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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., Competing Interests: Declaration of interests In the past 3 years, R.S. has received compensation from Bristol-Myers Squibb, ImmunAI, Resolve Biosciences, Nanostring, 10x Genomics, Neptune Bio, and the NYC Pandemic Response Lab. R.S., H.-H.W., and Y.H. are cofounders and equity holders of Neptune Bio. A.K. is a scientific cofounder of Ravel Biotechnology; is on the scientific advisory board of PatchBio, SerImmune, AINovo, TensorBio, and OpenTargets; is a consultant with Illumina; and owns shares in DeepGenomics, Immunai, and Freenome. J.L. is an employee of Calico Life Sciences, LLC, as of 11/21/2022. H.-H.W. and Y.H. are employees of Neptune Bio as of 8/1/2023., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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., Competing Interests: Declaration of interests In the past three years, R.S. has worked as a consultant for Bristol-Myers Squibb, Regeneron, and Kallyope and served as an SAB member for ImmunAI, Apollo Life Sciences GmbH, Nanostring, and the NYC Pandemic Response Lab. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, and Merck, he has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and he declares ownership in CellSpace Biosciences. P.S. is a co-inventor of a patent related to this work. B.Z.Y. is an employee at BioLegend Inc., which is the exclusive licensee of the New York Genome Center patent application related to this work., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The expression of inhibitory immune checkpoint molecules, such as programmed death-ligand (PD-L)1, is frequently observed in human cancers and can lead to the suppression of T cell-mediated immune responses. Here, we apply expanded CRISPR-compatible (EC)CITE-seq, a technology that combines pooled CRISPR screens with single-cell mRNA and surface protein measurements, to explore the molecular networks that regulate PD-L1 expression. We also develop a computational framework, mixscape, that substantially improves the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation. Applying these tools, we identify and validate regulators of PD-L1 and leverage our multimodal data to identify both transcriptional and post-transcriptional modes of regulation. Specifically, we discover that the Kelch-like protein KEAP1 and the transcriptional activator NRF2 mediate the upregulation of PD-L1 after interferon (IFN)-γ stimulation. Our results identify a new mechanism for the regulation of immune checkpoints and present a powerful analytical framework for the analysis of multimodal single-cell perturbation screens.

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)