Cross-tissue immune cell analysis reveals tissue-specific features in humans

Transendothelial Migration of Human B Cells: Chemokine versus Antigen

B cells, like T cells, can infiltrate sites of inflammation, but the processes and B cell subsets involved are poorly understood. Using human cells and in vitro assays, we find only a very small number of B cells will adhere to TNF-activated (but not to resting) human microvascular endothelial cells (ECs) under conditions of venular flow and do so by binding to ICAM-1 and VCAM-1. CXCL13 and, to a lesser extent, CXCL10 bound to the ECs can increase adhesion and induce transendothelial migration (TEM) of adherent naive and memory B cells in 10-15 min through a process involving cell spreading, translocation of the microtubule organizing center (MTOC) into a trailing uropod, and interacting with EC activated leukocyte cell adhesion molecule. Engagement of the BCR by EC-bound anti-κ L chain Ab also increases adhesion and TEM of κ+ but not λ+ B cells. BCR-induced TEM takes 30-60 min, requires Syk activation, is initiated by B cell rounding up and translocation of the microtubule organizing center to the region of the B cell adjacent to the EC, and also uses EC activated leukocyte cell adhesion molecule for TEM. BCR engagement reduces the number of B cells responding to chemokines and preferentially stimulates TEM of CD27+ B cells that coexpress IgD, with or without IgM, as well as CD43. RNA-sequencing analysis suggests that peripheral blood CD19+CD27+CD43+IgD+ cells have increased expression of genes that support BCR activation as well as innate immune properties in comparison with total peripheral blood CD19+ cells.

Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis

Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unravelled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggests that macrophages expressing osteopontin (SPP1) associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 15 single-cell RNA-sequencing datasets to profile 235,930 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin, and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodelling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarisation state within SPP1+ macrophages. Importantly, the MAM polarisation state follows a differentiation trajectory from SPP1+ macrophages and is associated with a core set of regulon activity. SPP1+ macrophages without the MAM polarisation state (SPP1+MAM-) show a positive association with ageing lung in mice and humans. These results suggest an advanced and conserved polarisation state of SPP1+ macrophages in fibrotic tissues resulting from prolonged inflammatory cues within each tissue microenvironment.

Single-cell profiling reveals inflammatory polarization of human carotid versus femoral plaque leukocytes

Femoral atherosclerotic plaques are less inflammatory than carotid plaques histologically, but limited cell-level data exist regarding comparative immune landscapes and polarization at these sites. We investigated intraplaque leukocyte phenotypes and transcriptional polarization in 49 patients undergoing femoral (n = 23) or carotid (n = 26) endarterectomy using single-cell RNA-Seq (scRNA-Seq; n = 13), flow cytometry (n = 24), and IHC (n = 12). Comparative scRNA-Seq of CD45+-selected leukocytes from femoral (n = 9; 35,265 cells) and carotid (n = 4; 30,655 cells) plaque revealed distinct transcriptional profiles. Inflammatory foam cell-like macrophages and monocytes comprised higher proportions of myeloid cells in carotid plaques, whereas noninflammatory foam cell-like macrophages and LYVE1-overexpressing macrophages comprised higher proportions of myeloid cells in femoral plaque (P < 0.001 for all). A significant comparative excess of CCR2+ macrophages in carotid versus plaque was observed by flow cytometry in a separate validation cohort. B cells were more prevalent and exhibited a comparatively antiinflammatory profile in femoral plaque, whereas cytotoxic CD8+ T cells were more prevalent in carotid plaque. In conclusion, human femoral plaques exhibit distinct macrophage phenotypic and transcriptional profiles as well as diminished CD8+ T cell populations compared with human carotid plaques.

ER stress mediates Angiotensin II-augmented innate immunity memory and facilitates distinct susceptibilities of thoracic from abdominal aorta to aneurysm development

