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SMiLE-seq identifies binding motifs of single and dimeric transcription factors

Nature Methods volume 14, pages 316–322 (2017)Cite this article

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Abstract

Resolving the DNA-binding specificities of transcription factors (TFs) is of critical value for understanding gene regulation. Here, we present a novel, semiautomated protein–DNA interaction characterization technology, selective microfluidics-based ligand enrichment followed by sequencing (SMiLE-seq). SMiLE-seq is neither limited by DNA bait length nor biased toward strong affinity binders; it probes the DNA-binding properties of TFs over a wide affinity range in a fast and cost-effective fashion. We validated SMiLE-seq by analyzing 58 full-length human, mouse, and Drosophila TFs from distinct structural classes. All tested TFs yielded DNA-binding models with predictive power comparable to or greater than that of other in vitro assays. De novo motif discovery on all JUN–FOS heterodimers and several nuclear receptor-TF complexes provided novel insights into partner-specific heterodimer DNA-binding preferences. We also successfully analyzed the DNA-binding properties of uncharacterized human C2H2 zinc-finger proteins and validated several using ChIP-exo.

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Figure 1: SMiLE-seq pipeline.
Figure 2: TF motifs confirmed by SMiLE-seq.
Figure 3: SMiLE-seq DNA-binding models provide insights into the DNA-binding energy landscape of TFs.
Figure 4: SMiLE-seq-based derivation of TF heterodimer DNA-binding motifs.
Figure 5: Novel TF binding motifs identified by SMiLE-seq.

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Sequence Read Archive

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Gene Expression Omnibus

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Acknowledgements

We would like to thank S. Maerkl (EPFL) for his guidance in applying microfluidic technologies; R. Dreos (EPFL) for helpful discussions on data analysis; and our lab members P. Schwalie and V. Gardeux (EPFL) for providing feedback on the manuscript. We also thank K. Harshman and B. Mangeat for their assistance in sample sequencing, as well as the VITAL-IT for providing the infrastructure for our computational analyses. This work has been supported by funds from the Swiss National Science Foundation (grant nos. 31003A_162735 and CRSII3_147684), by SystemsX.ch Special Opportunity Project 2015/323, and by institutional support from the EPFL.

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Authors and Affiliations

  1. Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Alina Isakova, Romain Groux, Pernille Rainer, Daniel Alpern, Riccardo Dainese & Bart Deplancke

  2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Alina Isakova, Romain Groux, Daniel Alpern, Riccardo Dainese, Giovanna Ambrosini, Philipp Bucher & Bart Deplancke

  3. Swiss Institute for Experimental Cancer Research (ISREC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Romain Groux, Giovanna Ambrosini & Philipp Bucher

  4. Global Health Institute (GHI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Michael Imbeault & Didier Trono

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  1. Alina Isakova
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Contributions

A.I. and B.D. conceived and planned the study and prepared the manuscript. A.I. performed the SMiLE-seq experiments. A.I. and R.G. analyzed SMiLE-seq data. P.R., D.A., and R.D. performed validation experiments including ChIP-seq. M.I. and D.T. performed ChIP-exo. R.G., G.A., and P.B. developed and implemented new bioinformatics methods and performed web server setup. All the authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Bart Deplancke.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 SMiLE-seq set-up.

Top right. SMiLE-seq set-up. Each SMiLE-seq device consists of a PDMS chip (approximately 2 x 5 cm) bonded to a plasma-activated glass slide. The SMiLE-seq device is placed on the microscope table and is connected to the microcontroller-based control unit. The microscope camera, connected to an external display, enables chip observation during a SMiLE-seq experiment. Center. Schematic design of a SMiLE-seq microchip. Blue and green colors denote flow and control layers respectively. Each unit of the device is connected to the collector unit on one side and the capillary pump on the other1. All units of the device are connected together by the continuous flow channel with four inlets (F1-F4) and three outlets (F5-F7). Switching between these two access modes can be done through the use of control micro valves (C1-C11).

1. Zimmermann, M., Hunziker, P. & Delamarche, E. Valves for autonomous capillary systems. Microfluid. Nanofluidics 5, 395–402 (2008).

Supplementary Figure 2 SMiLE-seq capacity and reproducibility.

a and b. Motifs for mouse (a) and Drosophila (b) TFs. c-f. Scatter plots showing enrichment of top 2000 k-mers, from two independent SMiLE-seq experiments for PAX7 (c), SRY (d), MAX (e) and FLI1 (f) TFs. rp denotes for Pearson correlation coefficient.

