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Fig. 2 | Genome Medicine

Fig. 2

From: Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

Fig. 2

Schematic of the application of the surprisal analysis algorithmA Preparation of fluorescently-tagged single-cell suspensions from different sample sources (control and post-RT) for multicolor FACS analysis. Each cell was labeled with a mixture of 11 fluorescently tagged antibodies. B Surprisal analysis reveals protein expression level distributions at the reference (steady) state and the deviations thereof due to constraints in the system (e.g., irradiation). An example for calculated distribution of the expression levels at the reference state and deviations thereof is presented for Her2, initially quantified by FACS and analyzed by SA, in 4T1 mice model of TNBC. C Proteins deviating from the steady state in a coordinated manner are grouped into altered subnetworks referred to as “unbalanced processes.” For example, in one 4T1 cell, the levels of Her2 and EGFR deviate significantly (upregulated) from the steady state and in the other cells, cMet levels deviate significantly (upregulated as well). Thus, the two cells are defined by the analysis as possessing different processes. D The unbalanced processes in each cell provide a cell-specific signaling signature (CSSS). Each CSSS is schematically transformed into a cell-specific barcode, indicating active and inactive processes. E Cells sharing the same barcode are organized into distinct subpopulations. F Tumor-specific targeted therapy combinations are tailored against the subpopulations expanding in response to RT

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