Search

Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. Data produced by scRNAseq is technically complex and requires analytical workflows that are an active field of bioinformatics research, whereas a wealth of biological background knowledge is needed to guide the investigation. Thus, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis so that they can focus on the science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools. We present SCHNAPPs, a Graphical User Interface (GUI), designed to enable bench-scientists to autonomously explore and interpret scRNAseq data and associated annotations. The R/Shiny-based application allows following different steps of scRNAseq analysis workflows from Seurat or Scran packages: performing quality control on cells and genes, normalizing the expression matrix, integrating different samples, dimension reduction, clustering, and differential gene expression analysis. Visualization tools for exploring each step of the process include violin plots, 2D projections, Box-plots, alluvial plots, and histograms. An R-markdown report can be generated that tracks modifications and selected visualizations. The modular design of the tool allows it to easily integrate new visualizations and analyses by bioinformaticians. We illustrate the main features of the tool by applying it to the characterization of T cells in a scRNAseq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) experiment of two healthy individuals. [ABSTRACT FROM AUTHOR]

Ankylosing spondylitis (AS) is an immune-mediated inflammatory disorder that primarily affects the axial skeleton, especially the sacroiliac joints and spine. This results in chronic back pain and, in extreme cases, ankylosis of the spine. Despite its debilitating effects, the pathogenesis of AS remains to be further elucidated. This study used single cell CITE-seq technology to analyze peripheral blood mononuclear cells (PBMCs) in AS and in healthy controls. We identified a number of molecular features associated with AS. CD52 was found to be overexpressed in both RNA and surface protein expression across several cell types in patients with AS. CD16 + monocytes overexpressed TNFSF10 and IL-18Rα in AS, while CD8 + T EM cells and natural killer cells overexpressed genes linked with cytotoxicity, including GZMH, GZMB , and NKG7 . Tregs underexpressed CD39 in AS, suggesting reduced functionality. We identified an overrepresented NK cell subset in AS that overexpressed CD16, CD161, and CD38, as well as cytotoxic genes and pathways. Finally, we developed machine learning models derived from CITE-seq data for the classification of AS and achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of > 0.95. In summary, CITE-seq identification of AS-associated genes and surface proteins in specific cell subsets informs our understanding of pathogenesis and potential new therapeutic targets, while providing new approaches for diagnosis via machine learning., Competing Interests: WL has received research grant funding from Abbvie, Amgen, Janssen, Leo, Novartis, Pfizer, Regeneron, and TRex Bio. LG has received research/grant support from Novartis, Pfizer and UCB and consulting fees from AbbVie, Eli Lilly, Gilead, Janssen, Novartis, Pfizer and UCB. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alber, Kumar, Liu, Huang, Paez, Hong, Chang, Bhutani, Gensler and Liao.)

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease., Competing Interests: Author WL has received funding from Abbvie, Amgen, Janssen, Leo, Novartis, Pfizer, Regeneron, and TRex Bio. Author CY is a Scientific Advisory Board member for and holds equity in Related Sciences and ImmunAI, a consultant for and holds equity in Maze Therapeutics, and a consultant for TReX Bio. Author CY has received funding from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, and Genentech. Author LG has received funding from Novartis, Pfizer, UCB, AbbVie, Eli Lilly, Gilead, Janssen, Novartis, Pfizer and UCB. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Kumar, Hong, Huang, Paez, Castillo, Calvo, Chang, Cummins, Chung, Yeroushalmi, Bartholomew, Hakimi, Ye, Bhutani, Matloubian, Gensler and Liao.)

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The CITE-seq workflow combines conventional single-cell transcriptomic analysis with simultaneous analysis of cell surface protein expression using oligonucleotide-conjugated antibodies. This addition of immunophenotyping to mRNA data allows for a more detailed characterization of single-cell heterogeneity and can help to identify markers for the prospective isolation of transcriptionally defined novel cell subsets. Here, we describe the workflow for the preparation of human cord blood mononuclear cells and CD34+-enriched hematopoietic progenitors for the simultaneous characterization of protein and RNA using the commercially available TotalSeq™ antibodies from BioLegend and the droplet-based single-cell RNA-seq commercial platform from 10x Genomics., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.

