Orthogonal multimodality integration and clustering in single-cell data

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Elucidating granulocytic myeloid-derived suppressor cell heterogeneity during Staphylococcus aureus biofilm infection

Myeloid-derived suppressor cells (MDSCs) are pathologically activated immature myeloid cells with immunosuppressive activity that expand during chronic inflammation, such as cancer and prosthetic joint infection (PJI). Myeloid-derived suppressor cells can be broadly separated into 2 populations based on surface marker expression and function: monocytic myeloid-derived suppressor cells (M-MDSCs) and granulocytic myeloid-derived suppressor cells (G-MDSCs). Granulocytic myeloid-derived suppressor cells are the most abundant leukocyte infiltrate during PJI; however, how this population is maintained in vivo and cellular heterogeneity is currently unknown. In this study, we identified a previously unknown population of Ly6G+Ly6C+F4/80+MHCII+ MDSCs during PJI that displayed immunosuppressive properties ex vivo. We leveraged F4/80 and MHCII expression by these cells for further characterization using cellular indexing of transcriptomes and epitopes by sequencing, which revealed a distinct transcriptomic signature of this population. F4/80+MHCII+ MDSCs displayed gene signatures resembling G-MDSCs, neutrophils, and monocytes but had significantly increased expression of pathways involved in cytokine response/production, inflammatory cell death, and mononuclear cell differentiation. To determine whether F4/80+MHCII+ MDSCs represented an alternate phenotypic state of G-MDSCs, Ly6G+Ly6C+F4/80-MHCII- G-MDSCs from CD45.1 mice were adoptively transferred into CD45.2 recipients using a mouse model of PJI. A small percentage of transferred G-MDSCs acquired F4/80 and MHCII expression in vivo, suggesting some degree of plasticity in this population. Collectively, these results demonstrate a previously unappreciated phenotype of F4/80+MHCII+ MDSCs during PJI, revealing that a granulocytic-to-monocytic transition can occur during biofilm infection.

S100A8-enriched microglia populate the brain of tau-seeded and accelerated aging mice

Long considered to fluctuate between pro- and anti-inflammatory states, it has now become evident that microglia occupy a variegated phenotypic landscape with relevance to aging and neurodegeneration. However, whether specific microglial subsets converge in or contribute to both processes that eventually affect brain function is less clear. To investigate this, we analyzed microglial heterogeneity in a tauopathy mouse model (K18-seeded P301L) and an accelerated aging model (Senescence-Accelerated Mouse-Prone 8, SAMP8) using cellular indexing of transcriptomes and epitopes by sequencing. We found that widespread tau pathology in K18-seeded P301L mice caused a significant change in the number and morphology of microglia, but only a mild overrepresentation of disease-associated microglia. At the cell population-level, we observed a marked upregulation of the calprotectin-encoding genes S100a8 and S100a9. In 9-month-old SAMP8 mice, we identified a unique microglial subpopulation that showed partial similarity with the disease-associated microglia phenotype and was additionally characterized by a high expression of the same calprotectin gene set. Immunostaining for S100A8 revealed that this population was enriched in the hippocampus, correlating with the cognitive impairment observed in this model. However, incomplete colocalization between their residence and markers of neuronal loss suggests regional specificity. Importantly, S100A8-positive microglia were also retrieved in brain biopsies of human AD and tauopathy patients as well as in a biopsy of an aged individual without reported pathology. Thus, the emergence of S100A8-positive microglia portrays a conspicuous commonality between accelerated aging and tauopathy progression, which may have relevance for ensuing brain dysfunction.

In-depth analysis of human virus-specific CD8+ T cells delineates unique phenotypic signatures for T cell specificity prediction

Following viral infection, the human immune system generates CD8+ T cell responses to virus antigens that differ in specificity, abundance, and phenotype. A characterization of virus-specific T cell responses allows one to assess infection history and to understand its contribution to protective immunity. Here, we perform in-depth profiling of CD8+ T cells binding to CMV-, EBV-, influenza-, and SARS-CoV-2-derived antigens in peripheral blood samples from 114 healthy donors and 55 cancer patients using high-dimensional mass cytometry and single-cell RNA sequencing. We analyze over 500 antigen-specific T cell responses across six different HLA alleles and observed unique phenotypes of T cells specific for antigens from different virus categories. Using machine learning, we extract phenotypic signatures of antigen-specific T cells, predict virus specificity for bulk CD8+ T cells, and validate these predictions, suggesting that machine learning can be used to accurately predict antigen specificity from T cell phenotypes.

