Single-cell transcriptomics: a revolutionary approach in skin and biomedical research

The field of dermatological R&D is currently undergoing significant advances in high-resolution analytical techniques that are transforming how we evaluate cosmetic efficacy and tissue biology. Traditional tissue-level analyses provide averaged signals that can mask the contributions of distinct or low-abundance cell populations. In contrast, single-cell transcriptomics allows researchers the characterization of individual gene expression of every cell within a tissue. This provides an unprecedented look at cellular heterogeneity, signaling pathways, and transitional cellular states.

When applied to reconstructed human skin models, this technology supports further the relevance of these platforms as physiologically representative systems for studying human skin biology. Such high-resolution characterization is the cornerstone of modern biomedical research, enabling the development of high-efficacy natural cosmetic actives that target specific biological activities based on specific cellular responses with surgical precision. 

What is single-cell transcriptomics?

Single-cell transcriptomics or single-cell RNA sequencing (scRNA-seq) is an advanced molecular biology technique used to analyze the total RNA content (the transcriptome) of individual cells within a heterogeneous population. While traditional bulk sequencing provides an average measure of gene expression across a whole sample—effectively blending the signals  of different cell types into a “smoothie”—single-cell methods isolate individual cells to identify distinct cell types, rare subpopulations, and transitional states that are otherwise masked by the dominant signal of the majority. 

By utilizing microfluidic-based or droplet-based platforms, such as the 10x Genomics Chromium system, researchers can partition thousands of individual cells into nanoliter-scale droplets, where the mRNA from each cell is uniquely barcoded. This process generates high dimensional datasets that reveal how different cells—even within the same lineage—can respond differently to environmental stressors, chronological aging, or cosmetic treatments.

These data are essential for the contribution to large-scale initiatives such as the Human Cell Atlas, which aim to map and characterize every cell type in the human body. For the cosmetic industry, such atlas provides a valuable framework to guide tissue engineering and skin biology research, supporting the development of more physiologically relevant skin models and more targeted and effective cosmetic active ingredients.

Exploring gene expression profiles at the level of individual cells 

The core objective of exploring gene expression at the single-cell level is to decode the functional diversity of the skin’s cellular ecosystem and the processes that govern cell state transitions (for example during skin cell renewal). The epidermis is not a static wall; it is a dynamic barrier where cells continuously progress through defined stages of differentiation. Single-cell transcriptomics captures these dynamics, providing a pseudotime analysis that allows researchers to visualize the trajectory of a cell from its birth in the basal layer to its eventual shedding at the surface. 

Through RNA sequencing data analysis, we can identify specific transcriptional signatures that define the state of a cell. For example, a keratinocyte in the basal layer will typically express high levels of markers such as keratins 5 and 14 (KRT5, KRT14), and the transcriptional factor TP63, which are genes indicative of proliferative potential. As this cell differentiates and moves into the spinous layer, it shifts its gene expression profile toward keratins 1 and 10 (KRT1, KRT10), and later toward proteins such as loricrin (LOR) and filaggrin (FLG) in the granular layer. Understanding these molecular transitions at the single-cell level can support cosmetic scientists to design ingredients that aim at modulating skin biology in a more targeted manner. This approach contributes to a more precise evaluation of how products interact with the skin, beyond effects limited to the outermost layers. For example, ensuring that the skin’s self-renewal machinery is optimized from within, rather than merely attempting to hydrate the dead cells on the tissue surface. 

Why single-cell transcriptomics in skin model development is important 

Single-cell analysis is highly valuable because it provides detailed molecular information that can be used to characterize reconstructed human skin models in the context of modern dermatological research. For an in vitro model to be considered relevant for human skin testing, it should demonstrate more than just histological similarity; it must function and behave like human skin at a molecular and genetic level. 

According to Provital-led studies such as Bajsert et al., 2024, and Jia et al., 2025 studies, the importance of this technology lies in its ability to: 

Verify physiological relevance: It confirms that lab-grown models (like PmtHEE) recapitulate the complex stratified architecture and the intricate signaling networks of native human epidermal tissue, including key aspects of cell–cell signaling (between melanocytes and keratinocytes). 

Eliminate biological “noise”: In traditional bulk testing, a positive signal might be misinterpreted if researchers cannot tell which cell type is responding. scRNA-seq separates these responses, allowing researchers to see how a melanocyte reacts differently than a fibroblast or a Langerhans cell when exposed to the same natural active. 

Advance ethical research and regulatory compliance: As global regulations move away from animal testing, single-cell data provides a more sophisticated, human-relevant in vitro alternative. These methods can improve the characterization of biological responses, for example identifying key hallmarks of skin aging, supporting more informed decision-making during product development.

