A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics.  This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures.
Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification or physical separation of RNA and genomic DNA. They allow insights that cannot be gathered solely from transcriptomic analysis, as RNA data do not contain non-coding genomic regions and information regarding copy-number variation, for example. An extension of this methodology is the integration of single-cell transcriptomes to single-cell methylomes, combining single-cell bisulfite sequencing to single cell RNA-Seq.  Other techniques to query the epigenome, as single-cell ATAC-Seq  and single-cell Hi-C also exist.
A different, but related, challenge is the integration of proteomic and transcriptomic data.  One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies.  A different approach uses a combination of heavy-metal RNA probes and protein antibodies to adapt mass cytometry for multiomic analysis.