Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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.


  1. Gallin, J.I. Human neutrophil heterogeneity exists, but is it meaningful? Blood 63, 977–983 (1984).

    CAS  PubMed  Google Scholar 

  2. Loo, L.H. et al. Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3–L1 preadipocytes. J. Cell Biol. (in the press).

  3. Rubin, H. The significance of biological heterogeneity. Cancer Metastasis Rev. 9, 1–20 (1990).

    Article  CAS  Google Scholar 

  4. Chang, H.H., Hemberg, M., Barahona, M., Ingber, D.E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008).

    Article  CAS  Google Scholar 

  5. Slack, M.D., Martinez, E.D., Wu, L.F. & Altschuler, S.J. Characterizing heterogeneous cellular responses to perturbations. Proc. Natl. Acad. Sci. USA 105, 19306–19311 (2008).

    Article  CAS  Google Scholar 

  6. Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).

    Article  CAS  Google Scholar 

  7. Malik, Z., Dishi, M. & Garini, Y. Fourier transform multipixel spectroscopy and spectral imaging of protoporphyrin in single melanoma cells. Photochem. Photobiol. 63, 608–614 (1996).

    Article  CAS  Google Scholar 

  8. Resch-Genger, U., Grabolle, M., Cavaliere-Jaricot, S., Nitschke, R. & Nann, T. Quantum dots versus organic dyes as fluorescent labels. Nat. Methods 5, 763–775 (2008).

    Article  CAS  Google Scholar 

  9. Tsurui, H. et al. Seven-color fluorescence imaging of tissue samples based on Fourier spectroscopy and singular value decomposition. J. Histochem. Cytochem. 48, 653–662 (2000).

    Article  CAS  Google Scholar 

  10. Boland, M.V. & Murphy, R.F. After sequencing: quantitative analysis of protein localization. IEEE Eng. Med. Biol. Mag. 18, 115–119 (1999).

    Article  CAS  Google Scholar 

  11. Loo, L.H., Wu, L.F. & Altschuler, S.J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445–453 (2007).

    CAS  PubMed  Google Scholar 

  12. Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    Article  CAS  Google Scholar 

  13. Rosen, E.D. & MacDougald, O.A. Adipocyte differentiation from the inside out. Nat. Rev. Mol. Cell Biol. 7, 885–896 (2006).

    Article  CAS  Google Scholar 

  14. Weiner, O.D. Regulation of cell polarity during eukaryotic chemotaxis: the chemotactic compass. Curr. Opin. Cell Biol. 14, 196–202 (2002).

    Article  CAS  Google Scholar 

  15. Yin, Z. et al. Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens. BMC Bioinformatics 9, 264 (2008).

    Article  Google Scholar 

  16. Cristianini, N. & Shawe-Taylor, J. An introduction to support vector machines: and other kernel-based learning methods. (Cambridge University Press, New York, 2000).

  17. Pudil, P., Novovicová, J. & Kittler, J. Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994).

    Article  Google Scholar 

  18. Brasaemle, D.L. Thematic review series: adipocyte biology. The perilipin family of structural lipid droplet proteins: stabilization of lipid droplets and control of lipolysis. J. Lipid Res. 48, 2547–2559 (2007).

    Article  CAS  Google Scholar 

  19. Weiner, O.D., Marganski, W.A., Wu, L.F., Altschuler, S.J. & Kirschner, M.W. An actin-based wave generator organizes cell motility. PLoS Biol. 5, e221 (2007).

    Article  Google Scholar 

  20. Eden, S., Rohatgi, R., Podtelejnikov, A.V., Mann, M. & Kirschner, M.W. Mechanism of regulation of WAVE1-induced actin nucleation by Rac1 and Nck. Nature 418, 790–793 (2002).

    Article  CAS  Google Scholar 

  21. Gratzner, H.G. Monoclonal antibody to 5-bromo- and 5-iododeoxyuridine: a new reagent for detection of DNA replication. Science 218, 474–475 (1982).

    Article  CAS  Google Scholar 

  22. Goto, H. et al. Identification of a novel phosphorylation site on histone H3 coupled with mitotic chromosome condensation. J. Biol. Chem. 274, 25543–25549 (1999).

    Article  CAS  Google Scholar 

  23. Darzynkiewicz, Z., Bedner, E. & Smolewski, P. Flow cytometry in analysis of cell cycle and apoptosis. Semin. Hematol. 38, 179–193 (2001).

    Article  CAS  Google Scholar 

  24. Dolbeare, F., Gratzner, H., Pallavicini, M.G. & Gray, J.W. Flow cytometric measurement of total DNA content and incorporated bromodeoxyuridine. Proc. Natl. Acad. Sci. USA 80, 5573–5577 (1983).

    Article  CAS  Google Scholar 

  25. Mittnacht, S. Control of pRB phosphorylation. Curr. Opin. Genet. Dev. 8, 21–27 (1998).

    Article  CAS  Google Scholar 

  26. Pines, J. & Hunter, T. Human cyclins A and B1 are differentially located in the cell and undergo cell cycle-dependent nuclear transport. J. Cell Biol. 115, 1–17 (1991).

    Article  CAS  Google Scholar 

  27. Dulic, V., Stein, G.H., Far, D.F. & Reed, S.I. Nuclear accumulation of p21Cip1 at the onset of mitosis: a role at the G2/M-phase transition. Mol. Cell. Biol. 18, 546–557 (1998).

    Article  CAS  Google Scholar 

  28. Whitfield, M.L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).

    Article  CAS  Google Scholar 

  29. Engelman, J.A. et al. Constitutively active mitogen-activated protein kinase kinase 6 (MKK6) or salicylate induces spontaneous 3T3–L1 adipogenesis. J. Biol. Chem. 274, 35630–35638 (1999).

    Article  CAS  Google Scholar 

  30. Sadowski, H.B., Wheeler, T.T. & Young, D.A. Gene expression during 3T3–L1 adipocyte differentiation. Characterization of initial responses to the inducing agents and changes during commitment to differentiation. J. Biol. Chem. 267, 4722–4731 (1992).

    CAS  PubMed  Google Scholar 

Download references


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

Authors and Affiliations



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)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing