Search bioRxiv⌕ Search

Biology subjects

Smibert, P.

Publications and source records attributed to Smibert, P..

At least 19 recordsLinked to original sources

Single-cell multi-omics reveals dynamics of purifying selection of pathogenic mitochondrial DNA across human immune cells

Cells experience intrinsic and extrinsic pressures that affect their proclivity to expand and persist in vivo. In congenital disorders caused by loss-of-function mutations in mitochondrial DNA (mtDNA), metabolic vulnerabilities may result in cell-type specific phenotypes and depletion of pathogenic alleles, contributing to purifying selection. However, the impact of pathogenic mtDNA mutations on the cellular hematopoietic landscape is not well understood. Here, we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from Pearson syndrome patients. We resolve the interdependence between pathogenic mtDNA and lineage, including purifying selection against deletions in effector/memory CD8 T-cell populations and recent thymic emigrants and dynamics in other hematopoietic populations. Our mapping of lineage-specific purifying selection dynamics in primary cells from patients carrying pathogenic heteroplasmy provides a new perspective on recurrent clinical phenotypes in mitochondrial disorders, including cancer and infection, with potential broader relevance to age-related immune dysfunction.

genetics↗

Type 1 interferon remodels normal and neoplastic hematopoiesis in human

Inflammation perturbs evolutionary dynamics of hematopoietic stem cell (HSC) clones in clonal hematopoiesis and myeloid neoplasms. We studied HSCs, progenitors and immune cells from patients with myeloproliferative neoplasm (MPN) at baseline and following interferon- (IFN) treatment, the only MPN therapy to deplete clonal stem cells. We focused on essential thrombocythemia, an informative model of early-phase neoplastic hematopoiesis. We integrated somatic genotyping, transcriptomes, immunophenotyping, and chromatin accessibility across single cells. IFN simultaneously activated HSCs into two polarized states, a lymphoid progenitor expansion associated with an anti-inflammatory state and an IFN-specific inflammatory granulocytic progenitor (IGP) state derived directly from HSCs. The augmented lymphoid differentiation balanced the typical MPN-induced myeloid bias, associated with normalized blood counts. Clonal fitness upon IFN exposure was due to resistance of clonal stem cells to differentiate into IGPs. These results support a paradigm wherein inflammation perturbs clonal dynamics by HSC induction into the precipitous IGP differentiation program. One-Sentence SummaryInflammation accelerates clonal evolution by driving stem cell differentiation into an alternate interferon--induced progenitor state.

cancer biology↗

Integrated Single-Cell Genotyping and Chromatin Accessibility Charts JAK2V617F Human Hematopoietic Differentiation

In normal somatic tissue differentiation, changes in chromatin accessibility govern priming and commitment of precursors towards cellular fates. In turn, somatic mutations can disrupt differentiation topologies leading to abnormal clonal outgrowth. However, defining the impact of somatic mutations on the epigenome in human samples is challenging due to admixed mutated and wildtype cells. To chart how somatic mutations disrupt epigenetic landscapes in human clonal outgrowths, we developed Genotyping of Targeted loci with single-cell Chromatin Accessibility (GoT-ChA). This high-throughput, broadly accessible platform links genotypes to chromatin accessibility at single-cell resolution, across thousands of cells within a single assay. We applied GoT-ChA to CD34+ cells from myeloproliferative neoplasm (MPN) patients with JAK2V617F-mutated hematopoiesis, where the JAK2 mutation is known to perturb hematopoietic differentiation. Differential accessibility analysis between wildtype and JAK2V617F mutant progenitors revealed both cell-intrinsic and cell state-specific shifts within mutant hematopoietic precursors. An early subset of mutant hematopoietic stem and progenitor cells (HSPCs) exhibited a cell-intrinsic pro-inflammatory signature characterized by increased NF-{kappa}B and JUN/FOS transcription factor motif accessibility. In addition, mutant HSPCs showed increased myeloid/erythroid epigenetic priming, preceding increased erythroid and megakaryocytic cellular output. Erythroid progenitors displayed aberrant regulation of the {gamma}-globin locus, providing an intrinsic epigenetic basis for the dysregulated fetal hemoglobin expression observed in MPNs. In contrast, megakaryocytic progenitors exhibited a more specialized inflammatory chromatin landscape relative to early HSPCs, with increased accessibility of pro-fibrotic JUN/FOS transcription factors. Notably, analysis of myelofibrosis patients treated with JAK inhibitors revealed an overall loss of mutant-specific phenotypes without modifying clonal burden, consistent with clinical responses. Finally, expansion of the multi-modality capability of GoT-ChA to integrate mitochondrial genome profiling and cell surface protein expression measurement enabled genotyping imputation and discovery of aberrant cellular phenotypes. Collectively, we show that the JAK2V617F mutation leads to epigenetic rewiring in a cell-intrinsic and cell type-specific manner. We envision that GoT-ChA will thus serve as a foundation for broad future explorations to uncover the critical link between mutated somatic genotypes and epigenetic alterations across clonal populations in malignant and non-malignant contexts.

cancer biology↗

Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq

Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq) which leverages the RNA-targeting CRISPR/Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable array which is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.

genomics↗

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↗

PySeq2500: An open source toolkit for repurposing HiSeq 2500 sequencing systems as versatile fluidics and imaging platforms.

Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument into an open platform for programmable applications. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. As demonstrated here, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.

genomics↗

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗

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↗

Scalable pooled CRISPR screens with single-cell chromatin accessibility profiling

Pooled CRISPR screens have been used to identify genes responsible for specific phenotypes and diseases, and, more recently, to connect genetic perturbations with multi-dimensional gene expression profiles. Here, we describe a method to link genome-wide chromatin accessibility to genetic perturbations in single cells. This scalable, cost-effective method combines pooled CRISPR perturbations with a single-cell combinatorial indexing assay for transposase-accessible chromatin (CRISPR-sciATAC). Using a human and mouse species-mixing experiment, we show that CRISPR-sciATAC separates single cells with a low doublet rate. Then, in human myelogenous leukemia cells, we apply CRISPR-sciATAC to target 21 chromatin-related genes that are frequently mutated in cancer and 84 subunits and cofactors of chromatin remodeling complexes, generating chromatin accessibility data for ~30,000 single cells. Using this large-scale atlas, we correlate loss of specific chromatin remodelers with changes in accessibility -- globally and at the binding sites of individual transcription factors. For example, we show that loss of the H3K27 methyltransferase EZH2 leads to increased accessibility at heterochromatic regions involved in embryonic development and triggers expression of multiple genes in the HOXA and HOXD clusters. At a subset of regulatory sites, we also analyze dynamic changes in nucleosome spacing upon loss of chromatin remodelers. CRISPR-sciATAC is a high-throughput, low-cost single-cell method that can be applied broadly to study the role of genetic perturbations on chromatin in normal and disease states.

genomics↗

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↗

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↗

Characterizing the molecular regulation of inhibitory immune checkpoints with multi-modal single-cell screens

The expression of inhibitory immune checkpoint molecules such as PD-L1 is frequently observed in human cancers and can lead to the suppression of T cell-mediated immune responses. Here we apply ECCITE-seq, a technology which 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 multi-modal data to identify both transcriptional and post-transcriptional modes of regulation. In particular, we discover that the kelch-like protein KEAP1 and the transcriptional activator NRF2, mediate levels of PD-L1 upregulation after IFN{gamma} stimulation. Our results identify a novel mechanism for the regulation of immune checkpoints and present a powerful analytical framework for the analysis of multi-modal single-cell perturbation screens.

genomics↗

Improving oligo-conjugated antibody signal in multimodal single-cell analysis

Simultaneous measurement of surface proteins and gene expression within single cells using oligo-conjugated antibodies offers high resolution snapshots of complex cell populations. Signal from oligo-conjugated antibodies is quantified by high-throughput sequencing and is highly scalable and sensitive. In this study, we investigated the response of oligo-conjugated antibodies towards four variables: Concentration, staining volume, cell number at staining, and tissue. We find that staining with recommended antibody concentrations cause unnecessarily high background and that concentrations can be drastically reduced without loss of biological information. Reducing volume only affects antibodies targeting abundant epitopes used at low concentrations and is counteracted by reducing cell numbers. Adjusting concentrations increases signal, lowers background and reduces costs. Background signal can account for a major fraction of the total sequencing and is primarily derived from antibodies used at high concentrations. Together, this study provides new insight into the titration response and background of oligo-conjugated antibodies and offers concrete guidelines on how such panels can be improved. Impact statementOligo-conjugated antibodies are a powerful tool but require thorough optimization to reduce background signal, increase sensitivity, and reduce sequencing costs.

genomics↗

Direct detection of RNA modifications and structure using single molecule nanopore sequencing

Many methods exist to detect RNA modifications by short-read sequencing, relying on either antibody enrichment of transcripts bearing modified bases or mutational profiling approaches which require conversion to cDNA. Endogenous modifications are present on several major classes of RNA including tRNA, rRNA and mRNA and can modulate diverse biological processes such as genetic recoding, mRNA export and RNA folding. In addition, exogenous modifications can be introduced to RNA molecules to reveal RNA structure and dynamics. Limitations on read length and library size inherent in short-read-based methods dissociate modifications from their native context, preventing single molecule analysis and modification phasing. Here we demonstrate direct RNA nanopore sequencing to detect endogenous and exogenous RNA modifications over long sequence distance at the single molecule level. We demonstrate comprehensive detection of endogenous modifications in E. coli and S. cerevisiae ribosomal RNA (rRNA) using current signal deviations. Notably 2-O-methyl (Nm) modifications generated a discernible shift in current signal and event level dwell times. We show that dwell times are mediated by the RNA motor protein which sits atop the nanopore. Further, we characterize a recently described small adduct-generating 2-O-acylation reagent, acetylimidazole (AcIm) for exogenously labeling flexible nucleotides in RNA. Finally, we demonstrate the utility of AcIm for single molecule RNA structural probing using nanopore sequencing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/126763v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1d13067org.highwire.dtl.DTLVardef@c0d0aaorg.highwire.dtl.DTLVardef@c362eeorg.highwire.dtl.DTLVardef@193ffdb_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

genomics↗

High Throughput pMHC-I Tetramer Library Production Using Chaperone Mediated Peptide Exchange

Peptide exchange technologies are essential for the generation of pMHC-multimer libraries, used to probe highly diverse, polyclonal TCR repertoires. Using the molecular chaperone TAPBPR, we present a robust method for the capture of stable, empty MHC-I molecules which can be readily tetramerized and loaded with peptides of choice in a high-throughput manner. Combined with tetramer barcoding using multi-modal cellular indexing technology (ECCITE-seq), our approach allows a combined analysis of TCR repertoires and other T-cell transcription profiles together with their cognate pMHC-I specificities in a single experiment.

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↗