Optical imaging of postsynaptic odor representation in the glomerular layer of the mouse olfactory bulb.

Olfactory glomeruli are the loci where the first odor-representation map emerges. The glomerular layer comprises exquisite local synaptic circuits for the processing of olfactory coding patterns immediately after their emergence. To understand how an odor map is transferred from afferent terminals to postsynaptic dendrites, it is essential to directly monitor the odor-evoked glomerular postsynaptic activity patterns. Here we report the use of a transgenic mouse expressing a Ca(2+)-sensitive green fluorescence protein (GCaMP2) under a Kv3.1 potassium-channel promoter. Immunostaining revealed that GCaMP2 was specifically expressed in mitral and tufted cells and a subpopulation of juxtaglomerular cells but not in olfactory nerve terminals. Both in vitro and in vivo imaging combined with glutamate receptor pharmacology confirmed that odor maps reported by GCaMP2 were of a postsynaptic origin. These mice thus provided an unprecedented opportunity to analyze the spatial activity pattern reflecting purely postsynaptic olfactory codes. The odor-evoked GCaMP2 signal had both focal and diffuse spatial components. The focalized hot spots corresponded to individually activated glomeruli. In GCaMP2-reported postsynaptic odor maps, different odorants activated distinct but overlapping sets of glomeruli. Increasing odor concentration increased both individual glomerular response amplitude and the total number of activated glomeruli. Furthermore, the GCaMP2 response displayed a fast time course that enabled us to analyze the temporal dynamics of odor maps over consecutive sniff cycles. In summary, with cell-specific targeting of a genetically encoded Ca(2+) indicator, we have successfully isolated and characterized an intermediate level of odor representation between olfactory nerve input and principal mitral/tufted cell output.

A PROCESS FOR ACHIEVING COMPARABLE DATA FROM HETEROGENEOUS DATABASES

The current state of health and biomedicine includes an enormity of heterogeneous data ‘silos’, collected for different purposes and represented differently, that are presently impossible to share or analyze in toto. The greatest challenge for large-scale and meaningful analyses of health-related data is to achieve a uniform data representation for data extracted from heterogeneous source representations. Based upon an analysis and categorization of heterogeneities, a process for achieving comparable data content by using a uniform terminological representation is developed. This process addresses the types of representational heterogeneities that commonly arise in healthcare data integration problems. Specifically, this process uses a reference terminology, and associated "maps" to transform heterogeneous data to a standard representation for comparability and secondary use. The capture of quality and precision of the “maps” between local terms and reference terminology concepts enhances the meaning of the aggregated data, empowering end users with better-informed queries for subsequent analyses. A data integration case study in the domain of pediatric asthma illustrates the development and use of a reference terminology for creating comparable data from heterogeneous source representations. The contribution of this research is a generalized process for the integration of data from heterogeneous source representations, and this process can be applied and extended to other problems where heterogeneous data needs to be merged.