Search

Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations).

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactionsvs. instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA-seq and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

Rapid advances in single-cell-, spatial-, and multi-omics, allow us to profile cellular ecosystems in tissues at unprecedented resolution, scale, and depth. However, both technical limitations, such as low spatial resolution and biological variations, such as continuous spectra of cell states, often render these data imperfect representations of cellular systems, best captured as continuous mixtures over cells or molecules. Based on this conceptual insight, we build a versatile framework, TACCO (Transfer of Annotations to Cells and their COmbinations) that extends an Optimal Transport-based core by different wrappers or boosters to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatio-molecular tissue structure at the cell and molecular level, and resolve differentiation trajectories. TACCO excels in speed, scalability, and adaptability, while successfully outperforming benchmarks across diverse synthetic and biological datasets. Along with highly optimized visualization and analysis functions, TACCO forms a comprehensive integrated framework for studies of high-dimensional, high-resolution biology.

While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq and spatial transcriptomics by Slide-seq, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled an inducible genetic CRC mouse model that recapitulates key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Three cellular neighborhood archetypes were associated with tumor progression, were active at the same time in different spatial parts of the same tumor, involved dysplasia-specific cellular layouts, and relied on distinct mechanisms: (1) inflammatory epithelial regions with endothelial cells and monocytes expressing angiogenesis, inflammation and invasion programs; (2) epithelial stem-like regions, associated with plasma and B cell activity; and (3) epithelial-to-mesenchymal transition (EMT) regions with dysplastic cells expressing a Wnt signaling program. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved in both species, with mouse archetypal neighborhoods correlated with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease.

Apolipoprotein B (ApoB) is the primary protein of chylomicrons, VLDLs and LDLs and is essential for their assembly. Defects in ApoB synthesis and secretion result in several human diseases, including abetalipoproteinemia and familial hypobetalipoproteinemia. Conversely, high levels of APOB in plasma are associated with increased risk for coronary heart disease and atherosclerosis.The involvement of APOB in lipid metabolism and atherogenesis prompted the generation of several mutant mice. However, as APOB is required for supplying nutrients to the developing embryo,ApoBnull mice are embryonic lethal, thereby precluding the study of the roles of this protein during development.Here, we established novel zebrafish mutants for twoapoBgenes:apoBaandapoBb.1. Double-mutant embryos display clear hallmarks of human hypolipidemia-related diseases, including intestinal defects and fatty liver, as well as profound vascular defects. We further use these models to identify the domains within ApoB responsible for its functions. By assessing the ability of different truncated forms of human APOB to rescue the mutant phenotypes, we demonstrate the benefits of this model for prospective therapeutic screens. Overall, our novel zebrafish models uncover new functions of ApoB in organ development and morphogenesis and shed new light on the mechanisms underlying hypolipidemia-related diseases.

Intrauterine growth restriction (IUGR) of fetuses affects 5-10% of pregnancies and is associated with perinatal morbidity, mortality and long-term health issues. Understanding genetic predisposition to IUGR is challenging, owing to extensive gene polymorphisms, linkage disequilibrium, and maternal and paternal influence. Here, we demonstrate that the inhibitory receptor, KIR2DL1, expressed on maternal uterine natural killer (uNK) cells, in interaction with the paternally-inherited HLA-C*05, an HLA-C group 2 allotype, expressed on fetal trophoblast cells, causes IUGR in a humanised mouse model. Micro-CT imaging of the uteroplacental vasculature revealed reduced uterine spiral artery diameter and increased segment length, increasing fetal blood flow resistance. Single cell RNA-Seq from the maternal-fetal interface highlighted expression programs activated by KIR2DL1-induced IUGR in several placental cell types, including degradation of extracellular matrix components, angiogenesis, and uNK cell communication, suggesting a complex response underlying IUGR. As current IUGR treatments are insufficient, our findings provide important insight for drug development.

