SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data.

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling.

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on increased Janus kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice by up-regulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed-lineage cells with increased JAK/STAT (signal transducer and activator of transcription) and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

Author Correction: Determination of RNA structural diversity and its role in HIV-1 RNA splicing.

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

Determination of RNA structural diversity and its role in HIV-1 RNA splicing.

Human immunodeficiency virus 1 (HIV-1) is a retrovirus with a ten-kilobase single-stranded RNA genome. HIV-1 must express all of its gene products from a single primary transcript, which undergoes alternative splicing to produce diverse protein products that include structural proteins and regulatory factors1,2. Despite the critical role of alternative splicing, the mechanisms that drive the choice of splice site are poorly understood. Synonymous RNA mutations that lead to severe defects in splicing and viral replication indicate the presence of unknown cis-regulatory elements3. Here we use dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) to investigate the structure of HIV-1 RNA in cells, and develop an algorithm that we name 'detection of RNA folding ensembles using expectation-maximization' (DREEM), which reveals the alternative conformations that are assumed by the same RNA sequence. Contrary to previous models that have analysed population averages4, our results reveal heterogeneous regions of RNA structure across the entire HIV-1 genome. In addition to confirming that in vitro characterized5 alternative structures for the HIV-1 Rev responsive element also exist in cells, we discover alternative conformations at critical splice sites that influence the ratio of transcript isoforms. Our simultaneous measurement of splicing and intracellular RNA structure provides evidence for the long-standing hypothesis6-8 that heterogeneity in RNA conformation regulates splice-site use and viral gene expression.

DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo.

Coupling of structure-specific in vivo chemical modification to next-generation sequencing is transforming RNA secondary structure studies in living cells. The dominant strategy for detecting in vivo chemical modifications uses reverse transcriptase truncation products, which introduce biases and necessitate population-average assessments of RNA structure. Here we present dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), which encodes DMS modifications as mismatches using a thermostable group II intron reverse transcriptase. DMS-MaPseq yields a high signal-to-noise ratio, can report multiple structural features per molecule, and allows both genome-wide studies and focused in vivo investigations of even low-abundance RNAs. We apply DMS-MaPseq for the first analysis of RNA structure within an animal tissue and to identify a functional structure involved in noncanonical translation initiation. Additionally, we use DMS-MaPseq to compare the in vivo structure of pre-mRNAs with their mature isoforms. These applications illustrate DMS-MaPseq's capacity to dramatically expand in vivo analysis of RNA structure.