Everybody tells me something different. My ex-supervisor even was not counting and just doing it
How many 293T or Hela cells should I seed for transfection in 10cm dishes (I want to harvest after 48h)?
And something else just came to my mind:
I know that if you have a selective medium with antibiotics (puromycin, g418 etc.), you shouldn't use the antibiotics in the medium when you are seeding cells for transfection. Is it the same for penicilin/str.?
thanks
-cellgene-
A good guide for the number of cells to plate can be found in the Fugene 6 protocol at
http://www.roche-applied-science.com/pack-...rt/1815091a.pdf
This recommends between 1 x 10^5 to 3 x 10^5 cells for one well of a 6-well dish to transfect the following day - so scaled up you'd be aiming for a ballpark figure of 1 x 10^6 cells for a 10cm plate. This works fine for most cell types (including HeLas), however 293Ts are very small so if you want them ready to transfect the following day I would aim to double or even triple this i.e. 2-3 x 10^6 cells for a 10cm plate. Depending on how long you transfect for they may end up very confluent by the time you do the experiment, but transfecting at lower density doesn't work as well. In my experience the confluency won't affect most experiments too much anyway.
Hopefully this helps!
cellgene on Apr 8 2010, 09:48 AM said:
Everybody tells me something different. My ex-supervisor even was not counting and just doing itHow many 293T or Hela cells should I seed for transfection in 10cm dishes (I want to harvest after 48h)?And something else just came to my mind:I know that if you have a selective medium with antibiotics (puromycin, g418 etc.), you shouldn't use the antibiotics in the medium when you are seeding cells for transfection. Is it the same for penicilin/str.?thanks
-killmusic1979-
Oh, also yes it's the same for Pen/Strep. The transfection reagents allow the antibiotics into the cell meaning that you'll get toxicity. However, this is dependent on the transfection reagent, I have done it with Fugene and not had a problem, but it is a problem with some others such as Lipofectamine 2000.
cellgene on Apr 8 2010, 09:48 AM said:
Everybody tells me something different. My ex-supervisor even was not counting and just doing itHow many 293T or Hela cells should I seed for transfection in 10cm dishes (I want to harvest after 48h)?And something else just came to my mind:I know that if you have a selective medium with antibiotics (puromycin, g418 etc.), you shouldn't use the antibiotics in the medium when you are seeding cells for transfection. Is it the same for penicilin/str.?thanks
-killmusic1979-
One quick point to mention, when seeding cells, it would depend on the transfection agent you are using. Some prefer a higher confluency, while others are slightly different.
Check the manufacturer's recommendation for hints on it.
-labrat612-
Thank you both.
We are doing transfections mostly with polyethylenimine and I think there is no user's manual for it
-cellgene-
cellgene on Apr 8 2010, 12:07 PM said:
Thank you both.We are doing transfections mostly with polyethylenimine and I think there is no user's manual for it
-labrat612-
I've used PEI for a few of my transient transfections as well. To start my optimization experiments, I based it off the manual for Fugene HD. The cell densities that they recommend for their product did work out well for my cell line. You could try it for yours as well.Hope that helps!
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Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, it is important to ensure that each colony originates from a single founding cell and to avoid subsequent merging of colonies during their growth. Clonal homogeneity can be obtained with low seeding densities; however, this leads to low yield and viability. It is therefore important to quantitatively assess how seeding density affects clonality loss so that experimental protocols can be optimised to meet the required standards. Here we develop a quantitative framework for modelling the growth of hESC colonies from a given seeding density based on stochastic exponential growth. This allows us to identify the timescales for colony merges and over which colony size no longer predicts the number of founding cells. We demonstrate the success of our model by applying it to our own experiments of hESC colony growth; while this is based on a particular experimental set-up, the model can be applied more generally to other cell lines and experimental conditions to predict these important timescales.
Here we focus on quantifying the possibilities of re-aggregation due to the physical proximity of colonies at different seeding densities and consider the optimal seeding densities to form clonal structures. From experimental observations of colonies after a fixed evolution time, we infer a stochastic model for colony growth, a method previously applied to other cell types, including bacterial and cancerous cells 19 , 20 . To correctly initialize the model for a given seeding density, we take into account the proportion of seeded cells which begin as isolated cells, pairs, triples, and so on. We find that this is essential to capture the experimental observations. We use the model to simulate hESC colony growth at different seeding densities, consider the area coverage with time and calculate the critical time at which the homogeneous colonies begin to merge. These results can help inform cell seeding decisions to form homogeneous colonies from single founder cells.
