Integrated Calendar

The ability to recognize individual conspecifics, termed social recognition, is crucial for survival of the individual, as it guides appropriate interactions with its social environment. In humans, social recognition can be based upon cues arriving from single sensory modalities. For example, humans can recognize a person just by looking at its face (visual modality) or hearing its voice (auditory modality). Such single-modality based social recognition seems to hold for other primates as well. Yet, how general is this ability among mammals is not clear.
Mice and rats, the main laboratory mammalian models in the field of neuroscience, are social species known to exhibit social recognition abilities, widely assumed to be mediated by stimulus-derived chemosensory cues received by the main and accessory olfactory systems of the subject. In the lecture, I will challenge this common assumption and show evidence that rodents' social recognition is based upon integration of olfactory, auditory and somatosensory cues, hence requires active behavior of the social stimuli. In that sense, social recognition in rodents seems to be fundamentally different from social recognition in humans.

The Visium Spatial Gene Expression Solution from 10x Genomics analyzes complete transcriptomes in
intact tissue sections, allowing you to discover genes and markers relevant to your research without
having to rely on known targets. Preserving spatial resolution offers critical information for
understanding the relationships between cellular function, phenotype, and location in the tissue.

Measuring quantitative distances are important for studying the structural properties of molecules at an atomic scale. Dipole-dipole coupling encodes for distance information between spin pairs. Additionally, the anisotropy of dipole-dipole coupling is also very sensitive to the sub-microsecond dynamics occurring in the molecules, and therefore can be used to estimate it. Rotational Echo Double Resonance (REDOR) and Correlation of Dipolar coupling and Chemical shift (DIPSHIFT) experiments are the most preferred experiments for measuring distances between a heteronuclear spin pair using Magic angle spinning (MAS) solid-state NMR. But these experiments can be used only for a small range of coupling strengths depending on the MAS frequency of the experiment. In the talk, I will discuss the latest developments we have made for measuring a wide range of coupling strengths using REDOR over a wide range of MAS frequencies. Further, I will also show that REDOR and DIPSHIFT are different realizations of a same experiment and this unification comes naturally out of our augmented REDOR sequence. Further, I will discuss a method to perform REDOR experiments with low radiofrequency amplitude pulses at MAS frequencies larger than 80~kHz. This method extends the application of REDOR and DIPSHIFT at very fast MAS frequencies, where the radiofrequency amplitude requirement becomes too high for nuclei other than 1H. Overall, the methods discussed here allow for measuring dipole-dipole coupling between heteronuclear spin-pairs over a wider range of MAS frequencies.

References:
1. T. Gullion and J. Schaefer, Journal of Magnetic Resonance, 1989.
2. M. G. Munowitz et al., Journal of American Chemical Society, 1981.
3. M. Hong et al., Journal of Magnetic Resonance, 1997.
4. P. Schanda et al., Journal of Magnetic Resonance, 2019.
5. M. G. Jain et al., Journal of Chemical Physics, 2017.
6. M. G. Jain et al., Journal of Chemical Physics, 2019.
7. M. G. Jain et al., Journal of Magnetic Resonance, 2019.

The past decade has witnessed the emergence of single-cell technologies that measure the expression level of genes at a single-cell resolution. These developments have revolutionized our understanding of the rich heterogeneity, structure, and dynamics of cellular populations, by probing the states of millions of cells, and their change under different conditions or over time. However, in standard experiments, information about the spatial context of cells, along with additional layers of information they encode about their location along dynamic processes (e.g. cell cycle or differentiation trajectories), is either lost or not explicitly accessible. This poses a fundamental problem for elucidating collective tissue function and mechanisms of cell-to-cell communication.
In this talk I will present computational approaches for addressing these challenges, by learning interpretable representations of structure, context and design principles for multicellular systems from single-cell information. I will first describe how the locations of cells in their tissue of origin and the resulting spatial gene expression can be probabilistically inferred from single-cell information by a generalized optimal-transport optimization framework that can flexibly incorporate prior biological assumptions or knowledge derived from experiments. Inference in this case is based on an organization principle for spatial gene expression, namely a structural correspondence between distances of cells in expression and physical space, which we hypothesized and supported for different tissues. We used this framework to spatially reconstruct diverse tissues and organisms, including the fly embryo, mammalian intestinal epithelium and cerebellum, and further inferred spatially informative genes. Since cells encode multiple layers of information, in addition to their spatial context, I will also discuss several approaches for the disentanglement of single-cell gene expression into distinct biological processes, based on ideas rooted in random matrix theory and manifold learning. I will finally discuss how these results can be generalized to reveal principles underlying self-organization of cells into multicellular structures, setting the foundation for the computationally-directed design of cell-to-cell interactions optimized for specific tissue structure or function.
Sunday, December 8, 2019 at 13:00
Sandwiches at 12:45
Drory Auditorium