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Best practices for reporting throughput in biomedical research

Nature Methods volume 19, pages 633–634 (2022)Cite this article

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To the Editor — In the last few decades, technological advances in photonics, electronics, computing, fluidics, robotics and chemistry have substantially boosted the rates of data acquisition and processing and the scale of automation and parallelization. This has enabled high-throughput performance in measurement, imaging, screening, sequencing, manipulation and sorting of molecules, compounds, genes and cells1,2,3,4,5,6. The term “throughput” is widely accepted and constantly used in the biomedical community, where high-throughput operations are indispensable for efficient, reproducible, time-sensitive, low-cost and rare-event applications.

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Fig. 1: Throughput definition and common pitfalls.

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Acknowledgements

This work is supported by JSPS Core-to-Core Program, JSPS KAKENHI grant numbers 19H05633 and 20H00317, JSPS-NUS/NTU Bilateral Joint Research Program 2021, AMED grant numbers JP20wm0325021 and 21ak0101163h0001, the White Rock Foundation, the Ogasawara Foundation, the Nakatani Foundation, the Konica Minolta Foundation and the Chemo-Sero-Therapeutic Research Institute.

Author information

Author notes

  1. These authors contributed equally: Maik Herbig, Akihiro Isozaki.

Authors and Affiliations

  1. Department of Chemistry, The University of Tokyo, Tokyo, Japan

    Maik Herbig, Akihiro Isozaki & Keisuke Goda

  2. Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo, Robert Damoiseaux & Keisuke Goda

  3. California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo & Robert Damoiseaux

  4. Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo

  5. Max Planck Institute for the Science of Light, Erlangen, Germany

    Jochen Guck

  6. Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany

    Jochen Guck

  7. CYBO, Inc., Tokyo, Japan

    Nao Nitta

  8. Department of Molecular and Medicinal Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA

    Robert Damoiseaux

  9. Chugai Pharmaceutical Co., Ltd., Tokyo, Japan

    Shogo Kamikawaji & Eigo Suyama

  10. RIKEN Cluster for Pioneering Research, Saitama, Japan

    Hirofumi Shintaku

  11. Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Angela Ruohao Wu

  12. Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Angela Ruohao Wu

  13. RIKEN Center for Biosystems Dynamics Research, Saitama, Japan

    Itoshi Nikaido

  14. Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan

    Itoshi Nikaido

  15. Graduate School of Science and Technology, University of Tsukuba, Saitama, Japan

    Itoshi Nikaido

  16. Institute of Technological Sciences, Wuhan University, Wuhan, China

    Keisuke Goda

Authors
  1. Maik Herbig
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  2. Akihiro Isozaki
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  11. Itoshi Nikaido
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  12. Keisuke Goda
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Contributions

K.G. conceived the project. M.H. prepared the initial manuscript with the support of K.G. and A.I. All authors revised and edited the manuscript. K.G. acquired funding.

Corresponding author

Correspondence to Keisuke Goda.

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Competing interests

K.G. and N.N. are shareholders of CYBO. All remaining authors declare no competing interests.

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Nature Methods thanks Kevin Tsia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Herbig, M., Isozaki, A., Di Carlo, D. et al. Best practices for reporting throughput in biomedical research. Nat Methods 19, 633–634 (2022). https://doi.org/10.1038/s41592-022-01483-6

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