Biology subjects

Stuart, T.

Publications and source records attributed to Stuart, T..

11 recordsLinked to original sources

bioRxiv2025

Regulatory element modules as universal features for single-cell chromatin analysis

Single-cell chromatin accessibility data provide important insights into the activity of DNA regulatory elements in health and disease. However, the analysis of these data is made challenging by the lack of a common set of features for use in downstream analysis. This results in individual studies quantifying dataset-specific peak regions that cannot be directly compared to other studies. To address this challenge, we developed a comprehensive set of DNA regulatory element modules (REMO) for the human genome. Here we show how REMO can be applied to single-cell chromatin data to better separate cell states in a low-dimensional space compared to peak matrix quantification, greatly improve the scalability of dimension reduction steps, and enable automated annotation of cell types. This is accompanied by new memory-efficient and scalable software for the quantification of single-cell chromatin accessibility data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/692786v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@17b5da0org.highwire.dtl.DTLVardef@3fbe64org.highwire.dtl.DTLVardef@ecc378org.highwire.dtl.DTLVardef@15363a_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Establishment of cell transcriptional identity during seed germination

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary to establish in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell-type specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell-type specific transcription begins and changes through germination. Here we describe a temporal analysis of the germinating Arabidopsis embryo at single-cell resolution. We define the highly dynamic cell-type specific patterns of gene expression and how these relate to changing cellular function as germination progresses. Underlying these are unique gene regulatory networks and transcription factor activity. We unexpectedly discover that most embryo cells transition through the same initial transcriptional state early in germination, after which cell-type specific gene expression is established. Furthermore, our analyses support previous findings that the earliest events leading to the induction of embryo growth take place in the vasculature. Overall, our study constitutes a general framework to characterise Arabidopsis cell states through embryo growth, allowing investigation of different genotypes and other plant species whose seed strategies may differ.

plant biology↗

bioRxiv2022

Nanobody-tethered transposition allows for multifactorial chromatin profiling at single-cell resolution

Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility, and other factors. However, most current single-cell-resolution methods are unable to measure more than one aspect of chromatin state in a single experiment, limiting our ability to accurately measure chromatin states. Here, we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), a new assay capable of measuring the genome-wide presence of multiple histone modifications and protein-DNA binding sites at single-cell resolution. NTT-seq utilizes recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk- and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and in cells of the human immune system, demonstrating the high accuracy and sensitivity of the method. We further extend NTT-seq to enable simultaneous profiling of cell-surface protein expression alongside multimodal chromatin states to study cells of the immune system.

genomics↗

bioRxiv2022

An environmentally-responsive transcriptional state modulates cell identities during root development

Roots are fundamental organs for plant development and response to their environment: they anchor the plant to its growth substrate, uptake nutrients and water vital to plant growth, and can sense and respond to a variety of biotic and abiotic stresses. The architecture of root systems and their growth are known to be strongly affected by the environmental conditions found in the soil. However, the acquisition of cell identities at the root meristem is still mainly viewed as ontogenetically driven, where a small number of stem cells generate all the cell types through stereotyped divisions followed by differentiation, along a simple developmental trajectory. The extent to which environmental cues precisely shape and affect these developmental trajectories remains an open question. We used single-cell RNA-seq, combined with spatial mapping, to deeply explore the trajectories of cell states at the tip of Arabidopsis roots, known to contain multiple developing lineages. Surprisingly, we found that most lineage trajectories exhibit a stereotyped bifid topology with two developmental trajectories rather than one. The formation of one of the trajectories is driven by a strong and specific activation of genes involved in the responses to various environmental stimuli, that affects only of a subset of the cells in multiple cell types simultaneously, revealing another layer of patterning of cell identities in the root that is independent of cell ontogeny. We demonstrate the robustness of this environmentally responsive transcriptional state by showing that it is present in a mutant where cell type identities are greatly perturbed, as well as in different Arabidopsis ecotypes. We also show that the root can adapt the proportion of cells that acquire this particular state in response to environmental signals such as nutrient availability. The discovery of this cell state reveals new layers of cell identity that may underpin the adaptive potential of plant development.

