A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates.

We present a multiomic cell atlas of human lung development that combines single-cell RNA and ATAC sequencing, high-throughput spatial transcriptomics, and single-cell imaging. Coupling single-cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial, and erythrocyte/leukocyte compartments from 5-22 post-conception weeks. We identify previously uncharacterized cell states in all compartments. These include developmental-specific secretory progenitors and a subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). To illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signaling and transcription factor hierarchies which we rigorously test using organoid models.

Gene-level alignment of single-cell trajectories.

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Cells of the human intestinal tract mapped across space and time.

The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. Here, to comprehensively map cell lineages, we use single-cell RNA sequencing and antigen receptor analysis of almost half a million cells from up to 5 anatomical regions in the developing and up to 11 distinct anatomical regions in the healthy paediatric and adult human gut. This reveals the existence of transcriptionally distinct BEST4 epithelial cells throughout the human intestinal tract. Furthermore, we implicate IgG sensing as a function of intestinal tuft cells. We describe neural cell populations in the developing enteric nervous system, and predict cell-type-specific expression of genes associated with Hirschsprung's disease. Finally, using a systems approach, we identify key cell players that drive the formation of secondary lymphoid tissue in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. This catalogue of intestinal cells will provide new insights into cellular programs in development, homeostasis and disease.

Blood and immune development in human fetal bone marrow and Down syndrome.

Haematopoiesis in the bone marrow (BM) maintains blood and immune cell production throughout postnatal life. Haematopoiesis first emerges in human BM at 11-12 weeks after conception1,2, yet almost nothing is known about how fetal BM (FBM) evolves to meet the highly specialized needs of the fetus and newborn. Here we detail the development of FBM, including stroma, using multi-omic assessment of mRNA and multiplexed protein epitope expression. We find that the full blood and immune cell repertoire is established in FBM in a short time window of 6-7 weeks early in the second trimester. FBM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell subsets emerging for the first time. The substantial expansion of B lymphocytes in FBM contrasts with fetal liver at the same gestational age. Haematopoietic progenitors from fetal liver, FBM and cord blood exhibit transcriptional and functional differences that contribute to tissue-specific identity and cellular diversification. Endothelial cell types form distinct vascular structures that we show are regionally compartmentalized within FBM. Finally, we reveal selective disruption of B lymphocyte, erythroid and myeloid development owing to a cell-intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in Down syndrome (trisomy 21).

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE.

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins.

Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion .

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Sensitive cluster-free differential expression testing.

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, 'neighbouring' cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

Precise identification of cell states altered in disease using healthy single-cell references.

Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use.

Early human lung immune cell development and its role in epithelial cell fate.

Studies of human lung development have focused on epithelial and mesenchymal cell types and function, but much less is known about the developing lung immune cells, even though the airways are a major site of mucosal immunity after birth. An unanswered question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. Here, we profiled human embryonic and fetal lung immune cells using scRNA-seq, smFISH, and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including innate lymphoid cells, natural killer cells, myeloid cells, and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes expressing high levels of cytotoxicity genes and the presence of mature B lymphocytes, including B-1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the presence and diversity of immune cells during development, we also investigated their possible effect on epithelial maturation. We found that IL-1ß drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1ß-producing myeloid cells were found throughout the lung and adjacent to epithelial tips, suggesting that immune cells may direct human lung epithelial development.

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.

A spatial human thymus cell atlas mapped to a continuous tissue axis.

T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassall's Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.

Differential abundance testing on single-cell data using k-nearest neighbor graphs.

Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a k-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell-cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at https://github.com/MarioniLab/miloR .

Cell2location maps fine-grained cell types in spatial transcriptomics.

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.

Mapping the developing human immune system across organs.

Single-cell genomics studies have decoded the immune cell composition of several human prenatal organs but were limited in describing the developing immune system as a distributed network across tissues. We profiled nine prenatal tissues combining single-cell RNA sequencing, antigen-receptor sequencing, and spatial transcriptomics to reconstruct the developing human immune system. This revealed the late acquisition of immune-effector functions by myeloid and lymphoid cell subsets and the maturation of monocytes and T cells before peripheral tissue seeding. Moreover, we uncovered system-wide blood and immune cell development beyond primary hematopoietic organs, characterized human prenatal B1 cells, and shed light on the origin of unconventional T cells. Our atlas provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine, and disease understanding.

Single-cell multi-omics analysis of the immune response in COVID-19.

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

Circulating tumor DNA profile recognizes transformation to castration-resistant neuroendocrine prostate cancer.

Loss of androgen receptor (AR) signaling dependence occurs in approximately 15%-20% of advanced treatment-resistant prostate cancers, and this may manifest clinically as transformation from a prostate adenocarcinoma histology to a castration-resistant neuroendocrine prostate cancer (CRPC-NE). The diagnosis of CRPC-NE currently relies on a metastatic tumor biopsy, which is invasive for patients and sometimes challenging to diagnose due to morphologic heterogeneity. By studying whole-exome sequencing and whole-genome bisulfite sequencing of cell free DNA (cfDNA) and of matched metastatic tumor biopsies from patients with metastatic prostate adenocarcinoma and CRPC-NE, we identified CRPC-NE features detectable in the circulation. Overall, there was markedly higher concordance between cfDNA and biopsy tissue genomic alterations in patients with CRPC-NE compared with castration-resistant adenocarcinoma, supporting greater intraindividual genomic consistency across metastases. Allele-specific copy number and serial sampling analyses allowed for the detection and tracking of clonal and subclonal tumor cell populations. cfDNA methylation was indicative of circulating tumor content fraction, reflective of methylation patterns observed in biopsy tissues, and was capable of detecting CRPC-NE-associated epigenetic changes (e.g., hypermethylation of ASXL3 and SPDEF; hypomethylation of INSM1 and CDH2). A targeted set combining genomic (TP53, RB1, CYLD, AR) and epigenomic (hypo- and hypermethylation of 20 differential sites) alterations applied to ctDNA was capable of identifying patients with CRPC-NE.

The power of professional nursing practice--an essential element of patient and family centered care.

Understanding power and learning how to use it is critical if nurses' efforts to shape their practice and work environments are to be successful. As part of our efforts to develop a Fast-Track BSN-to-PhD nursing program, we met with nurse leaders from six organizations to explore what power means, how nurses acquire it, and how they demonstrate it in their practice. Through these discussions, we identified eight characteristics of powerful nursing practice that, together, form a framework that can guide nurses' efforts to develop a powerful practice and shape the health care delivery settings and academic institutions in which they work. In this article we review recent studies of organizational power and share discussions which helped us better understand nursing power and the ways in which it is manifested. We also reflect on what power means for individual nurses and the profession and discuss how our insights influenced our Fast-Track program.