Applying single-cell transcriptome sequencing to reconstructed human skin models 

In recent pioneering studies, single-cell transcriptomics has been applied to characterize the cellular composition of two specific models: the fibroblast-supported human skin equivalent (FibHSE) and the pigmented human epidermal equivalent (PmtHEE). These models represent the apex of 3D bioengineering, simulating the dermal-epidermal interactions and pigmentation biology found in native skin with remarkable accuracy (Bajsert et al., 2024). 

By dissociating these 3D structures into single-cell suspensions, researchers can evaluate the cellular composition, stratification and the underlying dynamics of the tissue. This application reveals whether the reconstructed model is successfully producing the necessary subpopulations of keratinocytes—such as mitotically active basal cells and differentiated granular cells—and whether those cells are maintaining their specialized roles as they transition through the layers.

Compared to conventional in vitro systems, this level of analysis provides a more detailed view of cellular heterogeneity and differentiation dynamics. It can also offer insights into cell–cell interactions between epidermal and dermal compartments, which contribute to overall tissue function and stability.

Comparing human skin explants and 3D skin models using single-cell sequencing 

A key step in evaluating bioengineered skin models is their comparison with human skin explants. In a landmark study conducted by Provital in collaboration with academic partners, researchers compared the transcriptomic profiles of reconstructed skin models with neonatal foreskin epidermis (FsEpi), the industry standard for epidermal research. The result was a high-resolution benchmark that proved the biological fidelity of these 3D models (Jia et al., 2025). 

Using advanced data analysis and clustering algorithms, the study showed that both FibHSE and PmtHEE models accurately mimic the transcriptional states of native human skin. Specifically, the data confirmed that the reconstructed human skin reproduced major epidermal cell populations including basal, spinous, and granular layers found in human skin in situ, with comparable transcriptional profiles at the single-cell level (“fingerprints.”)

This type of benchmarking is essential for the industry, as it ensures that the results of a cosmetic efficacy test performed in the lab—whether for barrier repair, anti-aging, or brightening—are likely to be mirrored in real-world human application, providing a high degree of confidence for R&D teams (Jia et al., 2025). 

Key concepts to Provital in single-cell transcriptomics 

• Molecular-level evidence / Transcriptional relevance: Natural active ingredients can be evaluated based on their measurable effects on gene expression in every cell type, providing molecular-level evidence of biological activity beyond clinical observations.
• Multiple cellular targets: Given that the skin is composed of multiple interacting cell populations, this approach considers the response of every different cell type. This can support Provital’s selection of ingredients that can act across multiple cellular targets simultaneously. 
• Data-driven botanical selection: When comparing plant-derived compounds for raw material selection, transcriptomics can help decipher their biological effects on specific populations of skin cells, enabling a more systematic and evidence-based assessment of the bioactivity of botanical ingredients.  This could effectively help turning the plant kingdom into a searchable database of cosmetic solutions. 

Single-cell RNA sequencing: a powerful tool for cellular state analysis 

Single-cell RNA sequencing (scRNA-seq) provides the high throughput capabilities required to analyze thousands of cells simultaneously, providing a detailed view of cellular composition and states within a tissue, something not possible with previous techniques. In the context of 3D skin models such as PmtHEE model, scRNA-seq allows for the detailed study of melanocyte biology, which is notoriously difficult to isolate in traditional settings. 

Melanocytes are relatively rare, typically representing a small fraction of the epidermal population (5%), which means their signal is usually drowned out in bulk samples. However, scRNA-seq isolates these cells, allowing researchers to study the molecular signaling networks between melanocytes and keratinocytes. This powerful tool can support the investigation of pigmentation-related processes and the evaluation of how different compounds influence these pathways for developing efficacious natural treatments for hyperpigmentation and skin tone evenness. In this context, single-cell transcriptomic data can help identify key hallmarks that regulate biological processes involved in pigmentation—such as those related to melanogenesis or melanin transfer—and thereby enable the development of botanical ingredients that target these specific mechanisms to modulate such processes.  (Jia et al., 2025). 

Methodology: using scRNA-seq to characterize skin models 

The methodology for characterizing 3D models via single-cell transcriptomics involves a rigorous and highly technical multi-step process that requires expertise in both biochemical laboratory techniques and bioinformatics. First, the 3D reconstructed tissues are enzymatically dissociated into a suspension of individual cells, typically using specialized enzymes (proteases). This step is critical; if the dissociation is too harsh, most cells die; if it is too gentle, the tissue remains in clumps, ruining the single-cell resolution. 

