Chris Leung

Cell atlases as a phenotype

With the advent of single-cell RNA sequencing (scRNA-seq), some contend that the phenotype of a cell can be abstracted as a "bag of RNA" (Quake, 2021). Differential gene expression analysis then provides contrast and allows us to classify cells. Projects such as the Human Cell Atlas (HCA) aggregate single-cell and spatial data from healthy human cells, with the aim of connecting individual cell types to their collective functions and interactions (Amit et al., 2024). We analyse these claims in these ways:

  1. Does scRNA-seq provide sufficiently rich, reliable, and interpretable data to describe cell phenotypes?
  2. What do cell atlases tell us about phenotype and function?
  3. If cell atlases do provide a "molecular definition of cell type" (Quake, 2022), what might be missing?

Step 1 in building a cell atlas: single cell transcriptomics

Cell atlases are primarily built on scRNA-seq experiments. The readout provides a high-dimensional vector in RNA-space, which is then analysed by dimensionality reduction. We will examine the biases introduced in the experimental pipeline and consider the effects on the fidelity/utility of the RNA phenotypic readout.

A significant fraction of the RNA collected from a cell consists of ribosomal RNA, so mRNA is enriched either by poly-A or ribosomal-specific probes. The latter is particularly prone to introducing bias by off-target effects on similar sequences. As the average length of mRNAs exceeds the read length of high-throughput sequencing, the remaining RNA is fragmented, deeply sampled, then reconstructed in silico aided by unique molecular identifiers (UMI). These tags also serve to eliminate biases in PCR amplification in read counting. However, (Sena et al., 2018) showed that reads with identical UMIs sometimes map to slightly shifted but similar coordinates, possibly due to PCR stuttering. This results in an overestimation of certain genes' expression. In addition, low-expression genes often suffer from inadequate coverage, which is more efficiently improved by biological replicates rather than deeper sequencing. Quality control during data analysis can account for some of the biases (e.g. read duplication rate), but these examples indicate that experimental biases lead to a biased and lossy abstraction of the RNA content of a cell.

The exact choices taken in scRNA-seq experiments also impacts our ability to integrate this data usefully. Ongoing questions include the size of the cDNA fragments, accounting for inter-cell variability due to technical difficulties and the stochasticity of gene expression, choosing between 5' and 3' reads of the transcriptome, and dropouts in library preparation, as reviewed in (Jovic et al., 2022). Moreover, we can capture a cell at various stages of its development, depending on its cell cycle and stress state. Deep learning methods such as deepMNN (Zou et al., 2021) can counteract some of these batch effects, and the cell cycle can be accounted for by tools such as CellCycleScoring in Seurat, but the methodological differences complicate cell atlas integration and phenotypic comparisons between datasets.

Step 2: building a cell atlas

The scope of a cell atlas is subject to curation and validation. Some focus on single organs or entire, healthy organisms, whilst others sample for different stages of development or disease (Hrovatin et al., 2025). Setting aside the challenges of integrating cell atlases effectively, we will consider the phenotypic utility of cell atlases.

Differential gene expression analysis relies on dimensional reduction to provide a biological signal. Genes that are significantly variable in their expression within an atlas provide a strong signal for analysis. After dimensional reduction (e.g. by PCA, t-SNE or UMAP), clusters can then be identified.

In some cases, cell atlas analyses have contributed to our understanding of anatomy and physiology. An atlas of the Drosophila melanogaster olfactory projection neurons was produced in (Li et al., 2017). It is known that the classes of olfactory receptor neurons form an injective mapping to the classes of projection neurons (PNs) in the olfactory glomeruli; each class differs spatially and by the subset of olfactory receptor genes they express. Differential gene expression could be used to separate out astrocytes from olfactory PNs by labelling the latter with a GH146-GAL4 driver, and also identified new genes that were exclusively expressed in astrocytes. Unsupervised learning algorithms then separated the cells into PN classes by variations in gene expression within each cluster. The combination of new and existing genetic markers in vivo enabled them to then map these data clusters onto the corresponding olfactory glomeruli. Moreover, a developmental time-course cell atlas analysis showed that the transcriptomes of similar classes differ the most during circuit assembly. Within each lineage, a combinatorial transcription factor code enables precise dendrite and axon targeting. These experiments demonstrate the connection between transcriptional regulation and phenotypic effects.

