A Streamlined Approach to Bulk RNA and Protein Multiomics Profiling

[Bruker]

As life science research becomes more integrated and systems-focused, single-modality profiling can fall short in characterizing complex biological systems. Multiomics, which integrates data across gene expression and protein abundance, enables a more comprehensive understanding of regulatory networks, cell states, and disease mechanisms. Bulk multiomics, along with single-cell and spatial technologies, have advanced translational research by characterizing molecular mechanisms that underlie development and progression of diseases.

What Is Bulk Multiomics?
Bulk multiomics refers to the simultaneous measurement of multiple molecular analytes, typically RNA and protein, from samples where cells or analytes are combined and analyzed together (e.g., dissociated tissue or pooled cell lysates). Unlike single-cell or spatial methods that preserve positional or cellular resolution, bulk workflows average molecular signals across a sample [1]. These bulk workflows provide high-throughput processing, avoid tissue dissociation bias, and result in a simple data structure that is amenable to user-friendly analysis pipelines.
Bulk multiomics is particularly useful for:

• Identifying population-level transcriptional and proteomic trends
• Validating biomarkers discovered in more exploratory omics screens
• Processing large sample cohorts without extensive hands-on time or computational overhead

By measuring mRNA and protein in tandem from the same sample in bulk, researchers gain a more complete picture of regulatory states, while avoiding the cost and complexity of more granular approaches [2].

Why Choose Bulk Over Single-Cell or Spatial?
Single-cell and spatial technologies enable typing of individual cells and context-specific data but introduce several challenges [3,4]:

• Tissue dissociation artifacts can alter expression profiles, especially for surface proteins and labile transcripts.
• Sparse data and dropout in droplet-based single-cell workflows may obscure low-abundance signals.
• Throughput limitations can make large studies impractical or cost-prohibitive.

In contrast, bulk multiomics provides several notable advantages:

• Streamlined workflows avoid tissue dissociation, barcoding, and segmentation.
• Robust reproducibility from bulk samples minimizes the effects of dropouts and supports better replicate concordance.
• Greater scalability allows for large sample volumes, many timepoints, and/or analysis of multiple experimental conditions.

Bulk multiomics also serves as a practical bridge between whole transcriptome discovery and targeted validation or screening.

The nCounter® Bulk Multiomics Workflow
Broad approaches like whole-transcriptome RNA-seq are ideally suited to discovery research, where thousands of gene targets can be identified. Once key candidates are found, researchers need a more efficient and focused method for validating and monitoring these targets, especially in translational settings. As discussed in a recent SeqAnswers post, the nCounter Analysis System offers a high-throughput solution with direct transcript detection, robust 800-plex panels, and reproducible performance across sample types and conditions.
Now, Bruker Spatial Biology is expanding its nCounter platform to support simultaneous quantification of RNA and protein targets from the same tissue section. This new bulk multiomics workflow is based on direct digital detection and avoids enzymatic amplification, maintaining native expression levels and providing high reproducibility.

The key features of the nCounter multiomics workflow include:

• Fast: Results in 3 days, with minimal hands-on time.
• Broad compatibility: Works with FFPE or fresh frozen (FF) samples and requires only a single slide.
• Flexible: Measures RNA and protein targets on the same sample using stackable and customizable panels.
• Simplified data analysis: Integrate RNA and protein visualization with a single analysis software.

These features enable researchers to extract quantitative, multi-analyte data from samples that may not be suitable for single-cell analysis or mass spectrometry.

Workflow Overview

1. Sample preparation: FFPE or fresh frozen sections are mounted onto a slide.
2. Protein Hybridization: Target proteins are recognized by highly specific oligo-conjugated antibodies with photocleavable linkers. After overnight incubation, the oligos are removed by non-destructive photocleavage and retained for digital counting.
3. RNA Hybridization: After protein barcodes are removed, RNA is then extracted from the same slide and barcoded with pre-built nCounter panels.
4. Sample Processing: Both protein and RNA signals are counted during the same run on a single nCounter instrument.
5. Analysis: Data from both analyte types can be processed using a new integrated software.

