Single-cell multi-omics Market Outlook 2026–2035: Growth Drivers, Key Companies, Strategic Developments & Adoption Forecast — A Meticulous Next™ Foresight Brief

This Meticulous Next™ brief examines single-cell multi-omics, assays that measure gene expression, chromatin and proteins in individual cells, and the atlas scale datasets built from them for drug target discovery. It tests how fast falling cost per cell and large public datasets move these methods from research use into pharmaceutical target discovery, and which participants capture value among platform developers, sequencing suppliers, pharmaceutical sponsors and atlas programmes.

Dataset scale has jumped. The Arc Institute launched the Virtual Cell Atlas on 25 February 2025 with data from over 300 million cells, including Tahoe-100M, which profiles 100 million cells, 60,000 drug and cell interactions and 1,200 drug perturbations across 50 cancer cell lines, and which the release described as 50 times larger than all public drug perturbation data combined. The conclusion is that perturbation data at this scale is the training material for AI models of cell behaviour, and the owners of the largest open datasets set the reference for the field.

Suppliers built the supply side. Tahoe-100M used the GigaLab kit from Parse Biosciences for sample preparation and Ultima Genomics for sequencing, and the Chan Zuckerberg Initiative announced the Billion Cells Project with Scale Biosciences expecting nearly 500 million cells in its first year across more than ten laboratories. The conclusion is that cost per cell has fallen enough to make hundred million cell studies routine, and cheap sequencing and high throughput kits are the enabling inputs.

Demand is broadening. A 100 Million Cell Challenge run by Scale Biosciences with partners drew 147 proposals from researchers in 27 countries covering nearly 1 billion cells, and in June 2025 the Wellcome Sanger Institute, Parse Biosciences and Helmholtz Munich began a cancer plasticity atlas planned at hundreds of millions of cells. The conclusion is that public and philanthropic funders are creating demand for consumables and sequencing far above what individual pharmaceutical projects would buy.

The step from atlas data to validated targets is the one still to be demonstrated at scale, since atlases support target discovery and AI model training. The conclusion is that near term revenue rests on research spending, and pharmaceutical adoption will depend on early target validation results.

The brief treats single-cell multi-omics as an accelerating category with a nine year horizon to 2035. It identifies which assays, end users and regions scale first and what each means for platform developers, sequencing suppliers, pharmaceutical sponsors and atlas programmes. It is a focused 104-page decision brief that presents an indicative trajectory rather than a segmented market model.

Single cell assays isolate and barcode individual cells so that gene expression, open chromatin and surface proteins can be read for each cell, instead of an average across a tissue.

Multi-omic and perturbation assays measure several layers at once or the response of each cell to a drug or a gene edit, as in Tahoe-100M.

Other approaches combine single cell data with spatial data and with AI models that predict how cells respond to perturbations.

Uses are cell atlases, target identification and validation, mechanism and toxicity studies and, later, clinical sample profiling, each under research and laboratory test rules.

The limits are data volume and analysis burden, batch effects, the link from cell data to validated targets, and consumable cost at scale. The period turns on whether atlas data produces validated targets.

The single-cell multi-omics market, covering instruments, consumables, software and services for profiling gene expression, chromatin and proteins in individual cells, is valued at USD 4.5–6.5 billion in 2026 and is projected to reach USD 14.63–21.14 billion by 2035, at a CAGR of 14%.

The 2026 base rests on instruments, kits, sequencing, software and services sold to research institutes, pharmaceutical sponsors and core facilities. Growth then follows falling cost per cell, larger perturbation datasets and the extension to clinical samples.

The forecast is sensitive to sequencing cost, kit pricing and the pace of target validation. Regionally, North America leads on platforms and pharmaceutical use, Europe on atlas programmes and research institutes, and Asia-Pacific on sequencing makers and fast following laboratories.

The base case assumes cost per cell keeps falling and AI models trained on atlas data enter target programmes by 2029, giving the inflection in 2031. An accelerated case assumes early validated targets from atlas data, bringing the inflection to 2029 and lifting the 2035 value to the top of the range. A delayed case assumes no validated targets and flat research budgets, holding growth at the bottom of the range.

Growth drivers

• Falling cost per cell.
• Atlas and perturbation datasets for AI models.
• Pharma use in target discovery and validation.
• Extension to clinical samples and trials.

Enablers

• Philanthropic and public atlas funding.
• Vendor and pharma partnerships.
• Sequencing capacity.
• Cloud and compute.

Restraints and barriers

• Data volume and analysis burden.
• Batch effects and standardisation.
• Link from cell data to validated targets.
• Consumable cost at scale.

The direction of single-cell multi-omics is set by the need for cell level evidence in target discovery, standards and regulation, new chemistry and AI models, vendor, pharma and institute partnering, and sequencing capacity and cost per cell.

The key participants in single-cell multi-omics include 10x Genomics, Inc., Illumina, Inc., Becton, Dickinson and Company, Bio-Rad Laboratories, Inc., Parse Biosciences, Inc., Scale Biosciences, Inc., Ultima Genomics, Inc., Mission Bio, Inc., Standard BioTools Inc., Thermo Fisher Scientific Inc., Danaher Corporation, Agilent Technologies, Inc., Revvity, Inc., Tahoe Therapeutics, Inc., Element Biosciences, Inc., MGI Tech Co., Ltd., Pacific Biosciences of California, Inc., Oxford Nanopore Technologies plc, NVIDIA Corporation, Bruker Corporation, Arc Institute, Chan Zuckerberg Initiative, Human Cell Atlas, Wellcome Sanger Institute, Helmholtz Munich, Broad Institute, Allen Institute, AstraZeneca PLC, Pfizer Inc., Novartis AG, GSK plc, Sanofi, Merck & Co., Inc., U.S. Food and Drug Administration, European Medicines Agency, Medicines and Healthcare products Regulatory Agency, Pharmaceuticals and Medical Devices Agency, International Organization for Standardization, Global Alliance for Genomics and Health and Clinical and Laboratory Standards Institute.

The brief profiles representative participants in each archetype and assesses which are positioned to lead as single-cell multi-omics mature.

10x Genomics holds the largest installed base, Parse Biosciences and Scale Biosciences lead on high throughput low cost kits, Illumina and Ultima Genomics compete on sequencing cost, and the Arc Institute, the Chan Zuckerberg Initiative and the Wellcome Sanger Institute fund and host the atlases. Competitive intensity is high in 2026 and rises as kit prices fall.

Where value migrates.

In 2026 value sits with platform developers and sequencing suppliers that sell consumables to atlas programmes and pharmaceutical laboratories, because large studies buy kits and sequencing in volume. By 2031 it moves toward companies and institutes that hold large perturbation datasets and AI models, because data that predicts cell response is hard to reproduce. By 2035 it settles in integrated data, sequencing and analysis platforms that combine assays, datasets and models. Margins hold for assays with the lowest cost per cell and for proprietary datasets and narrow for commodity kits.

The signals converge on rapid growth in dataset scale and an open question on target validation. A Virtual Cell Atlas of over 300 million cells and a 100 million cell perturbation dataset were released openly in February 2025, and a philanthropic programme expects nearly 500 million cells in one year. Each signal supports growth, and data burden, batch effects and the lack of validated targets limit the pace.

Structural limits show where progress could slow: data volume and analysis cost, batch effects, the link from cell data to validated targets and consumable cost at scale. The brief therefore tracks four indicators: cells profiled in public atlases, cost per cell, targets validated from atlas data, and pharmaceutical partnerships with atlas builders.

Research atlases first, then pharmaceutical target discovery and perturbation screens, then clinical sample profiling are the expected order of adoption.