To determine the roles of endoplasmic reticulum (ER) stress and trained immunity, we performed transcriptome analyses on the thoracic aorta (TA) and abdominal aorta (AA) from the angiotensin II (Ang II)-HFD-ApoE-KO aneurysm model and made significant findings: 1) Ang II bypassed HFD-induced metabolic reprogramming and induced stronger inflammation in AA than in TA; 2) Ang II and HFD upregulated 890 genes in AA versus TA and induced cytokine signaling; 3) Ang II AA and TA upregulated 73 and 68 cytokines, scRNA-Seq identified markers of macrophages and immune cells, cell death regulators, respectively; transdifferentiation markers of neuron, glial, and squamous epithelial cells were upregulated by Ang II-AA and TA; and pyroptosis signaling with IL-1β and caspase-4 were more upregulated in Ang II-AA than in TA; 4) Six upregulated transcriptomes in patients with AAA, Ang II AA, Ang II TA, additional aneurysm models, PPE-AAA and BAPN-Ang II-AAA, were partially overlapped with 10 lists of new ER stress gene sets including 3 interaction protein lists of ER stress regulators ATF6, PERK, and IRE1, HPA ER localization genes, KEGG signal genes, XBP1 transcription targets, ATF4 (PERK) targets, ATF6 targets, thapsigargin ER stress genes, tunicamycin-ER stress genes, respectively; 5) Ang II-AA and TA upregulated ROS regulators, MitoCarta genes, trained immunity genes, and glycolysis genes; and 6) Gene KO transcriptomes indicated that ATF6 and PERK played more significant roles than IRE1 in promoting AAA and trained immunity whereas antioxidant NRF2 inhibited them. Our unprecedented ER-focused transcriptomic analyses have provided novel insights on the roles of ER as an immune organelle in sensing various DAMPs and initiating ER stress that triggers Ang II-accelerated trained immunity and differs susceptibilities of thoracic and abdominal aortas to diseases.

The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data

MOTIVATION

Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which use a variational approximation of the likelihood for inference.

RESULTS

We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori estimation. The DGD handles complex parameterized latent distributions naturally unlike variational autoencoders, which typically use a fixed Gaussian distribution, because of the complexity of adding other types. We first show its general functionality on a commonly used benchmark set, Fashion-MNIST. Secondly, we apply the model to multiple single-cell datasets. Here, the DGD learns low-dimensional, meaningful, and well-structured latent representations with sub-clustering beyond the provided labels. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable variational autoencoder.

AVAILABILITY AND IMPLEMENTATION

scDGD is available as a python package at https://github.com/Center-for-Health-Data-Science/scDGD. The remaining code is made available here: https://github.com/Center-for-Health-Data-Science/dgd.

A Roadmap for the Human Gut Cell Atlas

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Atypical B cells and impaired SARS-CoV-2 neutralization following heterologous vaccination in the elderly

Suboptimal responses to a primary vaccination course have been reported in the elderly, but there is little information regarding the impact of age on responses to booster third doses. Here, we show that individuals 70 years or older (median age 73, range 70-75) who received a primary two-dose schedule with AZD1222 and booster third dose with mRNA vaccine achieve significantly lower neutralizing antibody responses against SARS-CoV-2 spike pseudotyped virus compared with those younger than 70 (median age 66, range 54-69) at 1 month post booster. Impaired neutralization potency and breadth post third dose in the elderly is associated with circulating "atypical" spike-specific B cells expressing CD11c and FCRL5. However, when considering individuals who received three doses of mRNA vaccine, we did not observe differences in neutralization or enrichment in atypical B cells. This work highlights the finding that AdV and mRNA COVID-19 vaccine formats differentially instruct the memory B cell response.

Peripheral immune landscape for hypercytokinemia in myasthenic crisis utilizing single-cell transcriptomics

BACKGROUND

Myasthenia gravis (MG) is the most prevalent autoimmune disorder affecting the neuromuscular junction. A rapid deterioration in respiratory muscle can lead to a myasthenic crisis (MC), which represents a life-threatening condition with high mortality in MG. Multiple CD4+ T subsets and hypercytokinemia have been identified in the peripheral pro-inflammatory milieu during the crisis. However, the pathogenesis is complicated due to the many types of cells involved, leaving the underlying mechanism largely unexplored.

METHODS

We conducted single-cell transcriptomic and immune repertoire sequencing on 33,577 peripheral blood mononuclear cells (PBMCs) from two acetylcholine receptor antibody-positive (AChR +) MG patients during MC and again three months post-MC. We followed the Scanpy workflow for quality control, dimension reduction, and clustering of the single-cell data. Subsequently, we annotated high-resolution cell types utilizing transfer-learning models derived from publicly available single-cell immune datasets. RNA velocity calculations from unspliced and spliced mRNAs were applied to infer cellular state progression. We analyzed cell communication and MG-relevant cytokines and chemokines to identify potential inflammation initiators.

RESULTS

We identified a unique subset of monocytes, termed monocytes 3 (FCGR3B+ monocytes), which exhibited significant differential expression of pro-inflammatory signaling pathways during and after the crisis. In line with the activated innate immune state indicated by MC, a high neutrophil-lymphocyte ratio (NLR) was confirmed in an additional 22 AChR + MC patients in subsequent hemogram analysis and was associated with MG-relevant clinical scores. Furthermore, oligoclonal expansions were identified in age-associated B cells exhibiting high autoimmune activity, and in CD4+ and CD8+ T cells demonstrating persistent T exhaustion.

