Sequencing perturbed cortex development

doi:10.1126/science.abf3661

GSTDTAP > 气候变化

DOI	10.1126/science.abf3661
	Sequencing perturbed cortex development
	Barbara Treutlein; J. Gray Camp
	2020-11-27
发表期刊	Science
出版年	2020
英文摘要	Tissues are complex and contain a diverse compendium of cell types and states. Genetic disruptions can affect any one or all of these cells and, in doing so, sometimes give rise to disease. There are thousands of genes associated with human disease, and studying the effects of genetic disruption across all cell types within mammalian tissues remains difficult. Perturbations with single-cell sequencing readouts have promised extraordinary insight through increased throughput and enhanced resolution. On page 1057 of this issue, Jin et al. ([ 1 ][1]) combine in vivo genetic disruptions of genes associated with the risk of developing autism spectrum disorder (ASD) or neurodevelopmental delay (ND) with single-cell transcriptome sequencing to explore effects across diverse cells in the developing mouse cortex. Over the past decades, cleverly designed in vivo genetic screens have provided insight into the mechanisms that underpin organism development ([ 2 ][2]). Enormous effort has been applied to increase through-put and enhance readouts of genetic screen technologies. Still, most screen designs do not provide insight into phenotype and mechanism in the same experiment, and certainly not across all cell types in a tissue. The combination of single-cell RNA-sequencing and CRISPR-Cas technologies ([ 3 ][3]–[ 5 ][4]) has provided researchers with tools to generate information-rich descriptions of cells (such as the transcriptome) and at the same time introduce and assess the effects of a genetic perturbation. Because each cell is sequenced independently, a mosaic tissue with diverse genetic manipulations can be created and the effect of many genes assessed in one experiment across all cell types and states. This is the advantage of the experimental design in Jin et al. There are dozens of genes associated with ASD, and some of these genes are expressed in specific cell types, whereas others are broadly expressed in glial and neuronal cells. Through sparse infection of the developing cortex of mice constitutively expressing Cas9 ([ 6 ][5]) with lentiviral particles encoding guide RNAs (gRNAs) targeting these genes, Jin et al. used the Perturb-Seq method ([ 4 ][6]) to, in principle, rapidly assess all ASD-associated genes in one experiment using a single-cell RNA-sequencing readout (see the figure). Jin et al. find that recurrent gene regulatory modules may be perturbed by different gene mutations and also implicate additional cell types not previously linked to ASD or ND pathology. However, the reality of single-cell perturbation screens is that the data are sparse, the effects are correlative, and interpretations can be hazy. With current technologies, the mutations introduced in the analyzed cells are not read out directly but are linked to the detection of the gRNAs or, in the case of Perturb-Seq, the detection of a barcode linked to the gRNA. The efficiency of CRISPR-mediated “gene deletions” can be estimated on the basis of quantifying the proportion of frameshifts on a per-guide basis; however, this does not currently scale well. For example, Jin et al. evaluated perturbations of 35 genes and provide frameshift efficiencies in the mouse cortex for two genes. Therefore, even though gene ablation efficiency is likely high, it is not measured for all gRNAs. It is possible that a gene deletion with no effect is not effectively mutated, and hence, important genes may not be identified in the screen. The authors show that out of the 35 ASD- and ND-associated genes, 11 have a detectable effect. The data highlight the complexity of ASD and ND pathologies because both glial and neuronal cells showed perturbed expression networks. For example, perturbation of chromodomain helicase DNA-binding protein 8 ( Chd8 ) affected expression modules in glial cells, including oligodendrocytes and astrocytes. Chd8 is highly expressed in excitatory neurons in the mouse cortex, and previous work suggested that haploinsufficiency affects neuronal network development in the cortex ([ 7 ][7], [ 8 ][8]) and striatum ([ 9 ][9]). However, there was no significant effect of Chd8 perturbations on excitatory neurons. Thus, complex neuronal and glial interactions could lead to the diverse and variable pathologies associated with ASD and ND, and there could be specific effects within each brain region. ![Figure][10] Understanding genetic disease risk Perturb-Seq involves sparse infection of lentiviurses carrying guide RNAs (gRNAs) to ablate target genes followed by single-cell