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Abstract Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species’ cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.
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This content will become publicly available on July 1, 2026
Defining and benchmarking open problems in single-cell analysis
Single-cell genomics has enabled the study of biological processes at an unprecedented scale and resolution. These studies were enabled by innovative data generation technologies coupled with emerging computational tools specialized for single-cell data. As single-cell technologies have become more prevalent, so has the development of new analysis tools, which has resulted in over 1,700 published algorithms1 (as of February 2024). Thus, there is an increasing need to continually evaluate which algorithm performs best in which context to inform best practices2,3 that evolve with the field. In many fields of quantitative science, public competitions and benchmarks address this need by evaluating state-of-the-art methods against known criteria, following the concept of a common task framework4. Here, we present Open Problems, a living, extensive, community-guided platform including 12 current single-cell tasks that we envisage raising standards for the selection, evaluation and development of methods in single-cell analysis.
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- Award ID(s):
- 2047856
- PAR ID:
- 10617661
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Nature Biotechnology
- Volume:
- 43
- Issue:
- 7
- ISSN:
- 1087-0156
- Page Range / eLocation ID:
- 1035 to 1040
- Subject(s) / Keyword(s):
- Genomics, Single Cell, Benchmarking
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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