Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

A single-embryo, single-cell time-resolved model for mouse gastrulation.

Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.

Integrated analysis of multimodal single-cell data.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

A stony coral cell atlas illuminates the molecular and cellular basis of coral symbiosis, calcification, and immunity.

Stony corals are colonial cnidarians that sustain the most biodiverse marine ecosystems on Earth: coral reefs. Despite their ecological importance, little is known about the cell types and molecular pathways that underpin the biology of reef-building corals. Using single-cell RNA sequencing, we define over 40 cell types across the life cycle of Stylophora pistillata. We discover specialized immune cells, and we uncover the developmental gene expression dynamics of calcium-carbonate skeleton formation. By simultaneously measuring the transcriptomes of coral cells and the algae within them, we characterize the metabolic programs involved in symbiosis in both partners. We also trace the evolution of these coral cell specializations by phylogenetic integration of multiple cnidarian cell type atlases. Overall, this study reveals the molecular and cellular basis of stony coral biology.

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages.

Clear cell renal carcinoma (ccRCC) is a heterogeneous disease with a variable post-surgical course. To assemble a comprehensive ccRCC tumor microenvironment (TME) atlas, we performed single-cell RNA sequencing (scRNA-seq) of hematopoietic and non-hematopoietic subpopulations from tumor and tumor-adjacent tissue of treatment-naive ccRCC resections. We leveraged the VIPER algorithm to quantitate single-cell protein activity and validated this approach by comparison to flow cytometry. The analysis identified key TME subpopulations, as well as their master regulators and candidate cell-cell interactions, revealing clinically relevant populations, undetectable by gene-expression analysis. Specifically, we uncovered a tumor-specific macrophage subpopulation characterized by upregulation of TREM2/APOE/C1Q, validated by spatially resolved, quantitative multispectral immunofluorescence. In a large clinical validation cohort, these markers were significantly enriched in tumors from patients who recurred following surgery. The study thus identifies TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker for ccRCC recurrence, as well as a candidate therapeutic target.

Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing.

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNA-seq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell-state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap-junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

MorphoSeq: Full Single-Cell Transcriptome Dynamics Up to Gastrulation in a Chordate.

Single-cell RNA sequencing (scRNA-seq) provides a leap forward in resolving cellular diversity and developmental trajectories but fails to comprehensively delineate the spatial organization and precise cellular makeup of individual embryos. Here, we reconstruct from scRNA-seq and light sheet imaging data a canonical digital embryo that captures the genome-wide gene expression trajectory of every single cell at every cell division in the 18 lineages up to gastrulation in the ascidian Phallusia mammillata. By using high-coverage scRNA-seq, we devise a computational framework that stratifies single cells of individual embryos into cell types without prior knowledge. Unbiased transcriptome data analysis mapped each cell's physical position and lineage history, yielding the complete history of gene expression at the genome-wide level for every single cell in a developing embryo. A comparison of individual embryos reveals both extensive reproducibility between symmetric embryo sides and a large inter-embryonic variability due to small differences in embryogenesis timing.

Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for defining cellular diversity in tumors, but its application toward dissecting mechanisms underlying immune-modulating therapies is scarce. We performed scRNA-seq analyses on immune and stromal populations from colorectal cancer patients, identifying specific macrophage and conventional dendritic cell (cDC) subsets as key mediators of cellular cross-talk in the tumor microenvironment. Defining comparable myeloid populations in mouse tumors enabled characterization of their response to myeloid-targeted immunotherapy. Treatment with anti-CSF1R preferentially depleted macrophages with an inflammatory signature but spared macrophage populations that in mouse and human expresses pro-angiogenic/tumorigenic genes. Treatment with a CD40 agonist antibody preferentially activated a cDC population and increased Bhlhe40+ Th1-like cells and CD8+ memory T cells. Our comprehensive analysis of key myeloid subsets in human and mouse identifies critical cellular interactions regulating tumor immunity and defines mechanisms underlying myeloid-targeted immunotherapies currently undergoing clinical testing.

Lymphoma Driver Mutations in the Pathogenic Evolution of an Iconic Human Autoantibody.

Pathogenic autoantibodies arise in many autoimmune diseases, but it is not understood how the cells making them evade immune checkpoints. Here, single-cell multi-omics analysis demonstrates a shared mechanism with lymphoid malignancy in the formation of public rheumatoid factor autoantibodies responsible for mixed cryoglobulinemic vasculitis. By combining single-cell DNA and RNA sequencing with serum antibody peptide sequencing and antibody synthesis, rare circulating B lymphocytes making pathogenic autoantibodies were found to comprise clonal trees accumulating mutations. Lymphoma driver mutations in genes regulating B cell proliferation and V(D)J mutation (CARD11, TNFAIP3, CCND3, ID3, BTG2, and KLHL6) were present in rogue B cells producing the pathogenic autoantibody. Antibody V(D)J mutations conferred pathogenicity by causing the antigen-bound autoantibodies to undergo phase transition to insoluble aggregates at lower temperatures. These results reveal a pre-neoplastic stage in human lymphomagenesis and a cascade of somatic mutations leading to an iconic pathogenic autoantibody.

Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation.

Poor reproducibility within and across studies arising from lack of knowledge regarding the performance of extracellular RNA (exRNA) isolation methods has hindered progress in the exRNA field. A systematic comparison of 10 exRNA isolation methods across 5 biofluids revealed marked differences in the complexity and reproducibility of the resulting small RNA-seq profiles. The relative efficiency with which each method accessed different exRNA carrier subclasses was determined by estimating the proportions of extracellular vesicle (EV)-, ribonucleoprotein (RNP)-, and high-density lipoprotein (HDL)-specific miRNA signatures in each profile. An interactive web-based application (miRDaR) was developed to help investigators select the optimal exRNA isolation method for their studies. miRDar provides comparative statistics for all expressed miRNAs or a selected subset of miRNAs in the desired biofluid for each exRNA isolation method and returns a ranked list of exRNA isolation methods prioritized by complexity, expression level, and reproducibility. These results will improve reproducibility and stimulate further progress in exRNA biomarker development.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing.

Understanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single-neuron resolution and high throughput, a challenging combination with conventional methods. Here, we introduce barcoded anatomy resolved by sequencing (BARseq), a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3,579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (intratelencephalic [IT], pyramidal tract-like [PT-like], and corticothalamic [CT]) of projection neurons. In depth analysis uncovered a projection type restricted almost exclusively to transcriptionally defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.

Widespread and Functional RNA Circularization in Localized Prostate Cancer.

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.

The Landscape of Circular RNA in Cancer.

Circular RNAs (circRNAs) are an intriguing class of RNA due to their covalently closed structure, high stability, and implicated roles in gene regulation. Here, we used an exome capture RNA sequencing protocol to detect and characterize circRNAs across >2,000 cancer samples. When compared against Ribo-Zero and RNase R, capture sequencing significantly enhanced the enrichment of circRNAs and preserved accurate circular-to-linear ratios. Using capture sequencing, we built the most comprehensive catalog of circRNA species to date: MiOncoCirc, the first database to be composed primarily of circRNAs directly detected in tumor tissues. Using MiOncoCirc, we identified candidate circRNAs to serve as biomarkers for prostate cancer and were able to detect circRNAs in urine. We further detected a novel class of circular transcripts, termed read-through circRNAs, that involved exons originating from different genes. MiOncoCirc will serve as a valuable resource for the development of circRNAs as diagnostic or therapeutic targets across cancer types.

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

Atlas of Subcellular RNA Localization Revealed by APEX-Seq.

We introduce APEX-seq, a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome as a resource, revealing extensive patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.

Deciphering the "m6A Code" via Antibody-Independent Quantitative Profiling.

N6-methyladenosine (m6A) is the most abundant modification on mRNA and is implicated in critical roles in development, physiology, and disease. A major limitation has been the inability to quantify m6A stoichiometry and the lack of antibody-independent methodologies for interrogating m6A. Here, we develop MAZTER-seq for systematic quantitative profiling of m6A at single-nucleotide resolution at 16%-25% of expressed sites, building on differential cleavage by an RNase. MAZTER-seq permits validation and de novo discovery of m6A sites, calibration of the performance of antibody-based approaches, and quantitative tracking of m6A dynamics in yeast gametogenesis and mammalian differentiation. We discover that m6A stoichiometry is "hard coded" in cis via a simple and predictable code, accounting for 33%-46% of the variability in methylation levels and allowing accurate prediction of m6A loss and acquisition events across evolution. MAZTER-seq allows quantitative investigation of m6A regulation in subcellular fractions, diverse cell types, and disease states.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Phenotypic Convergence: Distinct Transcription Factors Regulate Common Terminal Features.

Transcription factors regulate the molecular, morphological, and physiological characteristics of neurons and generate their impressive cell-type diversity. To gain insight into the general principles that govern how transcription factors regulate cell-type diversity, we used large-scale single-cell RNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single cells and assigned them to 52 clusters. We validated and annotated many clusters using RNA sequencing of FACS-sorted single-cell types and cluster-specific genes. To identify transcription factors responsible for inducing specific terminal differentiation features, we generated a "random forest" model, and we showed that the transcription factors Apterous and Traffic-jam are required in many but not all cholinergic and glutamatergic neurons, respectively. In fact, the same terminal characters often can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.

RNA profiles are an informative phenotype of cellular and tissue states but can be costly to generate at massive scale. Here, we describe how gene expression levels can be efficiently acquired with random composite measurements-in which abundances are combined in a random weighted sum. We show (1) that the similarity between pairs of expression profiles can be approximated with very few composite measurements; (2) that by leveraging sparse, modular representations of gene expression, we can use random composite measurements to recover high-dimensional gene expression levels (with 100 times fewer measurements than genes); and (3) that it is possible to blindly recover gene expression from composite measurements, even without access to training data. Our results suggest new compressive modalities as a foundation for massive scaling in high-throughput measurements and new insights into the interpretation of high-dimensional data.