How Proteomics and Metabolomics Are Revolutionizing Single-Cell Analysis
Imagine if we could understand a city not by looking at its overall population statistics, but by following every individual resident—where they work, what they eat, how they interact with others.
This is the fundamental shift happening in biology as we move from studying tissues as bulk samples to investigating individual cells. Each cell in our body, though microscopic, contains an intricate universe of molecular activity that defines health, disease, and everything in between. Until recently, scientists could only measure average signals from millions of cells, masking critical differences between individual cells. The emergence of single-cell proteomics and metabolomics now allows researchers to peer into this previously invisible world, revealing how individual cells actually function rather than how we assume they function based on population averages 4 .
What a cell could do based on its DNA blueprint
What a cell might do based on RNA messages
What a cell is doing based on protein expression
What a cell is fueling based on metabolic activity
These technological advances come at a crucial time in biomedical research. While genomics tells us what a cell could do based on its DNA blueprint, and transcriptomics reveals what it might do based on RNA messages, proteomics and metabolomics show us what the cell is actually doing right now—the proteins executing functions and the metabolites fueling cellular processes 3 4 . This closer-to-function view provides unprecedented insights into why some cells become cancerous while others remain healthy, why some immune cells effectively fight pathogens while others fail, and how complex diseases develop and progress at the cellular level. The integration of these complementary perspectives is transforming our understanding of biology and opening new avenues for personalized medicine.
To appreciate the significance of these new technologies, we first need to understand what proteomics and metabolomics measure. The proteome represents the entire set of proteins in a cell—the molecular machines that perform virtually all cellular functions, from structural support to catalyzing biochemical reactions. Proteins are the workhorses that translate genetic information into actionable cellular activities. Single-cell proteomics allows scientists to identify which proteins are present, in what quantities, and where they're located within individual cells 4 . This is particularly important because proteins often undergo modifications after production that dramatically alter their function, and these changes can vary significantly between individual cells.
The metabolome, in contrast, comprises all the small molecules involved in cellular metabolism—sugars, lipids, amino acids, and other intermediates that serve as the currency of cellular energy. Metabolites represent the most downstream product of cellular processes and therefore provide the most immediate snapshot of a cell's functional state 3 7 . When immune cells switch to attack mode or cancer cells proliferate rapidly, their metabolic activity changes first, often before other visible transformations occur. Single-cell metabolomics captures these dynamic changes, revealing how individual cells are responding to their environment and meeting their energy demands 3 .
| Omics Type | What It Measures | Biological Significance | Key Challenge |
|---|---|---|---|
| Proteomics | Proteins and their modifications | Functional effectors of cellular processes | Cannot amplify proteins; wide dynamic range of abundance |
| Metabolomics | Small molecules (sugars, lipids, etc.) | Immediate snapshot of cellular metabolic state | High dynamism; metabolites change within seconds |
| Transcriptomics | RNA molecules | Blueprint of potentially active proteins | Doesn't always correlate with protein abundance |
The advent of single-cell proteomics and metabolomics has already yielded significant insights, particularly in understanding cancer heterogeneity and immune cell function. What makes these approaches so powerful is their ability to capture cellular heterogeneity—the differences between individual cells that were previously averaged out in bulk analyses 4 . In cancer research, for example, we now know that tumors contain diverse cell populations with different metabolic profiles, and the rare cells with unique metabolic adaptations often drive treatment resistance and disease recurrence 3 .
Traditional single-cell methods require dissociating tissues into individual cells, losing crucial information about where these cells were located. New spatial technologies now allow researchers to measure proteins and metabolites while preserving their spatial context. The scSpaMet framework, for instance, combines untargeted spatial metabolomics with targeted multiplexed protein imaging to profile immune and cancer cells in human tissues while maintaining information about their original locations and neighborhood relationships 1 .
Perhaps even more exciting are technologies that enable researchers to measure multiple types of molecules from the same single cell. A recently developed method called scMeT-seq successfully profiles both metabolome and transcriptome from the same individual cell by using nano-capillaries to extract minute amounts of cytoplasm for metabolic analysis while preserving cell viability for subsequent transcriptome sequencing 7 . This approach reveals direct relationships between gene expression and metabolic activity in individual cells.
Identification of rare tumor subpopulations with treatment-resistant metabolic profiles
Revealing metabolic adaptations of immune cells during activation and exhaustion
Mapping metabolic heterogeneity among neuronal and glial cell populations
To illustrate how these technologies work in practice, let's examine the scSpaMet (Single Cell Spatially resolved Metabolic) experiment published in Nature Communications in 2023 1 . This study exemplifies the power of combining proteomics and metabolomics while preserving spatial information in human tissues.
