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      Non-canonical odor coding in the mosquito

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          SUMMARY

          Aedes aegypti mosquitoes are a persistent human foe, transmitting arboviruses including dengue when they feed on human blood. Mosquitoes are intensely attracted to body odor and carbon dioxide, which they detect using ionotropic chemosensory receptors encoded by three large multi-gene families. Genetic mutations that disrupt the olfactory system have modest effects on human attraction, suggesting redundancy in odor coding. The canonical view is that olfactory sensory neurons each express a single chemosensory receptor that defines its ligand selectivity. We discovered that Ae. aegypti uses a different organizational principle, with many neurons co-expressing multiple chemosensory receptor genes. In vivo electrophysiology demonstrates that the broad ligand-sensitivity of mosquito olfactory neurons depends on this non-canonical co-expression. The redundancy afforded by an olfactory system in which neurons co-express multiple chemosensory receptors may increase the robustness of the mosquito olfactory system and explain our long-standing inability to disrupt the detection of humans by mosquitoes.

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          In brief

          Humans produce a complex blend of odor cues that attract female mosquitoes, and these cues are typically detected by olfactory neurons expressing a single receptor. In female Aedes aegypti mosquitos, however, many of these neurons co-express multiple chemosensory receptors directly affecting mosquito behavior and challenging the canonical one-receptor-to-one-neuron organization.

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          Most cited references132

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Fast, sensitive, and accurate integration of single cell data with Harmony

            The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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              Spatial reconstruction of single-cell gene expression

              Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                3 September 2022
                18 August 2022
                16 September 2022
                : 185
                : 17
                : 3104-3123.e28
                Affiliations
                [1 ]Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, NY 10065, USA
                [2 ]Kavli Neural Systems Institute, New York, NY 10065, USA
                [3 ]Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10065, USA
                [4 ]Huffington Center on Aging and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
                [5 ]Disease Vector Group, Unit of Chemical Ecology, Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp 234 22, Sweden
                [6 ]Howard Hughes Medical Institute, New York, NY 10065, USA
                [7 ]Department of Biology, Boston University, Boston, MA 02215, USA
                [8 ]These authors contributed equally
                [9 ]Present address: Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
                [10 ]Present address: University of British Columbia, Department of Zoology, Vancouver, BC V6S 0K3, Canada
                [11 ]Lead contact
                Author notes
                [* ]Correspondence: myounger@ 123456bu.edu

                AUTHOR CONTRIBUTIONS

                M.A.Y. carried out all central tissue immunofluorescence. M.H. carried out all peripheral tissue immunofluorescence and RNA in situ hybridization experiments. B.J.M. provided chemosensory gene and transcript analysis. Z.G. cloned and isolated Split-QF2 lines with M.H. Z.N.G. cloned and isolated QF2 stop codon replacement lines with B.J.M. and M.A.Y. S.R. worked with M.H. to generate RNA in situ hybridization data. O.V.G. developed the protocol to isolate nuclei and collected all tissue for snRNA-seq together with M.H. and M.A.Y. O.V.G. processed tissue for snRNA-seq experiments at Rockefeller. At Baylor, Y.Q. carried out sample preparation, flow cytometry, and 10X Genomics library preparation. T.-C.L. and O.V.G. carried out snRNA-seq data analysis. T.-C.L. performed read alignment and with O.V.G., carried out quality checking, cell and gene filtering, and data normalization. O.V.G. carried out additional downstream analysis. O.V.G. together with T.-C.L. generated the figure panels for Figure 4 and Figure 6. H.L. supervised Y.Q and T.-C.L. and oversaw snRNA-seq experimental design and data analysis. The single sensillum recordings in Figures 7A- 7E were carried out by M.G., and those in Figures 7F- 7I were carried out by G.C.-V. R.I. supervised M.G. and G.C.-V. and analyzed data in Figure 7 together with M.G. and G.C.-V. M.H., M.A.Y., and L.B.V. together conceived the study, designed the figures, and wrote the paper with input from all authors.

                Article
                NIHMS1828572
                10.1016/j.cell.2022.07.024
                9480278
                35985288
                16f78fd3-c01a-4168-b36b-a061f21da8c1

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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