Total turnaround time is approximately three days, with minimal hands-on time compared to sequencing or spatial workflows.

How It Compares: Bulk vs Single-Cell Multiomics
To help clarify where nCounter bulk multiomics fits among the current landscape of gene expression profiling technologies, the table below compares key features of bulk and single-cell approaches.

Feature	Bulk	Single-Cell
Platform	nCounter	Droplet-based scRNA-seq
Resolution	Bulk (sample-level average)	Individual cells
Throughput	High	Moderate
RNA detection	Direct, amplification-free	Enzymatic, sequencing-based
Protein detection	Same instrument; Oligo-labeled antibody	Separate instrument; using labeled antibody, mass spectrometry, or CITE-seq
Sample input	Intact tissue on slide	Dissociated cell suspension
Workflow complexity	Low	High
Ideal for	Screening, validation, large-scale cohort studies	Cell typing, discovery, rare cell identification

nCounter bulk multiomics is a practical choice for researchers needing efficient, high-throughput, multi-analyte data for non-discovery applications.

Practical Limitations to Consider
Bulk approaches capture averaged expression across all cells in a sample, which removes the ability to detect cellular heterogeneity or spatial context. The nCounter workflow is also targeted, requiring some prior knowledge to design RNA and protein panels. For well-characterized pathways or large sample sets, these constraints are often offset by the speed, reproducibility, and compatibility with FFPE tissue.

Use Cases Across Biology and Disease Research
With stackable protein panels for Immune Pathways and Tumor Signaling coming soon, the nCounter multiomics platform can support a wide range of applications, including:

• Tumor profiling: Assess gene and protein expression to characterize immune infiltration, tumor subtypes, or key signaling pathways in solid tumors. The workflow is particularly well suited to FFPE tissue and small biopsies, which are commonly used in translational oncology studies.
• Immunology: Profile cytokines, cell surface markers, and transcriptional signatures to characterize immune activation states in disease models or treatment conditions. Simultaneous RNA and protein profiling allows for cross-validation of activation markers and more comprehensive pathway assessment.
• Immuno-oncology: Analyze immune checkpoint signaling, tumor-immune interactions, and pathway modulation in response to immunotherapies. The combined measurement of RNA transcripts and corresponding protein targets provides a clearer view of the functional state of immune pathways, supporting mechanism-of-action studies, biomarker validation, and therapeutic development.

Researchers will be able to choose from predefined panels or develop custom content to align with specific biological questions. Multiomics data analysis will be supported by a new integrated software solution.

Early Access to nCounter Multiomics
Bruker Spatial Biology’s Technology Access Program (TAP) allows researchers to evaluate the nCounter multiomics workflow before it is commercially available by submitting samples for pilot studies. The nCounter TAP provides the first opportunity to assess data quality and understand the benefits of bulk multiomics firsthand.

Learn more about nCounter Multiomics

References

1. Chen, G., Yu, R., & Chen, X. (2023). Integrative analysis of single-cell and/or bulk multi-omics sequencing data. Frontiers in Genetics, 13, 1121999. https://doi.org/10.3389/fgene.2022.1121999
2. Li, Y., Ma, L., Wu, D., & Chen, G. (2021). Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Briefings in Bioinformatics, 22(5), bbab024. https://doi.org/10.1093/bib/bbab024
3. Mustachio, L. M., & Roszik, J. (2022). Single-cell sequencing: current applications in precision onco-genomics and cancer therapeutics. Cancers, 14(3), 657. https://doi.org/10.3390/cancers14030657
4. Luo, Y., Zhao, C., & Chen, F. (2024). Multiomics Research: Principles and Challenges in Integrated Analysis. BioDesign Research, 6, 0059. https://doi.org/10.34133/bdr.0059

nCounter bulk multiomics is for Research Use Only. Not for use in diagnostic procedures.

[David hoff]

This looks really promising! Being able to measure both RNA and protein from the same slide without all the complexity of single-cell or spatial methods is a big win. Especially useful for working with FFPE samples or large cohorts. Curious to see how flexible the panel customization will be—definitely keeping an eye on this.