While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency. [ABSTRACT FROM AUTHOR]

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. 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 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate 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 coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity. [Display omitted] • "Weighted nearest neighbor" analysis integrates multimodal single-cell data • A multimodal reference "atlas" of the circulating human immune system • Identification and validation of novel sources of lymphoid heterogeneity • "Reference-based" mapping of query datasets onto a multimodal atlas A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interpret immune responses to vaccinations. [ABSTRACT FROM AUTHOR]

A gene signature was previously found to be correlated with mosaic adenovirus 26 vaccine protection in simian immunodeficiency virus and simian-human immunodeficiency virus challenge models in non-human primates. In this report, we investigated the presence of this signature as a correlate of reduced risk in human clinical trials and potential mechanisms of protection. The absence of this gene signature in the DNA/rAd5 human vaccine trial, which did not show efficacy, strengthens our hypothesis that this signature is only enriched in studies that demonstrated protection. This gene signature was enriched in the partially effective RV144 human trial that administered the ALVAC/protein vaccine, and we find that the signature associates with both decreased risk of HIV-1 acquisition and increased vaccine efficacy (VE). Total RNA-seq in a clinical trial that used the same vaccine regimen as the RV144 HIV vaccine implicated antibody-dependent cellular phagocytosis (ADCP) as a potential mechanism of vaccine protection. CITE-seq profiling of 53 surface markers and transcriptomes of 53,777 single cells from the same trial showed that genes in this signature were primarily expressed in cells belonging to the myeloid lineage, including monocytes, which are major effector cells for ADCP. The consistent association of this transcriptome signature with VE represents a tool both to identify potential mechanisms, as with ADCP here, and to screen novel approaches to accelerate the development of new vaccine candidates., Competing Interests: SS, PE, AG, LY, GK, KM, SF, KN, LW, SS, EL, PP, SN, SC, SR, MR, DD, PG, GT, NM, SV, RT No competing interests declared

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17–23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19. [Display omitted] • Multiparameter metric captures finer shades of disease severity to empower analyses • Network analysis reveals primary disease severity correlates in pDCs and NK cells • Analysis of timing effects suggests a late immune response juncture • Inflammatory divergence at the late juncture predicts fatal-recovery outcomes A quantitative disease severity metric is developed by integrating clinical data and circulating cytokines to capture finer shades of disease severity. Multimodal single-cell profiling and immune cell analyses of COVID-19 patients over time reveal a late wave of diverging inflammatory and host immune responses that predicts recovery versus fatality. [ABSTRACT FROM AUTHOR]

Systematic understanding of immune aging on a whole-body scale is currently lacking. We characterized age-associated alterations in immune cells across multiple mouse organs using single-cell RNA and antigen receptor sequencing and flow cytometry-based validation. We defined organ-specific and common immune alterations and identified a subpopulation of age-associated granzyme K (GZMK)-expressing CD8+ T (Taa) cells that are distinct from T effector memory (Tem) cells. Taa cells were highly clonal, had specific epigenetic and transcriptional signatures, developed in response to an aged host environment, and expressed markers of exhaustion and tissue homing. Activated Taa cells were the primary source of GZMK, which enhanced inflammatory functions of non-immune cells. In humans, proportions of the circulating GZMK+CD8+ T cell population that shares transcriptional and epigenetic signatures with mouse Taa cells increased during healthy aging. These results identify GZMK+ Taa cells as a potential target to address age-associated dysfunctions of the immune system. • scRNA/CITE-seq and scATAC-seq reveal hallmarks of immune aging in mice and humans • Clonal exhausted-like GZMK+CD8+ T cells accumulate in tissues due to old environment • GZMK secreted by activated GZMK+CD8+ T cells can promote SASP from senescent cells • GZMK+CD8+ Taa rather than GZMB+CD8+ Tem accumulate in human blood with age Aging affects the immune system and establishes a chronic low-grade inflammation (inflammaging). Mogilenko et al. defined organ-specific and common alterations of immune cell populations in aging and identified a distinct subset of clonal GZMK+ CD8+ T cells as a conserved cellular hallmark of inflammaging in mice and humans. [ABSTRACT FROM AUTHOR]

Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells)., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data., Competing Interests: R.J.M.B.-R. is a co-founder of Alchemab Therapeutics Ltd and consultant for Alchemab Therapeutics Ltd and GSK. F.A.T. is a consultant for Alchemab Therapeutics Ltd., (© 2021 The Authors.)

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.

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. 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 causes unnecessarily high background and amount of antibody used can be drastically reduced without loss of biological information. Reducing staining 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 total sequencing and is primarily derived from antibodies used at high concentrations. This study provides new insight into titration response and background of oligo-conjugated antibodies and offers concrete guidelines to improve such panels., Competing Interests: TB, AH, EI, EM, AC, RH, TP, NO, SK No competing interests declared, PS is co-inventor of a patent related to the single cell technology utilized in this study (US provisional patent application 62/515-180)., (© 2021, Buus et al.)