Complete miRNA-15/16 loss in mice promotes hematopoietic progenitor expansion and a myeloid-biased hyperproliferative state

Dysregulated apoptosis and proliferation are fundamental properties of cancer, and microRNAs (miRNA) are critical regulators of these processes. Loss of miR-15a/16-1 at chromosome 13q14 is the most common genomic aberration in chronic lymphocytic leukemia (CLL). Correspondingly, the deletion of either murine miR-15a/16-1 or miR-15b/16-2 locus in mice is linked to B cell lymphoproliferative malignancies. However, unexpectedly, when both miR-15/16 clusters are eliminated, most double knockout (DKO) mice develop acute myeloid leukemia (AML). Moreover, in patients with CLL, significantly reduced expression of miR-15a, miR-15b, and miR-16 associates with progression of myelodysplastic syndrome to AML, as well as blast crisis in chronic myeloid leukemia. Thus, the miR-15/16 clusters have a biological relevance for myeloid neoplasms. Here, we demonstrate that the myeloproliferative phenotype in DKO mice correlates with an increase of hematopoietic stem and progenitor cells (HSPC) early in life. Using single-cell transcriptomic analyses, we presented the molecular underpinning of increased myeloid output in the HSPC of DKO mice with gene signatures suggestive of dysregulated hematopoiesis, metabolic activities, and cell cycle stages. Functionally, we found that multipotent progenitors (MPP) of DKO mice have increased self-renewing capacities and give rise to significantly more progeny in the granulocytic compartment. Moreover, a unique transcriptomic signature of DKO MPP correlates with poor outcome in patients with AML. Together, these data point to a unique regulatory role for miR-15/16 during the early stages of hematopoiesis and to a potentially useful biomarker for the pathogenesis of myeloid neoplasms.

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.

Temporal multimodal single-cell profiling of native hematopoiesis illuminates altered differentiation trajectories with age

Aging negatively affects hematopoiesis, with consequences for immunity and acquired blood cell disorders. Although impairments in hematopoietic stem cell (HSC) function contribute to this, the in vivo dynamics of such changes remain obscure. Here, we integrate extensive longitudinal functional assessments of HSC-specific lineage tracing with single-cell transcriptome and epitope profiling. In contrast to recent suggestions from single-cell RNA sequencing alone, our data favor a defined structure of HSC/progenitor differentiation that deviates substantially from HSC-derived hematopoiesis following transplantation. Native age-dependent attrition in HSC differentiation manifests as drastically reduced lymphoid output through an early lymphoid-primed progenitor (MPP Ly-I). While in vitro activation fails to rescue lymphoid differentiation from most aged HSCs, robust lymphopoiesis can be achieved by culturing elevated numbers of candidate HSCs. Therefore, our data position rare chronologically aged HSC clones, fully competent at producing lymphoid offspring, as a prime target for approaches aimed to improve lymphopoiesis in the elderly.

Human T cell generation is restored in CD3δ severe combined immunodeficiency through adenine base editing

CD3δ SCID is a devastating inborn error of immunity caused by mutations in CD3D, encoding the invariant CD3δ chain of the CD3/TCR complex necessary for normal thymopoiesis. We demonstrate an adenine base editing (ABE) strategy to restore CD3δ in autologous hematopoietic stem and progenitor cells (HSPCs). Delivery of mRNA encoding a laboratory-evolved ABE and guide RNA into a CD3δ SCID patient's HSPCs resulted in a 71.2% ± 7.85% (n = 3) correction of the pathogenic mutation. Edited HSPCs differentiated in artificial thymic organoids produced mature T cells exhibiting diverse TCR repertoires and TCR-dependent functions. Edited human HSPCs transplanted into immunodeficient mice showed 88% reversion of the CD3D defect in human CD34+ cells isolated from mouse bone marrow after 16 weeks, indicating correction of long-term repopulating HSCs. These findings demonstrate the preclinical efficacy of ABE in HSPCs for the treatment of CD3δ SCID, providing a foundation for the development of a one-time treatment for CD3δ SCID patients.

Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population

Gangliogliomas are brain tumors composed of neuron-like and macroglia-like components that occur in children and young adults. Gangliogliomas are often characterized by a rare population of immature astrocyte-appearing cells expressing CD34, a marker expressed in the neuroectoderm (neural precursor cells) during embryogenesis. New insights are needed to refine tumor classification and to identify therapeutic approaches. We evaluated five gangliogliomas with single nucleus RNA-seq, cellular indexing of transcriptomes and epitopes by sequencing, and/or spatially-resolved RNA-seq. We uncovered a population of CD34+ neoplastic cells with mixed neuroectodermal, immature astrocyte, and neuronal markers. Gene regulatory network interrogation in these neuroectoderm-like cells revealed control of transcriptional programming by TCF7L2/MEIS1-PAX6 and SOX2, similar to that found during neuroectodermal/neural development. Developmental trajectory analyses place neuroectoderm-like tumor cells as precursor cells that give rise to neuron-like and macroglia-like neoplastic cells. Spatially-resolved transcriptomics revealed a neuroectoderm-like tumor cell niche with relative lack of vascular and immune cells. We used these high resolution results to deconvolute clinically-annotated transcriptomic data, confirming that CD34+ cell-associated gene programs associate with gangliogliomas compared to other glial brain tumors. Together, these deep transcriptomic approaches characterized a ganglioglioma cellular hierarchy-confirming CD34+ neuroectoderm-like tumor precursor cells, controlling transcription programs, cell signaling, and associated immune cell states. These findings may guide tumor classification, diagnosis, prognostication, and therapeutic investigations.

Specialized functions and sexual dimorphism explain the functional diversity of the myeloid populations during glioma progression

Malignant gliomas are aggressive, hard-to-treat brain tumors. Their tumor microenvironment is massively infiltrated by myeloid cells, mostly brain-resident microglia, bone marrow (BM)-derived monocytes/macrophages, and dendritic cells that support tumor progression. Single-cell omics studies significantly dissected immune cell heterogeneity, but dynamics and specific functions of individual subpopulations were poorly recognized. We use Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) to precisely dissect myeloid cell identities and functionalities in murine GL261 gliomas. We demonstrate that the diversity of myeloid cells infiltrating gliomas is dictated by cell type and cell state. Glioma-activated microglia are the major source of cytokines attracting other immune cells, whereas BM-derived cells show the monocyte-to-macrophage transition in the glioma microenvironment. This transition is coupled with a phenotypic switch from the IFN-related to antigen-presentation and tumor-supportive gene expression. Moreover, we found sex-dependent differences in transcriptional programs and composition of myeloid cells in murine and human glioblastomas.

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.

Tissue-specific transcriptional profiles and heterogeneity of natural killer cells and group 1 innate lymphoid cells

Natural killer (NK) cells and type 1 innate lymphoid cells (ILC1s) are populations of non-T, non-B lymphocytes in peripheral tissues. Although NK and ILC1 subsets have been described, their identification and characteristics remain unclear. We performed single-cell RNA sequencing and CITE-seq to explore NK and ILC1 heterogeneity between tissues. We observed that although NK1 and NK2 subsets are conserved in spleen and liver, ILC1s are heterogeneous across tissues. We identified sets of genes expressed by related subsets or characterizing unique ILC1 populations in each organ. The syndecan-4 appeared as a marker discriminating murine ILC1 from NK cells across organs. Finally, we revealed that the expressions of EOMES, GZMA, IRF8, JAK1, NKG7, PLEK, PRF1, and ZEB2 define NK cells and that IL7R, LTB, and RGS1 differentiate ILC1s from NK cells in mice and humans. Our data constitute an important resource to improve our understanding of NK-ILC1 origin, phenotype, and biology.

