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

At its core, SUM-seq integrates the power of in situ barcoding specific to both accessible DNA regions and mRNA within individual nuclei, followed by droplet-based microfluidic barcoding. This sophisticated combinatorial approach introduces a multiplexing dimension previously unattainable, allowing multiple samples to be processed simultaneously in a single run. The ingenuity lies in its capacity to resolve droplets that encapsulate more than one nucleus, overcoming a notorious bottleneck in droplet microfluidics where multinucleated droplets confound data purity and interpretation. The consequent multiomic library preparation is not only robust but exquisitely scalable, accommodating the growing demand for large-scale single-cell atlasing projects and dynamic perturbation screens without compromising data quality.

When placed in context with existing multimodal assays for chromatin accessibility and transcriptome profiling, SUM-seq’s throughput and multiplexing capabilities are strikingly superior. Traditional methodologies often grapple with limitations such as lower cell capture rates, increased per-sample costs, and reduced flexibility when scaling to complex experimental designs involving multiple time points or treatment conditions. SUM-seq addresses these challenges head-on, providing a toolkit that dramatically expands the number of nuclei and samples analyzed in a single experiment. This leap in efficiency heralds a new era where large and diverse cellular populations from heterogeneous tissues can be interrogated thoroughly, accelerating discoveries in diverse fields ranging from immunology to oncology.

Of particular interest to researchers aiming to decode gene regulatory landscapes, SUM-seq simultaneously uncovers chromatin accessibility patterns—which reflect regulatory element activity—and gene expression profiles within the very same nucleus. This dual-layer insight paves the way for comprehensive modeling of cis-regulatory interactions that govern cell state transitions and phenotypic diversity. By mapping these epigenetic and transcriptomic features in tandem at a single-cell resolution, SUM-seq enables the construction of integrated gene regulatory networks with unprecedented fidelity, thereby enriching our understanding of cellular identity and functional heterogeneity.

SUM-seq’s methodological finesse extends into the realm of experimental design and sample preparation, accommodating a broad spectrum of sample types and experimental settings. The protocol offers detailed guidance on nuclei isolation, barcoding optimization, and droplet microfluidics parameters to ensure reproducibility and high data integrity. Moreover, the assay is optimized for completion within a remarkably short timeframe—approximately two to three days from sample collection to library preparation—and sequencing followed by only a single day for data processing. This rapid turnaround time is particularly advantageous in scenarios where timely data generation is critical, such as clinical diagnostics or iterative perturbation experiments.

The protocol’s accessibility is another commendable aspect, strategically crafted so that researchers equipped with general molecular biology expertise can implement it effectively. While prior exposure to single-cell assays is recommended to maximize success, the user-friendly design lowers the entry barrier for laboratories seeking to adopt cutting-edge multiomic profiling without necessitating overly specialized skill sets or prohibitively expensive equipment. This democratization of technology empowers a wider scientific community to harness high-resolution single-cell data for hypothesis generation and validation.

Crucially, SUM-seq addresses longstanding issues inherent to droplet-based approaches—issues that include sample cross-contamination and data deconvolution challenges arising from the multiplication of nuclei within single droplets. By introducing in situ barcoding strategies tailored to both accessible chromatin and transcriptomic content, the method meticulously preserves sample identity while capturing high-fidelity multiomic signatures. This technical nuance is instrumental in resolving complexities associated with heterogeneous or multinucleated samples, further expanding the assay’s utility.

The scalability and cost-effectiveness of SUM-seq hold transformative potential for large-scale biological endeavors. Projects such as the Human Cell Atlas or other tissue-centric atlases, which demand the processing of thousands to millions of cells across diverse conditions and subjects, stand to benefit immensely. Given the ability to multiplex numerous samples within a single experimental batch, researchers can now execute time-course studies or perturbation screens with high statistical power and biological resolution—ushering in more nuanced and data-rich insights into dynamic cellular responses.

In the sphere of data analysis, the protocol guides users through best practices for processing the complex multiomic data streams generated. Integrative computational frameworks tailored for chromatin accessibility and gene expression analyses enable the extraction of biologically meaningful patterns and regulatory relationships. This synergy between wet-lab innovation and computational rigor reinforces SUM-seq’s position as a holistic platform for single-cell systems biology.

A fascinating aspect of SUM-seq is its adaptability to diverse biological questions and experimental needs. Whether investigating developmental trajectories in embryonic tissues, immune cell activation in inflammatory contexts, or tumor heterogeneity in oncology, the simultaneous capture of epigenomic and transcriptomic layers offers a panoramic view of cellular function. Such detailed molecular portraits facilitate the identification of novel regulatory elements, transcription factor networks, and cell type-specific expression programs that might otherwise remain obscured in bulk analyses.

Moreover, by substantially reducing costs relative to existing multiomic approaches, SUM-seq not only expands accessibility but also encourages more extensive experimental replication and validation—key practices for ensuring reproducibility and robustness in scientific research. Institutions and consortia aiming to build comprehensive cellular atlases can leverage this cost-effectiveness to maximize sample breadth and depth, fueling discoveries that traverse species, disease states, and environmental exposures.

Looking ahead, the potential applications of SUM-seq are vast and varied. Its compatibility with nuclei from frozen or fixed tissues positions it as a valuable tool for retrospective studies and biobanking efforts, where sample preservation methods often preclude traditional cell-based assays. By overcoming these sample constraints, researchers can unlock hidden treasures in archived specimens with fresh genomic insights.

As single-cell technology continues to mature, methodologies like SUM-seq exemplify the commitment to convergence—bringing together multiple layers of molecular information in a unified, scalable, and cost-effective manner. This convergence is a leap toward comprehensively understanding the regulatory codes embedded within cellular genomes, ultimately informing precision medicine and targeted therapies.

In closing, the SUM-seq protocol, articulated with clarity and depth by Yildiz and colleagues, is more than a methodological advance; it is a paradigm shift in single-cell multiomic profiling. By balancing innovation with practicality, it equips the scientific community with a powerful instrument to chart the molecular choreography of life at the cellular level. As researchers begin to embrace and deploy SUM-seq across diverse biological inquiries, we can anticipate a surge in discoveries that illuminate the complexities of cellular identity, function, and disease with unprecedented clarity.

Subject of Research: Single-cell multimodal profiling of chromatin accessibility and gene expression to dissect gene regulatory networks and cellular heterogeneity.

Article Title: Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq).

Article References:
Yildiz, U., Lobato-Moreno, S., Claringbould, A. et al. Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq). Nat Protoc (2026). https://doi.org/10.1038/s41596-025-01310-0

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41596-025-01310-0