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Adapting systems biology to address the complexity of human disease in the single-cell era

Nature Reviews Genetics volume 26, pages 514–531 (2025)Cite this article

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Abstract

Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances — from single-cell and spatial profiling to CRISPR genome editing and machine learning — have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases. However, adapting systems-inspired approaches to dissect human disease complexity is challenging, given that discrepancies between the biological features of human tissues and the experimental models typically used to probe function (which we term ‘translational distance’) can confound insight. Here we review how samples, measurements and analyses can be contextualized within overall multiscale human disease processes to mitigate data and representation gaps. We then examine ways to bridge the translational distance between systems-inspired human discovery loops and model system validation loops to empower precision interventions in the era of single-cell genomics.

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Fig. 1: Challenges in applying systems biology to the study of human disease.
Fig. 2: Challenges in capturing the spatiotemporal scales of human disease with snapshots.
Fig. 3: Capturing descriptions of spatiotemporal scales of human disease.
Fig. 4: Perturbing scales of human disease in experimental model systems.
Fig. 5: Multiscale systems biology — a framework to study disease mechanisms based on human tissues and experimental models.

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Acknowledgements

The authors would like to thank the donors who have willingly partnered with them and others to enable the types of studies discussed in this review. The authors recognize that these individuals do so at a very precarious period in their lives with the goal of helping to improve understanding and treatment options for themselves and others. The authors appreciate the incredible privilege and responsibility that they have as part of this partnership, and are committed to do everything in their power to achieve the donors’ goals and to sharing what they have learn with those individuals. The authors also thank C.P. Couturier, W. Kattan, M. Ramseier, A. Rubin, Z. Steier and S. Triana for discussions and input on the manuscript. The authors’ work was supported in part by funding from the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. A.K.S. was supported in part by the Bill and Melinda Gates Foundation (INV-027498), the NIH (5DP1DA053731, 5R01AI149670, 75N93019C00071, 1P01AI177687, 5UM1AI164556), Break Through Cancer, Foundation MIT and the Wellcome Leap. P.S.W. acknowledges research support from Microsoft.

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Author notes

  1. These authors contributed equally: Peter S. Winter, Alex K. Shalek.

Authors and Affiliations

  1. Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    David S. Fischer

  2. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Martin A. Villanueva & Alex K. Shalek

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Peter S. Winter & Alex K. Shalek

  4. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  5. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  6. Ragon Institute of Mass General, MIT and Harvard, Cambridge, MA, USA

    Alex K. Shalek

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  2. Martin A. Villanueva
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Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Peter S. Winter or Alex K. Shalek.

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Competing interests

A.K.S. reports compensation for consulting and/or SAB membership from Honeycomb Biotechnologies, Cellarity, Ochre Bio, Bio-Rad Laboratories, Relation Therapeutics, IntrECate biotherapeutics, Parabalis Medicines, Quotient Therapeutics, Passkey Therapeutics, Danaher and Dahlia Biosciences unrelated to this work. P.S.W. reports compensation for consulting/speaking from Engine Ventures and AbbVie unrelated to this work. The other authors declare no competing interests.

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Glossary

Cellular scale

The spatial scale describing cells that includes, for example, variation in gene expression programmes among cells in response to a stimulus. Processes at the cellular scale can be understood on the basis of measurements of isolated cells.

Complexity

A measure of the completeness of the representation of human biology in an experimental model, specifically considering the presence of scales and features of these scales in the experimental model.

Molecular scale

The spatial scale of molecular circuits that includes, for example, intracellular signalling.

Multiscale dynamics

To develop quantitative models of the overall dynamics of a system, one often needs to account for different time scales, to capture both rapid and slow processes, and distinct spatial scales, to account for local and systemic processes.

Niches

Sets of interacting and colocated cells, typically within a tissue (for example, germinal centres). A niche is an intermediate spatial scale between cells and tissues that is often useful to understand how different cell types come together to create a phenotypic attribute (for example, affinity maturation of antibodies).

Niche scale

The spatial scale of niches that includes, for example, variation in cell–cell communication patterns.

Snapshots

Measurements that yield a characterization of states, for example, gene expression states, of a given sample. Most genomics measurements are destructive, which complicates the study of temporal phenomena through snapshots.

Spatial scales

Distance or length scales along which a system exhibits changes that can be related back to a mechanism that underlies its dynamics. It is a concept that is used to guide the placement of samples in an experiment.

Spatiotemporal scales

Scales that are both localized spatially in the anatomy of the organism and temporally with respect to disease progression.

Systems biology

In contrast to reductionist approaches applied to molecular and cell biology that isolate specific features, systems biology endeavours to holistically model the dynamics of a cellular system.

Temporal scales

Time intervals within which a system exhibits changes that can be related back to a mechanism that underlies its dynamics. It is a concept that is used to define timepoints for measuring a system and to define appropriate analyses.

Tensor

The multidimensional tensor represents a hypothetical set of measurements that covers the full disease process, that is, all analytes sampled across all involved tissues at all timepoints, covering the axes of anatomy, assayed modalities and time.

Tissue scale

The spatial scale of tissues that includes, for example, the interaction between lymph nodes and tumours through the adaptive immune system, or the spreading of tumours through metastasis. Understanding processes at the tissue scale often requires a consideration of phenomena that span tissues and organs.

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Fischer, D.S., Villanueva, M.A., Winter, P.S. et al. Adapting systems biology to address the complexity of human disease in the single-cell era. Nat Rev Genet 26, 514–531 (2025). https://doi.org/10.1038/s41576-025-00821-6

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