Loss of GM-CSF-dependent instruction of alveolar macrophages in COVID-19 provides a rationale for inhaled GM-CSF treatment

GM-CSF promotes myelopoiesis and inflammation, and GM-CSF blockade is being evaluated as a treatment for COVID-19-associated hyperinflammation. Alveolar GM-CSF is, however, required for monocytes to differentiate into alveolar macrophages (AMs) that control alveolar homeostasis. By mapping cross-species AM development to clinical lung samples, we discovered that COVID-19 is marked by defective GM-CSF-dependent AM instruction and accumulation of pro-inflammatory macrophages. In a multi-center, open-label RCT in 81 non-ventilated COVID-19 patients with respiratory failure, we found that inhalation of rhu-GM-CSF did not improve mean oxygenation parameters compared with standard treatment. However, more patients on GM-CSF had a clinical response, and GM-CSF inhalation induced higher numbers of virus-specific CD8 effector lymphocytes and class-switched B cells, without exacerbating systemic hyperinflammation. This translational proof-of-concept study provides a rationale for further testing of inhaled GM-CSF as a non-invasive treatment to improve alveolar gas exchange and simultaneously boost antiviral immunity in COVID-19. This study is registered at ClinicalTrials.gov (NCT04326920) and EudraCT (2020-001254-22).

A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation

CITE-seq, a single-cell multi-omics technology that measures RNA and protein expression simultaneously in single cells, has been widely applied in biomedical research, especially in immune related disorders and other diseases such as influenza and COVID-19. Despite the proliferation of CITE-seq, it is still costly to generate such data. Although data integration can increase information content, this raises computational challenges. First, combining multiple datasets is prone to batch effects that need to be addressed. Secondly, it is difficult to combine multiple CITE-seq datasets because the protein panels in different datasets may only partially overlap. Integrating multiple CITE-seq and single-cell RNA-seq (scRNA-seq) datasets is important because this allows the utilization of as many data as possible to uncover cell population heterogeneity. To overcome these challenges, we present sciPENN, a multi-use deep learning approach that supports CITE-seq and scRNA-seq data integration, protein expression prediction for scRNA-seq, protein expression imputation for CITE-seq, quantification of prediction and imputation uncertainty, and cell type label transfer from CITE-seq to scRNA-seq. Comprehensive evaluations spanning multiple datasets demonstrate that sciPENN outperforms other current state-of-the-art methods.

Identification of a distinct NK-like hepatic T-cell population activated by NKG2C in a TCR-independent manner

BACKGROUND & AIMS

The liver provides a unique niche of lymphocytes enriched with a large proportion of innate-like T cells. However, the heterogeneity and functional characteristics of the hepatic T-cell population remain to be fully elucidated.

METHODS

We obtained liver sinusoidal mononuclear cells from the liver perfusate of healthy donors and recipients with HBV-associated chronic liver disease (CLD) during liver transplantation. We performed a CITE-seq analysis of liver sinusoidal CD45⁺ cells in combination with T cell receptor (TCR)-seq and flow cytometry to examine the phenotypes and functions of liver sinusoidal CD8⁺ T cells.

RESULTS

We identified a distinct CD56^hiCD161^-CD8⁺ T-cell population characterized by natural killer (NK)-related gene expression and a uniquely restricted TCR repertoire. The frequency of these cells among the liver sinusoidal CD8⁺ T-cell population was significantly increased in patients with HBV-associated CLD. Although CD56^hiCD161^-CD8⁺ T cells exhibit weak responsiveness to TCR stimulation, CD56^hiCD161^-CD8⁺ T cells highly expressed various NK receptors, including CD94, killer immunoglobulin-like receptors, and NKG2C, and exerted NKG2C-mediated NK-like effector functions even in the absence of TCR stimulation. In addition, CD56^hiCD161^-CD8⁺ T cells highly respond to innate cytokines, such as IL-12/18 and IL-15, in the absence of TCR stimulation. We validated the results from liver sinusoidal CD8⁺ T cells using intrahepatic CD8⁺ T cells obtained from liver tissues.

CONCLUSIONS

In summary, the current study found a distinct CD56^hiCD161^-CD8⁺ T-cell population characterized by NK-like activation via TCR-independent NKG2C ligation. Further studies are required to elucidate the roles of liver sinusoidal CD56^hiCD161^-CD8⁺ T cells in immune responses to microbial pathogens or liver immunopathology.