Before cell atlases and scRNA-seq, studying the effects of disease on the genome was often restricted to mutations and limited the scope to regions of interest, e.g. BRCA-1 in breast and ovarian cancer. Cell atlases more fully capture the heterogeneity and interactions of cell phenotypes and identify gene expression programs with less prior knowledge. For instance, diffuse large B cell lymphoma (DLBCL) was studied in (Ye et al., 2022). Known markers were used to link clusters to cell types, then the transcriptome profiles of the malignant B cells were analysed to extract expression programs such as MAPK, mitochondrial RNA, glycolysis/proteasome, and cell cycle genes that were upregulated. Cell atlases also capture the interactions within the diverse tumour microenvironment, which cannot be obtained from genome sequencing alone. Using CellPhoneDB, infiltrating T cells were shown to interact with malignant B cells through CD70-CD27 binding and with regulatory T cells. This is a stronger indicator of phenotype than the genome alone: with this validation, CAR-T or BiTE immunotherapies can be used to target the tumour.

The two examples here show that cell atlases, despite the biases in their construction, provide actionable phenotypic information that is far harder to extract from the genome itself. Some aspects of transcriptional regulation can perhaps be gleaned from ATAC-seq and large-scale rearrangements in Hi-C, or from sequence alterations, but the phenotypic readout from RNA-seq is far richer and interpretable.

Multi-omics and the unknown unknowns

As mentioned above, the RNA expression vector is a useful abstraction of the phenotype caused by the genome. We will now consider the parts left out in this abstraction.

In well-understood gene regulatory networks (GRNs), genome-wide data from ChIP, ATAC, and histone marks combined with labelled cis-regulatory elements and transcription factor motifs have been used to guide experimental ablations of the network (as reviewed in (Zeitlinger et al., 2025)). However, this is limited by the selection of motifs and regions. Analysing cell atlases' expression of transcription factors and target genes can be used to construct GRNs as well. Moreover, comparing diseased and healthy samples enables us to understand how the GRN is rewired by disease or by cell state. The convergence of DNA genomics (e.g. single-cell ATAC) with cell atlas methods in understanding GRNs is an opportunity to validate phenotypic information obtained from atlases alone. Atlases tend to focus on gene expression rather than mutations and regulatory elements, and do not give a readout of how different transcription factor motifs interact: this information is only partially inferred from co-expressional patterns. A deeper understanding of the phenotype of the genome can be obtained from multi-modal approaches incorporating cell atlas scRNA-seq data with other modalities, e.g. sequence information, Hi-C and chromatin accessibility. Graph transformers such as scHiGeX (Shrestha et al., 2025) provide a computational framework for inferring cell atlas-style gene expression profiles from other modalities. Other multi-modal prediction frameworks such as AlphaGenome (Avsec et al., 2025) also enable phenotypic predictions from sequence alone. However, the combination of -omics technologies that will provide a good approximation of the genome's phenotype remains up for debate.

Conclusions

The contention that cell atlases should serve as a phenotypic companion to the genome has been experimentally validated. There are still concerns regarding biases: the resultant clustering maps well to biological function and has proven to be useful in labelling and classification tasks. Moreover, cell-cell interaction phenotypes can be inferred from atlases, which has enabled disease geneticists to go beyond genome sequence in designing targeted therapies. However, inferring the molecular mechanisms that contribute to the phenotype from correlative methods (including cell atlases) remains elusive. Whether computational methods can tell us anything about the mechanisms underlying the phenotypic output remains an open question.

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