Although fluorescence microscopy is ubiquitous in biomedical research, microscopy methods reporting is inconsistent and perhaps undervalued. We emphasize the importance of appropriate microscopy methods reporting and seek to educate researchers about how microscopy metadata impact data interpretation. We provide comprehensive guidelines and resources to enable accurate reporting for the most common fluorescence light microscopy modalities. We aim to improve microscopy reporting, thus improving the quality, rigor and reproducibility of image-based science.
Bibliographical noteFunding Information:
We thank C. Strambio De Castillia and C.M. Brown for helpful discussions and comments during manuscript preparation, and we thank R. Cole, A. Laude, G. Nelson, R. Nitschke and W. Salmon for feedback on the manuscript. We thank A. Vettiger (Bernhardt laboratory, Harvard Medical School) and G. Miner (Cohen laboratory, University of North Carolina at Chapel Hill) for the bacterial and U2OS samples, respectively. We thank Leica Microsystems for their help collecting the images in Fig. 3c and for supplying the custom six-color sample. We thank our microscopy core facility staff and users of the Microscopy Resources on the North Quad (MicRoN) Core at Harvard Medical School, the Duke University Light Microscopy Core Facility, the Neuroscience Microscopy Core Facility (RRID: SCR_019060) of the University of North Carolina School of Medicine and the University Imaging Centers of the University of Minnesota (RRID: SCR_020997). Imaging was performed at the MicRoN Core at Harvard Medical School and at the UNC Neuroscience Microscopy Core Facility supported, in part, by funding from the NIH-NINDS Neuroscience Center Support Grant P30 NS045892 and the NIH-NICHD Intellectual and Developmental Disabilities Research Center Support Grant P50 HD103573. M.S.I. is supported by an Imaging Scientist grant number 2019-198107 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. M.A.S. is supported by an Imaging Scientist grant from the Chan Zuckerberg Initiative. R.A.S. was supported by National Institutes of Health Grant F31-NS-108406.
© 2021, Springer Nature America, Inc.
PubMed: MeSH publication types
- Journal Article