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Computational Simulation of Optical Tracking of Cell Populations using Quantum Dot Fluorophores. Martyn R. Brown * , Huw D. Summers † , Paul Rees * , Kerenza Njoh ‡ , Sally C. Chappell ‡ , Paul J Smith ‡ and Rachel J. Errington ‡ - PowerPoint PPT Presentation
Citation preview
Computational Simulation of Optical Tracking of Cell Populations using
Quantum Dot Fluorophores
Martyn R. Brown*, Huw D. Summers†, Paul Rees*, Kerenza Njoh‡, Sally C. Chappell‡, Paul J Smith‡ and Rachel J. Errington‡
*Multidisciplinary Nanotechnology Centre, Swansea University, Singleton Park, Swansea, SA2 8PP, UK.
†School of Physics and Astronomy, Cardiff University, 5, The Parade, Cardiff, CF24 3YB, UK.
‡School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
Talk Outline Introduction Cell Division and Cycle
Population Studies Endocytosis
Experimental Methods and Measurements Imaging Quantum Dot Incorporation Population Tracking with Quantum Dots Flow Cytrometry
Theoretical Simulation Stochastic Cell Splitting Model Genetic Algorithm
Results Two-Four Parameter Optimisation
Summary
Introduction The ability to track the evolution of large cell populations over time is crucial
Provides a means of monitoring the general health of a population of cells Informing on the outcome of specific assays (e.g. pharmacodynamic assay)
Overall aim is to track the evolving generations within a growing cell culture and to identify the influence of drug intervention on the cell cycle i.e. can the cell division rate be slowed or even stopped
A key component to this has been the computational simulation of the QD dilution via cell mitosis
Modeling of this kind provides detailed insights into the evolution of cell lineage
Provides insight at the individual cell level from whole population experiments i.e. flow cytometry analysis as opposed to cell to cell tracking via time-lapse imaging
Traditional approaches used for determining cell proliferation require knowledge of population size or the behavior of a cellular marker diluted on a cell-to-cell basis
The use of this type of modeling provides a new avenue for large population cell-cycle analysis using flow cytometry
Cell Division and Cycle Biology focus – interference / blocking of cell cycle by
drugs
Anti-cancer therapeutics
Currently done by time lapse microscopy – time consuming
Exacerbated by the required statistical sampling of large populations because of the heterogeneous response
E.g. if you treat a tumour with a drug many of the cell lineages will die off but a few will be immune and it is these that survive and proliferate
Quantum Dot Fluorophores
Recent developments in semiconductor physics have produced a new class of fluorophore suitable for cytometry techniques
Nano-sized semiconductor crystals that emit specific wavelengths of light when excited by an optical source
Surface of fluorophores can be functionalised to bond or dock with specific sites within the biological cells
Provide long lived optical markers with the cell
Inorganic particles and so there is potential for them to be biologically inert
Properties applicable to tracking cell dynamics across generations
Passive optical reporters within the cell
Endocytosis Endocytosis - process whereby cells absorb material from the
outside by engulfing it with their cell membrane
QDot take-up in cell via endocytotic pathways Surface functionalised with peptides Specifically target the endosomal sites within cell
Concentration of nanoparticles within early / late endosomes
Process is repeatable, 5-6 runs and the same level of dot loading is seen.
Confirmed that by 24 hours the QDs are located in the endosomes and up until 72 hours the signal per dot is stable
from ‘Molecular Biology of the Cell’, Alberts et al
Imaging QD Incorporation
705 nm CdTe, QDs from Invitrogen (QTracker) functionalised surface coating to ensure cell uptake U2OS – Osteosarcoma cells
Images show QD uptake and evolution from membrane localisation, 1-3 hours through to clear compartmentalisation within the cell by 24hrs
1hr 24hr5hr3hr
Imaging Cell Mitosis Typical approach to tracking cell cycle dynamics, involves huge
amounts of data collection and painstaking post-capture analysis
Movie shows an example of the current experimental approach
Images taken at 15 min intervals
Time lapse movie of QDs in growing cell population followed by time consuming cell tracking done manually
Cell Population Tracking with Quantum Dots The basic concept is illustrated below in plot (a)
QDs are loaded into an initial population of cells As a cell undergoes mitosis the quantum dots are partitioned
into the two daughter cells The optical intensity, I, is reduced due to the reduction of dot
density per cell, N (figure (b)) Optical signal can be directly related to the cell lineage.
1
1/2
1/4 I
N (b)(a)
Flow Cytometry – FACS Scan Measurement of large data sets
(10,000 cells typically)
High measurement rates > 103 cells/s
Cells channelled through an interrogating laser beam
488 nm excitation of dots, fluorescence monitored with 670 nm long pass filter
Scattered/emitted light by cells is detected and used to analyse cell structure and function
Forward and side scatter signals from the cells used to gate a healthy population
data sets represent only live cells
Experiment – Fluorescence Distributions
Figure displays three typical experimental data sets, acquired from flow cytometry measurements on a population of 104 cells
The data sets are presented in the form of histograms derived by binning cells according to their quantum dot fluorescence intensity
Cells used are human osteosarcoma (U-2 OS; ATCC HTB-96)
100
102
104
0
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40
60
80
Cel
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Fluorescence Intensity (a.u.)
