M-DNB Factors Orchestrate Cell Fate Determination at Tipping Points during Mesendodermal Differentiation of Human Embryonic Stem Cells
The generation of ectoderm, mesoderm, and endoderm layers is the most critical biological process during the gastrulation of embryo development. Such a differentiation process in human embryonic stem cells (hESCs) is an inherently nonlinear multi-stage dynamical process which contain multiple tipping points playing crucial roles in the cell-fate decision. However, the tipping points of the process are largely unknown, letting alone the understanding of the molecular regulation on these critical events. Here by designing a module-based dynamic network biomarker (M-DNB) model, we quantitatively pinpointed two tipping points of the differentiation of hESCs towards definitive endoderm, which leads to the identification of M-DNB factors (FOS, HSF1, MYCN, TP53 and MYC) of this process.
The stage-specific and essential roles of M-DNB factors in the cell-fate decision were confirmed by the differentiation experiments. We demonstrate that before the tipping points, M-DNB factors are able to maintain the cell states and orchestrate cell fate determination during hESC (ES)-to-ME and ME-to-DE differentiation processes, which not only leads to better understanding of endodermal specification of hESCs but also reveals the power of the M-DNB model to identify critical transition points with their key factors in diverse biological processes including cell differentiation and transdifferentiation dynamics.
M-DNB model is designed based on matlab.
First, we should obtain the network from PPI network from STRING (https://cn.string-db.org/).
CI = Get_CI(data,time_Idx,feature,network);
Input:
data is time-series normailized scRNA-seq dataset(tpm,fpkm, or rpkm);
genes*cells
e.g. Chu-time dataset(download from https://www.ncbi.nlm.nih.gov/geo/download/acc=GSE75748&format=file&file=GSE75748%5Fsc%5Ftime%5Fcourse%5Fec%2Ecsv%2Egz, or https://github.com/LinLi-0909/M-DNB-model/blob/main/GSE75748_sc_time_course_ec.csv.gz).
time_Idx is time points of samples;
e.g Chu-time dataset contains 6 time points.The mat file can be downloaded from https://github.com/LinLi-0909/M-DNB-model/blob/main/timeIdx.mat. User can input time_Idx as below:
feature is gene list in dataset.
e.g.row name of Chu-time dataset.
network is PPI network obtained from STRING.
User can obtain network using the following code (matlab) and PPI network from STRING:
ex=importdata('9606.protein.links.v10.txt');
links=ex.textdata(2:end,1:2);
k=1;
network{1,1}=links{1,1};
network{1,2}{1,1}=links{1,2};
j=1;
for i=1:size(links,1)-1
if strcmpi(links{i,1},links{i+1,1})
network{k,2}{j+1,1}=links{i+1,2};
j=j+1;
else
j=1;
k=k+1;
network{k,1}=links{i+1,1};
network{k,2}{1,1}=links{i+1,2};
end
end
network(:,3)=importdata('towhole.csv');
Output:
CI: CI contains 4 columns for each gene module at each time point,which are CI value, sd, PCC_in and PCC_out,respectively.
And The CI of Chu-time dataset can be downloaded at https://github.com/LinLi-0909/M-DNB-model/blob/main/CI.mat
[topmCI,QI]=Get_Critical_Indicators(timeIdx,CI,m);
Input:
m is the top m-DNB genes with CI value in given critical point, e.g. m=50,100,150. users can also set the parameter as own request.
timeIdx and CI can be obtained from Step1 and Step2.
Output:
topmCI consists of genes with top m CI value at each time point.
topmCI can be downloaded at https://github.com/LinLi-0909/M-DNB-model/blob/main/top50CI.mat
QI is the mean of top m CI value.
We can find M-DNB fatcors (TFs of DNB genes) based on IPA. User can also investage the M-DNB factors (upstream regulator) based on publised datasets.
https://doi.org/10.1016/j.xinn.2022.100364
Please contact us:
Lin Li: [email protected]
©2022 Lin Li [Chen Lab]. All rights reserved.