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Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Abstract

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking—assigning identical cells on consecutive images to a track—is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

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Fig. 1: Cell tracking pipeline and common problems.
Fig. 2: CRF.
Fig. 3: CRF parameter evaluation for different cell types, image modalities and magnifications.
Fig. 4: Performance of TracX.
Fig. 5: Genealogy reconstruction.
Fig. 6: Effects of glucose availability on cell cycle regulation of S. cerevisiae.

Data availability

The data used in this study as well the analysis scripts are available for download from the ETH Research Collection at https://doi.org/10.3929/ethz-b-000550509 (ref. 54). A summary table of external data used can be found in the Supplementary Material.

Code availability

The TracX software can be downloaded from https://gitlab.com/csb.ethz/tracx as well as demo data from https://gitlab.com/csb.ethz/tracx_demo_data. Further documentation can be found under https://tracx.readthedocs.io/en/latest/.

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Acknowledgements

We thank M. Dürr for initial implementation of Ricicova’ tracker in R and U. Küchler for its translation to MATLAB. We thank G. Schmidt for training of the CellClamper and sharing data sets.

Author information

Authors and Affiliations

Authors

Contributions

A.P.C., F.R. and J.S. conceptualized the project. A.P.C. developed the CRF and wrote the TracX software. A.P. wrote the Gaussian vector filtering. T.K. initiated and codeveloped the graphical user interface and wrote the amorphous cell lineage reconstruction. A.P.C. engineered the cell strains and performed the microscopy experiments. A.P.C. and J.S. validated the software and analyzed the data. A.P.C. and J.S. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jörg Stelling.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Beth Cimini, Ralf Mikut and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 The effect of the re-size factor on CRF-based classification.

fr values are indicated per row and data presented as in Fig. 3b.

Extended Data Fig. 2 Sensitivity of cell region fingerprinting to cell rotations.

a: Schematics of rotation analysis. To evaluate the sensitivity of the df to cell rotations within a cell neighborhood, images were rotated between -90 and 90 degrees and then cropped with windows of sizes fl [25; 40; 100] px. b-f: The df is computed between the non-rotated and rotated image crops for each cell and we plot the fraction of correct assignments (defined as the sum of all occurrences where the df was smallest over all possible assignments) as a function of the rotation angle in degrees. Data sets used were: b: TTS-SC8-BF; c: TTS-60mrnaCropped; d: TTS-SC9-BF; e: TTS-SC9-FL; f: TTS-Fluo-N2DH-GOWT1. a-f n = 7/ 25/ 610/ 610/ 35 cells (repetitions) each from an independent experiment. c-f: scale bar, 32px.

Extended Data Fig. 3 Evaluation of the effect of the neighborhood fraction threshold (τf) for the different cell types and image modalities in Fig. 3.

For each data set, we chose a random parameter set yielding a high F-score according to the data in Fig. 3. Window sizes (fl), frequencies (fq) and resizing (fr) were [30, 8, 32], [300, 12, 40], [300, 10, 25], and [200, 20, 32]. We permuted assignments by the fractions indicated. The very conservative value τf = 0 can be relaxed to ≈ 0.2 while still accurately detecting matching consecutive (cellular) regions on all images and for all permutation frequencies.

Extended Data Fig. 4 Flow chart of the TracX core software architecture.

A tracking project defines all the required parameters and links to input files such as raw images and segmentation masks. Tracking is then started in a frame-by-frame manner. First, the CRF is calculated for each segmented centroid on each image frame. A first assignment is obtained from the LAP tracker process. These assignments are evaluated for correctness using the fingerprint distance (df), before refinements to handle unexpected motion where the image frequency was too low for the df to be informative enough. When all frames were tracked, results are saved and displayed to the user. Boxes encode for manual user input (trapezoid), documents (waved rectangle), process (rectangle), decision (square standing on the tip), start and end of the flow (ellipse), data display (ellipse with the tip to the right), complying with the commonly used ISO 5807:1985.

Extended Data Fig. 5 Effect of imaging frequency on the false positive rate.

The relative rate of false positives (FPs) as a function of neighborhood fraction thresholds (τf) evaluated for different imaging frequencies (colored dots). a False color image overlays of the first image frame with the one after 2, 6, 10, 14, 18, 22, 26, 30 minutes to simulate different imaging frequencies. Data set TTS-SC9; n = 8 representative image pairings out of 1980 possible pairings. Scale bar: 10μm. b Fraction of false positives as a function of neighborhood fraction threshold (τf). Neighborhood window size was 60 px or 8.80 ± 0.14 neighbors (left panel). Neighborhood window size was 80 px or 13.70 ± 0.31 neighbors (right panel). n = 520 assignments.