The quantification of stem cell behaviour assists in the development of mathematical models which can then be used as non-invasive predictive tools. The development of colonies is inherently stochastic containing many properties which occur with some randomness, such as cell growth, cell division and cell death. An overview of the modelling of cell processes is provided in 16 . For mammalian cells, colony formation from a single cell has been previously described using a stochastic Galton-Watson branching process model 17 . Cell proliferation or colony models can either be set on or off a regular or irregular lattice, and each cell can be modelled individually in an agent based model or using continuum models. A thorough summary of these different model types, along with their advantages and disadvantages with a view to tissue mechanics is provided in 18 .
Single hESCs are reported to have no effect on each other’s movement if they are greater than 150 μm apart 10 . It is therefore recommended to keep a minimum distance of 150 μm between colony boundaries throughout growth to assure the resulting clonal structures are from single founding hESCs. In our previous work we considered the kinematics of single and pairs of cells, and identified the occasional super-diffusive movements of cells which could lead to re-aggregation 14 , 15 .
An important measure of the self-renewal potential of stem cells is the clonality, the condition of being genetically identical. Generating homogeneous populations of clonal cells is of great importance 6 , 7 as clonally derived stem cell lines maintain pluripotency and proliferative potential for prolonged periods 8 . Some applications require clonal homogeneous populations, e.g. drug discovery 9 and iPSCs for personalised medicine. The selection of the best clones for further experimentation needs to be optimised to make clinical applications safe. If the seeding density is high, the migration of cells and the growth of closely-separated cell groups can cause aggregation of colonies; this is undesirable when a homogeneous clonal population with identical genetic composition is required. The seeding density of cells has been shown to not only have an effect on the clonality of stem cells 10 , but also on their differentiation potential 11 . Moreover, culturing at an overly high density can cause DNA damage and culture adaptation, leading to increasing occurrence of chromosomal aberrations 3 , 12 , 13 .
A typical in-vitro hESC experiment involves the distribution of cells upon a growth material (the ‘seeding’ of cells onto a plate). The seeding density is then the number of cells placed on the growth material per unit area. Cells need to attach to the plate surface, which is covered by Matrigel or similar, for viability and proliferation; however, some cells do not successfully attach and are lost. The hESCs then form colonies by repeated mitosis in which two genetically identical daughter cells are produced from the division of the mother cell. The proliferation of cells in this way results in colonies of tightly packed cells in mono-layers along the growth material. The doubling time of stem cells varies and can be affected by various environmental and chemical factors, including cell density 3 – 5 .
Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications, including the advancement of cellular therapies, disease modelling and drug development, due to their self renewal potential and the ability to differentiate into specialised cells (pluripotency) 1 , 2 . However, continued efforts in understanding the complex behaviour of hESCs and iPSCs are necessary to make their clinical applications a reality.
The notation used throughout the manuscript is outlined here for convenience. The number of cells in a colony at a given time, t, is N(t), with t always in hours. Therefore the number of cells at 72 h is N(72). The initial number of cells at time t = 0 is N(0) ≡ N0. The seeding density is n0 and the density of attached cells after 24 h is η0 = 0.35n0, given in cells/cm2. The growth rate of a colony is γ (given in h−1), the division rate is 1/γ (given in h) and the population doubling time is td (given in h). The time at which the number of founder cells is indistinguishable based on colony size is t* (given in h). The average time at which colonies merge due to physical proximity is τ (given in h).
hESCs were seeded at low density and grown into colonies. Two types of experiment were carried out: Experiment 1 collected data on colony numbers 72 hours post-attachment and Experiment 2 collected data on the rates of cell attachment and the time to the first colonies merging. Further details are given in the Methods section.
