Analysis of live cell images: Methods, tools and opportunities

Nketia, Thomas A.; Sailem, Heba; Rohde, Gustavo K.; Machiraju, Raghu; Rittscher, Jens

doi:10.1016/j.ymeth.2017.02.007

Cited by 70 publications

(45 citation statements)

References 167 publications

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“…This property can confound methods relying on the assumption that brighter regions reside at the centre of the shape. Also, nuclei boundaries which are key features for many methods (Dufour et al, 2017;Kan, 2017;Meijering, 2012;Nketia et al, 2017) become very difficult to distinguish even by eye. These problems are avoided when using a NE staining and this explains in part the success of the Nessys method.…”

Section: Features Of the Ne Signal Explain Nessys Performancementioning

confidence: 99%

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Nessys: a novel method for accurate nuclear segmentation in 3D

Blin

Sadurska

Migueles

et al. 2018

Preprint

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Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However current methods for segmenting nuclei in 3D tissues are not designed for situations where nuclei are densely packed, non-spherical, heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture.Here we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree structured ridge tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer's memory. We consistently obtain accurate segmentation rates of >90% even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation.We have also generated a live reporter of the NE that can be used to track live cells in three dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation.We provide these tools in an open-access user-friendly format. 2 35 40 45 50 2012; Carpenter et al., 2013; Meijering et al., 2016).Here, we report a new approach to overcome these bottlenecks in quantitative image analysis of individual cells in 3D. Rather than relying on staining for nuclear content (for example DAPI or Hoescht staining), we instead detect the nuclear envelope (NE). This makes it easier to identify individual nuclei that are in close contact with each other and does not suffer from segmentation problems associated with textured nuclear staining, unusually shaped nuclei, or cell debris. Furthermore, the NE of individual nuclei are easily discernable by eye in crowded tissues, and so manual correction of any mis-segmented nuclei becomes easier than is the case for DAPI stained nuclei. We provide a user-friendly 3D-4D editing tool to rapidly correct any

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Section: Features Of the Ne Signal Explain Nessys Performancementioning

confidence: 99%

“…In order to meet the need for automated nuclear segmentation in 3D, a wide array of methods have been developed, reviewed in (Dufour et al, 2017;Kan, 2017;Meijering, 2012;Nketia et al, 2017). It is becoming increasingly apparent, however, that there is no single 'one size fits all' solution to segmentation.…”

Section: Introductionmentioning

confidence: 99%

Nessys: a novel method for accurate nuclear segmentation in 3D

Blin

Sadurska

Migueles

et al. 2018

Preprint

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“…Various reviews describe the field from different angles [1,2,[12][13][14][15]. Bioimaging is special in that it crosses different disciplines and is now being taken to a new level by crucial advances in imaging processing and quantification [16,17]. It is timely to revisit the imaging evidence obtained so far and identify blind spots that demand a fresh look.…”

Section: Introductionmentioning

confidence: 99%

What Drives Symbiotic Calcium Signalling In Legumes? Insights And Challenges Of Imaging

Martins

Livina

2019

Preprint

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We review the contribution of bioimaging in building a coherent understanding of Ca 2+ signalling during legume-bacteria symbiosis. Currently, two different calcium signals are believed to control key steps of the symbiosis: a Ca 2+ gradient at the tip of the legume root hair is involved in the development of an infection thread, while nuclear Ca 2+ oscillations, the hallmark signal of this symbiosis, controls the formation of the root nodule, where bacteria fix nitrogen. Additionally, different Ca 2+ spiking signatures have been associated with specific infection stages. Bioimaging is intrinsically a cross-disciplinary area that requires integration of image recording, processing and analysis. We use experimental examples to critically evaluate previously established conclusions and draw attention to challenges caused by the varying nature of the signal-to-noise ratio in live imaging. We hypothesise that nuclear Ca 2+ spiking is a wide-range signal involving the entire root hair, and that Ca 2+ signature may be related to cytoplasmic streaming.

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“…To capture and study such behaviors, the process of interest should be followed over time at single cell resolution [6-8]. A widely used method to achieve this spatial and temporal resolution is live-cell time-lapse microscopy [9], which has two general requirements for extracting single-cell data: First, single-cell boundaries have to be identified for each time-point (segmentation), and second, cells have to be tracked over time (tracking) [10, 11].…”

Section: Introductionmentioning

confidence: 99%

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

Wood

2018

Preprint

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17 Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein 18 dynamics in single cells, relies on segmentation and tracking of individual cells for data 19 generation. The potential of the data that can be extracted from this technique is limited by the 20 inability to accurately segment a large number of cells from such microscopy images and track 21 them over long periods of time. Existing segmentation and tracking algorithms either require 22 additional dyes or markers specific to segmentation or they are highly specific to one imaging 23 condition and cell morphology and/or necessitate manual correction. Here we introduce a fully 24 automated, fast and robust segmentation and tracking algorithm for budding yeast that 25 overcomes these limitations. Full automatization is achieved through a novel automated seeding 26 method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using 27 these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately 28 segment and track individual yeast cells without any specific dye or biomarker. Moreover, we 29 show how existing channels devoted to a biological process of interest can be used to improve 30 the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells 31 with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and 32 pheromone treated cells). In addition, the algorithm is largely independent of the specific 33 imaging method (bright-field/phase) and objective used (40X/63X). We validate our algorithm's 34 performance on 9 cases each entailing a different imaging condition, objective magnification 35 and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and 36 tracking tool that can be adapted to numerous budding yeast single-cell studies.3 37 Introduction 38 Traditional life science methods that rely on the synchronization and homogenization of cell 39 populations have been used with great success to address numerous questions; however, they 40 mask dynamic cellular events such as oscillations, all-or-none switches, and bistable states [1][2][3][4][5].41 To capture and study such behaviors, the process of interest should be followed over time at 42 single cell resolution [6][7][8]. A widely used method to achieve this spatial and temporal resolution 43 is live-cell time-lapse microscopy [9], which has two general requirements for extracting single-44 cell data: First, single-cell boundaries have to be identified for each time-point (segmentation), 45 and second, cells have to be tracked over time (tracking) [10, 11]. 46 47 One of the widely-used model organisms in live-cell microscopy is budding yeast Sacchromyces 48 cerevisiae, which is easy to handle, has tractable genetics, and a short generation time [12, 13].49 Most importantly in the context of image analysis, budding yeast cells have smooth cell 50 boundaries and are mostly stationary while growing, which ca...

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Analysis of live cell images: Methods, tools and opportunities

Cited by 70 publications

References 167 publications

Nessys: a novel method for accurate nuclear segmentation in 3D

Nessys: a novel method for accurate nuclear segmentation in 3D

What Drives Symbiotic Calcium Signalling In Legumes? Insights And Challenges Of Imaging

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

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