Cell-population tracking using quantum dots in flow cytometry

Summers, Huw D.; Errington, Rachel J.; Smith, Paul J.; Chappell, Sally Claire; Rees, Paul; Brown, M. Rowan; Leary, James F.

doi:10.1117/12.763602

Cited by 2 publications

(7 citation statements)

References 7 publications

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“…Our approach to extracting cell‐cycle system parameters relies on fitting the intensity profile of an experimental flow data set, S E total ( t 1 ), measured at a time t 1 , to that of produced by an in‐silico virtual population, S V total ( t 1 ). The virtual population is initialized at time t 0 ( t 1 > t 0 ) to a further flow data set, S E total ( t 0 ), the virtual population, S V total ( t 0 ) , is then evolved from its state at t 0 to t 1 by means of a stochastic cell‐cycle model (9–11). Important ensemble parameters, within the cell‐cycle model, are iteratively refined by an evolutionary algorithm [15], to maximize correlation between S V total ( t 1 ) and S E total ( t 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Details of the stochastic cell‐cycle model used to evolve the initial Qdot705 intensity profile are given in the manuscripts Supplementary Information. Further information regarding the evolutionary strategy used to minimize cell‐cycle parameters are given in references (9–11).…”

Section: Methodsmentioning

confidence: 99%

“…Previously, we have tracked the inheritance of QTracker® 705 nm quantum dots (Qdot705) that have been compartmentalized within the endosomes of human osteosarcoma cells (U‐2 OS cells) (9–11). A combination flow cytometry fluorescent measurements and a stochastic cell‐cycle model determined the U‐2 OS population inheritance properties.…”

mentioning

confidence: 99%

“…These initial investigations, measured the redistribution of Qdot705 fluorescent intensity, over a 2‐day period, corresponds to approximately two cell‐cycle durations for the unperturbed U‐2 OS cell line (dilution due to two mitotic events). Even after two mitotic events, the quantum dot signal per cell was significantly above the intrinsic autofluorescence signal (9–11), produced primarily by intracellular chromaphores such as NADH, riboflavins, and flavin coenzymes (13). Therefore, for this small time‐frame the actual measured total fluorescence signal can be attributed to the quantum dot component only.…”

mentioning

confidence: 99%

“…To recover the quantum dot signal from the total measured signal, we developed an in‐silico cell population that has an associated stochastic cell‐cycle. Previously, the evolution or redistribution of quantum dot signal through mitosis was solely considered (9–11). Here, we present an updated stochastic cell‐cycle model that actually uses the autofluorescent signal to boost numerical fitting to measured data and allow important population parameters, such as intermitotic time to be predicted.…”

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confidence: 99%

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Long‐term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

Brown¹,

Summers²,

Rees³

et al. 2010

Cytometry Pt A

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The monitoring of cells labeled with quantum dot endosome-targeted markers in a highly proliferative population provides a quantitative approach to determine the redistribution of quantum dot signal as cells divide over generations. We demonstrate that the use of time-series flow cytometry in conjunction with a stochastic numerical simulation to provide a means to describe the proliferative features and quantum dot inheritance over multiple generations of a human tumor population. However, the core challenge for long-term tracking where the original quantum dot fluorescence signal over time becomes redistributed across a greater cell number requires accountability of background fluorescence in the simulation. By including an autofluorescence component, we are able to continue even when this signal predominates (i.e., [80% of the total signal) and obtain valid readouts of the proliferative system. We determine the robustness of the technique by tracking a human osteosarcoma cell population over 8 days and discuss the accuracy and certainty of the model parameters obtained. This systems biology approach provides insight into both cell heterogeneity and division dynamics within the population and furthermore informs on the lineage history of its members. ' 2010International Society for Advancement of Cytometry Key terms flow cytometry; cell-cycle; quantum dot; nano-toxicity; systems biology; proliferation; in-silico modeling FLOW cytometry is an essential tool for the study of the cell cycle by the measurement of the fluorescence properties of large cell populations (1-6). Using appropriate fluorescent markers, various elements of the cell-cycle or proliferation processes concealed within the population can be elucidated. To gain a deeper understanding of cell-cycle, its perturbation and potential clonogenic expansion within large cell populations over extended periods ([1 week) requires fluorescent labeling of individuals with stable, high intensity, and biologically compatible and adaptable markers. One candidate that meets these stringent criteria is the semiconductor colloidal quantum dot. The inorganic nature of these nanoparticles provides longevity to the fluorescence signal that is brighter than organic fluorophores and is unperturbed by intracellular biochemical reactions (7,8). Furthermore, their physical and chemical properties can be transformed to yield specific emission wavelengths and preferential binding to particular cellular compartments respectively. In addition, quantum dots are chemically stable and do not metabolize, facilitating their uptake by the cell and allowing passage to endosomic compartments, which through mitosis provide a means for daughter cells to inherit a diluted nanoparticle load (7).Previously, we have tracked the inheritance of QTracker 1 705 nm quantum dots (Qdot705) that have been compartmentalized within the endosomes of human

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Long‐term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

Brown¹,

Summers²,

Rees³

et al. 2010

Cytometry Pt A

Self Cite

View full text Add to dashboard Cite

show abstract

Statistical prediction of nanoparticle delivery: from culture media to cell

et al. 2015

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The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatment of various diseases, where judicious surface functionalization is exploited to increase efficacy of administered therapeutic dose. However, quantification of heterogeneity associated with absolute dose of a nanotherapeutic (NP number), how this is trafficked across biological barriers has proven difficult to achieve. The main issue being the quantitative assessment of NP number at the spatial scale of the individual NP, data which is essential for the continued growth and development of the next generation of nanotherapeutics. Recent advances in sample preparation and the imaging fidelity of transmission electron microscopy (TEM) platforms provide information at the required spatial scale, where individual NPs can be individually identified. High spatial resolution however reduces the sample frequency and as a result dynamic biological features or processes become opaque. However, the combination of TEM data with appropriate probabilistic models provide a means to extract biophysical information that imaging alone cannot. Previously, we demonstrated that limited cell sampling via TEM can be statistically coupled to large population flow cytometry measurements to quantify exact NP dose. Here we extended this concept to link TEM measurements of NP agglomerates in cell culture media to that encapsulated within vesicles in human osteosarcoma cells. By construction and validation of a data-driven transfer function, we are able to investigate the dynamic properties of NP agglomeration through endocytosis. In particular, we statistically predict how NP agglomerates may traverse a biological barrier, detailing inter-agglomerate merging events providing the basis for predictive modelling of nanopharmacology.

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Cell-population tracking using quantum dots in flow cytometry

Cited by 2 publications

References 7 publications

Long‐term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

Long‐term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

Statistical prediction of nanoparticle delivery: from culture media to cell

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