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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.

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describeGenes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories.Genes2Genesovercomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of expression dynamics. Across both real world and simulated datasets,Genes2Genesaccurately captured different alignment patterns, demonstrated its utility in disease cell state trajectory analysis, and revealed that T cells differentiatedin vitromatched to an immaturein vivostate while lacking the final TNFa signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from thein vivosystem, thus guiding the optimization ofin vitroculture conditions.

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.

During human development, lungs develop their roles of gas exchange and barrier function. Recent single cell studies have focused on epithelial and mesenchymal cell types, but much less is known about the developing lung immune cells, although the airways are a major site of mucosal immunity after birth. An open question is whether tissue-resident immune cells play a role in shaping the tissue as it developsin utero. In order to address this, we profiled lung immune cells using scRNAseq, smFISH and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including ILCs, NK, myeloid cells and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes high in cytotoxicity genes, and mature B lymphocytes, including B1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the abundance of immune cells, we investigated their possible effect on epithelial maturation and found that IL-1β drives epithelial progenitor exit from self-renewal and differentiation to basal cellsin vitro.In vivo, IL-1β-producing myeloid cells were found adjacent to epithelial tips, suggesting that immune cells may direct the developing lung epithelium.

Assessment of single-cell gene expression (scRNA-seq) and adaptive immune receptor sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here, we introduceDandelion, 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 non-productive and partially spliced contigs. We devised a novel strategy to create an adaptive immune receptor feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application ofDandelionimproved the alignment of human thymic development trajectories of double positive T cells to mature single-positive CD4/CD8 T cells, with important new predictions of factors regulating lineage commitment.Dandelionanalysis of other cell compartments provided novel insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach.Dandelionis an open access resource (https://www.github.com/zktuong/dandelion)that will enable future discoveries.

Human females undergo X-inactivation (Xi), whereby one copy of X is randomly inactivated early in development, then propagated through cell division. Because Xi state is inherited, its measurement in populations of cells encodes information about the phylogeny that created them and their relationships to other cells. We present a method, inactiveXX, to determine the Xi state of single cell transcriptomes, and demonstrate its accuracy using cancer and gold standard reference data. We apply inactiveXX to single cell transcriptomes from 190 human females, revealing that Xi in humans likely occurs around the 16 cell blastocyst stage and affects both embryonic and extra-embryonic tissues. We further find significant cell type specific variability in Xi skew, only detectable with cell type specific resolution, with certain cell types exhibiting strong population bottlenecks across tissues and disease state.

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 .

The yolk sac (YS) represents an evolutionarily-conserved extraembryonic structure that ensures timely delivery of nutritional support and oxygen to the developing embryo. However, the YS remains ill-defined in humans. We therefore assemble a complete single cell 3D map of human YS from 3-8 post conception weeks by integrating multiomic protein and gene expression data. We reveal the YS as a site of primitive and definitive haematopoiesis including a YS-specific accelerated route to macrophage production, a source of nutritional/metabolic support and a regulator of oxygen-carrying capacity. We reconstruct the emergence of primitive haematopoietic stem and progenitor cells from YS hemogenic endothelium and their decline upon stromal support modulation as intraembryonic organs specialise to assume these functions. The YS therefore functions as ‘three organs in one’ revealing a multifaceted relay of vital organismal functions as pregnancy proceeds.One Sentence SummaryHuman yolk sac is a key staging post in a relay of vital organismal functions during human pregnancy.

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 SummaryBy 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.

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 new cell states in all compartments. These include developmental-specific secretory progenitors and a new subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models.HighlightsSpatiotemporal atlas of human lung development from 5-22 post conception weeks identifies 144 cell types/states.Tracking the developmental origins of multiple cell compartments, including new progenitor states.Functional diversity of fibroblasts in distinct anatomical signalling niches.Resource applied to interrogate and experimentally test the transcription factor code controlling neuroendocrine cell heterogeneity and the origins of small cell lung cancer.

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). A single-cell atlas of human fetal bone marrow in healthy fetuses and fetuses with Down syndrome provides insight into developmental haematopoiesis in humans and the transcription and functional differences that occur in Down syndrome.

Funder: Medical Research Council, 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.

