SALL1 enforces microglia-specific DNA binding and function of SMADs to establish microglia identity

Spalt-like transcription factor 1 (SALL1) is a critical regulator of organogenesis and microglia identity. Here we demonstrate that disruption of a conserved microglia-specific super-enhancer interacting with the Sall1 promoter results in complete and specific loss of Sall1 expression in microglia. By determining the genomic binding sites of SALL1 and leveraging Sall1 enhancer knockout mice, we provide evidence for functional interactions between SALL1 and SMAD4 required for microglia-specific gene expression. SMAD4 binds directly to the Sall1 super-enhancer and is required for Sall1 expression, consistent with an evolutionarily conserved requirement of the TGFβ and SMAD homologs Dpp and Mad for cell-specific expression of Spalt in the Drosophila wing. Unexpectedly, SALL1 in turn promotes binding and function of SMAD4 at microglia-specific enhancers while simultaneously suppressing binding of SMAD4 to enhancers of genes that become inappropriately activated in enhancer knockout microglia, thereby enforcing microglia-specific functions of the TGFβ–SMAD signaling axis.</p

Properties of the Nucleo-Olivary Pathway: An In Vivo Whole-Cell Patch Clamp Study

The inferior olivary nucleus (IO) forms the gateway to the cerebellar cortex and receives feedback information from the cerebellar nuclei (CN), thereby occupying a central position in the olivo-cerebellar loop. Here, we investigated the feedback input from the CN to the IO in vivo in mice using the whole-cell patch-clamp technique. This approach allows us to study how the CN-feedback input is integrated with the activity of olivary neurons, while the olivo-cerebellar system and its connections are intact. Our results show how IO neurons respond to CN stimulation sequentially with: i) a short depolarization (EPSP), ii) a hyperpolarization (IPSP) and iii) a rebound depolarization. The latter two phenomena can also be evoked without the EPSPs. The IPSP is sensitive to a GABA(A) receptor blocker. The IPSP suppresses suprathreshold and subthreshold activity and is generated mainly by activation of the GABA(A) receptors. The rebound depolarization re-initiates and temporarily phase locks the subthreshold oscillations. Lack of electrotonical coupling does not affect the IPSP of individual olivary neurons, nor the sensitivity of its GABA(A) receptors to blockers. The GABAergic feedback input from the CN does not only temporarily block the transmission of signals through the IO, it also isolates neurons from the network by shunting the junction current and re-initiates the temporal pattern after a fixed time point. These data suggest that the IO not only functions as a cerebellar controlled gating device, but also operates as a pattern generator for controlling motor timing and/or learning

Functional imaging of cerebral oxygenation with intrinsic optical contrast and phosphorescent probes

Microscopic in vivo measurements of cerebral oxygenation are of key importance for understanding normal cerebral energy metabolism and its dysregulation in a wide range of clinical conditions. Relevant cerebral pathologies include compromised blood perfusion following stroke and a decrease in efficiency of single-cell respiratory processes that occurs in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In this chapter we review a number of quantitative optical approaches to measuring oxygenation of blood and cerebral tissue. These methods can be applied to map the hemodynamic response and study neurovascular and neurometabolic coupling, and can provide microscopic imaging of biomarkers in animal models of human disease, which would be useful for screening potential therapeutic approaches. © 2014 Springer Science+Business Media New York

Discriminative extended canonical correlation analysis for pattern set matching

In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.Comment: Machine Learning, 201