CONCLUSIONS

In summary, our integrated analysis of single-cell transcriptomics and TCR/BCR sequencing has underscored the role of innate immune activation which is associated with hypercytokinemia in MC. The identification of a specific monocyte cluster that dominates the peripheral immune profile may provide some hints into the etiology and pathology of MC. However, future functional studies are required to explore causality.

Yolk sac cell atlas reveals multiorgan functions during human early development

The extraembryonic yolk sac (YS) ensures delivery of nutritional support and oxygen to the developing embryo but remains ill-defined in humans. We therefore assembled a comprehensive multiomic reference of the human YS from 3 to 8 postconception weeks by integrating single-cell protein and gene expression data. Beyond its recognized role as a site of hematopoiesis, we highlight roles in metabolism, coagulation, vascular development, and hematopoietic regulation. We reconstructed the emergence and decline of YS hematopoietic stem and progenitor cells from hemogenic endothelium and revealed a YS-specific accelerated route to macrophage production that seeds developing organs. The multiorgan functions of the YS are superseded as intraembryonic organs develop, effecting a multifaceted relay of vital functions as pregnancy proceeds.

Analysis of matrisome expression patterns in murine and human dorsal root ganglia

The extracellular matrix (ECM) is a dynamic structure of molecules that can be divided into six different categories and are collectively called the matrisome. The ECM plays pivotal roles in physiological processes in many tissues, including the nervous system. Intriguingly, alterations in ECM molecules/pathways are associated with painful human conditions and murine pain models. Nevertheless, mechanistic insight into the interplay of normal or defective ECM and pain is largely lacking. The goal of this study was to integrate bulk, single-cell, and spatial RNA sequencing (RNAseq) datasets to investigate the expression and cellular origin of matrisome genes in male and female murine and human dorsal root ganglia (DRG). Bulk RNAseq showed that about 65% of all matrisome genes were expressed in both murine and human DRG, with proportionally more core matrisome genes (glycoproteins, collagens, and proteoglycans) expressed compared to matrisome-associated genes (ECM-affiliated genes, ECM regulators, and secreted factors). Single cell RNAseq on male murine DRG revealed the cellular origin of matrisome expression. Core matrisome genes, especially collagens, were expressed by fibroblasts whereas matrisome-associated genes were primarily expressed by neurons. Cell-cell communication network analysis with CellChat software predicted an important role for collagen signaling pathways in connecting vascular cell types and nociceptors in murine tissue, which we confirmed by analysis of spatial transcriptomic data from human DRG. RNAscope in situ hybridization and immunohistochemistry demonstrated expression of collagens in fibroblasts surrounding nociceptors in male and female human DRG. Finally, comparing human neuropathic pain samples with non-pain samples also showed differential expression of matrisome genes produced by both fibroblasts and by nociceptors. This study supports the idea that the DRG matrisome may contribute to neuronal signaling in both mouse and human, and that dysregulation of matrisome genes is associated with neuropathic pain.

Prolonged cytopenia following CD19 CAR T cell therapy is linked with bone marrow infiltration of clonally expanded IFNγ-expressing CD8 T cells

Autologous anti-CD19 chimeric antigen receptor T cell (CAR T) therapy is highly effective in relapsed/refractory large B cell lymphoma (rrLBCL) but is associated with toxicities that delay recovery. While the biological mechanisms of cytokine release syndrome and neurotoxicity have been investigated, the pathophysiology is poorly understood for prolonged cytopenia, defined as grade ≥3 cytopenia lasting beyond 30 days after CAR T infusion. We performed single-cell RNA sequencing of bone marrow samples from healthy donors and rrLBCL patients with or without prolonged cytopenia and identified significantly increased frequencies of clonally expanded CX3CR1hi cytotoxic T cells, expressing high interferon (IFN)-γ and cytokine signaling gene sets, associated with prolonged cytopenia. In line with this, we found that hematopoietic stem cells from these patients expressed IFN-γ response signatures. IFN-γ deregulates hematopoietic stem cell self-renewal and differentiation and can be targeted with thrombopoietin agonists or IFN-γ-neutralizing antibodies, highlighting a potential mechanism-based approach for the treatment of CAR T-associated prolonged cytopenia.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets

Background

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches.

Results

We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment.

Discussion and conclusion

We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

Intradermal skin test with mRNA vaccines as a surrogate marker of T cell immunity in immunocompromised patients

OBJECTIVES

Intradermal skin test (IDT) with mRNA vaccines may represent a simple, reliable, and affordable tool to measure T cell response in immunocompromised patients who failed to mount serological responses following vaccination with mRNA covid-19 vaccines.