Background: The availability and popularity of oral anti-cancer therapy has recently increased. In this shift of delivery, control over downstream steps in the process moves to patients, families, and the extended care team. Unique challenges have been identified including effective patient education, adherence, and monitoring. The purpose of this quality improvement project was to standardize the approach to initiating oral anti-cancer patient care. Methods: A quality improvement team developed and implemented a standard approach for before, during, and after initiation of oral anti-cancer therapy. Key components included ensuring completion of informed consent, electronic orders within an evidence-based treatment plan, and adherence monitoring with a specific emphasis on patient education. Education prior to initiating therapy was standardized through both an education folder and one-on-one teaching sessions with an oncology nurse. A voluntary, anonymous 4-question paper survey (deemed clinical quality improvement by the IRB) solicited feedback from patients to assess both the educational materials and teaching session. Questions included perceived acceptability of the content and whether materials guided conversation, provoked questions, and complimented the teaching. An opportunity for comments was provided. Results: A 3-month collection period yielded overwhelmingly positive results. 100% of patients (n=18) felt the education materials provided were “just right” and provoked discussion with the care team. 94% of patients felt the educational materials and teaching session were complimentary. Conclusions: As oral anti-cancer therapy becomes more common, it is vital to form a collaborative partnership with the patient, family, and the extended care team to ensure overall success. Standardized educational content supports the transfer of expert knowledge to ensure adherence, management, and patient safety, ultimately improving patient outcomes. A standard approach that includes informed consent, orders within a treatment plan, adherence monitoring, and patient education prior to initiating oral anti-cancer therapy is one way to ensure quality, comprehensive patient care.

Throughout postnatal life, haematopoiesis in the bone marrow (BM) maintains blood and immune cell production. Haematopoiesis first emerges in human BM at 12 post conception weeks while fetal liver (FL) haematopoiesis is still expanding. Yet, almost nothing is known about how fetal BM evolves to meet the highly specialised needs of the fetus and newborn infant. Here, we detail the development of fetal BM including stroma using single cell RNA-sequencing. We find that the full blood and immune cell repertoire is established in fetal BM in a short time window of 6-7 weeks early in the second trimester. Fetal BM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell (DC) subsets emerging for the first time. B-lymphocyte expansion occurs, in contrast with erythroid predominance in FL at the same gestational age. We identify transcriptional and functional differences that underlie tissue-specific identity and cellular diversification in fetal BM and FL. Finally, we reveal selective disruption of B-lymphocyte, erythroid and myeloid development due to cell intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in the fetal BM from constitutional chromosome anomaly Down syndrome during this crucial developmental time window.

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. To comprehensively map cell lineages in the healthy developing, pediatric and adult human gut from ten distinct anatomical regions, as well as draining lymph nodes, we used singlecell RNA-seq and VDJ analysis of roughly one third of a million cells. This reveals the presence of BEST4+ absorptive cells throughout the human intestinal tract, demonstrating the existence of this cell type beyond the colon for the first time. Furthermore, we implicate IgG sensing as a novel function of intestinal tuft cells, and link these cells to the pathogenesis of inflammatory bowel disease. We define novel glial and neuronal cell populations in the developing enteric nervous system, and predict cell-type specific expression of Hirschsprung’s disease-associated genes. Finally, using a systems approach, we identify key cell players across multiple cell lineages driving secondary lymphoid tissue formation 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. These data provide an unprecedented catalogue of intestinal cells, and new insights into cellular programs in development, homeostasis and disease.

The COVID-19 pandemic, caused by SARS coronavirus 2 (SARS-CoV-2), has resulted in excess morbidity and mortality as well as economic decline. To characterise the systemic host immune response to SARS-CoV-2, we performed single-cell RNA-sequencing coupled with analysis of cell surface proteins, providing molecular profiling of over 800,000 peripheral blood mononuclear cells from a cohort of 130 patients with COVID-19. Our cohort, from three UK centres, spans the spectrum of clinical presentations and disease severities ranging from asymptomatic to critical. Three control groups were included: healthy volunteers, patients suffering from a non-COVID-19 severe respiratory illness and healthy individuals administered with intravenous lipopolysaccharide to model an acute inflammatory response. Full single cell transcriptomes coupled with quantification of 188 cell surface proteins, and T and B lymphocyte antigen receptor repertoires have provided several insights into COVID-19: 1. a new non-classical monocyte state that sequesters platelets and replenishes the alveolar macrophage pool; 2. platelet activation accompanied by early priming towards megakaryopoiesis in immature haematopoietic stem/progenitor cells and expansion of megakaryocyte-primed progenitors; 3. increased clonally expanded CD8+ effector:effector memory T cells, and proliferating CD4+ and CD8+ T cells in patients with more severe disease; and 4. relative increase of IgA plasmablasts in asymptomatic stages that switches to expansion of IgG plasmablasts and plasma cells, accompanied with higher incidence of BCR sharing, as disease severity increases. All data and analysis results are available for interrogation and data mining through an intuitive web portal. Together, these data detail the cellular processes present in peripheral blood during an acute immune response to COVID-19, and serve as a template for multi-omic single cell data integration across multiple centers to rapidly build powerful resources to help combat diseases such as COVID-19.

