Understanding health and disease mechanisms in animals, plants, and microbes requires a systems-level investigation of regulatory networks at the levels of genes, transcripts, proteins, and metabolites. The majority of multiomic studies performed to date have been bulk analyses on blood [1], plasma [2], cell pellets [3], or whole tissues. While bulk analyses are often cost-effective and technologically less-demanding, such analyses cannot measure cell heterogeneity and biologically significant rare cell populations are often missed. Several ongoing initiatives, with a loosely shared goal of characterizing tissues at single-cell resolution, such as the Human Biomolecular Atlas Program [4], Human Tumor Atlas Network [5], Kidney Precision Medicine Project [6], Human Cell Atlas [7], and Cellular Senescence Network (SenNet) [8], are supporting the development of novel single-cell -omic techniques. Recently developed single-cell RNA sequencing (scRNAseq) techniques have revolutionized our understanding of developmental trajectories [9], identified new cell types [10], and enabled the mapping of entire organs [11]. Toward holistic understanding of biological processes, these sequencing methods have also been extended to multimodal measurements from the same single-cells 12, 13.

Although sequencing-based -omic approaches have many advantages (e.g. fidelity, cost, and throughput), there are limitations for protein measurements (e.g. intermediate antibody probes and limited access to intracellular targets) 14, 15, 16 and metabolites/lipids cannot be directly measured. Fortunately, it is within these classes of molecules that mass spectrometry (MS) thrives, demonstrated by decades of studies performed at tissue scale. Recent advancements in MS techniques have pushed the envelope further, enabling cellular and subcellular measurements. When combined with orthogonal techniques, measurements across modalities from the same single-cells become possible [17]. It is with this in mind that we focus this review on the same single-cell (or near-single-cell) MS-enabled multiomics.