The emergence of multiple retinal cell types through efficient coding of natural movies, bioRxiv, 2018-10-31
AbstractOne of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells.
biorxiv neuroscience 0-100-users 2018Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery, bioRxiv, 2018-10-31
Although single-cell RNA sequencing (scRNA-seq) technologies have shed light on the role of cellular diversity in human pathophysiology1–3, the resulting data remains noisy and sparse, making reliable quantification of gene expression challenging. Here, we show that a deep autoencoder coupled to a Bayesian model remarkably improves UMI-based scRNA-seq data quality by transfer learning across datasets. This new technology, SAVER-X, outperforms existing state-of-the-art tools. The deep learning model in SAVER-X extracts transferable gene expression features across data from different labs, generated by varying technologies, and obtained from divergent species. Through this framework, we explore the limits of transfer learning in a diverse testbed and demonstrate that future human sequencing projects will unequivocally benefit from the accumulation of publicly available data. We further show, through examples in immunology and neurodevelopment, that SAVER-X can harness existing public data to enhance downstream analysis of new data, such as those collected in clinical settings.
biorxiv bioinformatics 100-200-users 2018Charting a tissue from single-cell transcriptomes, bioRxiv, 2018-10-30
AbstractMassively multiplexed sequencing of RNA in individual cells is transforming basic and clinical life sciences. However, in standard experiments, tissues must first be dissociated. Thus, after sequencing, information about the spatial relationships between cells is lost although this knowledge is crucial for understanding cellular and tissue-level function. Recent attempts to overcome this fundamental challenge rely on employing additional in situ gene expression imaging data which can guide spatial mapping of sequenced cells. Here we present a conceptually different approach that allows to reconstruct spatial positions of cells in a variety of tissues without using reference imaging data. We first show for several complex biological systems that distances of single cells in expression space monotonically increase with their physical distances across tissues. We therefore seek to map cells to tissue space such that this principle is optimally preserved, while matching existing imaging data when available. We show that this optimization problem can be cast as a generalized optimal transport problem and solved efficiently. We apply our approach successfully to reconstruct the mammalian liver and intestinal epithelium as well as fly and zebrafish embryos. Our results demonstrate a simple spatial expression organization principle and that this principle (or future refined principles) can be used to infer, for individual cells, meaningful spatial position probabilities from the sequencing data alone.
biorxiv systems-biology 100-200-users 2018Genetic Consequences of Social Stratification in Great Britain, bioRxiv, 2018-10-30
Human DNA varies across geographic regions, with most variation observed so far reflecting distant ancestry differences. Here, we investigate the geographic clustering of genetic variants that influence complex traits and disease risk in a sample of ~450,000 individuals from Great Britain. Out of 30 traits analyzed, 16 show significant geographic clustering at the genetic level after controlling for ancestry, likely reflecting recent migration driven by socio-economic status (SES). Alleles associated with educational attainment (EA) show most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals that leave coal mining areas carry more EA-increasing alleles on average than the rest of Great Britain. In addition, we leveraged the geographic clustering of complex trait variation to further disentangle regional differences in socio-economic and cultural outcomes through genome-wide association studies on publicly available regional measures, namely coal mining, religiousness, 19702015 general election outcomes, and Brexit referendum results.
biorxiv genetics 200-500-users 2018Retinotopic maps of visual space in the human cerebellum, bioRxiv, 2018-10-29
While the cerebellum is instrumental for motor control, it is not traditionally implicated in vision. Here, we report the existence of 5 ipsilateral visual field maps in the human cerebellum. These maps are located within the oculomotor vermis and cerebellar nodes of the dorsal attention and visual networks. These findings imply that the cerebellum is closely involved in visuospatial cognition, and that its contributions are anchored in sensory coordinates.
biorxiv neuroscience 100-200-users 2018Specialized and spatially organized coding of sensory, motor, and cognitive variables in midbrain dopamine neurons, bioRxiv, 2018-10-29
There is increased appreciation that dopamine (DA) neurons in the midbrain respond not only to reward 1,2 and reward-predicting cues 1,3,4, but also to other variables such as distance to reward 5, movements 6–11 and behavioral choices 12–15. Based on these findings, a major open question is how the responses to these diverse variables are organized across the population of DA neurons. In other words, do individual DA neurons multiplex multiple variables, or are subsets of neurons specialized in encoding specific behavioral variables? The reason that this fundamental question has been difficult to resolve is that recordings from large populations of individual DA neurons have not been performed in a behavioral task with sufficient complexity to examine these diverse variables simultaneously. To address this gap, we used 2-photon calcium imaging through an implanted lens to record activity of >300 midbrain DA neurons in the VTA during a complex decision-making task. As mice navigated in a virtual reality (VR) environment, DA neurons encoded an array of sensory, motor, and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioral variables, in addition to encoding reward. These functional clusters were spatially organized, such that neighboring neurons were more likely to be part of the same cluster. Taken together with the topography between DA neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by DA neurons.
biorxiv neuroscience 0-100-users 2018