are predictable from cell imaging and that cell imaging
may potentially, in some settings and depending on the mechanisms of interest
and desired performance threshold, even be a scalable and resource-efficient
substitute for multi-omics measurements.
