MITOTIC PHASE BASED DETECTION OF CHROMOSOME SEGREGATION ERRORS INEMBRYONIC STEM CELLS

A. El-Labban 1∗ C. Arteta 1 A. Zisserman 1 A. W. Bird 2 A. Hyman 3

1Department of Engineering Science, University of Oxford, United Kingdom2Max Planck Institute for Molecular Physiology, Dortmund, Germany

3Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

ABSTRACT

The detection of chromosome segregation errors in mitosis isan important area of biological research. Due to the rarity andsubtle nature of such errors in untreated cell lines, there is aneed for automated, high-throughput systems for quantifyingthe rates at which such defects occur.

This paper presents a novel approach to detecting sub-tle chromosome segregation errors in mitosis in embryonicstem cells, targeting two cases: misaligned chromosomes in ametaphase cell, and lagging chromosomes between anaphasecells.

Our method builds on existing approaches for analysis ofother cell lines (e.g. HeLa) which label mitotic phases throughmitosis and detect substantial deviations from normal mitoticprogression. We apply these to more challenging, denser,stem cell lines. Leveraging the mitotic phase labelling allowsus to detect smaller, more subtle defects within mitosis. Thisresults in a very high recall rate, as necessary for detection ofsuch rare events.

1. INTRODUCTIONWhen a cell divides, it must segregate all of its chromosomesequally into two daughter cells. Errors in chromosome seg-regation can lead to the gain or loss of chromosomes in acell, resulting in a state known as aneuploidy. Aneuploidycan lead to problems in development, and cancer cells fre-quently display aneuploidy resulting from a high rate of chro-mosome missegregation, a condition termed chromosomal in-stability [1].

Compared to many cancer cell lines routinely studied,such as HeLa (human epithelial adenocarcinoma) cells, em-bryonic stem cells have relatively low levels of chromosomeinstability [2], making them useful for identifying condi-tions that lead to small but biologically significant increasesin chromosome segregation defects. It is also important tobe able to determine accurately the degree of chromosome

∗This work was supported by ERC grant VisRec no. 228180 andthe RCUK Centre for Doctoral Training in Healthcare Innovation(EP/G036861/1).

(a)

(b)

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(d) (e)

Fig. 1. Examples of chromosome segregation defects. (a) Trackof cell undergoing mitosis normally. (b) & (c) Tracks of two cells un-dergoing mitosis, frames with errors are highlighted in red (dashed).(d) & (e) Enlarged view of frames with defects, indicated withwhite arrows. (b) & (d) Misaligned chromosome, fails to align tometaphase plate during mitosis. (c) & (e) Lagging chromosomes,not segregated into either daughter cell during mitosis.

segregation errors and aneuploidy in stem cell lines, as theybecome candidates for medical treatments.

Due to the rarity of such errors in stem cell lines, largeamounts of data need to be analysed to detect statisticallysignificant changes in error frequency under different experi-mental conditions. Additionally, due to the subtlety of the de-fects, human annotation can be difficult and time-consuming.There is therefore a need for automated high-throughput

methods for quantifying this data.There has been substantial recent work on automated im-

age analysis in sequences of mitotic cells, predominantly fo-cusing on HeLa cells [3, 4, 5, 6]. These approaches eitherdetect perturbations in mitosis based on durations of mitoticphases [3, 4], or as morphologies which differ substantiallyfrom those observed in normal mitotic progression [3, 5] e.g.binuclear or polylobed cells. All prior work uses a commonoverall processing pipeline: frames are first segmented intoindividual cells using image processing operations such asadaptive thresholding and watershed algorithms [3, 4, 5], or acombination of a linear classifier and graph cuts [6], and thecells are then tracked throughout the sequence. Next, framesare classified into mitotic phases or a more extensive set ofmorphologies, either using a support vector machine followedby temporal correction [3, 4, 5], or by directly using temporalmodels [6].

The novel aspect of this work is that it aims to detect muchmore subtle mitotic errors than previous methods, which fo-cused on detection of substantial variations from normal mi-toses (i.e. durations or morphologies). Our method leveragesmitotic track phase labelling information to significantly re-duce the search space for defects to just the phases in whichthe desired defects are expected to occur. This approach usesexisting methods to detect and track mitotic cells and labelthe mitotic phases over time, though applied to a differentcell type than previous methods – embryonic stem cells ratherthan HeLa cells. The detection of errors targets two cases: ei-ther around a metaphase cell for misaligned chromosomes orbetween anaphase cells for lagging chromosomes, as illus-trated in Figure 1. This ‘phase aware search’ results in re-liable detections, with a high recall rate, as required for thisapplication given the rarity of defects.