plant biology↗

bioRxiv2022

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative to unsupervised analysis. Reference datasets, however, are predominantly constructed from single-cell RNA-seq data, and cannot be used to annotate datasets that do not measure gene expression. Here we introduce bridge integration, a method to harmonize singlecell datasets across modalities by leveraging a multi-omic dataset as a molecular bridge. Each cell in the multi-omic dataset comprises an element in a dictionary, which can be used to reconstruct unimodal datasets and transform them into a shared space. We demonstrate that our procedure can accurately harmonize transcriptomic data with independent single cell measurements of chromatin accessibility, histone modifications, DNA methylation, and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to substantially improve computational scalability, and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach aims to broaden the utility of single-cell reference datasets and facilitate comparisons across diverse molecular modalities. AvailabilityInstallation instructions, documentations, and vignettes are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2021

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

New technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization. However, the sparsity of these measurements and the challenge of integrating multiple binding maps represent significant challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

genomics↗

bioRxiv2021

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package (https://github.com/MarioniLab/geneBasisR).

bioinformatics↗

bioRxiv2020

Multimodal single-cell chromatin analysis with Signac

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a framework for the analysis of single-cell chromatin data, as an extension of the Seurat R toolkit for single-cell multimodal analysis. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis, and interactive visualization. Furthermore, Signac facilitates the analysis of multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance, and mitochondrial genotype. We demonstrate scaling of the Signac framework to datasets containing over 700,000 cells. AvailabilityInstallation instructions, documentation, and tutorials are available at: https://satijalab.org/signac/

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↗

bioRxiv2019

A Genome-Wide Association Study of Non-Photochemical Quenching in response to local seasonal climates in Arabidopsis thaliana

Field-grown plants have variable exposure to sunlight as a result of shifting cloud-cover, seasonal changes, canopy shading, and other environmental factors. As a result, they need to have developed a method for dissipating excess energy obtained from periodic excessive sunlight exposure. Non-photochemical quenching (NPQ) dissipates excess energy as heat, however the physical and molecular genetic mechanics of NPQ variation are not understood. In this study, we investigated the genetic loci involved in NPQ by first growing different Arabidopsis thaliana accessions in local and seasonal climate conditions, then measured their NPQ kinetics through development by chlorophyll fluorescence. We used genome-wide association studies (GWAS) to identify 15 significant quantitative trait loci (QTL) for a range of photosynthetic traits, including a QTL co-located with known NPQ gene PSBS (AT1G44575). We found there were large alternative regulatory segments between the PSBS promoter regions of the functional haplotypes and a significant difference in PsbS protein concentration. These findings parallel studies in rice showing recurrent regulatory evolution of this gene. The variation in the PSBS promoter and the changes underlying other QTLs could give insight to allow manipulations of NPQ in crops to improve their photosynthetic efficiency and yield. B.P. & J.B. conceived the project; B.P., J.B., P.W. and T.R. designed the research plan and analysis; P.W. supervised the experiments; T.R. performed most of the experiments and analysis; P.G., T.S., A.A. & E.A. designed and undertook experimental design, experiments and analysis for Figure 4; R.C. did the GWAS analysis; P.W., T.R. & A.A. wrote the article with contributions of all the authors. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/539379v1_fig4.gif" ALT="Figure 4"> View larger version (57K): org.highwire.dtl.DTLVardef@12ba2f9org.highwire.dtl.DTLVardef@1d88c15org.highwire.dtl.DTLVardef@1d4f21eorg.highwire.dtl.DTLVardef@16396aa_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4:C_FLOATNO (A-D) Coverage tracks of the PSBS genomic regions from five low NPQ (A and B) and five high NPQ (C and D) Arabidopsis accessions aligned with the TAIR 10 Col-0 reference genome (low NPQ accession; A and C) and KBS-Mac-74 genome (high NPQ accession; B and D). Values along x-axes indicate the base pair distance relative to the PSBS transcription start site. Genes along the track are coloured green and the intergenic region is coloured pink. (E) Graphical view of the alignment of the TAIR 10 Col-0 and the KBS-Mac-74 PSBS genomic regions. Axis values refer to base pair positions within the respective tracks. (F) Comparison of the average relative PsbS protein abundance between low and high NPQ accessions. Error bars represent standard deviations. N=30; *** P < 0.001 with paired Students T test. C_FIG

plant biology↗

bioRxiv2018

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↗