Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages BA.2.12.1, BA.4 and BA.5 exhibit higher transmissibility than the BA.2 lineage1. The receptor binding and immune-evasion capability of these recently emerged variants require immediate investigation. Here, coupled with structural comparisons of the spike proteins, we show that BA.2.12.1, BA.4 and BA.5 (BA.4 and BA.5 are hereafter referred collectively to as BA.4/BA.5) exhibit similar binding affinities to BA.2 for the angiotensin-converting enzyme 2 (ACE2) receptor. Of note, BA.2.12.1 and BA.4/BA.5 display increased evasion of neutralizing antibodies compared with BA.2 against plasma from triple-vaccinated individuals or from individuals who developed a BA.1 infection after vaccination. To delineate the underlying antibody-evasion mechanism, we determined the escape mutation profiles2, epitope distribution3 and Omicron-neutralization efficiency of 1,640 neutralizing antibodies directed against the receptor-binding domain of the viral spike protein, including 614 antibodies isolated from people who had recovered from BA.1 infection. BA.1 infection after vaccination predominantly recalls humoral immune memory directed against ancestral (hereafter referred to as wild-type (WT)) SARS-CoV-2 spike protein. The resulting elicited antibodies could neutralize both WT SARS-CoV-2 and BA.1 and are enriched on epitopes on spike that do not bind ACE2. However, most of these cross-reactive neutralizing antibodies are evaded by spike mutants L452Q, L452R and F486V. BA.1 infection can also induce new clones of BA.1-specific antibodies that potently neutralize BA.1. Nevertheless, these neutralizing antibodies are largely evaded by BA.2 and BA.4/BA.5 owing to D405N and F486V mutations, and react weakly to pre-Omicron variants, exhibiting narrow neutralization breadths. The therapeutic neutralizing antibodies bebtelovimab4 and cilgavimab5 can effectively neutralize BA.2.12.1 and BA.4/BA.5, whereas the S371F, D405N and R408S mutations undermine most broadly sarbecovirus-neutralizing antibodies. Together, our results indicate that Omicron may evolve mutations to evade the humoral immunity elicited by BA.1 infection, suggesting that BA.1-derived vaccine boosters may not achieve broad-spectrum protection against new Omicron variants. 

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BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infection

Diffuse intrinsic pontine glioma (DIPG) and other H3K27M-mutated diffuse midline gliomas (DMGs) are universally lethal paediatric tumours of the central nervous system1. We have previously shown that the disialoganglioside GD2 is highly expressed on H3K27M-mutated glioma cells and have demonstrated promising preclinical efficacy of GD2-directed chimeric antigen receptor (CAR) T cells2, providing the rationale for a first-in-human phase I clinical trial (NCT04196413). Because CAR T cell-induced brainstem inflammation can result in obstructive hydrocephalus, increased intracranial pressure and dangerous tissue shifts, neurocritical care precautions were incorporated. Here we present the clinical experience from the first four patients with H3K27M-mutated DIPG or spinal cord DMG treated with GD2-CAR T cells at dose level 1 (1 × 106 GD2-CAR T cells per kg administered intravenously). Patients who exhibited clinical benefit were eligible for subsequent GD2-CAR T cell infusions administered intracerebroventricularly3. Toxicity was largely related to the location of the tumour and was reversible with intensive supportive care. On-target, off-tumour toxicity was not observed. Three of four patients exhibited clinical and radiographic improvement. Pro-inflammatory cytokine levels were increased in the plasma and cerebrospinal fluid. Transcriptomic analyses of 65,598 single cells from CAR T cell products and cerebrospinal fluid elucidate heterogeneity in response between participants and administration routes. These early results underscore the promise of this therapeutic approach for patients with H3K27M-mutated DIPG or spinal cord DMG. 

/pdf/gd2-car-t-cell-therapy-for-h3k27m-mutated-diffuse-midline-1ai3enev.pdf

GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas

Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.

Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells

Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing. Description Immune cell diversity in the human body The human immune system is composed of many different cell types spread across the entire body, but little is currently known about the fine-grained variations in these cell types across organs. Using single-cell genomics, Domínguez Conde et al. examined the gene expression profile of more than 300,000 individual immune cells extracted from 16 different tissues in 12 deceased adult organ donors (see the Perspective by Liu and Zhang). Cell identity was assigned using CellTypist, an automated cell classification tool designed by the authors. In-depth data analysis revealed insights into how the immune system adapts to function effectively in different organ contexts. —LZ and DJ An immune cell atlas of human innate and adaptive immune cells across lymphoid, mucosal, and exocrine sites reveals tissue-specific compositions and features. INTRODUCTION Immune cells that seed peripheral tissues play a vital role in health and disease, yet most studies of human immunity focus on blood-derived cells. Immune cells adapt to local microenvironments, acquiring distinct features and functional specialization. Dissecting these molecular adaptations through the systematic assessment of cells across the human body promises to transform our understanding of the immune system at the organismal level. RATIONALE To comprehensively assess immune cell types, we collected donor-matched tissues from 12 deceased organ donors. We isolated immune cells and performed single-cell RNA sequencing and paired VDJ sequencing for T cell and B cell receptors, resulting in high-quality data for ~330,000 immune cells. To resolve cell identities, we developed CellTypist, a logistic regression–based framework using stochastic gradient descent learning. This cross-tissue annotation enabled interrogation of shared and tissue-specific expression modules and cell states within myeloid and lymphoid cell lineages. RESULTS We developed CellTypist by curating and harmonizing public datasets to assemble a comprehensive immune cell type reference database (https://www.celltypist.org). CellTypist was then applied to our data, generated across multiple tissues and individuals (https://www.tissueimmunecellatlas.org/). Altogether, we detected 101 immune populations and performed extensive cross-tissue comparisons for each subset. Although macrophages displayed prominent tissue-restricted features, some convergent features were also detected. For example, macrophages expressing erythrophagocytosis-related genes were widely found in spleen, liver, bone marrow, and lymph nodes. Heterogeneity within defined subpopulations was also observed, such as migratory dendritic cell adaptations. Within adaptive immune lineages, we identified tissue-specific distributions of memory populations. Plasma cells showed a restricted tissue distribution, whereas memory B cells were more widely distributed. Similarly, tissue-resident memory T (TRM) cells were more restricted in distribution compared with central and effector memory T cells. Notably, TRM cells harbored significant diversity, including αβ and γδ lineages, ascertained by VDJ sequencing. Assessment of clonal dynamics revealed the highest clonal expansions in TRM cells and the most frequent clonal sharing between resident and effector memory populations. CONCLUSION Here we present an immune cell atlas of myeloid and lymphoid lineages across adult human tissues. We developed CellTypist for automated immune cell annotation and performed an in-depth dissection of cell populations, identifying 101 cell types or states from more than one million cells, including previously underappreciated cell states. We also uncovered convergent phenotypes across tissues within given lineages and described tissue adaptation signatures for a number of cell types, including macrophages and resident memory T cells. Together, we have extended our understanding of how human immunity functions as an integrated, cross-tissue network, and we provide the scientific community with several key new resources. Cross-tissue atlas of human immune cells. Donor-matched tissues from 12 deceased organ donors were profiled using single-cell RNA sequencing [including single-cell VDJ sequencing (scVDJ-seq)]. Cell type annotation was achieved with CellTypist, an automated cell type annotation pipeline, followed by manual curation. In-depth analysis of transcriptional features revealed convergent and divergent gene expression programs for each cell lineage across lymphoid and nonlymphoid tissues. scVDJ-seq enabled repertoire analysis across cell subsets and tissues. scRNA-seq, single-cell RNA sequencing; TCR, T cell receptor; BCR, B cell receptor.

https://www.science.org/doi/pdf/10.1126/science.abl5197?download=true

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

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term ‘data integration’ has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods. As the number of single-cell experiments with multiple data modalities increases, Argelaguet and colleagues review the concepts and challenges of data integration.

Computational principles and challenges in single-cell data integration.

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. 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 hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of 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 COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

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Michael Zagar

Papers

Integrated analysis of multimodal single-cell data