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An approach for extensibly profiling the molecular states of cellular subpopulations


Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.

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Figure 1: Schematic of the three major steps in building extensible virtual phenotypic profiles of cellular subpopulations.
Figure 2: High-content features partially compensated the decrease in classification performance owing to removing a marker.
Figure 3: Virtual phenotypic profiles had low noise levels and were significantly different from population averages.
Figure 4: High-content features from adiponectin gave similar clustering and heatmap of virtual phenotypic profiles as the initial features.
Figure 5: Virtual phenotypic profiling of polarizing and of dividing cells.
Figure 6: Effectiveness of subpopulation profiling depends on the degree of cell-to-cell variability.


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We thank all members of the Altschuler and Wu lab at the University of Texas Southwestern Medical Center for critical discussion and for performing manual cell categorization; P.E. Scherer (University of Texas Southwestern Medical Center) and O.D. Weiner (University of California, San Francisco) for the gifts of the adiponectin and Hem1 antibodies, respectively; J. Rhorer at BD Biosciences for the gift of the cell cycle kit; and S.A. Kliewer, D.J. Mangelsdorf, J. Repa P.E. Scherer and H.T. Yu for stimulating conversations. This work was funded by the US National Institutes of Health (R01 GM081549 to L.F.W. and R01 GM085442 to S.J.A.), the Welch Foundation (I-1619 and I-1644 to L.F.W. and S.J.A.), the Rita Allen Foundation (S.J.A.) and the University of Texas Southwestern Endowment for Scholars in Biomedical Research (to L.F.W. and to S.J.A.). S.J.A. is a Rita Allen Scholar.

Author information




L.-H.L. designed the profiling methods and performed the analysis. L.-H.L. and H.-J.L. performed the 3T3-L1 experiments. R.J.S. performed the H460 experiments. Y.W. performed the HL-60 experiments. L.H.L., L.F.W. and S.J.A. contributed to the conception of the overall approach, statistical analysis of the methods and writing of the manuscript.

Corresponding authors

Correspondence to Lani F Wu or Steven J Altschuler.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Methods (PDF 1299 kb)

Supplementary Data

High-content feature list (XLS 221 kb)

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Loo, LH., Lin, HJ., Steininger, R. et al. An approach for extensibly profiling the molecular states of cellular subpopulations. Nat Methods 6, 759–765 (2009).

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