| Literature DB >> 31720237 |
C Elizabeth Caldon1,2, Andrew Burgess3,4.
Abstract
Historically, the ability to perform multi-day time-lapse imaging of adherent cells required expensive and specialized microscopy equipment. As byproduct of this cost, many labs would synchronize cells using inhibitors such as hydroxyurea and thymidine, and or use fluorescent biosensors to minimize time required on the microscope. These methods introduce significant artefacts including phototoxicity, increased DNA replication stress and mitotic defects, thereby limiting the ability to characterize various cell cycle phenotypes. However, increased access to low cost live cell microscopes has removed many of the economic barriers thereby allowing multi-day imaging on asynchronous cells on a regular basis. Here we describe our protocol for manually tracking individual cell fates across multiple generations of random daughter cells using only low toxicity brightfield based imaging. Importantly, our pipeline relies on the free open-source software ImageJ/Fiji and an easy to use Microsoft Excel spreadsheet. Furthermore, annotated files can be saved to allow later recall of any individual cell. In summary, our method provides quantitative data on interphase and mitotic transit time, points of cell cycle arrest and critically, the ability to link these events with cell fate.Entities:
Keywords: Anaphase; Brightfield; Cell cycle; Cell death; Interphase; Live cell imaging; Microscopy; Mitosis; NEBD
Year: 2019 PMID: 31720237 PMCID: PMC6838936 DOI: 10.1016/j.mex.2019.10.014
Source DB: PubMed Journal: MethodsX ISSN: 2215-0161
Fig. 1Steps to process your time-lapse movies in Fiji. (A) Importing data. (B) Scaling the data. (C) Inserting a time counter. (D) Adjusting for uneven lighting.
Fig. 2Identifying cells on your time-lapse movies. (A) Schematic of the cell cycle across two generations. (B) Identifying interphase cells. (C) Characterising the steps in mitosis. (D) Recognising cell death.
Fig. 3Creating single cell fate maps in Excel. (A) Adjusting for the total movie length. (B) Adjusting the data for death events. (C) Visualising the fate map.
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Enables quantitation of label free cells using time-lapse data Produces wealth of information on individual cell fates, enabling greater insight into biological responses. |