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organ’s common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7–10. Conversely, Spatial Transcriptomics allows forin-situmeasurements11–13at the histological level, but at lower spatial resolution and with limited sensitivity. Targetedin situtechnologies1–3solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4(Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ∼30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation ofin-situtechnologies.

Single nucleus RNA-seq (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-seq, massively parallel sNuc-Seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2′-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues., National Institute of Mental Health (U.S.) (Grant U01MH105960), National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706), National Institute of Mental Health (U.S.) (Grant 1R01-MH110049)

This article describes methods for isolating mouse monocytes and neutrophils, as well as in vitro protocols for measuring cell phagocytosis, migration, and polarization. The method employed here for the isolation of naive phagocytes overcomes many of the difficulties previously encountered concerning phagocyte activation. Three in vitro protocols are provided for the analysis of cell migration, one requiring no specialized equipment, one requiring a modified Boyden chamber, and the other employing a flow chamber, which measures cell adhesion, rolling, and migration. Three in vitro protocols to examine phagocytosis have been included in this updated version. Finally, a method is provided for imaging polarized cells by confocal microscopy. © 2018 by John Wiley & Sons, Inc.

Improved understanding of lipoproteins, particles that transport lipids throughout the circulation, is vital to developing new treatments for the dyslipidemias associated with metabolic syndrome. Apolipoproteins are a key component of lipoproteins. Apolipoproteins are proteins that structure lipoproteins and regulate lipid metabolism through control of cellular lipid exchange. Constraints of cell culture and mouse models mean that there is a need for a complementary model that can replicate the complex in vivo milieu that regulates apolipoprotein and lipoprotein biology. Here, we further establish the utility of the genetically tractable and optically clear larval zebrafish as a model of apolipoprotein biology. Gene ancestry analyses were implemented to determine the closest human orthologs of the zebrafish apolipoprotein A-I (apoA-I), apoB, apoE and apoA-IV genes and therefore ensure that they have been correctly named. Their expression patterns throughout development were also analyzed, by whole-mount mRNA in situ hybridization (ISH). The ISH results emphasized the importance of apolipoproteins in transporting yolk and dietary lipids: mRNA expression of all apolipoproteins was observed in the yolk syncytial layer, and intestinal and liver expression was observed from 4-6 days post-fertilization (dpf). Furthermore, real-time PCR confirmed that transcription of three of the four zebrafish apoA-IV genes was increased 4 hours after the onset of a 1-hour high-fat feed. Therefore, we tested the hypothesis that zebrafish ApoA-IV performs a conserved role to that in rat in the regulation of food intake by transiently overexpressing ApoA-IVb.1 in transgenic larvae and quantifying ingestion of co-fed fluorescently labeled fatty acid during a high-fat meal as an indicator of food intake. Indeed, ApoA-IVb.1 overexpression decreased food intake by approximately one-third. This study comprehensively describes the expression and function of eleven zebrafish apolipoproteins and serves as a springboard for future investigations to elucidate their roles in development and disease in the larval zebrafish model.

Currently, most single cell protocols require the preparation of a single cell suspension from fresh tissue, a major roadblock to clinical deployment, to archived materials and to certain tissues such asadult brain. In the adultbrainthe harsh enzymatic dissociation harms the integrity of the cells and theirRNA, and biases toward easily dissociated cell types, and is restricted to young animals. We developed DroNc-seq, a droplet microfluidic andDNAbarcoding technique for analysis ofRNAprofiles of single nuclei from fresh, frozen or lightly fixed tissues at high throughput and low cost. The utility of DroNc-Seq lies in working with hard-to-dissociate, frozen and/or archived tissues. To demonstrate the utility of this technique, we sequenced over 39 thousand nuclei from mouse and human archived brain samples, including post-mortem human brain tissue from GTEx project.

Single nucleus RNA-Seq (sNuc-Seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-Seq, massively parallel sNuc-Seq with droplet technology. We profile 29,543 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for charting systematic cell atlases.