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

AnnoGCD: a generalized category discovery framework for automatic cell type annotation

The identification of cell types in single-cell RNA sequencing (scRNA-seq) data is a critical task in understanding complex biological systems. Traditional supervised machine learning methods rely on large, well-labeled datasets, which are often impractical to obtain in open-world scenarios due to budget constraints and incomplete information. To address these challenges, we propose a novel computational framework, named AnnoGCD, building on Generalized Category Discovery (GCD) and Anomaly Detection (AD) for automatic cell type annotation. Our semi-supervised method combines labeled and unlabeled data to accurately classify known cell types and to discover novel ones, even in imbalanced datasets. AnnoGCD includes a semi-supervised block to first classify known cell types, followed by an unsupervised block aimed at identifying and clustering novel cell types. We evaluated our approach on five human scRNA-seq datasets and a mouse model atlas, demonstrating superior performance in both known and novel cell type identification compared to existing methods. Our model also exhibited robustness in datasets with significant class imbalance. The results suggest that AnnoGCD is a powerful tool for the automatic annotation of cell types in scRNA-seq data, providing a scalable solution for biological research and clinical applications. Our code and the datasets used for evaluations are publicly available on GitHub: https://github.com/cecca46/AnnoGCD/.

MoNETA: MultiOmics Network Embedding for SubType Analysis

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

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

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.

SARS-CoV-2 inflammation durably imprints memory CD4 T cells

Memory CD4 T cells are critical to human immunity, yet it is unclear whether viral inflammation during memory formation has long-term consequences. Here, we compared transcriptional and epigenetic landscapes of Spike (S)-specific memory CD4 T cells in 24 individuals whose first exposure to S was via SARS-CoV-2 infection or mRNA vaccination. Nearly 2 years after memory formation, S-specific CD4 T cells established by infection remained enriched for transcripts related to cytotoxicity and for interferon-stimulated genes, likely because of a chromatin accessibility landscape altered by inflammation. Moreover, S-specific CD4 T cells primed by infection had reduced proliferative capacity in vitro relative to vaccine-primed cells. Furthermore, the transcriptional state of S-specific memory CD4 T cells was minimally altered by booster immunization and/or breakthrough infection. Thus, infection-associated inflammation durably imprints CD4 T cell memory, which affects the function of these cells and may have consequences for long-term immunity.

Staphylococcus aureus induces drug resistance in cancer T cells in Sézary syndrome

ABSTRACT

Patients with Sézary syndrome (SS), a leukemic variant of cutaneous T-cell lymphoma (CTCL), are prone to Staphylococcus aureus infections and have a poor prognosis due to treatment resistance. Here, we report that S aureus and staphylococcal enterotoxins (SE) induce drug resistance in malignant T cells against therapeutics commonly used in CTCL. Supernatant from patient-derived, SE-producing S aureus and recombinant SE significantly inhibit cell death induced by histone deacetylase (HDAC) inhibitor romidepsin in primary malignant T cells from patients with SS. Bacterial killing by engineered, bacteriophage-derived, S aureus-specific endolysin (XZ.700) abrogates the effect of S aureus supernatant. Similarly, mutations in major histocompatibility complex (MHC) class II binding sites of SE type A (SEA) and anti-SEA antibody block induction of resistance. Importantly, SE also triggers resistance to other HDAC inhibitors (vorinostat and resminostat) and chemotherapeutic drugs (doxorubicin and etoposide). Multimodal single-cell sequencing indicates T-cell receptor (TCR), NF-κB, and JAK/STAT signaling pathways (previously associated with drug resistance) as putative mediators of SE-induced drug resistance. In support, inhibition of TCR-signaling and Protein kinase C (upstream of NF-κB) counteracts SE-induced rescue from drug-induced cell death. Inversely, SE cannot rescue from cell death induced by the proteasome/NF-κB inhibitor bortezomib. Inhibition of JAK/STAT only blocks rescue in patients whose malignant T-cell survival is dependent on SE-induced cytokines, suggesting 2 distinct ways SE can induce drug resistance. In conclusion, we show that S aureus enterotoxins induce drug resistance in primary malignant T cells. These findings suggest that S aureus enterotoxins cause clinical treatment resistance in patients with SS, and antibacterial measures may improve the outcome of cancer-directed therapy in patients harboring S aureus.

