Stanford Advisors

All Publications

  • Ratiometric Matryoshka biosensors from a nested cassette of green- and orange-emitting fluorescent proteins. Nature communications Ast, C. n., Foret, J. n., Oltrogge, L. M., De Michele, R. n., Kleist, T. J., Ho, C. H., Frommer, W. B. 2017; 8 (1): 431


    Sensitivity, dynamic and detection range as well as exclusion of expression and instrumental artifacts are critical for the quantitation of data obtained with fluorescent protein (FP)-based biosensors in vivo. Current biosensors designs are, in general, unable to simultaneously meet all these criteria. Here, we describe a generalizable platform to create dual-FP biosensors with large dynamic ranges by employing a single FP-cassette, named GO-(Green-Orange) Matryoshka. The cassette nests a stable reference FP (large Stokes shift LSSmOrange) within a reporter FP (circularly permuted green FP). GO- Matryoshka yields green and orange fluorescence upon blue excitation. As proof of concept, we converted existing, single-emission biosensors into a series of ratiometric calcium sensors (MatryoshCaMP6s) and ammonium transport activity sensors (AmTryoshka1;3). We additionally identified the internal acid-base equilibrium as a key determinant of the GCaMP dynamic range. Matryoshka technology promises flexibility in the design of a wide spectrum of ratiometric biosensors and expanded in vivo applications.Single fluorescent protein biosensors are susceptible to expression and instrumental artifacts. Here Ast et al. describe a dual fluorescent protein design whereby a reference fluorescent protein is nested within a reporter fluorescent protein to control for such artifacts while preserving sensitivity and dynamic range.

    View details for PubMedID 28874729

    View details for PubMedCentralID PMC5585204

  • Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors DEVELOPMENTAL CELL Sparks, E. E., Drapek, C., Gaudinier, A., Li, S., Ansariola, M., Shen, N., Hennacy, J. H., Zhang, J., Turco, G., Petricka, J. J., Foret, J., Hartemink, A. J., Gordan, R., Megraw, M., Brady, S. M., Benfey, P. N. 2016; 39 (5): 585-596


    Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue. Regulators of the SHORTROOT-SCARECROW transcriptional cascade were validated in planta. At the top of this cascade, we identified both activators and repressors of SHORTROOT. The aggregate spatial expression of these regulators is not sufficient to predict transcriptional specificity. Instead, modeling, transcriptional reporters, and synthetic promoters support a mechanism whereby expression at the top of the SHORTROOT-SCARECROW cascade is established through opposing activities of activators and repressors.

    View details for DOI 10.1016/j.devcel.2016.09.031

    View details for Web of Science ID 000389734700008

    View details for PubMedID 27923776

  • Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response PLOS ONE Koryachko, A., Matthiadis, A., Muhammad, D., Foret, J., Brady, S. M., Ducoste, J. J., Tuck, J., Long, T. A., Williams, C. 2015; 10 (8)


    Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.

    View details for DOI 10.1371/journal.pone.0136591

    View details for Web of Science ID 000360299100106

    View details for PubMedID 26317202

    View details for PubMedCentralID PMC4552565