LAY SUMMARY

The role of different immune cell populations in the liver is becoming an area of increasing interest. Herein, we identified a distinct T-cell population that had features similar to those of natural killer (NK) cells - a type of innate immune cell. This distinct population was expanded in the livers of patients with chronic liver disease and could thus have pathogenic relevance.

DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

HLA-B∗46 associates with rapid HIV disease progression in Asian cohorts and prominent differences in NK cell phenotype

Human leukocyte antigen (HLA) alleles have been linked to HIV disease progression and attributed to differences in cytotoxic T lymphocyte (CTL) epitope representation. These findings are largely based on treatment-naive individuals of European and African ancestry. We assessed HLA associations with HIV-1 outcomes in 1,318 individuals from Thailand and found HLA-B∗46:01 (B∗46) associated with accelerated disease in three independent cohorts. B∗46 had no detectable effect on HIV-specific T cell responses, but this allele is unusual in containing an HLA-C epitope that binds inhibitory receptors on natural killer (NK) cells. Unbiased transcriptomic screens showed increased NK cell activation in people with HIV, without B∗46, and simultaneous single-cell profiling of surface proteins and transcriptomes revealed a NK cell subset primed for increased responses in the absence of B∗46. These findings support a role for NK cells in HIV pathogenesis, revealed by the unique properties of the B∗46 allele common only in Asia.

Single Cell Transcriptome and Surface Epitope Analysis of Ankylosing Spondylitis Facilitates Disease Classification by Machine Learning

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.

A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers

A common theme across multiple successful immunotherapies for cancer is the recognition of tumor-specific mutations (neoantigens) by T cells. The rapid discovery of such antigen responses could lead to improved therapies through the adoptive transfer of T cells engineered to express neoantigen-reactive T cell receptors (TCRs). Here, through CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and TCR-seq of non-small cell lung cancer (NSCLC) tumor-infiltrating lymphocytes (TILs), we develop a neoantigen-reactive T cell signature based on clonotype frequency and CD39 protein and CXCL13 mRNA expression. Screening of TCRs selected by the signature allows us to identify neoantigen-reactive TCRs with a success rate of 45% for CD8+ and 66% for CD4+ T cells. Because of the small number of samples analyzed (4 patients), generalizability remains to be tested. However, this approach can enable the quick identification of neoantigen-reactive TCRs and expedite the engineering of personalized neoantigen-reactive T cells for therapy.

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

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.

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.

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches

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.

Simultaneous Analysis of Single-Cell Transcriptomes and Cell Surface Protein Expression of Human Hematopoietic Stem Cells and Progenitors Using the 10x Genomics Platform

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.

Multiomic analysis reveals decidual-specific transcriptional programing of MAIT cells

PROBLEM

Mucosal-Associated Invariant T (MAIT) cells have been recently identified at the maternal-fetal interface. However, transcriptional programming of decidual MAIT cells in pregnancy remains poorly understood.

METHOD OF STUDY

We employed a multiomic approach to address this question. Mononuclear cells from the decidua basalis and parietalis, and control PBMCs, were analyzed via flow cytometry to investigate MAIT cells in the decidua and assess their transcription factor expression. In a separate study, both decidual and matched peripheral MAIT cells were analyzed using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) coupled with gene expression analysis. Lastly, decidual MAIT cells were stimulated with E.coli and expression of MR1 by antigen presenting cells was measured to evaluate decidual MAIT cell function.

RESULTS

First, we identified MAIT cells in both the decidua basalis and parietalis. CITE-seq, coupled with scRNA-seq gene expression analysis, highlighted transcriptional programming differences between decidual and matched peripheral MAIT cells at a single cell resolution. Transcription factor expression analysis further highlighted transcriptional differences between decidual MAIT cells and non-matched peripheral MAIT cells. Functionally, MAIT cells are skewed towards IFNγ and TNFα production upon stimulation, with E.coli leading to IFNγ production. Lastly, we demonstrate that MR1, the antigen presenting molecule restricting MAIT cells, is expressed by decidual APCs.