Glutamine antagonist DRP-104 suppresses tumor growth and enhances response to checkpoint blockade in KEAP1 mutant lung cancer

Loss-of-function mutations in KEAP1 frequently occur in lung cancer and are associated with poor prognosis and resistance to standard of care treatment, highlighting the need for the development of targeted therapies. We previously showed that KEAP1 mutant tumors consume glutamine to support the metabolic rewiring associated with NRF2-dependent antioxidant production. Here, using preclinical patient-derived xenograft models and antigenic orthotopic lung cancer models, we show that the glutamine antagonist prodrug DRP-104 impairs the growth of KEAP1 mutant tumors. We find that DRP-104 suppresses KEAP1 mutant tumors by inhibiting glutamine-dependent nucleotide synthesis and promoting antitumor T cell responses. Using multimodal single-cell sequencing and ex vivo functional assays, we demonstrate that DRP-104 reverses T cell exhaustion, decreases Tregs, and enhances the function of CD4 and CD8 T cells, culminating in an improved response to anti-PD1 therapy. Our preclinical findings provide compelling evidence that DRP-104, currently in clinical trials, offers a promising therapeutic approach for treating patients with KEAP1 mutant lung cancer.

Ex vivo discovery of synergistic drug combinations for hematologic malignancies

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. In an exploratory analysis of PBMC datasets, we find that some droplets that were originally called 'empty' due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a 'spongelet' which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

demuxSNP: supervised demultiplexing single-cell RNA sequencing using cell hashing and SNPs

BACKGROUND

Multiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger-scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost-effectiveness.

FINDINGS

We propose a supervised algorithm, demuxSNP, which leverages both cell hashing and genetic variation between individuals (single-nucletotide polymorphisms [SNPs]). demuxSNP addresses fundamental limitations in demultiplexing methods that use only one data modality. Some cells may be confidently demultiplexed using probabilistic hashing methods. demuxSNP uses these data to infer the genotype of singlet and doublet clusters and predict on cells assigned as negative, uncertain, or doublet using a nearest-neighbor approach adapted for missing data.We benchmarked demuxSNP against hashing, genotype-free SNP and hybrid methods on simulated and real data from renal cell cancer. demuxSNP outperformed standalone hashing methods on low-quality hashing data benchmark, improved overall classification accuracy, and allowed more high RNA quality cells to be recovered. Through varying simulated doublet rates, we showed that genotype-free SNP and hybrid methods that leverage them were impacted by class size imbalance and doublet rate. demuxSNP's supervised approach was more robust to doublet rate in experiments with class size imbalance.

CONCLUSIONS

demuxSNP uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned or negative cells with high RNA quality are recovered, making more cells available for analysis. Data simulation and benchmarking pipelines as well as processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1mut erythroid progenitor cells. We uncovered distinct cryptic 3' splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3' splice site usage in SF3B1mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

Glutamine antagonist DRP-104 suppresses tumor growth and enhances response to checkpoint blockade in KEAP1 mutant lung cancer

Loss-of-function mutations in KEAP1 frequently occur in lung cancer and are associated with resistance to standard of care treatment, highlighting the need for the development of targeted therapies. We have previously shown that KEAP1 mutant tumors have increased glutamine consumption to support the metabolic rewiring associated with NRF2 activation. Here, using patient-derived xenograft models and antigenic orthotopic lung cancer models, we show that the novel glutamine antagonist DRP-104 impairs the growth of KEAP1 mutant tumors. We find that DRP-104 suppresses KEAP1 mutant tumor growth by inhibiting glutamine-dependent nucleotide synthesis and promoting anti-tumor CD4 and CD8 T cell responses. Using multimodal single-cell sequencing and ex vivo functional assays, we discover that DRP-104 reverses T cell exhaustion and enhances the function of CD4 and CD8 T cells culminating in an improved response to anti-PD1 therapy. Our pre-clinical findings provide compelling evidence that DRP-104, currently in phase 1 clinical trials, offers a promising therapeutic approach for treating patients with KEAP1 mutant lung cancer. Furthermore, we demonstrate that by combining DRP-104 with checkpoint inhibition, we can achieve suppression of tumor intrinsic metabolism and augmentation of anti-tumor T cell responses.

