Biology subjects

Yeung, B.

Publications and source records attributed to Yeung, B..

5 recordsLinked to original sources

bioRxiv2021

Simultaneous single cell measurements of intranuclear proteins and gene expression

Identifying gene regulatory targets of nuclear proteins in tissues remains a challenge. Here we describe intranuclear Cellular Indexing of Transcriptomes and Epitopes (inCITE-seq), a scalable method for measuring multiplexed intranuclear protein levels and the transcriptome in parallel in thousands of cells, enabling joint analysis of TF levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed the genome-wide effect of each TF and recovered known targets. Cell type-specific genes associated with each TF were co-expressed as distinct modules that each corresponded to positive or negative TF levels, showing that our approach can disentangle relative contributions of TFs to gene expression and add interpretability to gene networks. InCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

genomics↗

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. 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 hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of 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 COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2020

Scalable, multimodal profiling of chromatin accessibility and protein levels in single cells

Recent technological advances have enabled massively parallel chromatin profiling with single-cell Assay for Transposase Accessible Chromatin by sequencing (scATAC-seq) in thousands of individual cells. Here, we extend these approaches and present ATAC with Select Antigen Profiling by sequencing, ASAP-seq, a tool to simultaneously profile accessible chromatin and protein levels in thousands of single cells. 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 (mtDNA) for clonal tracking, thus concomitantly capturing three distinct modalities in single cells. Importantly, ASAP-seq uses a novel bridging approach that repurposes antibody:oligo conjugates designed for existing technologies that pair protein measurements with single cell RNA-seq. We demonstrate the utility of ASAP-seq by revealing coordinated and distinct changes in chromatin, RNA, and surface proteins during native hematopoietic differentiation, peripheral blood mononuclear cell stimulation, and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.

genomics↗

bioRxiv2018

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, including frozen clinical samples. This opens the way, in principle, to large studies, such as those required for human genetics, clinical trials, or precise cell atlases of large organs. However, such applications are currently limited by batch effects, sequential processing, and costs. To address these challenges, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies against the nuclear pore complex to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled in multiplex with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter the recovered transcriptome profiles. We further developed demuxEM, a novel computational tool that robustly detects inter-sample nucleus multiplets and assigns singlets to their samples of origin by antibody barcodes, and validated its accuracy using gender-specific gene expression, species-mixing and natural genetic variation. Nucleus hashing significantly reduces cost per nucleus, recovering up to about 5 times as many single nuclei per microfluidc channel. Our approach provides a robust technique for diverse studies including tissue atlases of isogenic model organisms or from a single larger human organ, multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.

genomics↗

bioRxiv2017

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

Despite rapid developments in single cell sequencing technology, sample-specific batch effects, detection of cell doublets, and the cost of generating massive datasets remain outstanding challenges. Here, we introduce cell \"hashing\", where oligo-tagged antibodies against ubiquitously expressed surface proteins are used to 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 sample of origin, and robustly identify doublets originating from multiple samples. We demonstrate our approach by pooling eight human PBMC samples on a single run of the 10x Chromium system, substantially reducing our per-cell costs for library generation. Cell \"hashing\" is inspired by, and complementary to, elegant multiplexing strategies based on genetic variation, which we also leverage to validate our results. We therefore envision that our approach will help to generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

genomics↗