Single-cell omic protocols applied to disease, development or mechanistic studies can reveal the emergence of aberrant cell states or changes in differentiation. These perturbations can manifest as a shift in the abundance of cells associated with a biological condition. Current computational workflows for comparative analyses typically use discrete clusters as input when testing for differential abundance between experimental conditions. However, clusters are not always an optimal representation of the biological manifold on which cells lie, especially in the context of continuous differentiation trajectories. To overcome these barriers to discovery, we present Milo, a flexible and scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighbourhoods on a k-nearest neighbour graph. Our method samples and refines neighbourhoods across the graph and leverages the flexibility of generalized linear models, making it applicable to a wide range of experimental settings. Using simulations, we show that Milo is both robust and sensitive, and can reveal subtle but important cell state perturbations that are obscured by discretizing cells into clusters. We illustrate the power of Milo by identifying the perturbed differentiation during ageing of a lineage-biased thymic epithelial precursor state and by uncovering extensive perturbation to multiple lineages in human cirrhotic liver. Milo is provided as an open-source R software package with documentation and tutorials at https://github.com/MarioniLab/miloR.

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 .

The spatial organization of cell types in tissues fundamentally shapes cellular interactions and function, but the high-throughput spatial mapping of complex tissues remains a challenge. We present сell2location, a principled and versatile Bayesian model that integrates single-cell and spatial transcriptomics to map cell types in situ in a comprehensive manner. We show that сell2location outperforms existing tools in accuracy and comprehensiveness and we demonstrate its utility by mapping two complex tissues. In the mouse brain, we use a new paired single nucleus and spatial RNA-sequencing dataset to map dozens of cell types and identify tissue regions in an automated manner. We discover novel regional astrocyte subtypes including fine subpopulations in the thalamus and hypothalamus. In the human lymph node, we resolve spatially interlaced immune cell states and identify co-located groups of cells underlying tissue organisation. We spatially map a rare pre-germinal centre B-cell population and predict putative cellular interactions relevant to the interferon response. Collectively our results demonstrate how сell2location can serve as a versatile first-line analysis tool to map tissue architectures in a high-throughput manner.

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.

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.

8 Background: Loss of androgen receptor signaling dependence occurs in approximately 20% of treatment resistant prostate cancers, and this may manifest as transformation to castration resistant neuroendocrine prostate cancer (CRPC-NE). The diagnosis of CRPC-NE relies on a metastatic tumor biopsy, which is invasive for patients. Here we study ctDNA as an approach to identify CRPC-NE-associated genomic and epigenomic changes. We also use this as a tool to understand tumor heterogeneity and clonal dynamics that occurs during CRPC-NE progression. Methods: 64 patients with metastatic prostate cancer (10 hormone naive, 35 castration resistant adenocarcinoma (CRPC-Adeno), 17 CRPC-NE) were prospectively enrolled for matched metastatic biopsy and blood collection. Twenty-four had multiple time-points, for total of 69 plasma and 98 metastatic tissues. Whole exome sequencing (WES) and whole genome bisulfite sequencing(of subset) of ctDNA,germline DNA, and metastatic biopsies were performed. Results: Overall there was high concordance of alterations between ctDNA and patient-matched metastasis, though this was highest in CRPC-NE. There was also less heterogeneity across patients with CRPC-NE. Alteration frequencies were consistent with published tissue-based studies with AR alterations enriched in CRPC-Adeno and TP53and RB1loss enriched in CRPC-NE ctDNA. The prognostic significance of these alterations differed based on histologic subtype. Allele-specific copy number analysis and serial sampling allowed for the detection and tracking of clonal and subclonal tumor cell populations. cfDNA methylation was reflective of methylation patterns in biopsies and detected CRPC-NE-associated epigenetic changes.A targeted set combining genomic ( TP53, RB1, CYLD, AR) and epigenomic(20 differential DNA methylation sites) was capable of identifying patients with CRPC-NE. Conclusions: WES and whole genome methylation of ctDNA is feasible, concordant with biopsy tissues, and identifies the spectrum and frequency of CRPC-NE genomic and epigenomic changes. A targeted panel extended and validated in larger cohorts could potentially improve the detection of CRPC-NE using non-invasive ctDNA profiling.

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.