Single-cell transcriptomics : a high-resolution avenue for plant functional genomics

Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Exploring Symbioses by Single-Cell Genomics

Single-cell genomics has advanced the field of microbiology from the analysis of microbial metagenomes where information is “drowning in a sea of sequences,” to recognizing each microbial cell as a separate and unique entity. Single-cell genomics employs Phi29 polymerase-mediated whole-genome amplification to yield microgram-range genomic DNA from single microbial cells. This method has now been applied to a handful of symbiotic systems, including bacterial symbionts of marine sponges, insects (grasshoppers, termites), and vertebrates (mouse, human). In each case, novel insights were obtained into the functional genomic repertoire of the bacterial partner, which, in turn, led to an improved understanding of the corresponding host. Single-cell genomics is particularly valuable when dealing with uncultivated microorganisms, as is still the case for many bacterial symbionts. In this review, we explore the power of single-cell genomics for symbiosis research and highlight recent insights into the symbiotic systems that were obtained by this approach

The future is now: single-cell genomics of bacteria and archaea

Interest in the expanding catalog of uncultivated microorganisms, increasing recognition of heterogeneity among seemingly similar cells, and technological advances in whole-genome amplification and single-cell manipulation are driving considerable progress in single-cell genomics. Here, the spectrum of applications for single-cell genomics, key advances in the development of the field, and emerging methodology for single-cell genome sequencing are reviewed by example with attention to the diversity of approaches and their unique characteristics. Experimental strategies transcending specific methodologies are identified and organized as a road map for future studies in single-cell genomics of environmental microorganisms. Over the next decade, increasingly powerful tools for single-cell genome sequencing and analysis will play key roles in accessing the genomes of uncultivated organisms, determining the basis of microbial community functions, and fundamental aspects of microbial population biology.National Institutes of Health (U.S.) (R01 HG004863)Burroughs Wellcome Fun

Single-cell genomics based on Raman sorting reveals novel carotenoid-containing bacteria in the Red Sea.

Cell sorting coupled with single-cell genomics is a powerful tool to circumvent cultivation of microorganisms and reveal microbial 'dark matter'. Single-cell Raman spectra (SCRSs) are label-free biochemical 'fingerprints' of individual cells, which can link the sorted cells to their phenotypic information and ecological functions. We employed a novel Raman-activated cell ejection (RACE) approach to sort single bacterial cells from a water sample in the Red Sea based on SCRS. Carotenoids are highly diverse pigments and play an important role in phototrophic bacteria, giving strong and distinctive Raman spectra. Here, we showed that individual carotenoid-containing cells from a Red Sea sample were isolated based on the characteristic SCRS. RACE-based single-cell genomics revealed putative novel functional genes related to carotenoid and isoprenoid biosynthesis, as well as previously unknown phototrophic microorganisms including an unculturable Cyanobacteria spp. The potential of Raman sorting coupled to single-cell genomics has been demonstrated

Single-cell genomic analysis in plants

Individual cells in an organism are variable, which strongly impacts cellular processes. Advances in sequencing technologies have enabled single-cell genomic analysis to become widespread, addressing shortcomings of analyses conducted on populations of bulk cells. While the field of single-cell plant genomics is in its infancy, there is great potential to gain insights into cell lineage and functional cell types to help understand complex cellular interactions in plants. In this review, we discuss current approaches for single-cell plant genomic analysis, with a focus on single-cell isolation, DNA amplification, next-generation sequencing, and bioinformatics analysis. We outline the technical challenges of analysing material from a single plant cell, and then examine applications of single-cell genomics and the integration of this approach with genome editing. Finally, we indicate future directions we expect in the rapidly developing field of plant single-cell genomic analysis

Single cell molecular alterations reveal target cells and pathways of concussive brain injury.

The complex neuropathology of traumatic brain injury (TBI) is difficult to dissect, given the convoluted cytoarchitecture of affected brain regions such as the hippocampus. Hippocampal dysfunction during TBI results in cognitive decline that may escalate to other neurological disorders, the molecular basis of which is hidden in the genomic programs of individual cells. Using the unbiased single cell sequencing method Drop-seq, we report that concussive TBI affects previously undefined cell populations, in addition to classical hippocampal cell types. TBI also impacts cell type-specific genes and pathways and alters gene co-expression across cell types, suggesting hidden pathogenic mechanisms and therapeutic target pathways. Modulating the thyroid hormone pathway as informed by the T4 transporter transthyretin Ttr mitigates TBI-associated genomic and behavioral abnormalities. Thus, single cell genomics provides unique information about how TBI impacts diverse hippocampal cell types, adding new insights into the pathogenic pathways amenable to therapeutics in TBI and related disorders

Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics.

