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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Gallin, J.I. Human neutrophil heterogeneity exists, but is it meaningful? Blood 63, 977–983 (1984).
Loo, L.H. et al. Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3–L1 preadipocytes. J. Cell Biol. (in the press).
Rubin, H. The significance of biological heterogeneity. Cancer Metastasis Rev. 9, 1–20 (1990).
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).
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).
Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).
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).
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).
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).
Boland, M.V. & Murphy, R.F. After sequencing: quantitative analysis of protein localization. IEEE Eng. Med. Biol. Mag. 18, 115–119 (1999).
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).
Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).
Rosen, E.D. & MacDougald, O.A. Adipocyte differentiation from the inside out. Nat. Rev. Mol. Cell Biol. 7, 885–896 (2006).
Weiner, O.D. Regulation of cell polarity during eukaryotic chemotaxis: the chemotactic compass. Curr. Opin. Cell Biol. 14, 196–202 (2002).
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).
Cristianini, N. & Shawe-Taylor, J. An introduction to support vector machines: and other kernel-based learning methods. (Cambridge University Press, New York, 2000).
Pudil, P., Novovicová, J. & Kittler, J. Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994).
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).
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).
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).
Gratzner, H.G. Monoclonal antibody to 5-bromo- and 5-iododeoxyuridine: a new reagent for detection of DNA replication. Science 218, 474–475 (1982).
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).
Darzynkiewicz, Z., Bedner, E. & Smolewski, P. Flow cytometry in analysis of cell cycle and apoptosis. Semin. Hematol. 38, 179–193 (2001).
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).
Mittnacht, S. Control of pRB phosphorylation. Curr. Opin. Genet. Dev. 8, 21–27 (1998).
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).
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).
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).
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).
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).
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.
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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). https://doi.org/10.1038/nmeth.1375
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