Extended Data Fig. 6 Effects of mis-segmentation on cell region fingerprint performance.

a: Schematic depicting randomly introduced erroneous merges (left) and splits (right) of cells on an image frame of test set TTS-SC7. Original segmentation (green or white outlines) and randomly merged cells (left, pink) or splits (right, pink). For better visibility, only half of the split cell is plotted. b: Erroneous merges (purple) and splits (orange) of cells were generated by randomly selecting 10% of the tracks and merging the segmentation masks of two neighboring cells or splitting single cells. For a selected track, we propagated the error for one to five consecutive frames (5-25 min). A true positive ratio (tpr) of one indicates that all introduced errors are captured after tracking with identical parameters compared to the ground truth by Ff > 0. c: Same as in b for five consecutive frames at different error rates. In both analyses, note the slightly higher scores for erroneous merges. We reason that the assignment score reflects erroneous merges better because the new cell center will be between the cells at a new position, which shifts the cell neighborhood relevant for the cell region fingerprint quite substantially. For erroneous splits, shifts of new cell centers relative to the correct cell center, and thereby changes to the neighborhood, are less pronounced.

Extended Data Fig. 7 The effect of the edge sensitivity threshold on asymmetric lineage reconstruction performance.

a Left: total variation regularization (ROF47) filtered false colored fluorescence image channel depicting the bud neck (green) with cell segmentation outlines (white). Middle and right: bud necks (magenta) detected for edge sensitivity thresholds of 0.09 (middle) and 0.225 (right). The data set used was TTS-SC7 (see Table S3). Three representative images shown out of n = 115/ 59 independent images from two independent data sets. Scale bar: 10μm. b Assignment counts as a function of edge sensitivity threshold classified into true positive (TP, bud to mother assignment correct), true negative (TN, no mother for bud), false positive (FP, wrong bud to mother assignment), and false negative (FN, missing bud to mother assignment).

Extended Data Fig. 8 Cell cycle regulation by Whi5 in S. cerevisiae for the selected track in Fig. 6a.

Merge of bright field image channel with the two fluorescent channels depicting Whi5 (magenta) and Myo1 (cyan; see Methods for details). Contrast adjusted to brightest pixels for both fluorescent channels. The selected cell from Fig. 6a is centered in each tile, with overlay of the outline of its segmentation mask (green). Scale bar: 10μm. The exemplary cell shown is one out of more than n =700 cells (repetitions) originating from 6 independently acquired data sets.

Extended Data Fig. 9 Cell cycle regulation by Whi5 in S. cerevisiae.

a,b Identification of threshold for nuclear concentrations of Whi5 by a two-component Gaussian mixture model (see Methods for details). a Example of empirical (bars) and fitted (lines) probability densities as well as inferred threshold (red line). b Ranking of cluster membership scores based on posterior probability indicates good cluster separation. c-e Cell cycle characteristics as a function of cell age and glucose concentration complementing Fig. 6b-e for growth rate in G1 (c), daughter volume at next division (d), and nuclear Whi5 in the mother at division (e). Sample sizes and statistical analysis are identical to Fig. 6b-e; see also Table S4. f Correlation plot as in Fig. 6f-i for growth rate in G1 and cellular Whi5 in mothers at the next division. g Effects plot for linear models for G1, G2/M, and total cell cycle duration (log-scaled response variables; see Fig. 6j). h Single-cell data vs model predictions for cell cycle duration as in Fig. 6k,l.

Extended Data Fig. 10 Whi5 concentration estimation based on cell volume.

Panels correspond to those for concentration estimation based on cell area as follows: a: Fig. 6d; b: Extended Data Fig. 9e; c: Fig. 6f; d: Fig. 6g; e: Fig. 6i; f: Extended Data Fig. 9f; g: Fig. 6j; h: Fig. 6k,l; i: Extended Data Fig. 9g; j: Extended Data Fig. 9h. Sample sizes, statistical analysis, and data presentation in a-b are identical to the referenced panels; see also Table S4 for details.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–3 and 5–7 and References.

Reporting Summary.

Peer Review File.

Supplementary Video 1

Lineage results for asymmetric cell division (S. cerevisiae). Cells colored by lineage tree of seeding cells from first frame. Details about data set TTS-SC7 are given in Supplementary Table 3.

Supplementary Video 2

Lineage results for symmetric cell division (S. pombe). Cells colored by lineage tree of seeding cells from first frame. Details about data set TTS-SP4 are given in Supplementary Table 3.

Supplementary Video 3

Lineage results for symmetric cell division (B. megaterium). Cells colored by lineage tree of seeding cells from first frame. Details about data set TTS-Bmeg are given in Supplementary Table 3.

Supplementary Video 4

Lineage results for symmetric cell division (HeLa cells). Subset of TTS-Fluo-N2DL-HeLa. Cells colored by lineage tree of seeding cells from first frame. Details about the data set are given in Supplementary Table 3.

Supplementary Video 5

Missing parent assignment errors for symmetric cell division (HeLa cells). Full data set (TTS-Fluo-N2DL-HeLa). Tracks that were not assigned to a parent are colored red in the first frame that they appear in. Details about the data set are given in Supplementary Table 3.

Supplementary Table 4

Detailed statistics (sample sizes, P values and confidence intervals) for Fig. 6 and Extended Data Figs. 9 and 10.

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Cuny, A.P., Ponti, A., Kündig, T. et al. Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction. Nat Methods 19, 1276–1285 (2022). https://doi.org/10.1038/s41592-022-01603-2

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