RNA-sequencing to identify the effects. Jin et al. targeted 35 genes associated with the risk of autism spectrum disorder and neurodevelopmental delay in the developing mouse cortex and uncovered common gene regulatory effects. GRAPHIC: A. KITTERMAN/ SCIENCE This study was a success because more is now known about the cell types affected by certain ASD risk–associated genes. However, it is still unclear whether at least 24 of the genes were deleted and have no effect. With Perturb-Seq, negatives are difficult to clearly interpret. Additionally, in contrast to classical in vivo genetic screens, the phenotypic effect of gene deletion is not obvious. Single-cell genomic perturbation measurements require layers of filtering, computational analyses, and statistical tests to assess the phenotypic effect. A simple differential expression analysis between gene-deleted and normal cells could not be performed because of the small number of cells for any given perturbation per cell type. The perturbation effect size was instead calculated on correlated expression modules across cell types compared with cells receiving control vectors. Data sparseness, technical variation between batches, uncertainty of genetic ablation status, and low cell numbers for certain cell types make it difficult to properly assess perturbation-associated phenotypes by using single-cell RNA-sequencing data. The authors validated certain key associations by using follow-up experiments in mice with single gene deletions, as well as with human brain tissues from patients with ASD. The data point to a small set of genes that are consistently dysregulated in humans and mice with ASD or ND. Emerging approaches that use massively parallel cell barcoding (called split and pool barcoding) ([ 10 ][11]), compressed sensing through measuring portions of the transcriptome ([ 11 ][12]), or image-based in situ sequencing ([ 12 ][13]) could enhance screen scalability with single-cell resolution. However, it is not yet clear whether these emerging technologies will yield practical solutions for complex tissues while providing high-information content phenotypes. Furthermore, many disease-associated mutations are not loss of function, but the approach used by Jin et al. aims to introduce mutations that cause gene ablation. Precise CRISPR editors could be an approach for more nuanced dissection of disease-associated mutations. The need to understand and to develop therapies for ASD and all other human diseases is urgent. Scientists are equipped with unprecedented tools. However, each scientist must decide how to spend valuable time and resources. Perhaps more pooled single-cell genomic perturbation screens in mice and human organoids should be the focus, or maybe disease-associated mutations should be introduced into models one by one for deep explorations of the phenotypic effect—or possibly both. Or perhaps that decision should be postponed until new technologies are developed. Progress often poses as many decisions as it provides answers. 1. [↵][14]1. X. Jin et al ., Science 370, eaaz6063 (2020). [OpenUrl][15][Abstract/FREE Full Text][16] 2. [↵][17]1. E. Wieschaus, 2. C. Nüsslein-Volhard , Annu. Rev. Cell Dev. Biol. 32, 1 (2016). [OpenUrl][18][CrossRef][19] 3. [↵][20]1. P. Datlinger et al ., Nat. Methods 14, 297 (2017). [OpenUrl][21][CrossRef][22][PubMed][23] 4. [↵][24]1. A. Dixit et al ., Cell 167, 1853 (2016). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. D. A. Jaitin et al ., Cell 167, 1883 (2016). [OpenUrl][29][CrossRef][30][PubMed][31] 6. [↵][32]1. R. J. Platt et al ., Cell 159, 440 (2014). [OpenUrl][33][CrossRef][34][PubMed][35][Web of Science][36] 7. [↵][37]1. O. Durak et al ., Nat. Neurosci. 19, 1477 (2016). [OpenUrl][38] 8. [↵][39]1. Y. Katayama et al ., Nature 537, 675 (2016). [OpenUrl][40][CrossRef][41][PubMed][42] 9. [↵][43]1. R. J. Platt et al ., Cell Rep. 19, 335 (2017). [OpenUrl][44][CrossRef][45][PubMed][46] 10. [↵][47]1. J. Cao et al ., Science 357, 661 (2017). [OpenUrl][48][Abstract/FREE Full Text][49] 11. [↵][50]1. D. Schraivogel et al ., Nat. Methods 17, 629 (2020). [OpenUrl][51] 12. [↵][52]1. D. Feldman et al ., Cell 179, 787 (2019). [OpenUrl][53][CrossRef][54] Acknowledgments: The authors thank R. Platt, J. Fleck, and Z. He for thoughts and comments. 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领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/304872
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Barbara Treutlein,J. Gray Camp. Sequencing perturbed cortex development[J]. Science,2020.
APA	Barbara Treutlein,&J. Gray Camp.(2020).Sequencing perturbed cortex development.Science.
MLA	Barbara Treutlein,et al."Sequencing perturbed cortex development".Science (2020).