Massively multiplexed sequencing of RNA in individual cells is transforming basic and clinical life sciences[1-4]. In standard experiments, however, tissues must be first dissociated. Thus, crucial information about spatial relationships between cells, along with the tissue-wide expression patterns they confer, is lost. This poses a fundamental problem for elucidating collective function of tissues, developmental pathways, and mechanisms of cell-to-cell communication[5, 6]. Considerable efforts to overcome this challenge have been undertaken. However, experimental methods are either technically challenging, or have limited resolution or throughput[5, 7, 8]. Existing computational approaches predict spatial positions by comparing each sequenced cell, independently, to an imaging-derived spatial gene expression database for that tissue [9, 10].However, these approaches rely on prior knowledge of spatial expression patterns which often does not exist, or is difficult to construct. Here, we explore a radically different idea. We postulate that cells in spatial proximity, overall, share more similar transcriptional profiles than cells farther apart. We validate this hypothesis for several complex biological systems. Consequently, we seek to find spatial arrangements of sequenced cells on tissue space which optimally preserve this principle. We show that this hard optimization problem can be cast as a generalized optimal transport problem for probabilistic embedding, for which we derived an efficient iterative algorithm. We successfully reconstruct the mammalian liver, intestinal epithelium, fly and zebrafish embryos, cerebellum sections and kidney. We then use the reconstructed tissues to infer spatially informative genes directly from single cell data. Our results demonstrate that we have identified a spatial expression organization principle in animal tissues which can be used to infer meaningful spatial position probabilities for individual cells. Our framework ("novoSpaRc") is flexible, can naturally incorporate prior spatial information, is scalable to large number of cells and compatible with any single-cell technology. We envision that novoSpaRc can be valuable in collaborative efforts to characterize various tissues [11,12], and that additional or generalized principles underlying spatial organization of gene expression can be formulated and tested using our approach.Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of the rich heterogeneous cellular populations that compose tissues, the dynamics of developmental processes, and the underlying regulatory mechanisms that control cellular function [1][2][3][4]. However, to understand how

By the onset of morphogenesis, embryos consist of about 6000 cells that express distinct gene combinations. Here, we used single-cell sequencing of precisely staged embryos and devised DistMap, a computational mapping strategy to reconstruct the embryo and to predict spatial gene expression approaching single-cell resolution. We produced a virtual embryo with about 8000 expressed genes per cell. Our interactive Virtual Expression eXplorer (DVEX) database generates three-dimensional virtual in situ hybridizations and computes gene expression gradients. We used DVEX to uncover patterned expression of transcription factors and long noncoding RNAs, as well as signaling pathway components. Spatial regulation of Hippo signaling during early embryogenesis suggests a mechanism for establishing asynchronous cell proliferation. Our approach is suitable to generate transcriptomic blueprints for other complex tissues.

Drosophila is a premier model system for understanding the molecular mechanisms of development. By the onset of morphogenesis, ~6000 cells express distinct gene combinations according to embryonic position. Despite extensive mRNA in situ screens, combinatorial gene expression within individual cells is largely unknown. Therefore, it is difficult to comprehensively identify the coding and non-coding transcripts that drive patterning and to decipher the molecular basis of cellular identity. Here, we single-cell sequence precisely staged embryos, measuring >3100 genes per cell. We produce a 'transcriptomic blueprint' of development -a virtual embryo where 3D locations of sequenced cells are confidently identified.Our "Drosophila-Virtual-Expression-eXplorer" performs virtual in situ hybridizations and computes expression gradients. Using DVEX, we predict spatial expression and discover patterned lncRNAs. DEVX is sensitive enough to detect subtle evolutionary changes in expression patterns between Drosophila species. We believe DVEX is a prototype for powerful single cell studies in complex tissues.

Although mRNAs are key molecules for understanding life, there exists no method to determine the full-length sequence of endogenous mRNAs including their poly(A) tails. Moreover, although poly(A) tails can be modified in functionally important ways, there also exists no method to accurately sequence them. Here, we present FLAM-seq, a rapid and simple method for high-quality sequencing of entire mRNAs. We report a cDNA library preparation method coupled to single-molecule sequencing to perform FLAM-seq. Using human cell lines, brain organoids, and C. elegans we show that FLAM-seq delivers high-quality full-length mRNA sequences for thousands of different genes per sample. We find that (a) 3' UTR length is correlated with poly(A) tail length, (b) alternative polyadenylation sites and alternative promoters for the same gene are linked to different tail lengths, (c) tails contain a significant number of cytosines. Thus, we provide a widely useful method and fundamental insights into poly(A) tail regulation.

BackgroundRecent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not altered by stress or ageing. Other challenges are rare cells that need to be collected over several days or samples prepared at different times or locations.MethodsHere, we used chemical fixation to address these problems. Methanol fixation allowed us to stabilise and preserve dissociated cells for weeks without compromising single-cell RNA sequencing data.ResultsBy using mixtures of fixed, cultured human and mouse cells, we first showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary cells from dissociated, complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells prepared by fluorescence-activated cell sorting, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data.ConclusionsWe expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single-cell resolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0383-5) contains supplementary material, which is available to authorized users.