METHODS

We compared anti-SARS-CoV-2 antibodies and cellular responses in vaccinated immunocompromised patients (n = 58), healthy seronegative naive controls (NC, n = 8), and healthy seropositive vaccinated controls (VC, n = 32) by Luminex, spike-induced IFN-γ Elispot and an IDT. A skin biopsy 24 h after IDT and single-cell RNAseq was performed in three vaccinated volunteers.

RESULTS

Twenty-five percent of seronegative NC had a positive Elispot (2/8) and IDT (1/4), compared to 95% (20/21) and 93% (28/30) in seropositive VC, respectively. Single-cell RNAseq data in the skin of VC showed a predominant mixed population of effector helper and cytotoxic T cells. The TCR repertoire revealed 18/1064 clonotypes with known specificities against SARS-CoV-2, among which six were spike-specific. Seronegative immunocompromised patients with positive Elispot and IDT were in 83% (5/6) treated with B cell-depleting reagents, while those with negative IDT were all transplant recipients.

CONCLUSIONS

Our results indicate that delayed local reaction to IDT reflects vaccine-induced T-cell immunity opening new perspectives to monitor seronegative patients and elderly populations with waning immunity.

A single-cell atlas of in vitro multiculture systems uncovers the in vivo lineage trajectory and cell state in the human lung

We present an in-depth single-cell atlas of in vitro multiculture systems on human primary airway epithelium derived from normal and diseased lungs of 27 individual donors. Our large-scale single-cell profiling identified new cell states and differentiation trajectories of rare airway epithelial cell types in human distal lungs. By integrating single-cell datasets of human lung tissues, we discovered immune-primed subsets enriched in lungs and organoids derived from patients with chronic respiratory disease. To demonstrate the full potential of our platform, we further illustrate transcriptomic responses to various respiratory virus infections in vitro airway models. Our work constitutes a single-cell roadmap for the cellular and molecular characteristics of human primary lung cells in vitro and their relevance to human tissues in vivo.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Pericardial Immune Cells and Their Evolving Role in Cardiovascular Pathophysiology

The pericardium plays several homeostatic roles to support and maintain everyday cardiac function. Recent advances in techniques and experimental models have allowed for further exploration into the cellular contents of the pericardium itself. Of particular interest are the various immune cell populations present in the space within the pericardial fluid and fat. In contrast to immune cells of the comparable pleura, peritoneum and heart, pericardial immune cells appear to be distinct in their function and phenotype. Specifically, recent work has suggested these cells play critical roles in an array of pathophysiological conditions including myocardial infarction, pericarditis, and post-cardiac surgery complications. In this review, we spotlight the pericardial immune cells currently identified in mice and humans, the pathophysiological role of these cells, and the clinical significance of the immunocardiology axis in cardiovascular health.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Tissue-specific features of the T cell repertoire after allogeneic hematopoietic cell transplantation in human and mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5 to 18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, and in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleens in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end stage of an inflammatory disorder after lymphocyte-directed therapy. These findings also underscore the importance of studying T cell in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and posttransplant T cell landscape.

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

Comparative phenotype of circulating versus tissue immune cells in human lung and blood compartments during health and disease

The lung is a dynamic mucosal surface constantly exposed to a variety of immunological challenges including harmless environmental antigens, pollutants, and potentially invasive microorganisms. Dysregulation of the immune system at this crucial site is associated with a range of chronic inflammatory conditions including asthma and Chronic Pulmonary Obstructive Disease (COPD). However, due to its relative inaccessibility, our fundamental understanding of the human lung immune compartment is limited. To address this, we performed flow cytometric immune phenotyping of human lung tissue and matched blood samples that were isolated from 115 donors undergoing lung tissue resection. We provide detailed characterization of the lung mononuclear phagocyte and T cell compartments, demonstrating clear phenotypic differences between lung tissue cells and those in peripheral circulation. Additionally, we show that CD103 expression demarcates pulmonary T cells that have undergone recent TCR and IL-7R signalling. Unexpectedly, we discovered that the immune landscape from asthmatic or COPD donors was broadly comparable to controls. Our data provide a much-needed expansion of our understanding of the pulmonary immune compartment in both health and disease.