From Experiment 1, the number of cells in each of the 48 colonies at 72 hours after cell attachment, N(72), was recorded and is shown for each colony arranged in ascending order in Fig. . The corresponding histogram of N(72) is shown in Fig. . The distribution is bimodal, confirmed by the kernel density estimation, with an outlier colony at N(72) = 77 cells. We remove this outlier colony for further analysis as it was most likely formed by several colony merges. We expect the number of cells at time t to evolve roughly as N(t) = N02t/td, where N0 ≡ N(0) is the initial number of cells and td is the time it takes for the population to double, or equivalently N(t) = N0eγt, where γ is the growth rate. The bimodal nature of N(72) implies that we have two distinct groups of colonies, lead by differences in N0 and/or differences in the growth rates between colonies. For a typical duration of the cell cycle, 16–18 hours, one expects 20 cells at 72 hours, corresponding to the first histogram peak. The second peak, at about 40 cells per colony, suggests that some groups of cells have merged to form larger colonies during the 72 hour period, or that the initial condition of the colony growth was in fact N0 = 2. K-means clustering, a standard algorithm which partitions observations into clusters based upon minimising within cluster variance, splits N(72) into the two ranges, 7 ≤ N(72) ≤ 29 and 34 ≤ N(72) ≤ 77.
Open in a separate windowTo ascertain the initial conditions that underlie the colony growth, we turn to Experiment 2, examining the cells after 24 hours; a typical image is shown in Fig. . There are several characteristic features of the cell distribution revealed in this experiment. Firstly, the random initial positioning of the seeded cells means that some cells are initially isolated with no cells within the interaction distance (estimated as 150 μm). Other cells lie within the interaction distance of each other, forming groups of varying sizes. In Fig. we colour-code the cells according to whether they are isolated, or are effectively in a pair or in a triple, to illustrate how N0 can vary at low seeding densities. We will return to this feature later.
Secondly, only a fraction of the originally seeded cells are attached to the plate at this time. Cells need to be attached to the plate for viability and proliferation - cells which do not attach are lost. We find for a range of seeding densities, 1000 ≤ n0 ≤ 7000 cells/cm2, that on average 35.19% ± 4.23[0.99] (the mean ± one standard deviation [standard error]) of initially seeded cells were attached 24 hours after plating. Figure shows the proportion of attached cells at different seeding densities. This rate of attachment is usual for stem cells in similar experiments. In the following modelling section of this paper we discuss N(t), the number of cells present in a colony over time, independent of original cell seeding densities. In the cell seeding section we discuss the effects of the cell plating densities, n0, where we assume that the actual density of cells present is η0 = 0.35n0 to account for the loss at the attachment stage. Note this relationship is easily adjustable for different attachment rates.
Throughout this paper we define time t = 0 to be the time that seeded cells have attached to the plate and their proliferation starts. Before this time some cells are lost (as reported above and shown in Fig. ) and there is a delay in the growth from the lag-phase experienced by newly plated in vitro hESCs as they adjust to the environment21. This is consistent with the experimental data which considers 72 h after cell attachment.
The simplest deterministic model for the number of cells in a colony at time t, N(t), assumes a constant cell division time 1/γ and simultaneous division of all the cells, leading to
dN(t)dt=γN,
which has the solution,
N(t)=N0eγt,
where N0 ≡ N(0) is the initial number of cells at t = 0. However, the cell cycle duration is variable due to various factors, such as inhomogeneities in the nutrient distribution within the growth medium and the inherent variation in the cell cycle duration between different clones. Such effects can be allowed for by considering a Gaussian random growth rate γ, with a mean value μ and standard deviation σ:
N(t)=N0eγt,γ~Norm(μ,σ2).
1
Different colonies thus grow at different rates sampled from the Gaussian probability distribution. The number of cells then follows a lognormal distribution, N(t)~LogNorm(μ0,σ02), where μ0 = tμ + log(N0) and σ02 = t2σ2. A short mathematical explanation can be found in the Supplementary Information. However, this model fails to explain the bimodal distribution of the colony sizes observed at t = 72 h. This is presented in the Supplementary Information, Fig. S1.
We suggest that a bimodal distribution of the colony sizes can be a consequence of a difference in the cell proliferation rates in cell groups of different sizes that may arise from their interactions. It can be expected that colonies starting from larger groups grow faster, not only due to the initial conditions but the preference of cells to growth in close proximity to neighbours22,23. To capture the bimodal nature of the colony size distribution, we consider two populations, A and B, each with a different initial condition,
N0={1,groupA,2,groupB,
where the probabilities for a colony to belong to groups A and B are α and β, respectively. Each population then follows Eq. (1) with its corresponding initial condition,
(NA(t)=eγAt,γA~Norm(μA,σA2),withprobabilityα,NB(t)=2eγBt,γB~Norm(μB,σB2),withprobabilityβ,
2
and each of NA and NB has a lognormal probability distribution. Thus we consider separately colonies that originate from a single cell and those from cell pairs. We consider the possible role of cell triples and larger progenitor groups in the Discussion.
