Integrated Calendar

Next generation sequencing (NGS) is revolutionizing all fields of biological research but it fails to extract the full range of information associated with genetic material. Complementary genomic technologies that analyze individual, unamplified genomic DNA are filling the gaps in the capabilities of NGS. Using such technologies we gain access to the structural variation and long range patterns of genetic and epigenetic information. Recent results from our lab demonstrate our ability to detect and map the epigenetic marks 5-methylacytosine and 5-hydroxymethylcytosine as well as various forms of DNA damage on individual genomic DNA molecules. This new technology allows genetic and epigenetic variation calling on the single cell level without the need to process single cells.

This joint MPI (Max Plank Institutes)-Weizmann one-day symposium will focus on fundamental, unanswered questions; foremost: How did the first proteins evolve, and whether and how do functional proteins arise de novo?

Classical fluorescence microscopy is limited in resolution by the wavelength of light (diffraction limit) restricting lateral resolution to ca. 200 nm, and axial resolution to ca. 500 nm (at typical excitation and emission wavelengths around 500 nm). However, recent years have seen a tremendous development in high- and super-resolution techniques of fluorescence microscopy, pushing spatial resolution to its diffraction-dictated limits and much beyond. One of these techniques is Image Scanning Microscopy (ISM). In ISM, the focus of a conventional laser-scanning confocal microscope (LCSM) is scanned over the sample, but instead of recording only the total fluorescence intensity for each scan position, as done in conventional operation of an LCSM, one records a small image of the illuminated region. The result is a four-dimensional stack of data: two dimensions refer to the lateral scan position, and two dimensions to the pixel position on the chip of the image-recording camera. This set of data can then be used to obtain a super-resolved image with doubled resolution, completely analogously to what is achieved with Structured Illumination Microscopy. However, ISM is conceptually and technically much simpler, suffers less from sample imperfections like refractive index variations, and can easily be implemented into any existing LSCM. I will also present recent results of combining ISM with two-photon excitation, which is important for deep-tissue imaging of e.g. neuronal tissue, and for performing non-linear coherent microscopy such as second-harmonic generation.

A second method which I will present is concerned with achieving nanometer resolution along the optical axis. It is called Metal Induced Energy Transfer or MIET and is based on the fact that, when placing a fluorescent molecule close to a metal, its fluorescence properties change dramatically. In particular, one observes a strongly modified lifetime of its excited state (Purcell effect). This coupling between an excited emitter and a metal film is strongly dependent on the emitter’s distance from the metal. We have used this effect for mapping the basal membrane of live cells with an axial accuracy of ~3 nm. The method is easy to implement and does not require any change to a conventional fluorescence lifetime microscope; it can be applied to any biological system of interest, and is compatible with most other super-resolution microscopy techniques which enhance the lateral resolution of imaging. Moreover, it is even applicable to localizing individual molecules, thus offering the prospect of three-dimensional single-molecule localization microscopy with nanometer isotropic resolution for structural biology.

Allosteric regulation - the control of protein function by sites far from the active
site, is a common feature that enables dynamic cellular responses. Reversible post-translational
modifications (such as phosphorylation) appear to be well suited to mediate dynamic cellular responses - yet
how new allosteric regulation evolves is not understood.
We mutationally scanned the surface of a prototypical kinase to identify readily evolvable phosphorylation
sites. Our data reveal spatially distributed "hotspots" on the surface of the protein that coevolve with the
active site and preferentially modulate kinase activity. By engineering simple consensus phosphorylation
sites at these hotspots, we successfully re-wired in vivo cell signaling.
Our results demonstrate a general strategy for engineering new cell signaling pathways, suggest cryptic sites for developing
small molecule allosteric kinase inhibitors and also provide a context for interpreting kinase mutations involved in disease.

Recent advances in single-cell technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression and epigenetics for thousands of single cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges in terms of scale, complexity, noise and measurement artifact which require advanced mathematical and algorithmic tools to extract underlying biological signals. In this talk, I cover one of most promising techniques to tackle these problems: manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to naturally process and the data, visualize the data and understand progressions as well as to find phenotypic diversity as well and infer patterns in it. I will cover two alternative approaches to manifold learning, diffusion-based and deep learning-based and show results in several projects including:1) MAGIC (Markov Affinity-based Graph Imputation of Cells): an algorithm for denoising and transcript recover of single cells applied to single-cell RNA sequencing data from the epithelial-to-mesenchymal transition in breast cancer, 2) PHATE (Potential of Heat-diffusion Affinity-based Transition Embedding): a visualization technique that offers an alternative to tSNE in that it emphasizes progressions and branching structures rather than cluster separations shown on several datasets including a newly generated embryoid body differentiation dataset, and 3) SAUCIE (Sparse AutoEncoders for Clustering Imputation and Embedding): a novel auto encoder architecture that performs denoising, batch normalization, clustering and visualization simultaneously for massive single-cell data sets from multi-patient cohorts shown on mass cytometry data from Dengue patients.