Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging

John W. Hickey; Elizabeth K. Neumann; Andrea J. Radtke; Jeannie M. Camarillo; Rebecca T. Beuschel; Alexandre Albanese; Elizabeth McDonough; Julia Hatler; Anne E. Wiblin; Jeremy Fisher; Josh Croteau; Eliza C. Small; Anup Sood; Richard M. Caprioli; R. Michael Angelo; Garry P. Nolan; Kwanghun Chung; Stephen M. Hewitt; Ronald N. Germain; Jeffrey M. Spraggins; Emma Lundberg; Michael P. Snyder; Neil L. Kelleher; Sinem K. Saka

doi:10.1038/s41592-021-01316-y

Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging

John W. Hickey, Elizabeth K. Neumann, Andrea J. Radtke^*, Jeannie M. Camarillo, Rebecca T. Beuschel, Alexandre Albanese, Elizabeth McDonough, Julia Hatler, Anne E. Wiblin, Jeremy Fisher, Josh Croteau, Eliza C. Small, Anup Sood, Richard M. Caprioli, R. Michael Angelo, Garry P. Nolan, Kwanghun Chung, Stephen M. Hewitt, Ronald N. Germain, Jeffrey M. SpragginsEmma Lundberg, Michael P. Snyder, Neil L. Kelleher, Sinem K. Saka^*

^*Corresponding author for this work

Molecular Biosciences

Research output: Contribution to journal › Article › peer-review

181 Scopus citations

Abstract

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

Original language	English (US)
Pages (from-to)	284-295
Number of pages	12
Journal	Nature Methods
Volume	19
Issue number	3
DOIs	https://doi.org/10.1038/s41592-021-01316-y
State	Published - Mar 2022

Funding

We are grateful for engaging and thoughtful discussions from the Affinity Reagent Imaging and Validation Working Group, HuBMAP Consortium. The authors would like to acknowledge funding from the following sources: NIH U54 DK120058, NIH U54 EY032442, NIH R01 AI145992, NIH R01 AI138581 (R. M. C. and J. M. S.), NIH T32ES007028 (E. K. N), NIH U54 HG010426-01 (M. P. S. and G. P. N.), NIH UG3 HL145600-01, NIH UH3 CA246633-01 (R. M. A), NIH UH3 CA246635-01 (N. L. K.), Swedish Research Council 2018-06461 (E. L.), Erling Persson Family Foundation (E. L.), Wallenberg Foundation (E. L.), NIH UH3 CA246594-01 (A. S. and E. M.), NIH T32CA196585 and ACS PF-20-032-01-CSM (J. W. H.), and NIH UH3 CA255133-03 (S.K.S.) and European Molecular Biology Laboratory (S. K. S.). This work was supported, in part, by the Intramural Research Program of the NIH, NIAID and NCI. We thank J. Hernandez and J. Davis (National Cancer Institute, NIH) for providing de-identified human tissues featured in this work, and K. Prummel (EMBL) for comments on the manuscript. Figures were made using the tools on Biorender.com.

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Hickey, J. W., Neumann, E. K., Radtke, A. J., Camarillo, J. M., Beuschel, R. T., Albanese, A., McDonough, E., Hatler, J., Wiblin, A. E., Fisher, J., Croteau, J., Small, E. C., Sood, A., Caprioli, R. M., Angelo, R. M., Nolan, G. P., Chung, K., Hewitt, S. M., Germain, R. N., ... Saka, S. K. (2022). Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nature Methods, 19(3), 284-295. https://doi.org/10.1038/s41592-021-01316-y

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abstract = "Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.",

author = "Hickey, {John W.} and Neumann, {Elizabeth K.} and Radtke, {Andrea J.} and Camarillo, {Jeannie M.} and Beuschel, {Rebecca T.} and Alexandre Albanese and Elizabeth McDonough and Julia Hatler and Wiblin, {Anne E.} and Jeremy Fisher and Josh Croteau and Small, {Eliza C.} and Anup Sood and Caprioli, {Richard M.} and Angelo, {R. Michael} and Nolan, {Garry P.} and Kwanghun Chung and Hewitt, {Stephen M.} and Germain, {Ronald N.} and Spraggins, {Jeffrey M.} and Emma Lundberg and Snyder, {Michael P.} and Kelleher, {Neil L.} and Saka, {Sinem K.}",

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year = "2022",

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doi = "10.1038/s41592-021-01316-y",

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Hickey, JW, Neumann, EK, Radtke, AJ, Camarillo, JM, Beuschel, RT, Albanese, A, McDonough, E, Hatler, J, Wiblin, AE, Fisher, J, Croteau, J, Small, EC, Sood, A, Caprioli, RM, Angelo, RM, Nolan, GP, Chung, K, Hewitt, SM, Germain, RN, Spraggins, JM, Lundberg, E, Snyder, MP, Kelleher, NL & Saka, SK 2022, 'Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging', Nature Methods, vol. 19, no. 3, pp. 284-295. https://doi.org/10.1038/s41592-021-01316-y

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AU - Chung, Kwanghun

AU - Hewitt, Stephen M.

AU - Germain, Ronald N.

AU - Spraggins, Jeffrey M.

AU - Lundberg, Emma

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AU - Kelleher, Neil L.

AU - Saka, Sinem K.

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AB - Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

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Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging

Abstract

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ASJC Scopus subject areas

UN SDGs

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