Once dissociated, the cells are partitioned, commonly into separate droplets where their mRNA is captured and uniquely barcoded. Following reverse transcription, the resulting libraries are sequenced using high throughput methods. The resulting RNA seq data are then processed through computational pipelines that include quality control, normalization, and dimensionality reduction. This allows for the clustering of cells based on their transcriptional similarity, effectively identifying the various cell types and differentiation stages present within the model (Jia et al., 2025). 

Results: insights from single-cell transcriptomics in skin research 

The application of single-cell transcriptomics to reconstructed human skin models has been transformative, providing a wealth of information that was previously “invisible.” Provital’s studies indicate that these models are not merely stratified layers of cells but physiologically dynamic environments that successfully replicate the architecture, signaling, and ultrastructural features of native human skin. 

Key insights from recent studies include (Jia et al., 2025)): 

• Differentiation trajectories: Computational analyses have provided evidence of a complete, healthy transition of keratinocytes, from basal progenitor cells to terminally differentiated granular cells, consistent with known patterns of epidermal differentiation. 
• Intercellular communication: Transcriptomic data supports the identification of signaling pathways between melanocytes and keratinocytes that govern skin homeostasis and cell renewal. These observations show that bioengineered models successfully preserve cell-cell communication, maintaining tissue balance. 

Provital: advanced technique such as single-cell transcriptomics 

Provital stands at the forefront of this research, being among the first in the global cosmetic industry to apply scRNA-seq to 3D skin models, including those incorporating melanocytes. This pioneering approach allows Provital to set a high-resolution benchmark for the entire industry, moving beyond traditional marketing claims into a more data-driven understanding of skin biology in cosmetic research.

By utilizing this advanced technique, Provital can: 

• Characterize in-depth molecular efficacy: Evaluate the effects of natural compounds on gene expression across the multiple cell types of the skin, helping to understand in depth the mechanism of action. 
• Predict long-term tissue effects: By observing and analyzing how cells transition through differentiation states, Provital can predict in vitro how an ingredient may influence epidermal processes such as skin barrier formation, maintenance and overall health over weeks of use. 

How single-cell transcriptomics will shape future biomedical and cosmetic research 

Single-cell transcriptomics is expected to play an increasingly important role in both biomedical and cosmetic research as sequencing technologies become more accessible and cost-effective., we will see a shift toward “digital twins” of human skin. These are AI-driven models trained on vast amounts of transcriptomic data to predict the outcome of any cosmetic formulation with near-perfect accuracy before a single drop of product is manufactured. 

In this context, emerging approaches aim to develop computational models of human skin that integrate large-scale molecular data. While still under development, these models may help simulate how different formulations interact with biological systems, supporting more informed decision-making during early stages of product development.

This technology will also accelerate the discovery of natural actives that can address silent cellular issues. For example, scRNA-seq can identify senescent or “zombie” cells—aged cells that have stopped dividing but refuse to die, secreting pro-inflammatory factors that affect the surrounding tissue. By better understanding the transcriptomic signature of these cells, can support Provital’s research and development into senolytic natural compounds that specifically target and clear these cells; leading to a new era of proactive, transcriptomic-based wellness. 

Implications for dermatological and cosmetic treatments 

In the realm of dermatology, the implications are profound. Researchers can now study the molecular mechanisms underlying different skin conditions, such as atopic dermatitis or rosacea, using reconstructed human skin models. While these models do not fully replace patient-derived samples, they provide a human-relevant system to investigate biological processes and to evaluate natural cosmetic compounds under controlled conditions. 

For the cosmetic industry, this means a new era of “Truth in Labeling.” The days of vague promises are ending. Claims related to effects such as “rejuvenation” or “skin repair at a cellular level,” can be increasingly supported by molecular data, including changes in gene expression associated with specific cellular processes. This level of transparency and scientific rigor will favor manufacturers and suppliers who invest in high-quality, ethically sourced, and scientifically validated natural ingredients. It marks the transition of the “natural” category from a lifestyle choice to a high-performance scientific standard. 

Key takeaways 

• Resolution vs. averaging: Single-cell transcriptomics complements bulk transcriptomics analysis by providing higher-resolution insights into individual cell populations, enabling the identification of less abundant or transient cellular states.
• Model validation and characterization: Reconstructed models like FibHSE and PmtHEE are proven to be physiologically competent substitutes for native human skin. scRNA-seq benchmarking has further characterized their cellular composition, supporting their use as relevant systems in R&D.
• Human-relevant ethical testing: Advanced 3D in vitro models combined with high-resolution molecular data, contribute to the development of ethical alternatives to animal testing, which are also more relevant to human biology.

For further information or insights on this topic, please do not hesitate to contact our team of experts, who are available to provide guidance and support in selecting the most suitable solutions for your requirements.