At around 150 000 neurons, the adult Drosophila melanogaster central nervous system is one of the largest species, for which a complete cellular catalogue is imminent. While numerically much simpler than mammalian brains, its complexity is still difficult to parse without grouping neurons into consistent types, which can number 1-1000 cells per hemisphere. We review how neuroanatomical and gene expression data are being used to discover neuronal types at scale. The correlation among multiple co-varying neuronal properties, including lineage, gene expression, morphology, connectivity, response properties and shared behavioral significance is essential to the definition of neuronal cell type. Initial studies comparing morphological and transcriptomic definitions of neuronal type suggest that these are highly consistent, but there is much to do to match these approaches brain-wide. Matched single-cell transcriptomic and morphological data provide an effective reference point to integrate other data types, including connectomics data. This will significantly enhance our ability to make functional predictions from brain wiring diagrams as well facilitating molecular genetic manipulation of neuronal types

Single-nucleus RNA sequencing of plant tissues using a nanowell‐based system

Single-cell genomics provides unprecedented potential for research on plant development and environmental responses. Here, we introduce a generic procedure for plant nucleus isolation combined with nanowell-based library preparation. Our method enables the transcriptome analysis of thousands of individual plant nuclei. It serves as an alternative to the use of protoplast isolation, which is currently a standard methodology for plant single-cell genomics, although it can be challenging for some plant tissues. We show the applicability of our nucleus isolation method by using different plant materials from different species. The potential of our single-nucleus RNA sequencing method is shown through the characterization of transcriptomes of seedlings and developing flowers from Arabidopsis thaliana. We evaluated the transcriptome dynamics during the early stages of anther development, identified stage-specific activities of transcription factors regulating this process, and predicted potential target genes of these transcription factors. Our nucleus isolation procedure can be applied in different plant species and tissues, thus expanding the toolkit for plant single-cell genomics experiments.Peer Reviewe

Non Equilibrium Physics of Single-Cell Genomics

The self-organisation of cells into complex tissues relies on the tight regulation of molecular processes governing their behaviour. Understanding these processes is a central questions in cell biology. In recent years, technological breakthroughs in single-cell sequencing experiments have enabled us to probe these processes with unprecedented molecular detail. However, biological function relies on collective processes on the mesoscopic and macroscopic scale, which do not necessarily obey the rules that govern it on the microscopic scale. Insights from these experiments on how collective processes determine cellular behaviour consequently remain severely limited. Methods from nonequilibrium statistical physics provide a rigorous framework to connect microscopic measurements to their mesoscopic or macroscopic consequences. In this thesis, by combining for the first time the possibilities of single-cell technologies and tools from nonequilbrium statistical physics, we develop theoretical frameworks that overcome these conceptual limitations. In particular, we derive a theory that maps measurements along the linear sequence of the DNA to mesoscopic processes in space and time in the cell nucleus. We demonstrate this approach in the context of the establishment of chemical modifications of the DNA (DNA methylation) during early embryonic development. Drawing on sequencing experiments both in vitro and in vivo, we find that the embryonic DNA methylome is established through the interplay between DNA methylation and 30-40 nm dynamic chromatin condensates. This interplay gives rise to hallmark scaling behaviour with an exponent of 5/2 in the time evolution of embryonic DNA methylation and time dependent, scale-free connected correlation functions, both of which are predicted by our theory. Using this theory, we successfully identify regions of the DNA that carry DNA methylation patterns anticipating cellular symmetry breaking in vivo. The primary layer determining cell identity is gene expression. However, read-outs of gene-expression profiling experiments are dominated by systematic technical noise and they do not provide “stochiometric” measurements that allow experimental data to be predicted by theories. Here, by developing effective spin glass methods, we show that the macroscopic propagation of fluctuations in the concentration of mRNA molecules gives direct information on the physical mechanisms governing cell states, independent of technical bias. We find that gene expression fluctuations may exhibit glassy behaviour such that they are long-lived and carry biological information. We demonstrate the biological relevance of glassy fluctuations by analysing single-cell RNA sequencing experiments of mouse neurogenesis. Taken together, we overcome important conceptual limitations of emerging technologies in biology and pioneer the application of methods from stochastic processes, spin glasses, field and renormalization group theories to single-cell genomics