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Decoding Neuronal Diversity by Single-Cell Convert-Seq

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Decoding Neuronal Diversity by Single-Cell Convert-Seq bioRxiv preprint doi: https://doi.org/10.1101/600239; this version posted April 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Decoding neuronal diversity by single-cell Convert-seq 2 Joachim Luginbühl1,2, Tsukasa Kouno1,2, Rei Nakano1,3,4, Thomas E Chater5, Divya M Sivaraman1,2,6, 3 Mami Kishima1,3, Filip Roudnicky7, Piero Carninci1,8, Charles Plessy1,8, Jay W Shin1,2,9,* 4 5 1RIKEN Center for Life Science Technologies, Division of Genomic Technologies, RIKEN Yokohama, 230-0045, 6 Japan; 2RIKEN Center for Integrative Medical Sciences, Laboratory for Advanced Genomic Circuits, RIKEN 7 Yokohama, 230-0045, Japan; 3RIKEN Center for Integrative Medical Sciences, Laboratory for Cellular Function 8 Conversion Technology, RIKEN Yokohama, 230-0045, Japan; 4Nihon University College of Bioresource Sciences, 9 Department of Veterinary Medicine, Laboratory of Veterinary Biochemistry, 252-0880, Japan; 5RIKEN Center for 10 Brain Science, Laboratory for Synaptic Plasticity and Connectivity, RIKEN Wako, 351-0198, Japan; 6Sree Chitra 11 Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, 695011, India; 7Institute of 12 Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland; 8RIKEN Center for Integrative Medical Sciences, 13 Laboratory for Single Cell Technologies, RIKEN Yokohama, 230-0045, Japan; 9Lead contact 14 15 *Materials and correspondence: [email protected] 16 17 Summary 18 The conversion of cell fates is controlled by hierarchical gene regulatory networks (GRNs) that 19 induce remarkable alterations in cellular and transcriptome states. The identification of key 20 regulators within these networks from myriad of candidate genes, however, poses a major 21 research challenge. Here we present Convert-seq, combining single-cell RNA sequencing 22 (scRNA-seq) and pooled ectopic gene expression with a new strategy to discriminate 23 sequencing reads derived from exogenous and endogenous transcripts. We demonstrate 24 Convert-seq by associating hundreds of single cells during reprogramming of human 25 fibroblasts to induced neurons (iN) with exogenous and endogenous transcriptional signatures. 26 Convert-seq identified GRNs modulating the emergence of developmental trajectories and 27 predicted combinatorial activation of exogenous transcription factors controlling iN subtype 28 specification. Functional validation of iN subtypes generated by novel combinations of 29 exogenous transcription factors establish Convert-seq as a broadly applicable workflow to 30 rapidly identify key transcription factors and GRNs orchestrating the reprogramming of virtually 31 any cell type. bioRxiv preprint doi: https://doi.org/10.1101/600239; this version posted April 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 32 Introduction 33 Fully differentiated cells can be reprogrammed to alternative fates via ectopic expression of defined 34 combinations of transcription factors and/or microRNAs (Chanda et al., 2014; Hu et al., 2015; Mertens 35 et al., 2015; Pang et al., 2011; Pfisterer et al., 2011; Ryoji Amamoto and Arlotta, 2014; Victor et al., 36 2014; Vierbuchen et al., 2010; Xue et al., 2013). Reprogrammed cells offer invaluable alternative routes 37 for the treatment of various diseases and represent a substantial improvement to the field of disease 38 modeling (Masserdotti et al., 2016). However, despite the establishment of new in silico prediction tools 39 (Rackham et al., 2016), it has remained a challenge to identify essential genetic drivers that determine 40 specific subtypes of cells during reprogramming. 41 Cell reprogramming is of particular interest for human neurons, which are notoriously difficult 42 to obtain from patients and cannot be expanded in culture (Ryoji Amamoto and Arlotta, 2014). Since 43 the first report on reprogramming of induced neurons (iN), several studies have succeeded in identifying 44 defined combinations of neurogenic transcription factors that allow the reprogramming of specific 45 neuronal subtypes (Masserdotti et al., 2016). For instance, distinct sets of neurogenic transcription 46 factors have been shown to create dopaminergic neurons and cholinergic motor neurons that are 47 selective targets of degeneration in patients with Parkinson’s disease and amyotrophic lateral sclerosis 48 (ALS), respectively (Caiazzo et al., 2011; Jiang et al., 2015; Kim et al., 2011; Liu et al., 2013, 2016; 49 Mazzoni et al., 2013; Pfisterer et al., 2011; Son et al., 2011). However, an