The single-cell revolution has granted us an unprecedented view into the workings of biology, resolving complex tissues into their individual cellular components, with huge implications for fundamental biological studies as and we major applicability in diagnosis and disease monitoring. However, this high-resolution view has brought its own challenges; thus, the field is facing concerns regarding reproducibility. As datasets grow larger and more complex, ensuring that the biological insights we derive are robust, consistent and comparable across different studies has become a paramount concern.

At Nexco Analytics, our commitment is to leverage the most advanced computational methods to deliver insights that are not only powerful but also reliable. As you have seen in our blog, staying current is not just a passive activity for us; it’s a core part of our process. Naturally, this includes making sure that conclusions are robust, reliable and reproducible. This month, a series of publications in Nature Methods perfectly captures the shift towards a more reproducible era in single-cell analysis, which we believe signals new standards for the field. For this month’s blog post we have opted to go through these papers in detail, for our own training and to distill the key points for you.

The Problem of Single Labels in RNA seq

For years, the standard approach to analyzing single-cell RNA sequencing (scRNA-seq) data has been clustering. While intuitive, this method often forces a single, discrete identity onto a cell that is, in reality, a composite of multiple biological processes. A T cell, for example, isn’t just its lineage (say for example a “basic” CD4+ helper T cell); rather, it also has a functional state. So, is a given T cell resting, activated, proliferating, or exhausted?

Traditional clustering can conflate these signals. A group of proliferating cells from different lineages might be incorrectly grouped together, masking their true identities and functions. This makes it incredibly difficult to compare findings between labs, datasets, and disease contexts. Is a “T-cell cluster 5” in one study the same as in another? Of course more often the answer is no, which hampers our ability to build upon previous work. However, if we could annotate the full gene expression program of that group of cells, then we could more reliable compare clusters across studies.

A New Paradigm: Annotating with Gene Expression Programs

The solution to the problem of single labels outlined above then lies in moving beyond single labels and towards a more multi-faceted description of a cell’s state. This is the core principle behind T-CellAnnoTator (TCAT), a new framework described by Kotliar et al in Nature Methods 2025. TCAT operates on the concept of Gene Expression Programs — groups of co-regulated genes that represent a distinct biological function, like defining a cell subset, its activation state, or its stage in the cell cycle.

Instead of clustering, TCAT scores each cell against a pre-defined, comprehensive catalog of 46 reproducible T-cell expression programs. This catalog was meticulously built by analyzing 1.7 million T cells across 38 tissues and multiple diseases. This approach provides a stable, consistent “coordinate system” to describe any T cell. This way, it allows researchers to disentangle a cell’s lineage identity from its transient functional state, revealing, for instance, the precise polarization of CD8+ T cells — a feature often obscured by other methods.

The power of this approach is not just there for academics. The authors demonstrated that TCAT could identify T-cell signatures that predict a patient’s response to cancer immunotherapy, highlighting its direct clinical relevance.

Making Advanced Models Accessible and Actionable

Developing a robust GEP catalog like TCAT is a massive computational undertaking. This raises another barrier to reproducibility: not every research group has the resources to process atlas-scale datasets. This is where a second, complementary innovation comes into play: scvi-hub.

Described by Ergen et al. in the same issue of Nature Methods, scvi-hub is essentially a “store” (like the one from which you download new apps in your phone) for pre-trained single-cell models. Scvi-hub thus provides a centralized platform where the scientific community can share and access state-of-the-art models that are ready to use. A researcher studying lung disease doesn’t need to download and process the entire Human Lung Cell Atlas; instead, the user can just pull a pre-trained model from scvi-hub and immediately use it to analyze the data with the power of the full atlas behind it.

This dramatically lowers the computational barrier to entry and, crucially, promotes standardization, which in turn increases reliability and improves reproducibility. By encouraging the use of common, well-validated reference models, scvi-hub facilitates analyses that are inherently more comparable and reproducible.

What This Means for Your Research

This shift from subjective clustering to standardized, model-based annotation with tools like TCAT represents the maturation of the single-cell field. Meanwhile, resources like scvi hub facilitate standardization and thus transparency and consistency across studies. This all means we can now move towards generating insights that are more robust, transferable, reliable, reproducible, and ultimately redound in more solid biological advances as well as clearer, safer, more controlled transfer to the clinic.

This is where Nexco’s expertise becomes critical. We do not just run data through default pipelines, but rather actively engage with these cutting-edge developments, understanding new frameworks like TCAT and leveraging platforms like scvi-hub. This ensures that when we analyze your data, we are applying the most powerful and reproducible methods available, tailored to your specific biological questions. By embracing these new standards, we can characterize cell states with greater precision, compare results against the backdrop of massive public atlases, and uncover subtle biological signals that would otherwise be missed.

The single-cell revolution is moving into its next, more rigorous phase. Staying ahead of this curve is not just a goal; it is our standard practice at Nexco Analytics. We are committed to ensuring your research benefits from the very forefront of bioinformatics innovation, delivering clarity and confidence from every single cell.

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Check out our bioinformatics services including our RNA-seq services, and contact us here.

References

• Reproducible annotation of T cell subsets and activation states with gene expression programs - Nature Methods
• Scvi-hub: an actionable repository for model-driven single-cell analysis - Nature Methods
• Reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions - Nature Methods