A lognormal mixture fit to the data, shown in Fig. , gives NA(72)~LogNorm(2.84,0.412) and NB(72)~LogNorm(3.76,0.132), with the mixture probabilities α = 0.77 and β = 0.23. Therefore, we have μA = 0.0394 h−1, σA = 0.0057 h−1, μB = 0.0426 h−1, and σB = 0.0018 h−1. A comparison between these growth parameter distributions is shown in Fig. . These values correspond to a doubling time of 17.5 h for the single founder cell population (group A) and 16.3 h for the pairs of cells population (group B). Note that here we have assumed the growth rates for the two populations are different. Our fitting suggests colonies starting with two cells grow faster than their single cell counterparts, consistent with the idea that cells growing in larger colonies proliferate more effectively than isolated cells22,23. The fitting under the assumption of identical growth rates, γA = γB, is presented in the Supplementary Information, Fig. S2. The fitting does not capture the experimental data and so we conclude it is appropriate to continue assuming different growth rates for the two populations.
Open in a separate windowWe have demonstrated how accurately the two-population model captures the experimental data at 72 hours. Now we proceed to develop it into a prognostic model for the colony size at later times. The evolution of the colony size, N(t), according to this two-population model is shown in Fig. . Because of the random scatter in the colony growth parameters the admissible range of colony size N(t) increases as t, and sooner or later, the size of the two colony types overlap. At early times, the sizes of the two colony types are distinct, where those beginning from two cells are larger than those from one founder cell, but as time progresses the stochasticity in the growth rates causes an overlap in the two populations. This overlap becomes more prominent for larger numbers of simulations as this increases the incidence of extreme growth rate values. Histograms of N(20) and N(72) in Fig. illustrate how at early times the two colony types are distinguishable but over time the distributions spread and merge to make single-clone colonies indistinguishable from heterogeneous colonies. The time at which NA first becomes equal to NB, t*, is the critical time after which it is not possible to distinguish which colonies originated from a single progenitor based on the colony size. This time is shown for increasing numbers of colonies in Fig. . As we increase the number of colonies, Ncols, we see more of the extreme values occurring with low probability, causing the blurring of the two populations to begin at an earlier time. The relationship is a power law, with the best fit t* = aNcols−b with a = 77.9 ± 4.7 h and b = 0.12 ± 0.01 with an R2 coefficient of 0.98. This allows us to estimate, for a given plating cell density, the time up to which colonies originating from a single founder cell are identifiable based on the current number of cells in the colony.
Open in a separate windowOpen in a separate windowTypical low seeding densities for hESCs, intended to grow colonies from single founder cells, commonly range from 500 to 3000 cells/cm2. Across a range of seeding densities, we find that the average proportion of cells attached to the substrate after 24 hours is 35 ± 4%, where the range represents one standard deviation within the sample, and the accuracy of the mean value (the standard error) is ±1.0%, presented in Fig. . For example, an initial seeding density of n0 = 1500 cells/cm2 results in around 500 cells continuing past day one of the experiment. Throughout this section we will present the initial seeding densities n0 and work on the assumption that 35% of these cells are successfully attached and survive, η0 = 0.35n0.
In this section we find the initial conditions corresponding to cell plating at different cell seeding densities and use this to inform the model for colony growth. The seeding of cells randomly across a growth area, A, can be simulated as a homogeneous Poisson point process in which the number of point counts is sampled from a Poisson distribution with the mean λA. The points are then independently and uniformly scattered across the region. For example, if we consider an initial seeding density of n0 = 1500 cells/cm2, we can simulate the seeding of η0 = 0.35n0 cells in a 1 cm2 area by a homogeneous point Poisson process in which the η0 point counts are sampled from Po(λA) where A = 1 cm2, and then locating cells according to the uniform distributions in x and y.
