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Quantitative mapping of fluorescently tagged cellular proteins using FCS-calibrated four-dimensional imaging

Nature Protocols volume 13, pages 1445–1464 (2018)Cite this article

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Abstract

The ability to tag a protein at its endogenous locus with a fluorescent protein (FP) enables quantitative understanding of protein dynamics at the physiological level. Genome-editing technology has now made this powerful approach routinely applicable to mammalian cells and many other model systems, thereby opening up the possibility to systematically and quantitatively map the cellular proteome in four dimensions. 3D time-lapse confocal microscopy (4D imaging) is an essential tool for investigating spatial and temporal protein dynamics; however, it lacks the required quantitative power to make the kind of absolute and comparable measurements required for systems analysis. In contrast, fluorescence correlation spectroscopy (FCS) provides quantitative proteomic and biophysical parameters such as protein concentration, hydrodynamic radius, and oligomerization but lacks the capability for high-throughput application in 4D spatial and temporal imaging. Here we present an automated experimental and computational workflow that integrates both methods and delivers quantitative 4D imaging data in high throughput. These data are processed to yield a calibration curve relating the fluorescence intensities (FIs) of image voxels to the absolute protein abundance. The calibration curve allows the conversion of the arbitrary FIs to protein amounts for all voxels of 4D imaging stacks. Using our workflow, users can acquire and analyze hundreds of FCS-calibrated image series to map their proteins of interest in four dimensions. Compared with other protocols, the current protocol does not require additional calibration standards and provides an automated acquisition pipeline for FCS and imaging data. The protocol can be completed in 1 d.

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Figure 1: Principle of FCS for point confocal microscopes.
Figure 2: Workflow for FCS-calibrated imaging.
Figure 3: Example of image quantification using FCS calibration.

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Acknowledgements

We thank the mechanical and electronics workshops of EMBL for custom hardware, the Advanced Light Microscopy Facility of EMBL for microscopy support, and the Flow Cytometry Core Facility of EMBL for cell sorting. We gratefully acknowledge B. Nijmeijer, S. Alexander, and A. Rybina for critical reading of the manuscript. We thank T. Ohrt (Zeiss) for support in the hardware control. This work was supported by grants to J.E. from the European Commission EU-FP7-Systems Microscopy NoE (grant agreement 258068), EU-FP7-MitoSys (grant agreement 241548), and iNEXT (grant agreement 653706), as well as by EMBL (A.Z.P., Y.C., N.W., M.J.H., B.K., M.W., and J.E.). Y.C. and N.W. were supported by the EMBL International PhD Programme (EIPP).

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  1. Yin Cai, Birgit Koch & Malte Wachsmuth

    Present address: Present addresses: Roche Diagnostics, Waiblingen, Germany (Y.C.); Max Planck Institute for Medical Research, Heidelberg, Germany (B.K.); Luxendo, Heidelberg, Germany (M.W.).,

Authors and Affiliations

  1. EMBL, Heidelberg, Germany

    Antonio Z Politi, Yin Cai, Nike Walther, M Julius Hossain, Birgit Koch, Malte Wachsmuth & Jan Ellenberg

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Contributions

A.Z.P. performed the experiments and analysis, and designed the software packages. A.Z.P. developed the protocol with the help of Y.C. and M.W. N.W. tested the protocol and helped in writing the software manuals. B.K. created the homozygous cell line and provided the dextran-labeling protocol. M.J.H. provided the code to segment the cells. A.Z.P. wrote the protocol with the help of N.W., B.K., and J.E.

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Correspondence to Jan Ellenberg.

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Integrated supplementary information

Supplementary Figure 1 Quality control of FCS traces

(a) Typical trace that passes the quality control (QC) according to the parameters shown in f. The QC is based on thresholds applied to fitting parameters such as the sum of squared residuals χ2, the coefficient of variation R2, and properties of the photon counts traces (e.g. Bleach coefficient, as calculated by FA). A visual inspection of the photon counts and autocorrelation traces can also be used for quality control. Scale bar 10 μm. (b) FCS measurement point is at the boundary of the cytoplasmic compartment causing a decreasing drift in photon counts. (c) FCS measurement point is at the boundary of the cytoplasmic compartment causing an increasing drift in photon counts. (d) FCS measurement point is at the boundary between a dim and a brighter cell causing large fluctuations in the photon counts. (e) FCS measurement point is in a cell that does not express a fluorescent protein. (f) Table of parameters used for the QC. The measurements in b and c do not pass the QC according to the bleach parameter. The measurement in d does not pass the QC according to the χ2 value. The measurement in e does not pass the QC according to the R2 value. None of the traces in b-e pass the visual inspection. The thresholds used were χ2 < 1.2, R2 > 0.9, 0.8 < Bleach < 1.2. The thresholds can be interactively set in the FCSCalibration software (Supplementary Software 4).

