The Diffusion Index
High-Plex Multi-Omics
The broad “multi-omics” dataset contains 37,000+ papers — but most of them use the word loosely, to describe any study combining two measurements. This dataset is different. It requires a named high-throughput method: CITE-seq, 10x Multiome, Olink + sequencing, spatial multi-omics, proteogenomics. The real revolution, with a curve to match.
Papers (2017–2026)
673
Peak tier 1+2 (2019)
464/1,000
Current (2026)
114/1,000
2025 papers
152
How many papers, and where they land
Annual high-plex multi-omics papers split into three tiers — Nature / Science / Cell, the rest of the top-tier specialist journals (Tier 1+2), and everything else. The log view keeps all three visible; the widening gap is the dilution the diffusion curve below captures as a ratio.
Log scale — each line is a count, so all three tiers stay visible despite spanning four orders of magnitude. The gap between the lines is the dilution: top-3 output barely moves while total volume explodes. 2006–2025 (2026 partial year omitted).
Peak: 255 per 1,000 in 2021 · current (2026): 64.3 per 1,000 — field in Early Majority, still diffusing
CITE-seq changed what was possible
In 2017, Stoeckius et al. published CITE-seq in Nature Methods — simultaneous RNA and protein measurement from the same single cell, at scale. It was genuinely new. The first papers were instantly in the top journals. Two years later, 10x Genomics released the Chromium Multiome (joint RNA + ATAC-seq), and the field began to industrialize. Unlike the loose “multi-omics” literature, these methods were cited by name — and the citation record shows a field that knows exactly what it’s doing.
The 2021 inflection: landmark papers
2021 had three top-3 journal papers in this dataset — the highest single-year count. This includes Hao et al. (Seurat v4 / Weighted Nearest Neighbor, Cell), which gave every lab a practical toolkit for joint RNA+protein analysis. That paper alone was cited thousands of times. When a methods paper lands in Cell and gets replicated everywhere within 18 months, the Innovators phase is over and Early Adopters have arrived. The score stayed high (452/1,000 tier 1+2) while volume nearly doubled from 32 to 53 papers.
The proteomics + sequencing layer
Olink and SomaScan are not single-cell tools — they measure thousands of proteins from blood or tissue using proximity extension assays or aptamer-based detection. Pairing them with RNA-seq or whole-genome sequencing in large cohorts (UK Biobank, All of Us, deCODE) is real high-plex integration: proteome-scale + genome-scale in the same sample. These studies are driving a meaningful fraction of the volume growth since 2022. The key distinction from “multi-omics”: these papers name the platform. Olink appears in 107 papers in this dataset; SomaScan in 42. Both are rising.
Where the curve is heading
The tier 1+2 score has declined from its 2019 peak of 464/1,000 to 114/1,000 in 2026 — but volume has grown 15× over the same period (9 to 140+ papers/year). This is the classic Early Majority signature: the novelty premium erodes as the method spreads, but the field is very much alive. Unlike the broad multi-omics word, which lost signal because the practice became universal and unnamed, high-plex methods are still being cited by name — which means the market signal in these tools remains trackable. That’s the point. When CITE-seq becomes as generic as “PCR,” this index will tell you.
Methods captured in this dataset
A single paper can match multiple methods. Counts reflect papers where the method appears in title or abstract.
Where the papers come from
Share of high-plex multi-omics papers by first-author affiliation, 2019–2026. Parsed from 660 affiliations.
High-plex multi-omics is the youngest field here and still the most US-led: US institutions held 34–45% of first-author papers through 2024, reflecting the US origins of CITE-seq, 10x Multiome, and Olink-based work. China crossed the US only in 2025, and on small numbers. If the trajectory of the older platforms holds, expect that gap to close quickly.
Methodology: Papers fetched from PubMed using a query requiring at least one named high-plex multi-omics method (CITE-seq, 10x Multiome, Chromium Multiome, SHARE-seq, SNARE-seq, TEA-seq, ASAP-seq, REAP-seq, ECCITE-seq, spatial multi-omics, proteogenomics, CPTAC, multi-modal single-cell, joint profiling, Olink + [transcriptomics / RNA-seq / multi-omics], or SomaScan + [same]). No generic “multi-omics” or “multiomics” terms included without a co-occurring named method. Diffusion score = top-tier papers ÷ total papers × 1,000. Default view shows Tier 1+2 journals. Journal tiers assigned locally using a curated list. 673 papers, 2017–2026.