inherent limitation of studies 50 aiming to identify key transcription factors controlling cell reprogramming is that the identification of 51 candidates from myriad of transcription factors still is based on trial and error. This is exacerbated by 52 the time and labor intensive retesting and validation of newly identified candidates. Pooled screens, 53 instead of one-by-one tests, offer a more efficient and scalable strategy, but they either rely on simple 54 phenotypic readouts or require specific markers to enrich for target cell populations, which are often not 55 available (Chen et al., 2015; Hsu et al., 2014; Liu et al., 2018; Shalem et al., 2015). This is particularly 56 problematic with regard to reprogramming, which is intrinsically inefficient, resulting in heterogenous 57 cell populations containing unreprogrammed, partially reprogrammed, and successfully reprogrammed 58 cells. 59 To address this challenge, we developed Convert-seq, combining ectopic expression of 60 transcription factors with single-cell RNA sequencing (scRNA-seq) to retrospectively associate single 61 cells with their exogenous and endogenous expression profiles. Convert-seq represents a pooled bioRxiv preprint doi: https://doi.org/10.1101/600239; this version posted April 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 62 (multiplexed) screening strategy that allows to identify transcription factors and GRNs governing the 63 reprogramming of many different celltypes in a single experiment. We demonstrate Convert-seq in iN 64 generated by the pooled infection of human fibroblasts with 20 pro-neuronal transcription factors using 65 two single-cell sequencing platforms, the Fluidigm C1 system based on microfluidic technology and the 66 10x Genomics system based on nano-droplets. By association of different transcriptional states and 67 developmental trajectories emerging during reprogramming with distinct exogenous and endogenous 68 transcriptional signatures, we identified defined combinations of key transcription factors orchestrating 69 the reprogramming of human fibroblasts into multiple neuronal subtypes. Our data demonstrate the 70 ability of Convert-seq to efficiently gain rich insight into biological processes governing cell plasticity 71 and cell fate decisions and to systematically dissect GRNs controlling subtype specification during cell 72 reprogramming. 73 74 Results 75 76 Generation of a heterogenous population of human induced neurons 77 To enable systematic identification of combinations of transcription factors governing the 78 reprogramming of human fibroblasts into specific neuronal subtypes, we generated a pool of lentiviruses 79 (hereafter termed transcription factor-pool or TF-pool) encoding 20 pro-neuronal transcription factors. 80 We included the pioneer factor ASCL1 (Wapinski et al., 2013) and several transcription factors that 81 increase the efficiency of neuronal reprogramming (POU3F2, ZIC1, OLIG2, NEUROD1) (Pang et al., 82 2011; Vierbuchen et al., 2010). To promote reprogramming of different subtypes of neurons, we further 83 included transcription factors which have been previously shown to convert human fibroblasts into 84 GABAergic (DLX1, DLX2) (Victor et al., 2014), cholinergic (NEUROG2, ISL1) (Liu et al., 2013; Son et 85 al., 2011), serotonergic (FEV) (Xu et al., 2016) or dopaminergic (FOXA2, NR4A2, PITX3) (Liu et al., 86 2012; Pfisterer et al., 2011) neurons when co-expressed with other transcription factors. Additionally, 87 we selected 7 transcription factors which have not yet been used to generate specific neuronal 88 subtypes, but showed significant up-regulation during the differentiation of human induced pluripotent 89 stem cells (iPSC) into neuronal progenitor cells (NPC) (Figure S1A). Each transcription factor was 90 expressed under the elongation factor 1 alpha (EF1A) promoter together with a gene encoding Venus 91 yellow fluorescent protein (YFP) (Figure S1B). Based on quantifications of YFP expression, we tuned bioRxiv preprint doi: https://doi.org/10.1101/600239; this version posted April 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 92 the multiplicity of infection (MOI) of each lentivirus to allow ~85% of fibroblasts to express mixed 93 combinations of 2 - 6 transcription factors after stochastic infection with the TF-pool (Figure 1A and 94 Figure S1C). Using successive application of neuronal induction medium for 14 days followed by 95 neuronal maturation medium for 7 days, we successfully transformed fibroblasts into cells exhibiting 96 hetergeneous neuronal morphologies and expressing canonical neuronal marker
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