Search

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. [Display omitted] • "Weighted nearest neighbor" analysis integrates multimodal single-cell data • A multimodal reference "atlas" of the circulating human immune system • Identification and validation of novel sources of lymphoid heterogeneity • "Reference-based" mapping of query datasets onto a multimodal atlas A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interpret immune responses to vaccinations. [ABSTRACT FROM AUTHOR]

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3′ scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis -regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation. [Display omitted] • Genetic perturbations of CPA regulators reveal coordinated changes in polyA site usage • PASTA quantifies heterogeneity in polyA site usage in scRNA-seq data • Distinct components of RNA life cycle affect intronic polyA site usage • Deep learning identifies sequence features that dictate perturbation response CRISPR perturbation screens of 42 cleavage and polyadenylation regulators followed by 3′ scRNA-seq (CPA-Perturb-seq) identifies modules of co-regulated polyA sites, their connection with distinct components of the RNA life cycle, and the sequence determinants that drive perturbation responses. [ABSTRACT FROM AUTHOR]

Targeting glycolysis has been considered therapeutically intractable owing to its essential housekeeping role. However, the context-dependent requirement for individual glycolytic steps has not been fully explored. We show that CRISPR-mediated targeting of glycolysis in T cells in mice results in global loss of Th17 cells, whereas deficiency of the glycolytic enzyme glucose phosphate isomerase (Gpi1) selectively eliminates inflammatory encephalitogenic and colitogenic Th17 cells, without substantially affecting homeostatic microbiota-specific Th17 cells. In homeostatic Th17 cells, partial blockade of glycolysis upon Gpi1 inactivation was compensated by pentose phosphate pathway flux and increased mitochondrial respiration. In contrast, inflammatory Th17 cells experience a hypoxic microenvironment known to limit mitochondrial respiration, which is incompatible with loss of Gpi1. Our study suggests that inhibiting glycolysis by targeting Gpi1 could be an effective therapeutic strategy with minimum toxicity for Th17-mediated autoimmune diseases, and, more generally, that metabolic redundancies can be exploited for selective targeting of disease processes. • PPP and OXPHOS compensate for Gpi1 loss in Th17 cells in normoxic environment • Gpi1 is essential in hypoxic inflamed tissue due to reduced OXPHOS compensation • Metabolic redundancy can vary according to the microenvironment • Targeting Gpi1 could be a tolerable approach for therapeutic glycolysis inhibition Metabolic redundancy differs according to microenvironments, making the glycolysis gene Gpi1 dispensable for homeostatic Th17 cells in normal tissue but essential for pathogenic Th17 cells in hypoxic inflamed tissue. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets. • Seurat v3 identifies correspondences between cells in different experiments • These "anchors" can be used to harmonize datasets into a single reference • Reference labels and data can be projected onto query datasets • Extends beyond RNA-seq to single-cell protein, chromatin, and spatial data A computational approach to integrate diverse modalities associated with single-cell sequencing datasets can be used to better understand cellular identity and function. [ABSTRACT FROM AUTHOR]

Sodium-ion batteries (SIBs) with abundant resource and high safety are attracting intensive interest from both research and industry communities in meeting the ever-increasing energy demands. Despite the rapid advance of SIBs, it is difficult yet necessary to enhance the cycling and rate performance at anode due to the sluggish kinetics of "fat" Na + . This review provides an overview of two-dimensional (2D) nanomaterials with a short ion diffusion pathway and a superior active sites exposure from the perspectives of synthesis, material chemistry, and structure engineering. We present the design principle of ideal carbon materials in SIBs. Moreover, we discuss the structure and chemistry regulations of different 2D materials to promote the efficient ion mass transfer and storage according to the different mechanisms of alloying, conversion, and insertion. Finally, we propose the remaining challenges and the possible solutions, in hope of guiding the future development of this booming field., Competing Interests: There are no conflicts to declare., (© 2023 The Authors.)