Supplementary Figure 3 AUC profiles of TF binding predicted by SMiLE-seq models.

Each plot represents the AUC value computed for SMiLE-seq, HT-SELEX, JASPAR, UniPROBE (if available) and HOCOMOCO DNA binding models on intervals of 500 peaks obtained from ranked (from high-to-low) ENCODE ChIP-seq peak data.

Supplementary Figure 4 The predictive power of SMiLE-seq data.

a. The predictive power of SMiLE-seq motifs compared to the motifs that are retrievable from HT-SELEX data or computed from HT-SELEX data cycle 1 using the HMM-based analysis pipeline. For each motif, we computed area under the ROC curve (AUC) values on the 500 top peaks of the ENCODE ChIP-seq datasets for a given TF. The heat map represents the AUC values computed for SMiLE-seq, HT-SELEX and HT-SELEX cycle1 motifs on the respective ChIP-seq datasets that were selected based on the highest mean AUC values among all five models. b. Each box plot represents the AUC value computed for SMiLE-seq, HT-SELEX, JASPAR and HOCOMOCO DNA binding models on a 500bp peak interval obtained from ranked (from high-to-low) ENCODE ChIP-seq data. c-f. Egr1 binding affinity. (c) Correlation between the k-mer enrichment of all possible SNP variants of the GCGTGGGCG 9-mer data derived from either the SMiLE-seq experiment or different selection cycles of HT-SELEX (SRA ID: ERR185027 for cycle 2, ERR185028 for cycle 3 and ERR185029 for cycle 4) and corresponding binding affinities computed from Kd values2 of the Egr1 mouse TF. (d) Same, but the binding affinities of 9-mers computed from Kon/Koff values. (e-f). Correlation between normalized PBM (UniPROBE Accession Number: UP00007) 9-mer counts of all possible GCGTGGGCG SNP variants as well as the respective 9-mer SMiLE-seq counts and corresponding binding affinity values of Egr1 TF computed either from Kds (e) or Kon/Koff values (f). rp and rs denote Pearson and Spearman correlation coefficients respectively.

2. Geertz, M., Shore, D. & Maerkl, S. J. Massively parallel measurements of molecular interaction kinetics on a microfluidic platform. Proc. Natl. Acad. Sci. U. S. A. 109, 16540–16545 (2012).

Supplementary Figure 5 Identification of binding motifs for TF heterodimers using SMiLE-seq.

a. Schematic representation of the experimental setup. Step 1. Biotinylated anti-eGFP antibody is immobilized under the button of the SMiLE-seq device. Step 2. Dimerizing transcription factor (TF1) fused to an eGFP tag, dimer partner (TF2) tagged with mCherry and Cy5-labeled DNA baits are introduced into the chip. Step 3. Antibody-immobilized complexes consisting of TF1, TF2, and DNA are trapped under the flexible PDMS membrane; dimer formation is confirmed by fluorescent read-out. Step 4. Unbound molecules as well as molecular complexes are washed away. b. TOMTOM3 comparison of JASPAR and SMiLE-seq binding motifs for mouse PPARγ:RXRα and human ARNTL:CLOCK heterodimers.

3. Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).

Supplementary Figure 6 JUN:FOS motifs.

Primary (top) and secondary (bottom) motifs identified for JUN:FOS heterodimers.

Supplementary Figure 7 Genomic regions bound by KRAB ZFPs.

Peak annotation of the genomic regions bound by ZFP14 (a), ZNF135 (b), ZNF682 (c) obtained from HOMER4 and GREAT5 analyses.

4. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

5. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

Supplementary Figure 8 An example of an initial HMM with a seed sequence 'ATGCCC'.

The emission states in the boxes correspond to 'A', 'C', 'G' and 'T' respectively. The red values are the values that are not subjected to EM training.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1–8 and Supplementary Tables 3–6 (PDF 1823 kb)

Supplementary Table 1

TFs used in the study. (XLSX 107 kb)

Supplementary Table 2

AUC values computed for SMiLE-seq, HTSELEX, JASPAR, HOCOMOCO and UniPROBE models on ChIP-seq peak intervals. (XLSX 132 kb)

Supplementary Data

SMiLE-seq-derived PWMs (ZIP 36 kb)

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Isakova, A., Groux, R., Imbeault, M. et al. SMiLE-seq identifies binding motifs of single and dimeric transcription factors. Nat Methods 14, 316–322 (2017). https://doi.org/10.1038/nmeth.4143

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