Cross-organ single-cell transcriptome profiling reveals macrophage and dendritic cell heterogeneity in zebrafish

Tissue-resident macrophages (TRMs) and dendritic cells (DCs) are highly heterogeneous and essential for immunity, tissue regeneration, and homeostasis maintenance. Here, we comprehensively profile the heterogeneity of TRMs and DCs across adult zebrafish organs via single-cell RNA sequencing. We identify two macrophage subsets: pro-inflammatory macrophages with potent phagocytosis signatures and pro-remodeling macrophages with tissue regeneration signatures in barrier tissues, liver, and heart. In parallel, one conventional dendritic cell (cDC) population with prominent antigen presentation capacity and plasmacytoid dendritic cells (pDCs) featured by anti-virus properties are also observed in these organs. Remarkably, in addition to a single macrophage/microglia population with potent phagocytosis capacity, a pDC population and two distinct cDC populations are identified in the brain. Finally, we generate specific reporter lines for in vivo tracking of macrophage and DC subsets. Our study depicts the landscape of TRMs and DCs and creates valuable tools for in-depth study of these cells in zebrafish.

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

Single-cell RNA sequencing reveals that HSD17B2 in cancer-associated fibroblasts promotes the development and progression of castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) responds poorly to existing therapy and appears as the lethal consequence of prostate cancer (PCa) progression. The tumour microenvironment (TME) has been thought to play a crucial role in CRPC progression. Here, we conducted single-cell RNA sequencing analysis on two CRPC and two hormone-sensitive prostate cancer (HSPC) samples to reveal potential leading roles in castration resistance. We described the single-cell transcriptional landscape of PCa. Higher cancer heterogeneity was explored in CRPC, with stronger cell cycling status and heavier copy number variant burden of luminal cells. Cancer-associated fibroblasts (CAFs), which are one of the most critical components of TME, demonstrated unique expression and cell-cell communication features in CRPC. A CAFs subtype with high expression of HSD17B2 in CRPC was identified with inflammatory features. HSD17B2 catalyses the conversion of testosterone and dihydrotestosterone to their less active forms, which was associated with steroid hormone metabolism in PCa tumour cells. However, the characteristics of HSD17B2 in PCa fibroblasts remained unknown. We found that HSD17B2 knockdown in CRPC-CAFs could inhibit migration, invasion, and castration resistance of PCa cells in vitro. Further study showed that HSD17B2 could regulate CAFs functions and promote PCa migration through the AR/ITGBL1 axis. Overall, our study revealed the important role of CAFs in the formation of CRPC. HSD17B2 in CAFs regulated AR activation and subsequent ITGBL1 secretion to promote the malignant behaviour of PCa cells. HSD17B2 in CAFs could serve as a promising therapeutic target for CRPC.

Classic costimulatory interactions in MAIT cell responses: from gene expression to immune regulation

Mucosa-associated invariant T (MAIT) cells are evolutionarily conserved, innate-like T lymphocytes with enormous immunomodulatory potentials. Due to their strategic localization, their invariant T cell receptor (iTCR) specificity for major histocompatibility complex-related protein 1 (MR1) ligands of commensal and pathogenic bacterial origin, and their sensitivity to infection-elicited cytokines, MAIT cells are best known for their antimicrobial characteristics. However, they are thought to also play important parts in the contexts of cancer, autoimmunity, vaccine-induced immunity, and tissue repair. While cognate MR1 ligands and cytokine cues govern MAIT cell maturation, polarization, and peripheral activation, other signal transduction pathways, including those mediated by costimulatory interactions, regulate MAIT cell responses. Activated MAIT cells exhibit cytolytic activities and secrete potent inflammatory cytokines of their own, thus transregulating the biological behaviors of several other cell types, including dendritic cells, macrophages, natural killer cells, conventional T cells, and B cells, with significant implications in health and disease. Therefore, an in-depth understanding of how costimulatory pathways control MAIT cell responses may introduce new targets for optimized MR1/MAIT cell-based interventions. Herein, we compare and contrast MAIT cells and mainstream T cells for their expression of classic costimulatory molecules belonging to the immunoglobulin superfamily and the tumor necrosis factor (TNF)/TNF receptor superfamily, based not only on the available literature but also on our transcriptomic analyses. We discuss how these molecules participate in MAIT cells' development and activities. Finally, we introduce several pressing questions vis-à-vis MAIT cell costimulation and offer new directions for future research in this area.

Spatially resolved multiomics of human cardiac niches

The function of a cell is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here we combine single-cell and spatial transcriptomics data to discover cellular niches within eight regions of the human heart. We map cells to microanatomical locations and integrate knowledge-based and unsupervised structural annotations. We also profile the cells of the human cardiac conduction system1. The results revealed their distinctive repertoire of ion channels, G-protein-coupled receptors (GPCRs) and regulatory networks, and implicated FOXP2 in the pacemaker phenotype. We show that the sinoatrial node is compartmentalized, with a core of pacemaker cells, fibroblasts and glial cells supporting glutamatergic signalling. Using a custom CellPhoneDB.org module, we identify trans-synaptic pacemaker cell interactions with glia. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions to provide mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches that may contribute to infection defence. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be applied to other tissues and organs.