Inflammation durably imprints memory CD4+ T cells

Adaptive immune responses are induced by vaccination and infection, yet little is known about how CD4+ T cell memory differs when primed in these two contexts. Notably, viral infection is generally associated with higher levels of systemic inflammation than is vaccination. To assess whether the inflammatory milieu at the time of CD4+ T cell priming has long-term effects on memory, we compared Spike-specific memory CD4+ T cells in 22 individuals around the time of the participants' third SARS-CoV-2 mRNA vaccination, with stratification by whether the participants' first exposure to Spike was via virus or mRNA vaccine. Multimodal single-cell profiling of Spike-specific CD4+ T cells revealed 755 differentially expressed genes that distinguished infection- and vaccine-primed memory CD4+ T cells. Spike-specific CD4+ T cells from infection-primed individuals had strong enrichment for cytotoxicity and interferon signaling genes, whereas Spike-specific CD4+ T cells from vaccine-primed individuals were enriched for proliferative pathways by gene set enrichment analysis. Moreover, Spike-specific memory CD4+ T cells established by infection had distinct epigenetic landscapes driven by enrichment of IRF-family transcription factors, relative to T cells established by mRNA vaccination. This transcriptional imprint was minimally altered following subsequent mRNA vaccination or breakthrough infection, reflecting the strong bias induced by the inflammatory environment during initial memory differentiation. Together, these data suggest that the inflammatory context during CD4+ T cell priming is durably imprinted in the memory state at transcriptional and epigenetic levels, which has implications for personalization of vaccination based on prior infection history.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Strategies for optimizing CITE-seq for human islets and other tissues

Defining the immunological landscape of human tissue is an important area of research, but challenges include the impact of tissue disaggregation on cell phenotypes and the low abundance of immune cells in many tissues. Here, we describe methods to troubleshoot and standardize Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) for studies involving enzymatic digestion of human tissue. We tested epitope susceptibility of 92 antibodies commonly used to differentiate immune lineages and cell states on human peripheral blood mononuclear cells following treatment with an enzymatic digestion cocktail used to isolate islets. We observed CD4, CD8a, CD25, CD27, CD120b, CCR4, CCR6, and PD1 display significant sensitivity to enzymatic treatment, effects that often could not be overcome with alternate antibodies. Comparison of flow cytometry-based CITE-seq antibody titrations and sequencing data supports that for the majority of antibodies, flow cytometry accurately predicts optimal antibody concentrations for CITE-seq. Comparison by CITE-seq of immune cells in enzymatically digested islet tissue and donor-matched spleen not treated with enzymes revealed little digestion-induced epitope cleavage, suggesting increased sensitivity of CITE-seq and/or that the islet structure may protect resident immune cells from enzymes. Within islets, CITE-seq identified immune cells difficult to identify by transcriptional signatures alone, such as distinct tissue-resident T cell subsets, mast cells, and innate lymphoid cells (ILCs). Collectively this study identifies strategies for the rational design and testing of CITE-seq antibodies for single-cell studies of immune cells within islets and other tissues.

BD Rhapsody™ Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data

Advancements in single-cell sequencing have revolutionized our understanding of complex biological systems such as the immune system and allowed us to overcome limitations in various disciplines of life science research such as oncology, developmental biology, or neurobiology (Perkel, Nature 595. https://www.nature.com/articles/d41586-021-01994-w , 2021).The BD Rhapsody™ Single-Cell Analysis System enables us to capture multimodal information from thousands of single cells in parallel ("Multiomics") covering mRNA expression levels, protein expression levels, the immune repertoire for T-cell receptors (TCR) and B-cell receptors (BCR), and the identification of antigen-specific T cells and B cells using dCODE Dextramer^® (RiO) from Immudex. The system utilizes microwell-based cartridges that allow to capture a broad range of single cells and an imaging device for sample quality control and workflow quality control (including viability and multiplets). The power of Multiomics relies on simultaneously measuring several aspects of single cells, including gene expression and protein abundance, using next generation sequencing (NGS) as a single readout.Here we describe the complete BD Rhapsody™ Single-Cell Analysis System from the sample preparation including different options for the antibody and/or dCODE Dextramer^® staining through to the data analysis.For updated protocols, guides, and technical bulletins, please visit the BD Scomix page: https://scomix.bd.com/hc/en-us or the BDB webpage: https://www.bdbiosciences.com/en-eu .

Titration of 124 antibodies using CITE-Seq on human PBMCs

Single-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies will improve the analysis and may reduce costs. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Combined protein and transcript single-cell RNA sequencing in human peripheral blood mononuclear cells

BACKGROUND

Cryopreserved peripheral blood mononuclear cells (PBMCs) are frequently collected and provide disease- and treatment-relevant data in clinical studies. Here, we developed combined protein (40 antibodies) and transcript single-cell (sc)RNA sequencing (scRNA-seq) in PBMCs.

