Tissue and cellular spatiotemporal dynamics in colon aging.

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease.

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE-gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP-ARE-gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

Assessment of spatial transcriptomics for oncology discovery.

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function.

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Author Correction: Nuclei multiplexing with barcoded antibodies for single-nucleus genomics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Human and Mouse Enteric Nervous System at Single-Cell Resolution.

The enteric nervous system (ENS) coordinates diverse functions in the intestine but has eluded comprehensive molecular characterization because of the rarity and diversity of cells. Here we develop two methods to profile the ENS of adult mice and humans at single-cell resolution: RAISIN RNA-seq for profiling intact nuclei with ribosome-bound mRNA and MIRACL-seq for label-free enrichment of rare cell types by droplet-based profiling. The 1,187,535 nuclei in our mouse atlas include 5,068 neurons from the ileum and colon, revealing extraordinary neuron diversity. We highlight circadian expression changes in enteric neurons, show that disease-related genes are dysregulated with aging, and identify differences between the ileum and proximal/distal colon. In humans, we profile 436,202 nuclei, recovering 1,445 neurons, and identify conserved and species-specific transcriptional programs and putative neuro-epithelial, neuro-stromal, and neuro-immune interactions. The human ENS expresses risk genes for neuropathic, inflammatory, and extra-intestinal diseases, suggesting neuronal contributions to disease.

Nuclei multiplexing with barcoded antibodies for single-nucleus genomics.

Single-nucleus RNA-seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate or are frozen, and opens the way to human genetics studies, clinical trials, and precise cell atlases of large organs. However, such applications are currently limited by batch effects, processing, and costs. Here, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter recovered profiles. We develop DemuxEM, a computational tool that detects inter-sample multiplets and assigns singlets to their sample of origin, and validate its accuracy using sex-specific gene expression, species-mixing and natural genetic variation. Our approach will facilitate tissue atlases of isogenic model organisms or from multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.

Genome-Wide Maps of m6A circRNAs Identify Widespread and Cell-Type-Specific Methylation Patterns that Are Distinct from mRNAs.

N6-methyladenosine (m6A) is the most abundant internal modification of mRNAs and is implicated in all aspects of post-transcriptional RNA metabolism. However, little is known about m6A modifications to circular (circ) RNAs. We developed a computational pipeline (AutoCirc) that, together with depletion of ribosomal RNA and m6A immunoprecipitation, defined thousands of m6A circRNAs with cell-type-specific expression. The presence of m6A circRNAs is corroborated by interaction between circRNAs and YTHDF1/YTHDF2, proteins that read m6A sites in mRNAs, and by reduced m6A levels upon depletion of METTL3, the m6A writer. Despite sharing m6A readers and writers, m6A circRNAs are frequently derived from exons that are not methylated in mRNAs, whereas mRNAs that are methylated on the same exons that compose m6A circRNAs exhibit less stability in a process regulated by YTHDF2. These results expand our understanding of the breadth of m6A modifications and uncover regulation of circRNAs through m6A modification.

m(6)A-LAIC-seq reveals the census and complexity of the m(6)A epitranscriptome.

N(6)-Methyladenosine (m(6)A) is a widespread, reversible chemical modification of RNA molecules, implicated in many aspects of RNA metabolism. Little quantitative information exists as to either how many transcript copies of particular genes are m(6)A modified ('m(6)A levels') or the relationship of m(6)A modification(s) to alternative RNA isoforms. To deconvolute the m(6)A epitranscriptome, we developed m(6)A-level and isoform-characterization sequencing (m(6)A-LAIC-seq). We found that cells exhibit a broad range of nonstoichiometric m(6)A levels with cell-type specificity. At the level of isoform characterization, we discovered widespread differences in the use of tandem alternative polyadenylation (APA) sites by methylated and nonmethylated transcript isoforms of individual genes. Strikingly, there is a strong bias for methylated transcripts to be coupled with proximal APA sites, resulting in shortened 3' untranslated regions, while nonmethylated transcript isoforms tend to use distal APA sites. m(6)A-LAIC-seq yields a new perspective on transcriptome complexity and links APA usage to m(6)A modifications.