A.k.a. time-resolved 3D transcriptomics

The various spatial omics are revolutionizing our ability to study cellular function and interaction by preserving the spatial context of molecular data within tissues, as we have covered in our blog with multiple examples:

• Advanced Tissue Analysis via Spatial Transcriptomics at Nexco
• Introduction to Spatial Proteomics And The Spatial -Omics Revolution

In particular, we have explored how 3D -omics such as 3D transcriptomics and 3D proteomics provide unprecedented insight into the architecture of complex tissues and organs. However, these methods provide what is essentially a static snapshot, and the next great leap forward is the addition of a fourth dimension: time, essential to really unveil mechanisms. Welcome to 4D -omics!

A recent and exciting perspective article titled “Temporal and spatial omics technologies for 4D profiling” (Reynolds et al, Nature Methods 2025) delves into this nascent frontier, and we found it so inspiring that we wanted to share our take on what this means for the future of life sciences, particularly with an eye on the bioinformatics, modeling, multimodal AI systems for biology, and data analysis aspects.

The core challenge is moving from static snapshots to time-dependent information that can better help us to unveil biological processes and mechanisms. This is useful to better understand processes of development and differentiation, response to treatments, relapses and adaptation, etc. all of which have time as the key variable. Naturally, the challenge — that new technologies address — is monitoring molecular changes in the same living tissue over time. This is precisely the promise of 4D profiling: capturing dynamic 3D spatial molecular changes as they happen.

Computational challenges to 4D omics

Adding the temporal dimension entails of course devising new experimental setups, as Reynolds et al cover extensively — very worth a read. But this in turn entails dealing with massive datasets and new challenges for data processing, analysis, visualization and interpretation. Fortunately, the computational biology community is already laying the groundwork, developing software for processing and visualization as well as thinking bigger on how to develop multimodal AI systems that can process such kind of information massively in order to deduce outcomes from unseen data and even to do complex system-level simulations. Read on to discover the exciting prospect!

Several innovative approaches aim to infer temporal dynamics from single-cell data. Unsupervised algorithms like Monocle have been developed to reconstruct the temporal progression of processes like stem cell differentiation from scRNA-seq datasets taken at different time points. The software can track changes over time or what its developers call “pseudotime”, a measure of how far a cell has moved through biological progress. This is useful to unveil how cells transition from one state to another throughout development, disease, and life. Furthermore, single-cell trajectory analysis discloses how cells choose their “fate” among several possible end states. The reconstruction algorithms introduced in Monocle version 2 can pinpoint branching trajectories along with the genes that cells use to navigate these decisions.

The pseudotime idea comes up again in SpaceFlow, a tool designed to process spatial transcriptomic datasets to identify key spatiotemporal patterns and components. With such kinds of tools researchers can build spatiotemporal atlases of development and disease, by mapping trajectories and cell-cell interactions in four dimensions.

Similarly, tools like MAGIC use data diffusion to reveal temporal patterns. By diffusing information across similar cells, MAGIC denoises the cell count matrix coming from a single-cell RNA experiment and thus fills in the missing transcripts. This way, the method alleviates technical noise coming from under-sampling of some mRNA molecules, which often obscures important gene-gene relationships.

Meanwhile, the concept of RNA velocity has been a game-changer, allowing researchers to predict the future state of individual cells on a timescale of hours. Tools like CellRank build on RNA velocity to map cellular fates, and mathematical models like Carta are emerging to create more accurate cell differentiation maps from lineage tracing data. These methods are essential for extracting dynamic insights from complex single-cell datasets.

Modeling and simulating tissues and organs in 4D

Looking ahead, the integration of computational modeling and machine learning will be paramount. The perspective points towards a future where multiscale modeling, i.e. computational simulation of complex biological systems spanning multiple levels of resolution, becomes a key tool for 4D profiling. It is just exciting to think how such virtual models, possibly informed by multimodal AI systems for biology like those we have also discussed, will allow us to simulate everything from protein binding kinetics to tissue development patterns, enabling us to design better experiments and explore biological hypotheses in silico before ever stepping into the lab. We can even imagine digital twins of actual living tissues, organs or whole bodies, used for computational interrogation before acting with a treatment on the real living unit.

As spatiotemporal atlases grow, their data will be used to train better, more comprehensive AI systems that slowly start to make the reality of such digital twins real, facilitating the recognition of patterns in new data, the optimization of settings for new experiments, and eventually reliable simulations of how cells, tissues and organs will evolve in 3D over time. This synergy between experimental data and computational simulation is where the deepest insights will be found, and it aligns perfectly with the data-driven, AI-powered approach we champion at Nexco.

4D omics at Nexco

As experimental techniques for spatiotemporal monitoring mature, they will generate data of immense size and complexity. Handling and interpreting this data is where advanced bioinformatics will be most critical. The Nature Methods perspective article highlights that the effectiveness of these new technologies will depend heavily on downstream processing methods. That’s where our services and expertise come in, and where continuous acquaintance with new techniques, software and databases is key.

The move towards time-resolved 3D transcriptomics and vastly informed AI systems could well represent a paradigm shift for biology. At Nexco, we are thrilled by these advancements and are poised to develop and apply the sophisticated bioinformatics, data analysis, and modeling solutions required to turn this flood of 4D data into actionable biological knowledge.

Want help with your 3D or 4D omics? Contact Nexco today, and let’s decode genome’s spatial secrets together.

And check in particular Spatial Transcriptomics services.

References and related posts

The perspective that inspired this blog post:

DE Reynolds, YH Roh, D Oh, P Vallapureddy, R Fan and J Ko. Temporal and spatial omics technologies for 4D profiling. Nature Methods 22, 1408–1419 (2025). Link: https://www.nature.com/articles/s41592-025-02683-6

Some related posts from our archive:

• Discover ONex: our end-to-end solution for Omics analysis
• Unveiling the 3D Organization of Tissues Through Spatial Multi-Omics
• Introduction to Spatial Proteomics And The Spatial -Omics Revolution
• Advanced Tissue Analysis via Spatial Transcriptomics at Nexco
• Integrating Single-Cell and Spatial Transcriptomics: An Exciting New Era
• How Cutting-Edge Bioinformatics Drive Advances in Life Sciences and Medicine
• Foundation Models for Biology to Support Bioinformatics