Chromatin accessibility dynamics of neurogenic niche cells reveal defects in neural stem cell adhesion and migration during aging

The regenerative potential of brain stem cell niches deteriorates during aging. Yet the mechanisms underlying this decline are largely unknown. Here we characterize genome-wide chromatin accessibility of neurogenic niche cells in vivo during aging. Interestingly, chromatin accessibility at adhesion and migration genes decreases with age in quiescent neural stem cells (NSCs) but increases with age in activated (proliferative) NSCs. Quiescent and activated NSCs exhibit opposing adhesion behaviors during aging: quiescent NSCs become less adhesive, whereas activated NSCs become more adhesive. Old activated NSCs also show decreased migration in vitro and diminished mobilization out of the niche for neurogenesis in vivo. Using tension sensors, we find that aging increases force-producing adhesions in activated NSCs. Inhibiting the cytoskeletal-regulating kinase ROCK reduces these adhesions, restores migration in old activated NSCs in vitro, and boosts neurogenesis in vivo. These results have implications for restoring the migratory potential of NSCs and for improving neurogenesis in the aged brain.

Functional heterogeneity of human skin-resident memory T cells in health and disease

The human skin is populated by a diverse pool of memory T cells, which can act rapidly in response to pathogens and cancer antigens. Tissue-resident memory T cells (TRM ) have been implicated in range of allergic, autoimmune and inflammatory skin diseases. Clonal expansion of cells with TRM properties is also known to contribute to cutaneous T-cell lymphoma. Here, we review the heterogeneous phenotypes, transcriptional programs, and effector functions of skin TRM . We summarize recent studies on TRM formation, longevity, plasticity, and retrograde migration and contextualize the findings to skin TRM and their role in maintaining skin homeostasis and altered functions in skin disease.

scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics

Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of cellular dynamics with minimal influence from batch effects. For inference, scTour simultaneously estimates the developmental pseudotime, delineates the vector field, and maps the transcriptomic latent space under a single, integrated framework. For prediction, scTour precisely reconstructs the underlying dynamics of unseen cellular states or a new independent dataset. scTour's functionalities are demonstrated in a variety of biological processes from 19 datasets.

Heterogeneity of cancer-associated fibroblasts in head and neck squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) consist of heterogeneous cellular populations that contribute critical roles in head and neck squamous cell carcinoma (HNSCC). A series of computer-aided analyses were performed to determine various aspects of CAFs in HNSCC, including their cellular heterogeneity, prognostic value, relationship with immune suppression and immunotherapeutic response, intercellular communication, and metabolic activity. The prognostic significance of CKS2+ CAFs was verified using immunohistochemistry. Our findings revealed that fibroblasts group demonstrated prognostic significance, with the CKS2+ subset of inflammatory CAFs (iCAFs) exhibiting a significant correlation with unfavorable prognosis and being localized in close proximity to cancer cells. Patients with a high infiltration of CKS2+ CAFs had a poor overall survival rate. There is a negative correlation between CKS2+ iCAFs and cytotoxic CD8+ T cells and natural killer (NK) cells, while a positive correlation was found with exhausted CD8+ T cells. Additionally, patients in Cluster 3, characterized by a high proportion of CKS2+ iCAFs, and patients in Cluster 2, characterized by a high proportion of CKS2- iCAFs and CENPF-/MYLPF- myofibroblastic CAFs (myCAFs), did not exhibit significant immunotherapeutic responses. Moreover, close interactions was confirmed to exist between cancer cells and CKS2+ iCAFs/ CENPF+ myCAFs. Furthermore, CKS2+ iCAFs demonstrated the highest level of metabolic activity. In summary, our study enhances the understanding of the heterogeneity of CAFs and provided insights into improving the efficacy of immunotherapies and prognostic accuracy for HNSCC patients.

Single-cell profiling identifies a novel human polyclonal unconventional T cell lineage

In the human thymus, a CD10+ PD-1+ TCRαβ+ differentiation pathway diverges from the conventional single positive T cell lineages at the early double-positive stage. Here, we identify the progeny of this unconventional lineage in antigen-inexperienced blood. These unconventional T cells (UTCs) in thymus and blood share a transcriptomic profile, characterized by hallmark transcription factors (i.e., ZNF683 and IKZF2), and a polyclonal TCR repertoire with autoreactive features, exhibiting a bias toward early TCRα chain rearrangements. Single-cell RNA sequencing confirms a common developmental trajectory between the thymic and blood UTCs and clearly delineates this unconventional lineage in blood. Besides MME+ recent thymic emigrants, effector-like clusters are identified in this heterogeneous lineage. Expression of Helios and KIR and a decreased CD8β expression are characteristics of this lineage. This UTC lineage could be identified in adult blood and intestinal tissues. In summary, our data provide a comprehensive characterization of the polyclonal unconventional lineage in antigen-inexperienced blood and identify the adult progeny.

