MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

Ovarian cancer and the evolution of subtype classifications using transcriptional profiling†

Ovarian cancer is a complex disease with multiple subtypes, each having distinct histopathologies and variable responses to treatment. This review highlights the technological milestones and the studies that have applied them to change our definitions of ovarian cancer. Over the past 50 years, technologies such as microarrays and next-generation sequencing have led to the discovery of molecular alterations that define each of the ovarian cancer subtypes and has enabled further subclassification of the most common subtype, high-grade serous ovarian cancer (HGSOC). Improvements in mutational profiling have provided valuable insight, such as the ubiquity of TP53 mutations in HGSOC tumors. However, the information derived from these technological advances has also revealed the immense heterogeneity of this disease, from variation between patients to compositional differences within single masses. In looking forward, the emerging technologies for single-cell and spatially resolved transcriptomics will allow us to better understand the cellular composition and structure of tumors and how these contribute to the molecular subtypes. Attempts to incorporate the complexities ovarian cancer has resulted in increasing sophistication of model systems, and the increased precision in molecular profiling of ovarian cancers has already led to the introduction of inhibitors of poly (ADP-ribose) polymerases as a new class of treatments for ovarian cancer with DNA repair deficiencies. Future endeavors to define increasingly accurate classification strategies for ovarian cancer subtypes will allow for confident prediction of disease progression and provide important insight into potentially targetable molecular mechanisms specific to each subtype.

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.

Single-nucleus RNA-seq enables interrogation of complex tissues but is limited due to batch effects and processing costs. Here the authors use barcoded antibodies against the nuclear pore complex to label nuclei from distinct samples, and develop a computational tool to assign the sample of origin.

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.

Single-Cell High-Throughput Technologies in Cerebrospinal Fluid Research and Diagnostics

High-throughput single-cell technologies have recently emerged as essential tools in biomedical research with great potential for clinical pathology when studying liquid and solid biopsies. We provide an update on current single-cell methods in cerebrospinal fluid research and diagnostics, focusing on high-throughput cell-type specific proteomic and genomic technologies. Proteomic methods comprising flow cytometry and mass cytometry as well as genomic approaches including immune cell repertoire and single-cell transcriptomic studies are critically reviewed and future directions discussed.

CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.

Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

Despite rapid developments in single cell sequencing, sample-specific batch effects, detection of cell multiplets, and experimental costs remain outstanding challenges. Here, we introduce Cell Hashing, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its original sample, robustly identify cross-sample multiplets, and "super-load" commercial droplet-based systems for significant cost reduction. We validate our approach using a complementary genetic approach and demonstrate how hashing can generalize the benefits of single cell multiplexing to diverse samples and experimental designs.