TY - JOUR
T1 - On comparing heterogeneity across biomarkers
AU - Steininger, Robert J.
AU - Rajaram, Satwik
AU - Girard, Luc
AU - Minna, John D.
AU - Wu, Lani F.
AU - Altschuler, Steven J.
N1 - Publisher Copyright:
© 2014 International Society for Advancement of Cytometry.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.
AB - Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.
KW - Bioimage informatics
KW - Biological networks
KW - Biomarker selection; systems biology
KW - Heterogeneity
KW - Information theory
KW - Microscopy
KW - Single-cell variability
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U2 - 10.1002/cyto.a.22599
DO - 10.1002/cyto.a.22599
M3 - Article
C2 - 25425168
AN - SCOPUS:84929887927
VL - 87
SP - 558
EP - 567
JO - Cytometry. Part A : the journal of the International Society for Analytical Cytology
JF - Cytometry. Part A : the journal of the International Society for Analytical Cytology
SN - 1552-4922
IS - 6
ER -