RESULTS

Among 31 participants in the Women's Interagency HIV Study (WIHS), we sequenced 41,611 cells. Using Boolean gating followed by Seurat UMAPs (tool for visualizing high-dimensional data) and Louvain clustering, we identified 50 subsets among CD4+ T, CD8+ T, B, NK cells, and monocytes. This resolution was superior to flow cytometry, mass cytometry, or scRNA-seq without antibodies. Combined protein and transcript scRNA-seq allowed for the assessment of disease-related changes in transcriptomes and cell type proportions. As a proof-of-concept, we showed such differences between healthy and matched individuals living with HIV with and without cardiovascular disease.

CONCLUSIONS

In conclusion, combined protein and transcript scRNA sequencing is a suitable and powerful method for clinical investigations using PBMCs.

Antibodies to combat viral infections: development strategies and progress

Monoclonal antibodies (mAbs) are appealing as potential therapeutics and prophylactics for viral infections owing to characteristics such as their high specificity and their ability to enhance immune responses. Furthermore, antibody engineering can be used to strengthen effector function and prolong mAb half-life, and advances in structural biology have enabled the selection and optimization of potent neutralizing mAbs through identification of vulnerable regions in viral proteins, which can also be relevant for vaccine design. The COVID-19 pandemic has stimulated extensive efforts to develop neutralizing mAbs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with several mAbs now having received authorization for emergency use, providing not just an important component of strategies to combat COVID-19 but also a boost to efforts to harness mAbs in therapeutic and preventive settings for other infectious diseases. Here, we describe advances in antibody discovery and engineering that have led to the development of mAbs for use against infections caused by viruses including SARS-CoV-2, respiratory syncytial virus (RSV), Ebola virus (EBOV), human cytomegalovirus (HCMV) and influenza. We also discuss the rationale for moving from empirical to structure-guided strategies in vaccine development, based on identifying optimal candidate antigens and vulnerable regions within them that can be targeted by antibodies to result in a strong protective immune response.

Induced Human Regulatory T Cells Express the Glucagon-like Peptide-1 Receptor

The glucagon-like peptide-1 receptor (GLP-1R) plays a key role in metabolism and is an important therapeutic target in diabetes and obesity. Recent studies in experimental animals have shown that certain subsets of T cells express functional GLP-1R, indicating an immune regulatory role of GLP-1. In contrast, less is known about the expression and function of the GLP-1R in human T cells. Here, we provide evidence that activated human T cells express GLP-1R. The expressed GLP-1R was functional, as stimulation with a GLP-1R agonist triggered an increase in intracellular cAMP, which was abrogated by a GLP-1R antagonist. Analysis of CD4⁺ T cells activated under T helper (Th) 1, Th2, Th17 and regulatory T (Treg) cell differentiation conditions indicated that GLP-1R expression was most pronounced in induced Treg (iTreg) cells. Through multimodal single-cell CITE- and TCR-sequencing, we detected GLP-1R expression in 29–34% of the FoxP3⁺CD25⁺CD127^- iTreg cells. GLP-1R⁺ cells showed no difference in their TCR-gene usage nor CDR3 lengths. Finally, we demonstrated the presence of GLP-1R⁺CD4⁺ T cells in skin from patients with allergic contact dermatitis. Taken together, the present data demonstrate that T cell activation triggers the expression of functional GLP-1R in human CD4⁺ T cells. Given the high induction of GLP-1R in human iTreg cells, we hypothesize that GLP-1R⁺ iTreg cells play a key role in the anti-inflammatory effects ascribed to GLP-1R agonists in humans.

Detection of cell markers from single cell RNA-seq with sc2marker

Background

Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data.

Results

Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04817-5.

Normalizing and denoising protein expression data from droplet-based single cell profiling

Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".

Single-cell immunology: Past, present, and future

The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

BACKGROUND

Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing."

RESULTS

Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

CONCLUSIONS

Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

Optimising protein detection with fixable custom oligo-labelled antibodies for single-cell multi-omics approaches

BACKGROUND AND AIM

Single-cell RNA sequencing (scRNA-seq) is a powerful method utilising transcriptomic data for detailed characterisation of heterogeneous cell populations. The use of oligonucleotide-labelled antibodies for targeted proteomics addresses the shortcomings of the scRNA-seq-only based approach by improving detection of low expressing targets. However, optimisation of large antibody panels is challenging and depends on the availability of co-functioning oligonucleotide-labelled antibodies.