The study began with human lung cancer, tonsil, and endometrium tissues sectioned into thin slices and mounted on slides. An adjacent section from each tissue was stained with hematoxylin and eosin (H&E) for pathological identification of regions of interest.
The researchers first performed untargeted spatial metabolomics using Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). This technique bombards the tissue surface with ions, causing the ejection and ionization of molecules from the tissue. The instrument then measures the mass-to-charge ratio of these ions, identifying hundreds of metabolites and lipids at submicron resolution—detailed enough to distinguish features within individual cells.
Following metabolic imaging, the same tissue sections were subjected to multiplexed protein analysis using Imaging Mass Cytometry (IMC). This method uses antibodies tagged with metal isotopes to target and label specific protein markers. The tissue is then ablated with a laser, and the metal tags are detected by mass spectrometry, generating maps of protein expression at single-cell resolution.
The crucial innovation was developing a computational pipeline to align the metabolic and protein datasets. Using histone and DNA markers as landmarks, the researchers applied affine transformation to precisely register both imaging modalities, enabling correlation of metabolic and protein profiles at the single-cell level. This integration allowed them to analyze how metabolite levels varied between different cell types identified by their protein markers.
The scSpaMet approach generated remarkable insights into cellular metabolism within its native tissue context:
| Tissue Type | Number of Cells Analyzed | Key Metabolic Finding | Biological Significance |
|---|---|---|---|
| Lung Cancer | 19,507 | Unique metabolic competition between cancer and immune cells | Reveals how tumors create metabolically hostile microenvironments for immune cells |
| Tonsil | 31,156 | Cell-type specific metabolite profiles in immune cells | Shows how different immune cell types maintain distinct metabolic identities |
| Endometrium | 8,215 | Metabolic changes along tissue differentiation paths | Illustrates how metabolism supports tissue remodeling and specialization |
| Step | Technique | What Is Measured | Resolution |
|---|---|---|---|
| 1. Metabolic Imaging | Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) | 200+ metabolites and lipids | Submicron (<1 μm) |
| 2. Protein Imaging | Imaging Mass Cytometry (IMC) | 25-35 protein markers | 1 μm |
| 3. Data Alignment | Computational cross-modality registration | Spatial alignment of metabolic and protein data | Single-cell level |
| 4. Analysis | Deep learning joint embedding | Metabolic patterns across cell types and spaces | Single-cell resolution |
The study analyzed a total of 58,928 individual cells across the three tissue types. The results revealed striking cell-type-dependent metabolite profiles, with different immune and cancer cell types showing distinct metabolic signatures.
— scSpaMet Study, Nature Communications 2023 1
Conducting single-cell proteomics and metabolomics research requires specialized reagents and technologies. Here are some key tools that enable this cutting-edge science:
| Tool | Function | Example Use Cases |
|---|---|---|
| Metal-tagged Antibodies | Label specific proteins for detection by mass cytometry | Cell type identification in Imaging Mass Cytometry 1 |
| Tandem Mass Tags (TMT) | Label peptides from different samples for multiplexed analysis | Comparing protein expression across multiple single cells simultaneously 4 |
| NanoPOTS Chips | Miniaturized platform for processing single-cell samples | Reducing sample loss in single-cell proteomics workflows 4 |
| Matrix for MALDI Imaging | Enables ionization of molecules for mass spectrometry imaging | Spatial mapping of metabolites in tissues 1 |
| Nano-capillaries | Extract minute amounts of cytoplasm from living cells | Simultaneous metabolome and transcriptome profiling from the same cell 7 |
Bulk proteomics and metabolomics
Single-cell transcriptomics emerges
First single-cell proteomics methods
Multi-omics and spatial technologies
Single-cell proteomics and metabolomics represent more than just technical achievements—they signify a fundamental shift in how we study biology. By examining the molecular components that most directly influence cellular function at the level of individual cells, we gain unprecedented resolution into the mechanisms of health and disease. These approaches have already begun transforming our understanding of cancer metabolism, immune cell function, and tissue organization 1 3 .
The future of this field lies in further integration—both technological and analytical. Technologically, we're moving toward methods that can simultaneously measure proteins, metabolites, and potentially other molecular types from the same single cell with minimal destruction.
Analytically, we're developing more sophisticated computational frameworks to integrate these multimodal datasets and extract biologically meaningful patterns 7 . As these technologies become more accessible and widespread, they will undoubtedly reveal new aspects of cellular biology.
Perhaps most exciting is the potential for clinical translation. As single-cell proteomic and metabolic profiling become faster and more cost-effective, they could revolutionize diagnostic medicine by identifying rare cell populations responsible for disease progression or treatment resistance.
The cellular universe within us is vast and largely unexplored, but with these powerful new tools, we're poised to map its intricacies in unprecedented detail, potentially unlocking new frontiers in personalized medicine and therapeutic intervention.