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies

Vaccination strategies aimed at maturing broadly neutralizing antibodies (bnAbs) from naïve precursors are hindered by unusual features that characterize these Abs, including insertions and deletions (indels). Longitudinal studies of natural HIV infection cases shed light on the complex processes underlying bnAb development and have suggested a role for superinfection as a potential enhancer of neutralization breadth. Here we describe the development of a potent bnAb lineage that was elicited by two founder viruses to inform vaccine design. The V3-glycan targeting bnAb lineage (PC39-1) was isolated from subtype C-infected IAVI Protocol C elite neutralizer, donor PC39, and is defined by the presence of multiple independent insertions in CDRH1 that range from 1-11 amino acids in length. Memory B cell members of this lineage are predominantly atypical in phenotype yet also span the class-switched and antibody-secreting cell compartments. Development of neutralization breadth occurred concomitantly with extensive recombination between founder viruses before each virus separated into two distinct population "arms" that evolved independently to escape the PC39-1 lineage. Ab crystal structures show an extended CDRH1 that can help stabilize the CDRH3. Overall, these findings suggest that early exposure of the humoral system to multiple related Env molecules could promote the induction of bnAbs by focusing Ab responses to conserved epitopes.

Transitioning single-cell genomics into the clinic

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

Chronic Kidney Disease Transdifferentiates Veins into a Specialized Immune-Endocrine Organ with Increased MYCN-AP1 Signaling

Most patients with end-stage renal disease (ESRD) and advanced chronic kidney disease (CKD) choose hemodialysis as their treatment of choice. Thus, upper-extremity veins provide a functioning arteriovenous access to reduce dependence on central venous catheters. However, it is unknown whether CKD reprograms the transcriptome of veins and primes them for arteriovenous fistula (AVF) failure. To examine this, we performed transcriptomic analyses of bulk RNA sequencing data of veins isolated from 48 CKD patients and 20 non-CKD controls and made the following findings: (1) CKD converts veins into immune organs by upregulating 13 cytokine and chemokine genes, and over 50 canonical and noncanonical secretome genes; (2) CKD increases innate immune responses by upregulating 12 innate immune response genes and 18 cell membrane protein genes for increased intercellular communication, such as CX3CR1 chemokine signaling; (3) CKD upregulates five endoplasmic reticulum protein-coding genes and three mitochondrial genes, impairing mitochondrial bioenergetics and inducing immunometabolic reprogramming; (4) CKD reprograms fibrogenic processes in veins by upregulating 20 fibroblast genes and 6 fibrogenic factors, priming the vein for AVF failure; (5) CKD reprograms numerous cell death and survival programs; (6) CKD reprograms protein kinase signal transduction pathways and upregulates SRPK3 and CHKB; and (7) CKD reprograms vein transcriptomes and upregulates MYCN, AP1, and 11 other transcription factors for embryonic organ development, positive regulation of developmental growth, and muscle structure development in veins. These results provide novel insights on the roles of veins as immune endocrine organs and the effect of CKD in upregulating secretomes and driving immune and vascular cell differentiation.

ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts

The distinct functions and phenotypes of human tissues and cells derive from the activity of biological processes that varies in a context-dependent manner. Here, we present the Process Activity (ProAct) webserver that estimates the preferential activity of biological processes in tissues, cells, and other contexts. Users can upload a differential gene expression matrix measured across contexts or cells, or use a built-in matrix of differential gene expression in 34 human tissues. Per context, ProAct associates gene ontology (GO) biological processes with estimated preferential activity scores, which are inferred from the input matrix. ProAct visualizes these scores across processes, contexts, and process-associated genes. ProAct also offers potential cell-type annotations for cell subsets, by inferring them from the preferential activity of 2001 cell-type-specific processes. Thus, ProAct output can highlight the distinct functions of tissues and cell types in various contexts, and can enhance cell-type annotation efforts. The ProAct webserver is available at https://netbio.bgu.ac.il/ProAct/.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Multi-omics delineation of cytokine-induced endothelial inflammatory states

Vascular endothelial cells (ECs) form a dynamic interface between blood and tissue and play a crucial role in the progression of vascular inflammation. Here, we aim to dissect the system-wide molecular mechanisms of inflammatory endothelial-cytokine responses. Applying an unbiased cytokine library, we determined that TNFα and IFNγ induced the largest EC response resulting in distinct proteomic inflammatory signatures. Notably, combined TNFα + IFNγ stimulation induced an additional synergetic inflammatory signature. We employed a multi-omics approach to dissect these inflammatory states, combining (phospho-) proteome, transcriptome and secretome and found, depending on the stimulus, a wide-array of altered immune-modulating processes, including complement proteins, MHC complexes and distinct secretory cytokines. Synergy resulted in cooperative activation of transcript induction. This resource describes the intricate molecular mechanisms that are at the basis of endothelial inflammation and supports the adaptive immunomodulatory role of the endothelium in host defense and vascular inflammation.