72hr
48hr
24hr
These have a typical mean cell inter-mitotic time of ~22 hours and so measurements at 24 hour intervals effectively sample sequential cell generations
It is apparent that each successive generation has a lower fluorescence due to the dilution of quantum dot number by cell mitosis
Theoretical Simulation and Optimisation The computer simulation consists of two components
A cell mitosis model (CMM) Genetic algorithm (GA)
The aim of the CMM is to generate a theoretical equivalent to the experimental fluorescence intensity histograms
The CMM is the function that the GA minimises, f(X)
Through the use of a GA the important ensemble parameters are optimized
To obtain agreement with experimental data Subsequently provide a more detailed picture of the quantum dot
partitioning during cell division
Cell Mitosis Model (CMM) – Two Parameters
Flowchart indicating main steps of the CMM
Two parameter version: Mean partition ratio of parent to daughter
cells, μp, i.e. distribution of QDs
Associated standard deviation, σp
Firstly, the recorded data describing the cellular fluorescence intensity from the quantum dots within a population of 104 cells is taken as an input set for the program Measured 24 hours following QD loading
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (2)
Each of the 104 input cells is stochastically allocated a time within its cell-cycle
Randomly from a normal distribution centered on the mean inter-mitotic time, μIMT with an associated standard deviation, σIMT
This step mimics the fact that each of the 104 cells in the experiment will be at different stages within the cell-cycle
For our model the cell-cycle is simply defined by an inter-mitotic time, i.e. a time relative to the cell’s birth at which the cell will split into two daughter cells
Therefore, from birth the cell moves through its cycle unchanged until it reaches its inter-mitotic time
The cell-cycle is far more complicated than this and different compartments of the cycle can be included in the model however, this is not required for this present analysis
The variables μIMT and σIMT are the two other of the four parameters to be optimized by the genetic algorithm
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (3) The next step of the algorithm determines if a particular
parent cell has split or not
Again this choice is stochastically determined
The previously assigned cycle time of a cell together with the laboratory time is used to generate a cumulative distribution specific to each individual cell
This choice is illustrated in the figure below where a particular cell has been randomly given a cell-cycle time of 12 hours
10 20 30 40 50 60
0.2
0.4
0.6
0.8
1.0
Laboratory Time (hrs)
Cum
ulat
ive
Pro
babi
lity
m
s
27
0.1
0.0
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (4) If for example μIMT is 23 hours and σIMT is 6 hours the resulting
cumulative distribution will be centered on 35 hours
A splitting event occurs if a random number, uniformly distributed over the interval [0 1], lies below the cumulative probability curve at the laboratory time
For example, the filled black circle indicates the probability of a split occurring for this particular cell at a laboratory time of 27 hours, the graph indicates a 10% chance of this split occurring
This sampling occurs at every time interval (1 hour in our case)
10 20 30 40 50 60
0.2
0.4
0.6
0.8
1.0
Laboratory Time (hrs)
Cum
ulat
ive
Pro
babi
lity
m
s
27
0.1
0.0
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (5)
If the parent cell has not split it is returned to the populace
If a splitting event occurs the algorithm next decides how the quantum dots are distributed to its daughters
When splitting occurs we assume that the number of quantum dots is always conserved
The total number of dots in each daughter cell is equal to the number of dots in the parent cell
The number of dots allocated to each daughter cell is chosen at random from a normal distribution centered on a mean partition ratio, μP, which has an associated standard deviation, σP
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (6)
Once the daughter cells have been assigned their respective quantum dot population, the algorithm resets their cycle time equal to their parents plus the value of μIMT
This action ensures that the probability of two newly formed daughter cells splitting again in the immediate future is small
The final stage of the algorithm simply stores both daughter and the initial parent cells yet to split in the first hour in the laboratory frame of reference
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
CMM – Two Parameters (7)
The total population is now > 104
Laboratory and cycle time of the cells are incremented by 1 hr
At the set ‘measurement’ time (typically a 24 hour increment) a fluorescent histogram is calculated by determining the number of dots in each cell from a random sample population of 104
This histogram can then be compared directly with the experimental data
Specifically, the Euclidean norm of the two histogram curves is calculated and compared for particular values of μp and σp
Yes
No
Input 24 hourexperimental data
Stochastically assignlocal time to parent cell
Determine if parentcell has split
Reset mean lifetimeof daughters
Update cellpopulation
Next parent cell
Increment timeby 1 hour
Randomly determine how the number of quantum dots is
distributed to the daughter cells
Genetic Algorithm (GA) (1) Flowchart indicating main steps of the GA
Initial population of chromosomes randomly generated to span the whole parameter space
10 chromosomes 8 genes per optimisation parameter Each gene randomly given a 0 or 1
Fitness of the initial populace is evaluated by running through the CMM
Fitness is determined by calculation of the Euclidean norm of the experimental and simulated data over the entire intensity range
Although, the simulated data does not produce a fluorescent signal, but rather a number detailing the number of quantum dots per cell, a meaningful comparison between the experimental and simulated data can be made on the supposition that florescence intensity is proportional to cell dot density
Generate randompopulation of
10 chromosomes
Evaluatefitness
Mate chromosomesaccording to fitness
Mutate chromosomeelements
Evaluate function, f(X)
Converged?