MAIN METHODS AND RESULTS

We present here a simple adjustable oligonucleotide-antibody conjugation method which enables desired level of oligo-conjugation per antibody. The mean labelling in the produced antibody batches varied from 1 to 6 oligos per antibody. In the scRNA-seq multimodal experiment, the highest sensitivity was seen with moderate antibody labelling as the high activation and/or labelling was detrimental to antibody performance. The conjugates were also tested for compatibility with the fixation and freeze storage protocols. The oligo-antibody signal was stable in fixed cells indicating feasibility of the stain, fix, store, and analyse later type of workflow for multimodal scRNA-seq.

CONCLUSIONS AND IMPLICATIONS

Optimised oligo-labelling will improve detection of weak protein targets in scRNA-seq multimodal experiments and reduce sequencing costs due to a more balanced amplification of different antibody signals in CITE-seq libraries. Furthermore, the use of a pre-stain, fix, run later protocol will allow for flexibility, facilitate sample pooling, and ease logistics in scRNA-seq multimodal experiments. This article is protected by copyright. All rights reserved.

Antibody-Oligonucleotide Conjugation Using a SPAAC Copper-Free Method Compatible with 10× Genomics' Single-Cell RNA-Seq

Recent advances in multimodal approaches toward single-cell analyses present valuable data points that can complement standard flow cytometry data. In particular, the overlay of cell-surface proteome data with gene expression analysis presents a necessary advancement, particularly in the field of immunology. Here we describe a copper-free click chemistry method for the generation of antibody-oligonucleotide complexes and present the steps for its employment in the context of the 10× genomics droplet-based single-cell RNA-seq workflow, providing a method for coupling proteomic and transcriptomic analyses in an efficient and cost-effect manner.

OUP accepted manuscript

Motivation

The advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities.

Results

Here we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering.

Availability and implementation

The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128.

Supplementary information

Supplementary data are available at Bioinformatics online.

Acquisition of murine splenic myeloid cells for protein and gene expression profiling by advanced flow cytometry and CITE-seq

Here, we outline detailed protocols to isolate and profile murine splenic dendritic cells (DCs) through advanced flow cytometry of the myeloid compartment and single-cell transcriptomic profiling with integrated cell surface protein expression through CITE-seq. This protocol provides a general transferrable road map for different tissues and species.

For complete details on the use and execution of this protocol, please refer to Lukowski et al. (2021).

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Protocol to obtain integrated single-cell gene and protein expression data

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Optimized flow cytometry panel for confident delineation of six main myeloid lineages

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Gating strategy identifies large cell state heterogeneity within each lineage

Joint single-cell measurements of nuclear proteins and RNA in vivo

Identifying gene-regulatory targets of nuclear proteins in tissues is a challenge. Here we describe intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq), a scalable method that measures multiplexed intranuclear protein levels and the transcriptome in parallel across thousands of nuclei, enabling joint analysis of transcription factor (TF) levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed genome-wide associations of each TF and recovered known gene targets. TF-associated genes were coexpressed as distinct modules that each reflected positive or negative TF levels, showing that our approach can disentangle relative putative contributions of TFs to gene expression and add interpretability to inferred gene networks. inCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

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. 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.

Absence of Batf3 reveals a new dimension of cell state heterogeneity within conventional dendritic cells

Conventional dendritic cells (cDCs) are traditionally subdivided into cDC1 and cDC2 lineages. Batf3 is a cDC1-required transcription factor, and we observed that Batf3−/− mice harbor a population of cDC1-like cells co-expressing cDC2-associated surface molecules. Using single-cell RNA sequencing with integrated cell surface protein expression (CITE-seq), we found that Batf3−/− mitotic immature cDC1-like cells showed reduced expression of cDC1 features and increased levels of cDC2 features. In wild type, we also observed a proportion of mature cDC1 cells expressing surface features characteristic to cDC2 and found that overall cDC cell state heterogeneity was mainly driven by developmental stage, proliferation, and maturity. We detected population diversity within Sirpa+ cDC2 cells, including a Cd33+ cell state expressing high levels of Sox4 and lineage-mixed features characteristic to cDC1, cDC2, pDCs, and monocytes. In conclusion, these data suggest that multiple cDC cell states can co-express lineage-overlapping features, revealing a level of previously unappreciated cDC plasticity.

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Single-cell proteogenomics identifies additional layers of DC heterogeneity

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cDC diversity is driven by proliferation, developmental stage, and maturation

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Lack of Batf3 increases cDCs with lineage-mixed features

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Sox4+ cDCs represent a cell state of lineage-mixed features