Dataset Information

Uncovering distinct protein-network topologies in heterogeneous cell populations.

ABSTRACT:

Background

Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation.

Results

Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins.

Conclusions

Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.

SUBMITTER: Wieczorek J

PROVIDER: S-EPMC4480582 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The majority of oligodendrocytes in the neocortex originate from neural progenitors that reside in the dorsal forebrain. We recently showed that Sonic Hedgehog (Shh) signaling in these dorsal progenitors is required to produce normal numbers of neocortical oligodendrocytes during embryonic development. Conditional deletion of the Shh signaling effector, Smo, in dorsal progenitors caused a dramatic reduction in oligodendrocyte numbers in the embryonic neocortex. In the current study, we show that the depleted oligodendrocyte lineage in Smo conditional mutants is able to recover to control numbers over time. This eventual recovery is achieved in part by expansion of the ventrally-derived wild-type lineage that normally makes up a minority of the total oligodendrocyte population. However, we find that the remaining dorsally-derived mutant cells also increase in numbers over time to contribute equally to the recovery of the total population. Additionally, we found that the ways in which the dorsal and ventral sources cooperate to achieve recovery is different for distinct populations of oligodendrocyte-lineage cells. Oligodendrocyte precursor cells (OPCs) in the neocortical white matter recover completely by expansion of the remaining dorsally-derived Smo mutant cells. On the other hand, mature oligodendrocytes in the white and gray matter recover through an equal contribution from dorsal mutant and ventral wild-type lineages. Interestingly, the only population that did not make a full recovery was OPCs in the gray matter. We find that gray matter OPCs are less proliferative in Smo cKO mutants compared to controls, which may explain their inability to fully recover. Our data indicate that certain populations of the dorsal oligodendrocyte lineage are more affected by loss of Shh signaling than others. Furthermore, these studies shed new light on the complex relationship between dorsal and ventral sources of oligodendrocytes in the developing neocortex.