End
Yes
No
Genetic Algorithm (GA) (2) The fitness of chromosome generation is analyzed to
see if a desired convergence criterion is met
If true the simulation is halted
The population is ranked in order of fitness and chosen stochastically to generate the succeeding generation
The simulation utilizes two methods to produce the next chromosome generation, mating and elitism
Chromosome mating, utilizes 65% gene crossover rate between stochastically selected parents
The random choice of the parents is weighted in favor of individual fitness
Higher their fitness the more likely they will be chosen to mate
Generate randompopulation of
10 chromosomes
Evaluatefitness
Mate chromosomesaccording to fitness
Mutate chromosomeelements
Evaluate function, f(X)
Converged?
End
Yes
No
Genetic Algorithm (GA) (3)
Elitism is included to ensure that the fittest individual of one generation survives to the next without modification
Also, in each new-generation there is a small probability that a chromosome may undergo a random mutation
This is set to occur to 5% of the total number of genes available at each generation
The new-generation of chromosomes is again evaluated in the manner above until a suitable convergence criterion is achieved
The magnitude of the optimized parameters varied by less than 5% across the whole chromosome population
Generate randompopulation of
10 chromosomes
Evaluatefitness
Mate chromosomesaccording to fitness
Mutate chromosomeelements
Evaluate function, f(X)
Converged?
End
Yes
No
Results – Two Parameter Model (a) Experimental and (b) simulated quantum
dot fluorescence intensity histograms taken at 24 hour intervals following take-up
(c) Computed (blue trace) and measured (black trace) fluorescence histograms 72 hours after quantum dot uptake
Excellent fit between computed and measured traces
The modeled fit has a peak probability of partitioning ratio of 74:26 % with a 6 % standard deviation
The importance and relevance of the asymmetric splitting is very unexpected and the subject of much further work
Asymmetry, verified using microscopic techniques
Hypothesised to be due to the presence of QDs within the cell
10 0 10 1 10 2 10 3 10 40
20
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60Cel
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Fluorescence Intensity (a.u.)
0
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(b)
(a)
72hr48hr
24hr
72hr48hr
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Cel
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Fluorescence Intensity (a.u.)10 10 1010 100 1 32 4
(c)
Results – Four Parameter Model (1)
In addition to the parent partition ratio and its standard deviation we include the mean inter-mitotic time, μIMT and its deviation σIMT
ParametersSample Space
μP, σP [0 1]
μIMT [0 48]
σIMT [1 20]
Including these two supplementary parameters provides detailed analysis of cell growth dynamics without the requirement of prior knowledge of cell growth parameters other than the measurements themselves
Initial population of 50 chromosomes
Each chromosomes with 32 genes split evenly between the 4 parameters
Results – Four Parameter Model (2)
Figure displays both the experimental (black trace) and the simulated quantum dot fluorescence intensity at 48 hours (blue trace) using the 4 parameter cell-cycle model in conjunction with the genetic algorithm
The values of inter-mitotic time and its associated standard deviation predicted by the simulation are 22.5 and 4 hours respectively
Using microscopic techniques the inter-mitosis time for the human osteosarcoma cell line has been estimated at 21 hours with a standard deviation of 4 hours
The values of the cell partitioning ratio and its standard deviation are found to be 0.733 and 0.14
Again a strong asymmetry in the parent to daughter portioning values is apparent
Fluorescence Intensity (a.u.)
0
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30
40
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50
100 101 102 103 10
Summary of Results Outlined the use of a genetic algorithm coupled with a stochastic cell-
cycle model, which when compared with experimental flow cytometry data enables tracking of quantum dot fluorophores within large cell populations over multiple generations
The cell-cycle model complements the experimental investigations in that it mimics the cell division behavior of individual cells within large populations
By utilizing a genetic algorithm in conjunction with the cell-cycle model we have been able to achieve excellent fits of the theoretically predicted quantum dot distributions with that measured experimentally
Using the genetic algorithm we obtain an inter-mitotic time of 22.5 hours with a standard deviation of 4 hours for the four parameter version
We also obtain an asymmetric cell partition ratio of 73:27% with a standard deviation of 14%
These results are in excellent agreement with single cell microscopic studies
Importance of these Results
The ability of this computer model to fit to experimental flow cytometry data provides a unique and novel analysis that allows tracking of cell population growth and lineage whilst maintaining information at the single cell level
It is also extremely powerful in that it provides the biologist with a detailed analysis of cell growth dynamics without the requirement of prior knowledge of the cell growth parameters
These results demonstrate that flow cytometry measurements, of quantum dot intensity, in conjunction with our model can give the single cell information required to assess anti-cancer therapeutics