Modern biology has entered an age in which the hardest part of an experiment may no longer be collecting data, but understanding what the data are trying to say. Technologies such as single-cell RNA sequencing can now record the activity of tens of thousands of genes in hundreds of thousands, and increasingly millions, of individual cells. The result is an extraordinarily detailed view of living systems—but also a dataset with thousands of dimensions, far beyond the limits of ordinary human visualization. Researchers at the University of Basel in Switzerland have introduced a software tool called Bonsai that aims to make these immense datasets intelligible by reconstructing their hidden structure as a branching tree.
The challenge is fundamental. Each cell can be represented as a point in a high-dimensional space, with every measured gene, molecular feature, or biological signal contributing another coordinate. In a single-cell RNA sequencing experiment, two cells may be considered close to one another if they have similar patterns of gene activity, while cells with very different molecular programs may occupy distant regions of the same abstract space. Yet no screen can display a 10,000-dimensional landscape directly. Scientists therefore commonly rely on algorithms that compress the information into two dimensions, producing maps that are visually accessible but potentially misleading.
These two-dimensional methods are useful for exploring data, but they inevitably discard information. A projection can place two biologically unrelated cells next to each other, or separate cells that are genuinely close in the original dataset. It may also distort the paths connecting cell states, making a gradual developmental process appear fragmented or creating apparent clusters that are mathematical artefacts. Professor Erik van Nimwegen of the University of Basel describes the problem in intuitive terms: people are skilled at recognizing patterns in two or three dimensions, but have little intuition for the types of structures that can exist in spaces with thousands of dimensions. Bonsai was designed to address that gap without pretending that the underlying data are flat.
Rather than forcing every cell onto a two-dimensional map, Bonsai constructs a branching tree in which individual cells appear at the leaves of the branches. The central idea is that the geometry of the tree should preserve meaningful relationships from the original high-dimensional space. Cells that are similar in their molecular profiles are placed close together along the branches, while larger distances represent greater biological or statistical differences. The branching structure also offers a natural way to represent trajectories: in developmental biology, for example, a common precursor population may appear near the trunk, with progressively specialized cell states emerging along separate branches.
The tree is not simply a decorative alternative to a conventional scatterplot. Its value depends on how accurately the branching architecture and distances reproduce relationships that exist in the full dataset. According to the researchers, Bonsai was tested on simulated data, where the true underlying structure is known, as well as on real single-cell RNA sequencing datasets. In these tests, the method reconstructed developmental pathways more accurately than existing approaches, retained relationships between cells more faithfully, and identified similar cells more reliably. Such performance is especially important when researchers are trying to determine whether a sequence of molecular changes reflects genuine development, disease progression, or merely the distortions introduced by data processing.
Single-cell RNA sequencing provides a particularly demanding test for this type of tool. The technique works by isolating individual cells and measuring RNA molecules, creating a profile of which genes are active in each one. Those profiles can reveal subtle differences between cells that look identical under a microscope. They can also expose transitional states in which a cell is changing from one identity to another. However, the data are sparse and noisy: many genes are not detected in every cell, and biological variation can be mixed with technical effects. A useful visualization must therefore distinguish robust structure from random fluctuations while preserving the relationships that matter for interpretation. Bonsai’s tree-based representation is intended to make those relationships easier to inspect and test.
The researchers’ analysis of human blood cells illustrates how a more faithful representation can lead to an unexpected biological result. Bonsai automatically recovered established relationships among different blood cell types, indicating that the resulting tree reflected known organization rather than producing arbitrary clusters. It also highlighted a previously unrecognized subtype of natural killer, or NK, cells. NK cells are immune cells that can destroy infected or abnormal cells, and they have traditionally been associated with the lymphoid lineage of blood-cell development. The molecular signature of the newly identified subtype suggested that it arose from the myeloid lineage, a distinct developmental route. If confirmed by further biological experiments, the finding could revise assumptions about how at least some NK cells are generated.
That discovery is precisely the kind of outcome the Basel team believes high-dimensional visualization should enable. Algorithms are often treated as neutral instruments, but the way data are represented can determine which patterns scientists notice and which they overlook. A projection that distorts distances may obscure a rare population or make a transitional cell state appear unrelated to its origin. Conversely, a faithful representation can reveal that a seemingly isolated group is connected to a broader developmental process. “When you can trust the picture, you have a much better chance of making new discoveries,” van Nimwegen says. The researchers emphasize that visual evidence does not replace experimental validation, but it can help identify the hypotheses most worth testing.
Bonsai’s potential applications extend beyond gene-expression studies. The same mathematical problem appears whenever researchers collect measurements across many variables and need to understand relationships among observations. The tool could be used with chromatin-state data, which describe how accessible different regions of DNA are; medical datasets containing numerous clinical measurements; microbiological data charting the composition of species in complex communities; or neuroscience experiments recording patterns of neural firing. In each case, the tree could offer a way to represent similarity, divergence, and branching organization without reducing the original structure to a potentially deceptive flat image. The software is being made freely available to the research community, giving laboratories an opportunity to evaluate it across different biological systems.
The arrival of Bonsai reflects a broader shift in computational biology: visualization is becoming not merely a presentation step, but part of the process of scientific discovery. As experimental technologies continue to increase the scale and dimensionality of biological measurements, researchers will need methods that preserve the geometry of their data while making it accessible to human reasoning. A branching tree cannot capture every detail of a complex dataset, and no algorithm can eliminate the need for careful statistical analysis or laboratory confirmation. But by offering a structure that more closely mirrors relationships in high-dimensional space, Bonsai could help scientists see developmental trajectories, rare cell populations, and hidden biological connections that conventional maps leave invisible.
Subject of Research: High-dimensional biological data visualization, single-cell RNA sequencing, cell relationships, and developmental trajectories.
Web References: https://doi.org/10.1038/s41587-026-03220-2
References: Nature Biotechnology, DOI: 10.1038/s41587-026-03220-2
Image Credits: Daan de Groot, Biozentrum, University of Basel
Keywords: Bonsai software, high-dimensional data, single-cell RNA sequencing, single-cell genomics, data visualization, computational biology, cell development, natural killer cells, immune-cell biology, biological big data
Tags: advanced bioinformatics tools for cell analysisbiological data clustering algorithmsbiological data pattern recognitionBonsai software for biological datadimensionality reduction in biologyhidden structure in complex biological datasetshigh-dimensional biological data visualizationinterpreting large-scale biological datasetsreconstructing biological data treessingle-cell gene expression profilingsingle-cell RNA sequencing data analysisvisualization of multi-gene cellular activity
