The cell populations were manually examined based on their CD14 and CD11b intensities to identify DN, SP and DP cell populations and the frequency count and a mean intensity value for each channel were calculated.

Estimation of the relative contribution of each cell population in the total cytokine production

The coefficients were then used to infer the amount of cytokine by DN, SP and DP cell populations .

Thus, cumulated TEM secretions from ivdTEM were measured experimentally and the secretions for TEM were mathematically inferred (ivdTEM correspond to double positive DP cell population , see Materials and Methods and S2 Fig.)

The bacterial li-popolysaccharide membrane receptor CD14 is a component of the innate immune system mainly expressed by monocytes and macrophages and commonly used as a marker of these cell populations .

CD11b+, CD 14+ cells are gated from live and single cell population and the expression of Tie-2 and VEGFR-l was assessed in this population either in peripheral blood (A) or dissociated tumors (B).

double positive DP cells) while in vitro differentiated cells encompassed three cell populations : DN: double negative (CD1 1b", CD14"), SP: single positive (CD1 1b", CD14+) and DP: double positive (CD11b+, CD14+).

In vitro differentiated TEM correspond to the DP cell population and display a phenotype and functions intermediate to blood and tumor patient TEM (Fig.

As a consequence of this cellular heterogeneity, knockdowns result in variable effects among cells and lead to weak average phenotypes on the cell population level.

Like for the simulation study, we derived the cell population effects for the NEM from Wilcoxon tests, comparing the knockdown experiment to the control.

Nested effects models, a method tailored to reconstruct signaling networks from high-dimensional readouts of gene silencing experiments, have so far been only applied on the cell population level.

Here, we have identified one confounding factor, namely heterogeneous signaling pathway activation within a cell population , and incorporated it directly into a novel probabilistic model for pathway reconstruction.

Only at this data resolution, the heterogeneity within a cell population can be accounted for and it becomes possible to investigate potentially confounding factors, such as, for example, pathway activity.

Otherwise, an ambiguous signal is obtained, when averaging over the cell population of a knockdown.

To assess the impact that pathway disruption has on the cell population level, we ran the simulations on a standard NEM using the log-likelihood model introduced in [23].

For these gene-level data sets we used p-values of a Wilcoxon test comparing the cell population of a knockdown to the control distribution.

In this theoretical study, we cannot address the question to what extent such a mechanism is responsible for the activation of T cell populations in vivo.

That means, the cell population recognizes relative rather than absolute increases in the stimulus strength (here, the amount of secreted cyto-kine molecules per time).

In silico T cell population

upon receiving an antigen stimulus, only about one quarter of a Th cell population releases IL-2 molecules [34—36].

For the simulations of the three-dimensional in silico T cell population (Figs 3—5), a problem specific software was developed in the Heidelberg Numerical Methods Group, based on the open source C++ library deal.II [41].

Although we use the specific parameters for IL-2 here, this model is of more general interest and applies to other situations with few signaling cells and many responder cells (e.g., IL-4 secreting Th cells in a B cell population [9]), or can be thought of as representing a cluster of several cytokine secreting cells in a population with a small density of cytokine secreting cells elsewhere.

To investigate the origins and consequences of spatially inhomogeneous dynamics of cyto-kine signaling, we performed extensive three-dimensional simulations of a T cell population (Fig 3A and 3B).

However, competition for the cytokine can cause heterogeneity in the response of a cell population and result in bulk IL-2 levels that are much lower than local concentration peaks and in agreement With concentration levels measured by ELISA (see Discussion).

Bursting transcription can cause individual cells to remain in synchrony transiently, offering an explanation of transcriptional cycling as observed in cell populations , both on promoter chromatin status and mRNA levels.

This provides an explanation for transient transcriptional cycles observed at the level of cell populations .

This finding is in good agreement with experimental observations that mRNA burst-size distributions for regulated genes across a cell population are often geometric [54, 55].

If no mRNA degradation occurred on the considered timescale the bursts led to stepwise accumulation of mRNA in individual cells (Fig 5D) as well as on the cell population level (Fig 5F).

The model does not, however, exclude a possibility of the transcription cycle times being less precise, due, for instance, to the presence of a very slow step in one or more of the cycle transitions, or a possibility of variable gene induction times due to heterogeneity of the initial promoter states in a cell population .

In some cases transcription dynamics at the cell population level proceeds in an oscillatory fashion [23—28] at frequencies between 30 and 60 min [24, 27], i.e.

Infra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology.

The Cancer Stem Cell (CSC) hypothesis, the idea that a small population of tumour cells have the capacity to seed and grow the tumour, and intra-tumour heterogeneity, the diversity of the cancer cell population Within the tumour of an individual patient, have long been considered the basis of potential prognostic indicators in oncology.

These results were derived mostly from cell-lines, which are characterised by relatively homogeneous cell populations , and were further validated in time-course differentiation experiments [16].

In addition to quantifying stemness of the signalling regime of a homogeneous cell population, signalling entropy, if computed over a heterogeneous cell population , should also quantify the intercellular diversity in pathway activation.

We derived a sufficient condition on the eXpression profiles of homogeneous cell populations for signalling entropy to be a measure of intra-sample heterogeneity on average.

Thus, given a homogeneous cell population , a high signalling entropy suggests that signalling within each cell is very promiscuous and that the cells may therefore have a plastic stem cell like phenotype.

Note that in our model we consider only one cell, instead of a heterogeneous cell population .

As shown in our aforementioned study, the impact of the cell cycle asynchrony on the average fi-catenin dynamics in cell populations is negligible [44].

Naturally, in a cell population , the released WNT molecules will most likely induce WNT/fi-catenin signaling in the neighboring cells as well (paracrine activation).

Human neural progenitor cells offer the promising perspective of using in-vitro grown neural cell populations for replacement therapies in the context of neurodegenerative diseases, such as Parkinson’s or Huntington’s disease.

Also, at later time points the cell population of ReNcell VM197 is already so heterogeneous due to differentiation, that potential signal activities may originate from multiple sources.

Through inheritance, we let the culture evolve over time, generating fully traceable cell populations for analysis [32].

Model-1 and Model-2 within the cell populations simulator.

The script can be used to simulate complete pedigrees of growing and dividing cells from single progenitor cells and contains routines to create basic plots for analysis of the resulting cell population .

In [27] , the model assumed that the dynamics of the immune cell population took the form dy/ dt = pr xy (Equation 3 in that paper).

The growth of the immune cell population is modelled using a logistic-growth curve:

To proceed with finding an analytical solution for the emergence probability, we proceed as in previous analyses [21, 24, 31], and note that since y is monotonically increasing, we can use the immune cell population size as a surrogate measure of time.