Supplementary Figure 2 CPM and effective volume dependency on pinhole diameter and laser power

(a) Counts per molecule (CPM) as function of the pinhole diameter for Alexa488 in water. (b) Effective confocal focal volume estimated as described in the supplement (Supplementary Notes 1–2 and Box 2) as function of the pinhole diameter for Alexa488. (c) Effective confocal focal volume as function of the excitation laser (Argon 488 nm). The pinhole was 34 μm.

Supplementary Figure 3 Simulated FCS-calibrated imaging

(a) Illustration of a homogeneous fluorophore distribution. (b) The slope of the fluorophore concentration as function of pixel fluorescence intensity gives the calibration coefficient. Simulated PSF is a 3D Gaussian (Eq. S1). The size of the PSF is characterized by its e2 decay w0 and z0 in XY and Z direction, respectively. Here and in all subsequent panels background and detector noise are not simulated. (c) Simulated Z-stack of a point source (50 fluorophores). (d) Using the calibration coefficient and Eqs. S15, S16, and S19 the total number of proteins in each plane is calculated. Several planes along the Z-direction are summed (circles). The expected protein number is obtained when a large region along Z is considered (>2*z0). Simulated images have a pixel-size of 0.4, 0.6, 0.8, 1 times w0 in all directions (different colors). Triangles give the result when all pixels are considered (Eq. S19). Squares give the approximation using the integral of the PSF along Z (Eq. S21). Diamonds give the approximation when the integral of the whole PSF and the pixel with the highest intensity is used (Eq. S23). (e) Simulated fluorophores distributed in a XZ plane (density of 100 fluorophores/μm2). Scale bar 10 μm. (f) Protein density is computed using Eqs. S15 and S24. The expected density is obtained for a region width along the X direction of > 2*w0. Triangles give the result when all pixels are considered. Squares give the approximation when the integral of the PSF along X is used (Eq. S26). (g) Simulated fluorophores distributed in a XY plane (density of 100 fluorophores/μm2). (h) The average protein density is summed for planes along Z (Eqs. S15 and S17). The expected density is obtained for a region width > 2*z0. Triangles give the result when all pixels are considered. Squares give the approximation when the integral of the PSF along Z is used (Eq. S29). Each fluorophore has a simulated intensity of 1000 (a.u.), the simulated PSF is characterized by w0 = 250 nm, z0 = 1500 nm. The black lines give the theoretically expected result after integration of the Gaussian function where N e is the expected protein number/density (50 in d, and 100 in f and h) and Rs is the region size in unit of the PSF characteristic size.

Supplementary information

Supplementary Text and Figures

Supplementary figures 1–3, Supplementary Tables 1–2 and Supplementary Notes 1–6 (PDF 1377 kb)

Supplementary Software 1

The FCSRunner software to manually acquire FCS and imaging data. (ZIP 2057 kb)

Supplementary Software 2

The MyPiC software to automatically acquire FCS and imaging data using adaptive-feedback microscopy. (ZIP 7646 kb)

Supplementary Software 3

A FiJi software package to automatically acquire FCS and imaging data using adaptive-feedback microscopy; to be used in combination with Supplementary Software 2. (ZIP 7971 kb)

Supplementary Software 4

FiJi and R tools to analyze the FCS and imaging data and compute image calibration coefficients. (ZIP 114307 kb)

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Politi, A., Cai, Y., Walther, N. et al. Quantitative mapping of fluorescently tagged cellular proteins using FCS-calibrated four-dimensional imaging. Nat Protoc 13, 1445–1464 (2018). https://doi.org/10.1038/nprot.2018.040

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