Once cells have been scattered, we can then consider the distances between cells and their nearest neighbours with the aim to estimate the fraction of isolated cells, their pairs (defined as two cells separated by less than 150 μm) and triples etc. The probability density function of the distance, r, to the kth nearest neighbour is known from the theory of Poisson point processes as dk(r)=2(λπr2)ke−lpr2/r(k−1)24. This reduces to d1(r)=2πλr2e−lpr2 for the first nearest neighbour. The theoretical distributions along with histograms from simulated data for d1(r) are shown in Fig. for initial seeding densities of n0 = 500, 1500 and 5000 cells/cm2 corresponding to λ = η0 = 0.35n0. These distributions allow us to calculate the proportion of seeded cells with the nearest neighbour at a given distance. The nearest neighbour cumulative distribution function for the proportion of cells with a nearest neighbour at a distance < r for a 2D homogeneous Poisson process is given by D1(r)=1−e−lpr224. This theoretical proportion of cells with a first nearest neighbour less than r away, D1, for initial seeding densities n0 = 500, 1500 and 5000 cells/cm2 is shown in Fig. along with data from a simulation at each seeding density. For the initial seeding density of n0 = 1500 cells/cm2 the nearest neighbour distance between cells will be less than 150 μm in around 30% of cases, similar to the experimental estimate of 23%. We have neglected the movement of cells as, based on observed migration speeds of approximately 16 μm/h14, the time required to traverse the critical interaction distance of 150 μm is around 9 h, a large portion of the cell cycle time.
Open in a separate windowTo consider the groupings of seeded cells we use a density based clustering algorithm. Cells less than 150 μm apart are considered as being part of the same cluster, and any neighbouring cell less than 150 μm away from any other cell in the cluster is also considered to be part of the cluster. This allows for clusters of elongated shapes. Note that this definition of a cluster of cells is non-trivial and has implications for the interactions of cells, but from the experimental images we see examples of clusters in elongated shapes as well as the more common regular shapes. Examples of different cluster formations for triples are shown in Fig. . The proportion of cells in a cluster of a size n at different seeding densities is shown in Fig. . At low initial seeding densities, e.g. n0 = 500 cells/cm2 the majority of cells have no close neighbours. As the seeding density increases, the proportion of pairs of cells increases. The proportion of each cluster size first rises with n0 before reaching a maximum and then tends to zero as more possible cluster sizes become available. The distributions shown in Fig. provide the initial conditions corresponding to cell seeding at different densities.
Now the initial conditions of cell seeding are known, the growth of colonies from these cells can be considered. Cells are seeded at density n0 according to a Poisson point process as described above, and then the division of the cells and growth into colonies can be described by the two-population model. The area coverage of the plate can be estimated from the number of cells we expect to be present. The average area of a cell, Acell, is approximately 250 μm2 [S. Orozco-Fuentes, private communication], corresponding to a cell diameter of 18 μm. The percentage of area covered by cells evolves as shown in Fig. . Taking this value, the proportion of area coverage is the area covered by the cells, N(t)Acell, divided by the growth area of the plate and Aplate = Nseeded/n0. We therefore expect the percentage area coverage over time to tend to an exponential relationship due to the growth of N(t), scaled by a factor equal to n0Acell/Nseeded, as we see in Fig. . The time taken for the growth area to be 100% covered, t100%, for varying initial seeding densities, is shown in Fig. .
Open in a separate windowSimulating the initial conditions as described above and the colony growth allows us to estimate the crucial time at which the colonies begin to merge. The cells are seeded at density n0, with η0 cells attached, and are then sorted into clusters based on their spatial distances away from each other. Each cluster grows according to the two-population model, estimated as a circle with centre at the geometric centre of the cluster and radius based on the number of cells present, N(t). The growth rate for triples and larger clusters of cells is assumed to be the same as that for pairs of cells. The time at which any colony begins to merge with its neighbour is critical as the time that clonality is lost, τ, illustrated in Fig. . The time the first colony merge occurs at varying seeding densities is shown in Fig. , with least squares fitting τ = (−0.007 ± 0.0001)n0 + (102 ± 3) with R2 = 0.99, τ in hours and n0 in cells/cm2. We are therefore able to estimate the time taken for the first colony merge to occur from the equation
τ1h≈100−n0140cm−2,
3
where n0 is the initial seeding density of cells before attachment in cells/cm2 and τ is produced in hours. Experimental values were extracted for τ from Experiment 2 and the model captures these values within errors for the seeding densities 3000, 4000 and 7000 cells/cm2. These results are summarised in Table for convenience. The results are also shown for extrapolated growth rates in Fig. , under the assumption growth rates continue to increase with cluster size. The least squares fitting is τ = (−0.01 ± 0.001)n0 + (97 ± 4) with R2 = 0.99, τ in hours and n0 in cells/cm2. The increasing growth rates cause an earlier first merger time, particularly at the higher seeding densities where larger clusters are more likely.
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