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Stoeckius, M.

Publications and source records attributed to Stoeckius, M..

10 recordsLinked to original sources

Integrated protein and transcriptome high-throughput spatial profiling

Spatial transcriptomics and proteomics have independently transformed our understanding of complex biological processes; however, integration of these modalities is currently limited. To overcome this challenge, we developed Spatial PrOtein and Transcriptome Sequencing (SPOTS) for high-throughput integration of transcriptome and protein profiling within the spatial context. Applying SPOTS to spleen and breast cancer samples revealed that spatially-resolved multi-omic integration provides a comprehensive perspective on key biological processes in health and disease.

genomics↗

Antigen presentation by type 3 innate lymphoid cells instructs the differentiation of gut microbiota-specific regulatory T cells

The mutualistic relationship of gut-resident microbiota and cells of the host immune system promotes homeostasis that ensures maintenance of the microbial community and of a poised, but largely non-aggressive, immune cell compartment1, 2. Consequences of disturbing this balance, by environmental or genetic factors, include proximal inflammatory conditions, like Crohns disease, and systemic illnesses, both metabolic and autoimmune. One of the means by which this equilibrium is achieved is through induction of both effector and suppressor or regulatory arms of the adaptive immune system. In mice, Helicobacter species induce regulatory (iTreg) and follicular helper (Tfh) T cells in the colon-draining mesenteric lymph nodes under homeostatic conditions, but can instead induce inflammatory Th17 cells when iTreg cells are compromised3, 4. How Helicobacter hepaticus and other gut bacteria direct T cells to adopt distinct functions remains poorly understood. Here, we investigated which cells and molecular components are required to convey the microbial instruction for the iTreg differentiation program. We found that antigen presentation by cells expressing ROR{gamma}t, rather than by classical dendritic cells, was both required and sufficient for iTreg induction. These ROR{gamma}t+ cells, likely to be type 3 innate lymphoid cells (ILC3) and/or a recently-described population of Aire+ cells termed Janus cells5, require the MHC class II antigen presentation machinery, the chemokine receptor CCR7, and v integrin, which activates TGF-{beta}, for iTreg cell differentiation. In the absence of any of these, instead of iTreg cells there was expansion of microbiota-specific pathogenic Th17 cells, which were induced by other antigen presenting cells (APCs) that did not require CCR7. Thus, intestinal commensal microbes and their products target multiple APCs with pre-determined features suited to directing appropriate T cell differentiation programs, rather than a common APC that they endow with appropriate functions. Our results illustrate the ability of microbiota to exploit specialized functions of distinct innate immune system cells, targeting them to achieve the desired composition of equipoised T cells, thus maintaining tolerance.

immunology↗

Single cell analysis of blood mononuclear cells stimulated through CD3 and CD28 shows collateral activation of B and NK cells and demise of monocytes

Immune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e.g., CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq.jax.org/). Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗

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↗

The functional and phenotypic diversity of single T-cell infiltrates in human colorectal cancer as correlated with clinical outcome

Although degree of T-cell infiltration in CRC was shown to correlate with a positive prognosis, the contribution of phenotypically and functionally distinct T cell subtypes within tumors remains unclear. We analyzed 37,931 single T cells with respect to transcriptome, TCR sequence and 23 cell surface proteins, from tumors and adjacent normal colon of 16 patients. Our comprehensive analysis revealed two phenotypically distinct cytotoxic T cell populations within tumors, including positively prognostic effector memory cells and non-prognostic resident memory cells. These cytotoxic T cell infiltrates transitioned from effector memory to resident memory in a stage-dependent manner. We further defined several Treg subpopulations within tumors. While Tregs overall were associated with positive clinical outcomes, CD38+ peripherally-derived Tregs, phenotypically related to Th17 cells, correlated with poor outcomes independent of cancer stage. Thus, our data highlight the diversity of T cells in CRC and demonstrate the prognostic significance of distinct T cell subtypes, which could inform therapeutic strategies.

immunology↗

Expanding the CITE-seq tool-kit: Detection of proteins, transcriptomes, clonotypes and CRISPR perturbations with multiplexing, in a single assay

Rapid technological progress in the recent years has allowed the high-throughput interrogation of different types of biomolecules from single cells. Combining several of these readouts into integrated multi-omic assays is essential to comprehensively understand and model cellular processes. Here, we report the development of Expanded CRISPR-compatible Cellular Indexing of Transcriptomes and Epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell: transcriptome, immune receptor clonotypes, surface markers, sample identity and sgRNAs. We demonstrate the use of ECCITE-seq to directly and efficiently capture sgRNA molecules and measure their effects on gene expression and protein levels, opening the possibility of performing high throughput single cell CRISPR screens with multimodal readout using existing libraries and commonly used vectors. Finally, by utilizing the combined phenotyping of clonotype and cell surface markers in immune cells, we apply ECCITE to study a lymphoma sample to discriminate cells and define molecular signatures of malignant cells within a heterogeneous population.

genomics↗

Comprehensive integration of single cell data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to \"anchor\" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.\n\nAvailability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

genomics↗

A post-transcriptional regulatory code for mRNA stability during the zebrafish maternal-to-zygotic transition

Post-transcriptional regulation is crucial to shape gene expression. During the Maternal-to-Zygotic Transition (MZT), thousands of maternal transcripts are regulated upon fertilization and genome activation. Transcript stability can be influenced by cis-elements and trans-factors, but how these inputs are integrated to determine the overall mRNA stability is unclear. Here, we show that most transcripts are under combinatorial regulation by multiple decay pathways during zebrafish MZT. To identify cis-regulatory elements, we performed a massively parallel reporter assay for stability-influencing sequences, which revealed that 3-UTR poly-U motifs are associated with mRNA stability. In contrast, miR-430 target sequences, UAUUUAUU AU-rich elements (ARE), CCUC and CUGC elements emerged as the main destabilizing motifs in the embryo, with miR-430 and AREs causing mRNA deadenylation in a genome activation-dependent manner. To identify the trans-factors interacting with these cis-elements, we comprehensively profiled RNA-protein interactions and their associated regulatory activities across the transcriptome during the MZT. We find that poly-U binding proteins are preferentially associated with 3-UTR sequences and stabilizing motifs, and that antagonistic sequence contexts for poly-C and poly-U binding proteins shape the binding landscape and magnitude of regulation across the transcriptome. Finally, we integrate these regulatory motifs into a machine learning model that accurately predicts the stability of mRNA reporters in vivo. Our findings reveal how mechanisms of post-transcriptional regulation are coordinated to direct changes in mRNA stability within the early zebrafish embryo.

genomics↗

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↗

Large-scale simultaneous measurement of epitopes and transcriptomes in single cells.

Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.

genomics↗