Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel

Image data

This dataset contains multiplexed fluorescence microscopy images of the protein nanobarcodes with the immunostaining protocol described in the manuscript.Data are provided for 4 different expression times, namely overnight, 24h, 48h, and 72h, for all the constructs used in the research. The datasets used in for training, validation, and testing of the deep network used for identification of nanobarcodes are solely obtained from the provided images.Multiplexed cellular imaging typically relies on the sequential application of detection probes, such as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations

Data corresponding to the Main and Supplementary Figures/Tables

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multi-cell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel

An analysis of the colocalization of epitope-tagged proteins to their expected compartments.

The images from S5 and S16 Figs were analyzed by measuring the Pearson’s correlation coefficient in different image regions. The box plot indicates the respective values, compared to a control, consisting of similar measurements across the same regions in the protein-of-interest channel, and mirrored regions in the compartment channel. All proteins show a colocalization that is significantly above the control values (Kruskal–Wallis test followed by Tukey post hoc test, p S1 Data file, Sheet “SFig 17_all_loc_func,” available from http://dx.doi.org/10.17169/refubium-40101. (TIF)</p

Summary of the image data used for training and testing the deep network.

Number of confocal images obtained for each protein (with the given nanobarcode) as well as number of pixels that have been sampled for the deep learning dataset. All images have the same dimensions of 512 × 512 pixels. With 72-hour samples, each image contains 5 slices in a z-stack. Network training, validation, and testing are done only based on the subsampled pixels (with the numbers given in the last column), while the precision matrices in S20 Fig are obtained on full-frame images. (DOCX)</p

Training and testing of the deep network.

(A) Pipeline through which data are prepared for training and testing the deep network for SNAP25 from 48-hour protocol as an example. Ten-dimensional vectors containing pixel-wise intensities across all channels are mapped along one dimension using kPCA transform. A relative threshold on the principal component separates foreground from background and results in a binary mask, based on which data can be gathered from points than contain proteins in the confocal image. (B) The result of Isomap, kPCA, t-SNE, and Sepctral Embedding “shallow-learning” methods for dimensionality reduction applied directly to the data gathered according to the pipeline explained in (A). (C) Training and validation accuracies averaged over all proteins in the dataset, sampled in each training epoch. Red dashed line shows the early stopping used based on the monitored validation accuracy. (D) Results of the ablation study, in which in each case one protein is removed from the training dataset and the performance of the deep network is evaluated based on the given metrics after training and validation procedure is performed. The data underlying this Figure are available as file “Fig 3_ABCD.xlsx” from http://dx.doi.org/10.17169/refubium-40101.</p

Endogenous SNAP25 and SNAP25(1100) have a similar cellular distribution within SNAP25(1100) transfected PC12 cells.

(A) Visualization of both endogenous SNAP25 and SNAP25(1100) using SNAP25 specific primary and secondary antibodies, plus NbALFA. (B, C) Negative control experiments, leaving out either primary antibodies (B) or NbALFA (C). (D) Imaging control, using a mixture of the same secondary antibody with 2 distinct fluorophores (targeting both endogenous SNAP25 and SNAP25(1100)), to provide a visual indication of the maximum expected colocalization. Bottom part of the figure: legend for used symbols and schemes. Scale bars: 2.5 μm. For quantification, see S17 Fig. (TIF)</p

Deep network analysis results.

(A) The prediction accuracy matrix of trained deep networks, estimated over all the images in the dataset. To increase the complexity of the training and testing procedure, we expressed each construct for different time periods, and we then trained and tested the deep networks with all of these different datasets. Each row corresponds to a separate network that has been trained solely on the given dataset. Columns are the average pixel-wise prediction accuracy, assuming that all the pixels picked by the network in an image should belong to the protein with which the cells have been transfected. The given accuracy values may include effects of misexpressed proteins, weak fluorescence signals, and imaging noise. (B) From left to right, first column: merged channels (405 nm/CH1, 488 nm/CH2, 561 nm/CH3, 633 nm/CH4), before being processed by the network. Second column: images produced by assigning false colors to bright pixels, assuming that all the proteins in the image exactly match the given nanobarcode. Third column: output of the deep network, with each pixel given the false color representing the protein picked by the network. Colors are scaled based on class probabilities (Fig 2). Fourth column: false color output of the network overlaid on the gray “cell halos” produced from the brightfield images. Brightfield images have been processed to remove noise and background gradients and to enhance the contrast. (C, D) As (A) and (B), for additional nanobarcode proteins. The data underlying this Figure are available as file “FigS20_AC.xlsx” from http://dx.doi.org/10.17169/refubium-40101. (TIFF)</p

Transferrin and EGF imaging assays, tested for nanobarcoded syntaxin 6.

(A) Visualization of transferrin-Alexa488 (green) and EGF-Alexa647 (magenta), as well as the transfected protein, visualized with the ALFA nanobody (NbALFA) conjugated to AZdye568 (white). The 3 rows show the 10-minute pulse with the ligands (endocytosis), followed by the 10- and 20-minute chase (wash-off). To enable optimal visualization, the images are scaled differently, with the image scaling indicated in all panels. Scale bar: 20 μm. (B) The nanobarcoding scheme and the expected localization of the protein. (C) The NbALFA fluorescence intensity is plotted against the transferrin (green) and EGF (magenta) intensity, for all signals measured in 2 independent experiments, for all conditions. All intensities were normalized to the medians of the distributions and were then grouped in 20 bins of ALFA intensity, each containing similar numbers of values. The mean and SEM of each bin in the respective channels are plotted. The data underlying this Figure can be found in the S1 Data file, Sheet “SFig 9C_STX6,” available from http://dx.doi.org/10.17169/refubium-40101. (D) The Pearson’s correlation coefficients for the distributions from panel C are shown, with the p-values corrected for multiple testing using a Bonferroni correction. (TIF)</p