2. DATAThe data consists of 40 sequences of mouse embryonic stemcells expressing fluorescent histone-GFP protein to visualizechromosomes. Images were acquired on a widefield fluores-cence microscope with a 40× objective. Cells were imagedevery 4 or 5 minutes, and for each time point a z-stack of 5images at 2.5 micron spacing was acquired. Final image se-quences were then deconvolved and z-stacks projected. Theresolution is 480 × 480 pixels. Sequences initially contain 19cells on average, approximately 760 in all sequences, a num-ber which increases as cells divide or new ones move intofocus, and last for an average duration of 170 frames. In to-tal, over all frames in all sequences, there are approximately13 × 104 possible individual cell detections. Sequences con-tain from 2 up to 41 mitoses. In total, 62% of all cells in thedataset undergo mitosis, giving 473 mitoses, of which 10 con-tain a misaligned chromosome error and 10 contain a laggingchromosome error (slightly over 4% of the 473 mitoses aredefective). Mitoses typically last 10-12 frames, and defectsare visible for at most 3 frames in mitotic tracks i.e. fewer

(a) (b)

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Fig. 2. Four typical frames from the data. There is one visiblemitosis in each frame. The challenges of this dataset include denselyclustered cells, overlap of mitotic and interphase cells (e.g. in (b)-(c)), artifacts from dead cells (e.g. top of (b) and middle of (d)),and significant variations in interphase cell brightness (e.g. the fourinterphase cells on the right of (d)).

than 0.2% of all possible individual cell detections are mitoicand 0.03% contain chromosome segregation errors.

The data presents a number of challenges, illustrated inFigure 2, including densely clustered cells, overlap of mitoticand interphase cells, artifacts from dead cells, and significantvariations in interphase cell brightness. The chromosomesthat must be detected are extremely small: either blobs only2-3 pixels in diameter, or as slightly elongated, strand-likeobjects 4-5 pixels long and 1-2 pixels wide.

3. METHODWe follow a three stage approach for detecting chromosomesegregation defects: first detecting and tracking mitotic cells,second labelling the phase of mitosis in every frame of thetracks, and finally detecting defects in metaphase (misalignedchromosomes) and anaphase (lagging chromosomes).

3.1. Detection & Tracking

Due to the very high density of cells in the data, resulting ina large proportion of cells touching or overlapping, detectingand tracking all cells in a sequence would be an extremelychallenging task; simple foreground segmentation techniqueswould not differentiate between cells. Also, it is unneces-sary to detect and track all cells when the purpose is detect-ing defects in mitosis. Instead, we detect only mitotic cells(prometaphase, metaphase, anaphase and telophase) whichcan be distinguished from interphase cells as they are typi-

(a) (b) (c) (d)

Fig. 3. Example detection results. Detected extremal regionboundaries overlaid on images. Detection gives good segmentationboundaries and copes with overlapping or touching cells e.g. in (c)and (d). View in colour to see overlaid boundaries.

(a) (b)

Fig. 4. Aligned regions of interest for defect detection. (a) Mis-aligned chromosomes occur along the major axis of metaphase cells.(b) Lagging chromosomes appear between anaphase cells. Detec-tions within these regions are encoded in a 3 × 3 bin spatial his-togram. Segmented cell regions, shown in Figure 3, are excludedfrom the region of interest.

cally substantially brighter in appearance. The extremal re-gion based approach of [7] is used for this purpose. Thismethod involves first proposing hundreds of candidate ex-tremal regions, and then selecting the regions of interest us-ing a classifier together with a non-overlap constraint. It canrobustly detect mitotic cells even when they overlap with in-terphase cells, as can be seen in Figure 3, and the resultingregions provide good chromatin segmentations for use in sub-sequent steps. For brevity, the chromatin segmentations willbe referred to as cells in the remaining sections.

Cells are then associated from frame to frame by a near-est neighbour approach using a feature consisting of centroidposition and size, and incorporating validation gating in theform of an association threshold. This approach is sufficientto cope with the relatively well spaced out detections, bothspatially and temporally.

A post-processing stage is applied to remove false posi-tive tracks: a linear classifier is used to remove tracks of arti-facts from cell death, and non-mitotic cells which are brightenough to be detected. The features used for this consist ofthe mean, standard deviation, maximum and minimum valuesof cell brightness and size over a track. These features shouldvary substantially as a cell undergoes mitosis but remain rea-sonably constant for artifacts and interphase cells.

3.2. Phase labellingFor phase labelling we adopt the approach of [6]. As the per-formance of this method improves when a track is sufficiently

long to contain all mitotic phases, the detected mitotic tracksare continued three frames forward and backwards in time toobtain some frames with the cells in interphase, allowing forbetter normalisation of the features.

3.3. Defect detectionFor detection of the defective chromosomes, a two stage ap-proach is used: first, candidate structures are detected in aregion of interest around the cell, and second detections arefiltered based on a number of intensity and spatial character-istics. The region of interest is defined where defects are ex-pected to occur – orthogonal to the major axis of metaphasecells, or between dividing anaphase cells. Example regionsare illustrated in Figure 4.

