Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative to unsupervised analysis. Reference datasets, however, are predominantly constructed from single-cell RNA-seq data, and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to harmonize singlecell datasets across modalities by leveraging a multi-omic dataset as a molecular bridge. Each cell in the multi-omic dataset comprises an element in a ‘dictionary’, which can be used to reconstruct unimodal datasets and transform them into a shared space. We demonstrate that our procedure can accurately harmonize transcriptomic data with independent single cell measurements of chromatin accessibility, histone modifications, DNA methylation, and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to substantially improve computational scalability, and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach aims to broaden the utility of single-cell reference datasets and facilitate comparisons across diverse molecular modalities. Availability Installation instructions, documentations, and vignettes are available at http://www.satijalab.org/seurat

/pdf/dictionary-learning-for-integrative-multimodal-and-scalable-eqwpqrte.pdf

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

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. 

https://www.nature.com/articles/s41576-023-00586-w.pdf

Best practices for single-cell analysis across modalities

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. 

/pdf/an-integrated-cell-atlas-of-the-lung-in-health-and-disease-3nmpcxnm.pdf

An integrated cell atlas of the lung in health and disease

The emerging landscape of spatial profiling technologies

Recent advances in single cell genomics technologies have facilitated studies on the developing immune system at unprecedented scale and resolution. However, these studies have focused on one or a few organs and were thus limited in understanding the developing immune system as a distributed network across tissues. Here, we profiled prenatal haematopoietic organs, lymphoid organs and non-lymphoid tissues using a combination of single-cell RNA sequencing, paired antigen-receptor sequencing and spatial transcriptomics to reconstruct the developing human immune system. Our analysis revealed the acquisition of immune effector transcriptome profiles in macrophages, mast cells and NK cells from the second trimester, and the transcriptomic changes accompanying the late-stage maturation of developing monocytes and T cells that extended from their organ of origin to peripheral tissues. We uncovered system-wide blood and immune cell development beyond the conventional primary haematopoietic organs. We further identified, extensively characterised and functionally validated the human prenatal B1 cells. Finally, we provide evidence for thymocyte-thymocyte selection origin for αβTCR- expressing unconventional T cells based on TCR gene usage and an in vitro artificial thymic organoid culture model. Our comprehensive atlas of the developing human immune system provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine and disease understanding. One-Sentence Summary By performing a comprehensive single-cell RNA sequencing atlas of human developing immune system together with antigen-receptor sequencing and spatial transcriptomics, we explored the cross-gestation and cross-organ variability in immune cells, discovered system-wide blood and immune cell development, identified, characterised and functionally validated the properties of human prenatal B1 cells and the origin of unconventional T cells.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612819

Mapping the developing human immune system across organs

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

Background: Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with a novel airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods: Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Results: Forty patients (32M:8F, age:22-98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Conclusions: The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established.

https://www.medrxiv.org/content/medrxiv/early/2023/05/07/2023.05.05.23289594.full.pdf

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

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

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19