Deciphering and advancing CAR T-cell therapy with single-cell sequencing technologies

Chimeric antigen receptor (CAR) T-cell therapy has made remarkable progress in cancer immunotherapy, but several challenges with unclear mechanisms hinder its wide clinical application. Single-cell sequencing technologies, with the powerful unbiased analysis of cellular heterogeneity and molecular patterns at unprecedented resolution, have greatly advanced our understanding of immunology and oncology. In this review, we summarize the recent applications of single-cell sequencing technologies in CAR T-cell therapy, including the biological characteristics, the latest mechanisms of clinical response and adverse events, promising strategies that contribute to the development of CAR T-cell therapy and CAR target selection. Generally, we propose a multi-omics research mode to guide potential future research on CAR T-cell therapy.

Single-cell transcriptomics for the assessment of cardiac disease

Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

Repression of the aryl-hydrocarbon receptor prevents oxidative stress and ferroptosis of intestinal intraepithelial lymphocytes

The aryl-hydrocarbon receptor (AHR) is a ligand-activated transcription factor that buoys intestinal immune responses. AHR induces its own negative regulator, the AHR repressor (AHRR). Here, we show that AHRR is vital to sustaining intestinal intraepithelial lymphocytes (IELs). AHRR deficiency reduced IEL representation in a cell-intrinsic fashion. Single-cell RNA sequencing revealed an oxidative stress profile in Ahrr-/- IELs. AHRR deficiency unleashed AHR-induced expression of CYP1A1, a monooxygenase that generates reactive oxygen species, increasing redox imbalance, lipid peroxidation, and ferroptosis in Ahrr-/- IELs. Dietary supplementation with selenium or vitamin E to restore redox homeostasis rescued Ahrr-/- IELs. Loss of IELs in Ahrr-/- mice caused susceptibility to Clostridium difficile infection and dextran sodium-sulfate-induced colitis. Inflamed tissue of inflammatory bowel disease patients showed reduced Ahrr expression that may contribute to disease. We conclude that AHR signaling must be tightly regulated to prevent oxidative stress and ferroptosis of IELs and to preserve intestinal immune responses.

Intercellular crosstalk in adult dental pulp is mediated by heparin-binding growth factors Pleiotrophin and Midkine

BACKGROUND

In-depth knowledge of the cellular and molecular composition of dental pulp (DP) and the crosstalk between DP cells that drive tissue homeostasis are not well understood. To address these questions, we performed a comparative analysis of publicly available single-cell transcriptomes of healthy adult human DP to 5 other reference tissues: peripheral blood mononuclear cells, bone marrow, adipose tissue, lung, and skin.

RESULTS

Our analysis revealed that DP resident cells have a unique gene expression profile when compared to the reference tissues, and that DP fibroblasts are the main cell type contributing to this expression profile. Genes coding for pleiotrophin (PTN) and midkine (MDK), homologous heparin-binding growth-factors, possessed the highest differential expression levels in DP fibroblasts. In addition, we identified extensive crosstalk between DP fibroblasts and several other DP resident cells, including Schwann cells, mesenchymal stem cells and odontoblasts, mediated by PTN and MDK.

CONCLUSIONS

DP fibroblasts emerge as unappreciated players in DP homeostasis, mainly through their crosstalk with glial cells. These findings suggest that fibroblast-derived growth factors possess major regulatory functions and thus have a potential role as dental therapeutic targets.

An artificial intelligence network-guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms

The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes-based model remains largely unknown. In the current study, by analysing single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data, the tumour-infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been discovered that the TIIC signature score can forecast genomic change, chemotherapeutic drug susceptibility, and-most significantly-immunotherapeutic response. As a newly demonstrated biomarker, the TIIC signature score facilitated the selection of the LUAD population who would benefit from future clinical stratification.

Advancing CAR T cell therapy through the use of multidimensional omics data

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

Pan-cancer classification of single cells in the tumour microenvironment

Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells' identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC's accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC's practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours' primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.