Within the interest regions, candidate misaligned/laggingchromosomes are initially detected as blobs as local maximaof Lapacian of Gaussians filter response. The filtering oper-ation is carried out at two different scales (σ = 1, 2 pixels)to account for variation in defect sizes. Note, the chromatinsegmented region determined in Section 3.1 is excluded whenproposing candidates and in other subsequent processing.

The candidates contain a large number of false positivesprimarily due to interference from other nearby cells. Thepurpose of the second stage is a filter to remove these falsepositives.

For the intensity based filtering step, we observe that ac-tual mitotic defect detections have pixel intensities closer tothe chromatin in the segmented cell than the background por-tion of the interest region. Therefore, we assume that theprobability of a blob corresponding to a mitotic defect is pro-portional to the ratio of intensity differences:

p(defect|iblob) ∝ exp

(−(iblob − icell)

2

2σ2cell

− −(iblob − ibg)2

2σ2bg

)

where iblob is the pixel intensity at the blob detection, icelland σcell are the mean and standard deviation of chromatinpixel intensities within the segmented cell, and ibg and σbg

are the mean and standard deviation of pixel intensities in theinterest region background. A threshold, τ , on this measureremoves false positives. The image is then thresholded basedon intensity at each detection, with the resulting segmentationused to estimate the size of the detected blob. Segmentationslarger than individual chromosomes are discarded.

The remaining detections are then encoded in a 3 × 3bin spatial histogram. Histogram bin counts are weighted bythe magnitude of detected peaks in the filter response, reduc-ing the contribution of any remaining peaks caused by imagenoise. Entropy is then used as a measure to define defects,based on the observation that true defects will appear as welllocalised detections, and consequently generate histogramswith low entropy.

4. RESULTSResults are reported in Table 1 for the cell detection and mi-totic defect detection components of this work. For each of

True Pos. False Pos. False Neg. True Neg. Precision RecallMitosis detection (frame) 2853 2658 852 ≈ 13× 104 0.52 0.77Mitosis detection (track) 409 19 64 ≈ 750 0.96 0.86Defect detection (frame) 23 80 12 4491 0.22 0.66Defect detection (track) 18 47 2 364 0.29 0.90

Table 1. Results for the two key stages of the method – mitotic cell detection, and defect detections. The performance of eachstage is assessed on a frame-by-frame basis and on a whole track basis. Mitotic defect detection results are based on detectedmitotic tracks from the previous stage. The true negative values for mitosis detection are estimates, as ground truth data isunavailable for non-mitotic cells and tracks.

0 0.2 0.4 0.6 0.8 10

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Fig. 5. ROC curve of defective mitotic track detection. The red(dashed) curve uses only candidate defects (before filtering) as de-scribed in the first stage of Section 3.3. The blue curve incorporatesmitotic phase and spatial information filters. The operating pointused for results in Table 1 is indicated with a blue ‘×’.

these, performance values are given for two conditions: in-dividual frame detections and whole track detections. Thetrack performance is particularly important since detecting adefective mitotic track is still useful for analysing large scalebiological data even if one or two frames containing defectsare missed.

For the mitotic cell detection part of this work, the 40 se-quences in the dataset are divided into two equal partitions of20 for training and testing. Within the training partition, two-fold cross-validation is used to set parameters of the detectionsystem. Training is then carried out on the full 20 sequencepartition and tested on the remaining half of the data. The par-titions are then switched and the training and testing process isrepeated to obtain mitotic cell detections for all 40 sequences.This results in very high detection accuracies, with overallprecision and recall values of 0.96 and 0.86 for mitotic trackdetection. False positive track detections are mostly causedby artifacts remaining from cell death and oversegmentationsof correctly detected cells.

The defect detection results are based only on detectedmitotic tracks from the first stage. All of the 20 mitotic tracksin the dataset containing defects are detected. For the mitoticdefect detection, the threshold, τ , described in Section 3.3 isvaried to produce ROC curves, as shown in Figure 5. Thisresults in a very high rate of recall, with only two tracks withdefects missed. Precision is lower due to the significant im-

balance in the data. The two false negative tracks which occurare both lagging chromosomes in anaphase cells. All mis-aligned chromosomes in metaphase are correctly detected.

5. CONCLUSIONWe have presented a novel method for detecting very rare andsubtle mitotic errors in large volumes of microscopy images.Our method is useful as a filter for further biological analy-sis, as it reduces the amount of data to be analysed manuallyby several orders of magnitude, although further work canimprove the precision. With the availability of more data ad-ditional types of mitotic errors may be detected, or the twoaddressed here may be further sub-categorized. Additionally,classifiers could be trained to further improve precision.

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