Introduction: Recent research has found that World Trade Center (WTC) responders in their mid-50s have an elevated prevalence of mild cognitive impairment (MCI) that is associated with neural degeneration and subcortical thinning. This article extends our understanding of the molecular complexity of MCI through gene expression profiling of blood., Methods: The transcriptomics of 40 male WTC responders were profiled across two cohorts (discovery: nine MCI and nine controls; replication: 11 MCI and 11 controls) using CITE-Seq at single-cell resolution in blood., Results: Comparing the transcriptomic signatures across seven major cell subpopulations, the largest differences were observed in monocytes in which 226 genes were differentially expressed. Pathway analysis on the genes unique to monocytes identified processes associated with cerebral immune response., Discussion: Our findings suggested monocytes may constitute a key cell type to target in blood-based biomarker studies for early detection of risk of MCI and development of new interventions., Competing Interests: The authors reported no biomedical financial interests or other potential conflicts of interest., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

The diversity of circulating human B cells is unknown. We use single-cell RNA sequencing (RNA-seq) to examine the diversity of both antigen-specific and total B cells in healthy subjects and malaria-exposed individuals. This reveals two B cell lineages: a classical lineage of activated and resting memory B cells and an alternative lineage, which includes previously described atypical B cells. Although atypical B cells have previously been associated with disease states, the alternative lineage is common in healthy controls, as well as malaria-exposed individuals. We further track Plasmodium-specific B cells after malaria vaccination in naive volunteers. We find that alternative lineage cells are primed after the initial immunization and respond to booster doses. However, alternative lineage cells develop an atypical phenotype with repeated boosts. The data highlight that atypical cells are part of a wider alternative lineage of B cells that are a normal component of healthy immune responses., Competing Interests: Declaration of interests S.C., N.K., B.K.L.S., and S.L.H. are salaried employees of Sanaria, the developer and owner of PfSPZ Vaccine and the investigational new drug (IND) application sponsor of the clinical trials. S.L.H. and B.K.L.S. have a financial interest in Sanaria. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Phenotypic and functional plasticity of brain immune cells contribute to brain tissue homeostasis and disease. Immune cell plasticity is profoundly influenced by tissue microenvironment cues and systemic factors. Aging and gut microbiota dysbiosis that reshape brain immune cell plasticity and homeostasis has not been fully delineated. Using Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), we analyze compositional and transcriptional changes of the brain immune landscape in response to aging and gut dysbiosis. Discordance between canonical surface-marker-defined immune cell types and their transcriptomes suggest transcriptional plasticity among immune cells. Ly6C + monocytes predominate a pro-inflammatory signature in the aged brain, while innate lymphoid cells (ILCs) shift toward an ILC2-like profile. Aging increases ILC-like cells expressing a T memory stemness (T scm ) signature, which is reduced through antibiotics-induced gut dysbiosis. Systemic changes due to aging and gut dysbiosis increase propensity for neuroinflammation, providing insights into gut dysbiosis in age-related neurological diseases., Competing Interests: declaration of interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The intestinal mucosa represents a unique environment where the coordinated function of diverse epithelial, mesenchymal, and immune cells maintains a physiologically balanced environment in the presence of gut microbiota. The intestinal mucosa plays a central role in the pathogenesis of inflammatory bowel disease (IBD), yet the molecular and cellular composition of this diverse environment is poorly understood. However, the recent advent of multimodal single-cell technologies, including single-cell RNA sequencing (scRNA-seq), now provides an opportunity to accurately map the tissue architecture, characterize rare cell types that were previously overlooked, and define function at a single-cell level. In this review, we summarize key advances in single-cell technology and provide an overview of important aspects of computational analysis. We describe emerging data in the field of IBD and discuss how the characterization of novel intestinal mucosa cell populations is reshaping our understanding of this complex disease. We conclude by considering the potential clinical applications, including the definition of novel drug targets and the opportunity for personalization of care in this exciting new era of precision medicine., (© 2020 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation.)

Single-cell transcriptomic analysis has become a new and powerful tool to study complex multicellular systems. Single-cell RNA sequencing provides an unbiased classification of heterogeneous cellular states at the transcriptional level, but it does not always correlate to cell-surface protein expression. A recently developed method called cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) simultaneously measures surface proteins and gene expression from single cells. Briefly, based on the existing single-cell sequencing technology, oligonucleotide-labeled antibodies and barcoded primer gel beads are used to bind to corresponding cell-surface proteins and mRNA, respectively. Further, libraries of labeled protein and RNA information are sequenced to integrate cellular protein and transcriptome reads together efficiently. CITE-seq is transforming comprehensive genomic studies into models of causal gene-protein investigation.

Transcriptome analysis at a single-cell level with single-cell RNA sequencing (scRNA-seq) is a powerful method for detailed characterization of heterogeneous cell populations. Recent developments have enabled parallel analysis of both transcript and protein levels by using antibodies conjugated to barcoded oligonucleotides. These antibodies enable protein levels to be converted into nucleotide format, allowing the sequencing-based detection of both modalities at single-cell level. Here we present a simple and reliable method for conjugation of oligonucleotides with antibodies and a protocol for their use in single-cell transcriptome sequencing.