Downloaded from and Summarized As

bioRxiv preprint doi: https://doi.org/10.1101/524561; this version posted January 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Transcriptomic correlates of electrophysiological and morphological diversity 2 within and across neuron types 3 Short title: Transcriptomic correlates of neuronal diversity 4 Claire Bomkamp*1,2, Shreejoy J. Tripathy*1,3,5, Carolina Bengtsson Gonzales4, Jens Hjerling-Leffler4, 5 Ann Marie Craig1,2, Paul Pavlidis1,3 6 *These authors contributed equally 7 1 Department of Psychiatry, University of British Columbia, Vancouver BC, Canada 8 2Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver BC, Canada 9 3Michael Smith Laboratories, University of British Columbia, Vancouver BC, Canada 10 4Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, 11 Karolinska Institutet, SE-17177 Stockholm, Sweden 12 5Present address: Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health and 13 Department of Psychiatry, University of Toronto, Toronto ON, Canada 14 Abstract 15 In order to further our understanding of how gene expression contributes to key functional properties of 16 neurons, we combined publicly accessible gene expression, electrophysiology, and morphology 17 measurements to identify cross-cell type correlations between these data modalities. Building on our 18 previous work using a similar approach, we distinguished between correlations which were “class- 1 bioRxiv preprint doi: https://doi.org/10.1101/524561; this version posted January 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 19 driven,” meaning those that could be explained by differences between excitatory and inhibitory cell 20 classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity 21 into account increased the degree to which our results replicated in an independent dataset as well as 22 their correspondence with known modes of ion channel function based on the literature. We also found a 23 smaller set of genes whose relationships to electrophysiological or morphological properties appear to 24 be specific to either excitatory or inhibitory cell types. Next, using data from Patch-seq experiments, 25 allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found 26 that some of the gene-property correlations observed across cell types were further predictive of within- 27 cell type heterogeneity. In summary, we have identified a number of relationships between gene 28 expression, electrophysiology, and morphology that provide testable hypotheses for future studies. 29 Author Summary 30 The behavior of neurons is governed by their electrical properties, for example how readily they respond 31 to a stimulus or at what rate they are able to send signals. Additionally, neurons come in different shapes 32 and sizes, and their shape defines how they can form connections with specific partners and thus 33 function within the complete circuit. We know that these properties are governed by genes, acting 34 acutely or during development, but we do not know which specific genes underlie many of these 35 properties. Understanding how gene expression changes the properties of neurons will help in advancing 36 our overall understanding of how neurons, and ultimately brains, function. This can in turn help to 37 identify potential treatments for brain-related diseases. In this work, we aimed to identify genes whose 38 expression showed a relationship with the electrical properties and shape measurements of different 39 types of neurons. While our analysis does not identify causal relationships, our findings provide testable 40 predictions for future research. 2 bioRxiv preprint doi: https://doi.org/10.1101/524561; this version posted January 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 41 Introduction 42 Two prominent features that distinguish neurons from other cells are their electrical activity and their 43 characteristic morphology. The specific pattern of electrophysiological activity displayed by a given 44 neuron is a core property of its identity as one type of neuron or another. Similarly, different cell types 45 often show striking differences in their size, branching complexity, and other morphological features. 46 Neuronal cell types defined according to their electrophysiological or morphological characteristics 47 show substantial correspondence with one another as well as with those defined using classification 48 schemes based on transcriptomic criteria (1). Electrophysiological characteristics of neurons, as well as 49 their connectivity patterns, give rise to the computational properties of a given circuit (2,3). Additionally, 50 modeling studies show that morphological changes in simulated neurons can critically change their 51 signaling capabilities (4–6). Thus, understanding the origins of neuronal electrophysiology and 52 morphology is an important step in understanding the mechanisms of brain function, both in the context 53 of basic research and in the search for treatments for neuropsychiatric disorders. 54 A comprehensive understanding of the mechanisms that give rise to electrophysiological or 55 morphological diversity must necessarily include a catalogue of the genes whose products contribute to 56 these properties. Many genes have been shown experimentally to influence neuronal electrophysiology 57 through a variety of mechanisms, including but not limited to ion channel activity, protein trafficking, 58 and transcription factor activity (7–9). Processes such as axon guidance and the development of dendrite 59 morphology are also known to be under genetic control (10). Despite this, our understanding of the 60 relationship between gene expression and electrophysiological or morphological properties is quite 61 limited. 3 bioRxiv preprint doi: https://doi.org/10.1101/524561; this version posted January 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 62 In previous work (11), we combined publicly accessible electrophysiological and gene expression 63 datasets in order to examine the relationship between gene expression and electrophysiological 64 properties. By matching groups of cells inferred to be similar based on multiple information sources, 65 such as the transgenic reporter line and the brain region cells were isolated from, we were able to 66 combine separate datasets containing gene expression and electrophysiological data to generate lists of 67 genes which were correlated with one of several electrophysiological properties (as outlined in Fig 1A). 68 The goal of this approach was to identify candidate genes that could be further studied using knockout 69 or knockdown approaches in order to determine whether a causal relationship was present. 70 One caveat in our prior study is that the gene-electrophysiology correlations we identified may have 71 been confounded by overall differences between broad cell classes. Across multiple datasets and cellular 72 characterization methods, including gene expression (12–15), and electrophysiology and morphology (1), 73 clustering cellular phenotypes in an unbiased manner reveals the major taxonomic difference between 74 neurons to be between projecting and non-projecting neurons (13), or in the case of those cell types 75 present in the cortex or hippocampus, excitatory and inhibitory neurons (12,14,15). Thus, the commonly 76 held view that a neuron’s identity is first and foremost defined by its excitatory or inhibitory identity 77 (16) is corroborated across multiple data sources and experimental modalities. 78 Therefore, we reasoned that the dataset we used previously was potentially susceptible to this 79 confounding effect of cell class, since it contained a mixture of cells from different broad cell classes. In 80 this work, we will use the term “cell type” to refer to narrowly-defined cell types, and “cell class” to 81 refer to those which are broadly-defined (excitatory versus inhibitory or projecting versus non- 82 projecting). We refer to correlations between gene expression and electrophysiological or morphological 83 properties that are explained by differences between cell classes as “class-driven,” (e.g. Fig 1B) and to 84 those that exist based on graded differences within broad cell classes as “non-class-driven” (e.g. Fig 1C). 4 bioRxiv preprint doi: https://doi.org/10.1101/524561; this version posted January 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 85 We reason that gene-property

Load more