Real-Time Cell Cycle Imaging in a 3D Cell Culture Model of Melanoma, Quantitative Analysis, Optical Clearing, and Mathematical Modeling.

Loredana Spoerri, Kimberley A Beaumont, Andrea Anfosso, Ryan J Murphy, Alexander P Browning, Gency Gunasingh, Nikolas K Haass

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Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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5 · Who and what money

Authors and funding

7 authors.

Loredana SpoerriFrazer Institute, The University of Queensland, Brisbane, QLD, Australia.

Kimberley A BeaumontThe Centenary Institute, Sydney, NSW, Australia.

Andrea AnfossoThe Centenary Institute, Sydney, NSW, Australia.

Ryan J MurphyMathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.

Alexander P BrowningMathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.

Gency GunasinghFrazer Institute, The University of Queensland, Brisbane, QLD, Australia.

Nikolas K HaassFrazer Institute, The University of Queensland, Brisbane, QLD, Australia. n.haass1@uq.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aberrant cell cycle progression is a hallmark of solid tumors. Therefore, cell cycle analysis is an invaluable technique to study cancer cell biology. However, cell cycle progression has been most commonly assessed by methods that are limited to temporal snapshots or that lack spatial information. In this chapter, we describe a technique that allows spatiotemporal real-time tracking of cell cycle progression of individual cells in a multicellular context. The power of this system lies in the use of 3D melanoma spheroids generated from melanoma cells engineered with the fluorescent ubiquitination-based cell cycle indicator (FUCCI). This technique, combined with mathematical modeling, allows us to gain further and more detailed insight into several relevant aspects of solid cancer cell biology, such as tumor growth, proliferation, invasion, and drug sensitivity.

Indexed as

MelanomaCell Culture Techniques, Three DimensionalCell CycleCell DivisionDiagnostic ImagingHumansSpheroids, Cellular3D spheroidCancer drug resistanceFluorescent ubiquitination-based cell cycle indicator (FUCCI)InvasionMathematical modelingMigrationReal-time imagingTumor heterogeneityTumor microenvironment

Identifiers

PMID38393602

DOI10.1111/bjd.12430

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.