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Comprehensive benchmarking of CITE-seq versus DOGMA-seq single cell multimodal omics.

ABSTRACT: The recently developed method TEA-seq and similar DOGMA-seq single cell trimodal omics assays provide unprecedented opportunities for understanding cell biology, but independent evaluation is lacking. We explore the utility of DOGMA-seq compared to the bimodal CITE-seq assay in activated and stimulated human peripheral blood T cells. We find that single cell trimodal omics measurements after digitonin (DIG) permeabilization were generally better than after an alternative "low-loss lysis" (LLL) permeabilization condition. Next, we find that DOGMA-seq with optimized DIG permeabilization and its ATAC library provides more information, although its mRNA and cell surface protein libraries have slightly inferior quality, compared to CITE-seq.

SUBMITTER: Xu Z

PROVIDER: S-EPMC9219143 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Comprehensive benchmarking of CITE-seq versus DOGMA-seq single cell multimodal omics.

Xu Zhongli Z Heidrich-O'Hare Elisa E Chen Wei W Duerr Richard H RH

Genome biology 20220623 1

The recently developed method TEA-seq and similar DOGMA-seq single cell trimodal omics assays provide unprecedented opportunities for understanding cell biology, but independent evaluation is lacking. We explore the utility of DOGMA-seq compared to the bimodal CITE-seq assay in activated and stimulated human peripheral blood T cells. We find that single cell trimodal omics measurements after digitonin (DIG) permeabilization were generally better than after an alternative "low-loss lysis" (LLL) p ...[more]

PMID: 35739535

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Comprehensive Benchmarking of CITE-seq versus DOGMA-seq Single Cell Multimodal Omics

Project description:The recently developed transcription, epitopes, and chromatin accessibility by sequencing (TEA-seq) and similar DOGMA-seq single-cell trimodal omics assays provide unprecedented opportunities for understanding cell biology, but independent optimization, benchmarking and evaluation are lacking. We explored the utility, pros and cons of DOGMA-seq compared to the bimodal cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) assay in activated and stimulated human peripheral blood T cells. We identified an optimal incubation time and concentration of digitonin (DIG) for cell permeabilization and found that single-cell trimodal omics measurements after DIG permeabilization were generally better than after an alternative “low-loss lysis” (LLL) permeabilization condition. Next, we found that DOGMA-seq with optimized DIG permeabilization and its ATAC library provides more information, even though its mRNA and cell surface protein antibody-derived tag (ADT) libraries have slightly inferior quality, compared to CITE-seq. Finally, we recognized the additional value of DOGMA-seq for studying lineage-specific T helper cells.

2022-06-13 | GSE200417 | GEO

Comprehensive Benchmarking of CITE-seq versus DOGMA-seq Single Cell Multimodal Omics

Project description:Comprehensive Benchmarking of CITE-seq versus DOGMA-seq Single Cell Multimodal Omics

| PRJNA824482 | ENA

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.

Project description:Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates this multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate CITE-seq annotation of cell types and developmental states across diverse lineages, tissues, or individuals.

| S-EPMC10350048 | biostudies-literature

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.

Project description:Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

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ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

Project description:CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

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Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.

Project description:BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.ResultsHere, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis.ConclusionsIn summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

| S-EPMC8672463 | biostudies-literature

Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Project description:Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

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The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow.

Project description:Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.

| S-EPMC8163004 | biostudies-literature

Artificial-cell-type aware cell-type classification in CITE-seq.

Project description:MotivationCellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping.ResultsWe propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq.Availability and implementationhttp://github.com/QiuyuLian/CITE-sort.Supplementary informationSupplementary data is available at Bioinformatics online.

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Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion.

Project description:Resistance to immune checkpoint inhibitors (ICIs) is a key challenge in cancer therapy. To elucidate underlying mechanisms, we developed Perturb-CITE-sequencing (Perturb-CITE-seq), enabling pooled clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 perturbations with single-cell transcriptome and protein readouts. In patient-derived melanoma cells and autologous tumor-infiltrating lymphocyte (TIL) co-cultures, we profiled transcriptomes and 20 proteins in ~218,000 cells under ~750 perturbations associated with cancer cell-intrinsic ICI resistance (ICR). We recover known mechanisms of resistance, including defects in the interferon-γ (IFN-γ)-JAK/STAT and antigen-presentation pathways in RNA, protein and perturbation space, and new ones, including loss/downregulation of CD58. Loss of CD58 conferred immune evasion in multiple co-culture models and was downregulated in tumors of melanoma patients with ICR. CD58 protein expression was not induced by IFN-γ signaling, and CD58 loss conferred immune evasion without compromising major histocompatibility complex (MHC) expression, suggesting that it acts orthogonally to known mechanisms of ICR. This work provides a framework for the deciphering of complex mechanisms by large-scale perturbation screens with multimodal, single-cell readouts, and discovers potentially clinically relevant mechanisms of immune evasion.

| S-EPMC8376399 | biostudies-literature

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