CONCLUSION

MAIT cells are present in the decidua basalis and obtain a unique gene expression profile. The presence of MR1 on APCs coupled with in vitro activation by E.coli suggests that MAIT cells might be involved in tissue-repair mechanisms at the maternal-fetal interface.

Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as a potential mechanism of vaccine-induced protection against HIV-1

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.

Improved integration of single-cell transcriptome and surface protein expression by LinQ-View

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).

Integrated analysis of multimodal single-cell data

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.

Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal

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.

Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling

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.

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

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.

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.

The Conjugation of Antibodies for the Simultaneous Detection of Surface Proteins and Transcriptome Analysis at a Single-Cell Level

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.

Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28

Immune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e.g., CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq.jax.org/).

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

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 CD56^dimCD16^hi 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.

Understanding cell-cell communication and signaling in the colorectal cancer microenvironment

Carcinomas are complex heterocellular systems containing epithelial cancer cells, stromal fibroblasts, and multiple immune cell-types. Cell-cell communication between these tumor microenvironments (TME) and cells drives cancer progression and influences response to existing therapies. In order to provide better treatments for patients, we must understand how various cell-types collaborate within the TME to drive cancer and consider the multiple signals present between and within different cancer types. To investigate how tissues function, we need a model to measure both how signals are transferred between cells and how that information is processed within cells. The interplay of collaboration between different cell-types requires cell-cell communication. This article aims to review the current in vitro and in vivo mono-cellular and multi-cellular cultures models of colorectal cancer (CRC), and to explore how they can be used for single-cell multi-omics approaches for isolating multiple types of molecules from a single-cell required for cell-cell communication to distinguish cancer cells from normal cells. Integrating the existing single-cell signaling measurements and models, and through understanding the cell identity and how different cell types communicate, will help predict drug sensitivities in tumor cells and between- and within-patients responses.

Atypical B cells are part of an alternative lineage of B cells that participates in responses to vaccination and infection in humans

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.

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Single-cell RNA-seq reveals two distinct B cell lineages

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An alternative lineage contains CXCR3⁺ and atypical B cells

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Alternative B cells are primed after primary vaccination and respond to boosters

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Alternative B cells adopt a more atypical phenotype following repeated antigen exposure

Comprehensive Profiling of an Aging Immune System Reveals Clonal GZMK+ CD8+ T Cells as Conserved Hallmark of Inflammaging

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.

Multi-modal Single-Cell Analysis Reveals Brain Immune Landscape Plasticity during Aging and Gut Microbiota Dysbiosis

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.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Inflammatory Bowel Disease Through the Lens of Single-cell RNA-seq Technologies

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.

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data

Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as a cost-effective substitute of single-cell proteomics, may not accurately reflect protein expression levels due to measurement error, noise, post-transcriptional and translational regulation, etc. The recently emerging single-cell multimodal omics data, e.g. CITE-seq and REAP-seq, can simultaneously profile RNA and protein abundances in single cells, providing labeled data for predictive modeling in a supervised learning framework. Deep neural network-based transfer learning method has been applied to imputation of surface protein abundances from single-cell transcriptomic data. However, it is unclear if the artificial neural network is the best model, and it is desirable to improve the prediction performance (e.g. accuracy, interpretability) of machine learning models. In this paper, we compared several tree-based ensemble learning methods with neural network models, and found that ensemble learning often performed better than neural network, and Random Forest (RF) performed the best overall. Moreover, we used the feature importance scores from RF to interpret biological mechanisms underlying the prediction. Our study demonstrates the effectiveness of ensemble learning for reliable protein abundances prediction using single-cell multimodal omics data, and paves the way for knowledge discovery by mining single-cell multi-omics data in large scale.

Simultaneous Measurement of Surface Proteins and Gene Expression from Single Cells

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.

Integration of Flow Cytometry and Single Cell Sequencing

Integrating cytometric analysis of cells, mitochondria, and other polynucleotide-containing biological particles with high-throughput single particle sequencing would provide an ultimate bioanalytical tool, simultaneously assessing phenotype, functionality, genome, and transcriptome of each particle in a large population. Here, we describe how such integration could be performed by adapting existing, well-established technologies.