Imaging the transcriptome in situ with high accuracy has been a major challenge in single cell biology, particularly hindered by the limits of optical resolution and the density of transcripts in single cells [1][2][3][4][5] . Here, we demonstrate seqFISH+, that can image the mRNAs for 10,000 genes in single cells with high accuracy and sub-diffraction-limit resolution, in the mouse brain cortex, subventricular zone, and the olfactory bulb, using a standard confocal microscope. The transcriptome level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand-receptor pairs across neighboring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ. Spatial genomics, the analysis of the transcriptome and other genomic information directly in the native context of tissues, is crucial to many fields in biology, including neuroscience and developmental biology. Pioneering work in single molecule Fluorescence in situ Reprints and permissions information is available at www.nature.com/reprintsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

Fetal hemoglobin (HbF, αγ) level is genetically controlled and modifies severity of adult hemoglobin (HbA, αβ) disorders, sickle cell disease, and β-thalassemia. Common genetic variation affects expression of BCL11A, a regulator of HbF silencing. To uncover how BCL11A supports the developmental switch from γ- to β- globin, we use a functional assay and protein binding microarray to establish a requirement for a zinc-finger cluster in BCL11A in repression and identify a preferred DNA recognition sequence. This motif appears in embryonic and fetal-expressed globin promoters and is duplicated in γ-globin promoters. The more distal of the duplicated motifs is mutated in individuals with hereditary persistence of HbF. Using the CUT&RUN approach to map protein binding sites in erythroid cells, we demonstrate BCL11A occupancy preferentially at the distal motif, which can be disrupted by editing the promoter. Our findings reveal that direct γ-globin gene promoter repression by BCL11A underlies hemoglobin switching.

The rapid development of novel spatial transcriptomics technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational algorithms and pipelines. Here we present Giotto, a comprehensive, flexible, robust, and open-source pipeline for spatial transcriptomic data analysis and visualization. The data analysis module implements a wide range of algorithms ranging from basic tasks such as data pre-processing to innovative approaches for cell-cell interaction characterization. The data visualization module provides a user-friendly workspace that allows users to interactively visualize, explore and compare multiple layers of information. These two modules can be used iteratively for refined analysis and hypothesis development. We illustrate the functionalities of Giotto by using the recently published seqFISH+ dataset for mouse brain. Our analysis highlights the utility of Giotto for characterizing tissue spatial organization as well as for the interactive exploration of multi-layer information in spatial transcriptomic and imaging data. We find that single-cell resolution spatial information is essential for the investigation of ligandreceptor mediated cell-cell interactions. Giotto is generally applicable and can be easily integrated with external software packages for multi-omic data integration. Giotto facilitates the comprehensive analysis of single-cell spatial transcriptomic dataGiotto Analyzer is written in the popular language R. The core data structure is a simple and flexible S4 object ( Fig. 2A). Raw and processed count matrices are represented as a base matrix in R, while other annotations and metadata is encoded by an igraph network or a data.table. The former is a powerful library to work with networks, and the latter is a simple but intuitive table format with excellent performance for large-scale operations. In total, the Giotto uncovers different layers of spatial expression variabilityA key component of Giotto Analyzer is the implementation of a wide range of computational methods for spatial gene expression pattern identification. On a basic level, Giotto Analyzer can reduce the single-cell resolution data to a spatial grid through averaging (Supplementary Fig. 2A, B). Principal component analysis (PCA) is applied to the gridaverage data and significant principal components, along with their associated genes, are identified and reported. Using the aforementioned seqFISH+ dataset as an example, we found that the first principal component (PC) separates the outer layer extremities from the other layers. This is likely due to differences in cell-type compositions as most layers correlate with Slc17a7 expression, a marker for glutamatergic neurons, while the extremities show higher abundance of astrocytes and oligodendrocytes (Fig. 3A, top, Fig. 2D). In contrast, the second PC separates the outer and inner layers, which have similar cell-type composit...

Varying degrees of reduction of genetic diversity in crops relative to their wild progenitors occurred during the process of domestication. Such information, however, has not been available for the Asian cultivated rice (Oryza sativa) despite its importance as a staple food and a model organism. To reveal levels and patterns of nucleotide diversity and to elucidate the genetic relationship and demographic history of O. sativa and its close relatives (Oryza rufipogon and Oryza nivara), we investigated nucleotide diversity data from 10 unlinked nuclear loci in species-wide samples of these species. The results indicated that O. rufipogon and O. nivara possessed comparable levels of nucleotide variation ((sil) = 0.0077 approximately 0.0095) compared with the relatives of other crops. In contrast, nucleotide diversity of O. sativa was as low as (sil) = 0.0024 and even lower ((sil) = 0.0021 for indica and 0.0011 for japonica), if we consider the 2 subspecies separately. Overall, only 20-10% of the diversity in the wild species was retained in 2 subspecies of the cultivated rice (indica and japonica), respectively. Because statistic tests did not reject the assumption of neutrality for all 10 loci, we further used coalescent to simulate bottlenecks under various lengths and population sizes to better understand the domestication process. Consistent with the dramatic reduction in nucleotide diversity, we detected a severe domestication bottleneck and demonstrated that the sequence diversity currently found in the rice genome could be explained by a founding population of 1,500 individuals if the initial domestication event occurred over a 3,000-year period. Phylogenetic analyses revealed close genetic relationships and ambiguous species boundary of O. rufipogon and O. nivara, providing additional evidence to treat them as 2 ecotypes of a single species. Lowest linkage disequilibrium (LD) was found in the perennial O. rufipogon where the r(2) value dropped to a negligible level within 400 bp, and the highest in the japonica rice where LD extended to the entirely sequenced region ( approximately 900 bp), implying that LD mapping by genome scans may not be feasible in wild rice due to the high density of markers needed.

Cell-lineage–specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage–specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage–specific expression, even in lineages not separable by experimental microdissection. Our machine-learning–based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage–specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.

Summary• The A-genome group in Oryza consists of eight diploid species and is distributed world-wide. Here we reconstructed the phylogeny among the A-genome species based on sequences of nuclear genes and MITE (miniature inverted-repeat transposable elements) insertions.• Thirty-seven accessions representing two cultivated and six wild species from the A-genome group were sampled. Introns of four nuclear single-copy genes on different chromosomes were sequenced and analysed by both maximum parsimony (MP) and Bayesian inference methods.• All the species except for Oryza rufipogon and Oryza nivara formed a monophyletic group and the Australian endemic Oryza meridionalis was the earliest divergent lineage. Two subspecies of Oryza sativa (ssp. indica and ssp. japonica ) formed two separate monophyletic groups, suggestive of their polyphyletic origin. Based on molecular clock approach, we estimated that the divergence of the Agenome group occurred c . 2.0 million years ago (mya) while the two subspecies ( indica and japonica ) separated c . 0.4 mya.• Intron sequences of nuclear genes provide sufficient resolution and are informative for phylogenetic inference at lower taxonomic levels.