A Visionary Think Tank to Address the Nursing Workforce Crisis.

The COVID-19 pandemic exacerbated an existing problem plaguing hospital systems across the United States: a nursing workforce shortage. This article describes how one institution applied the American Organization for Nursing Leadership Nurse Executive Competencies to convene an immersive think tank to reimagine the nursing workforce.

Surge Planning and Implementation: A Required Competency for Today's Nurse Leaders.

The ability to respond effectively and efficiently during times of crisis, including a pandemic, has emerged as a competency for nurse leaders. This article describes one institution's experience using the American Organization of Nurse Leaders Competencies for Nurse Executives in operationalizing the concept of surge capacity.

Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterising transcriptional changes in cord blood-derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the 2 stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the 2 phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology, and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process (which is different from a simple binary switch between 2 options, as it is usually envisioned).

Integrating the Social Determinants of Health into Nursing Practice: Nurses' Perspectives.

PURPOSE: The purpose of this study was to assess nurses' knowledge, perceived self-efficacy, and intended behaviors relative to integrating the social determinants of health (SDoH) into clinical practice. DESIGN AND METHODS: A cross-sectional study was completed with 768 nurses working in three hospitals within a large regional healthcare system located in the Midwest. Data were collected using an adapted 71-item SDoH Survey, which measured nurses' confidence in and frequency of discussing the SDoH with patients, general knowledge of the SDoH, familiarity with patients' social and economic conditions, and awareness of their institution's health equity strategic plan to achieve health equity. The institution's health equity strategic plan reflects the organization's commitment to improving the health of individuals and neighborhoods by addressing the SDoH known to influence health status and life expectancy. Finally, participants were asked to describe barriers to incorporating the SDoH into practice along with completing five demographic items. Descriptive statistics were used to describe the findings. FINDINGS: Of the 768 respondents, 63% had a baccalaureate degree in nursing and 33.1% reported more than 20 years in nursing. Fifty percent of respondents reported feeling more knowledgeable or confident in their ability to discuss access to care issues with patients compared to the other SDoH. Identified barriers to discussing the SDoH included insufficient time to address identified needs and unfamiliarity with internal and external resources. Respondents stressed the need for interdisciplinary education and collaboration along with more information on the role of social workers. CONCLUSIONS: Nurses are more confident in discussing certain determinants of health and could benefit from more skill development in discussing SDoH issues and stronger collaborative partnerships to address identified needs. CLINICAL RELEVANCE: Findings from the study have implications for supporting the educational and resource needs of front-line nurses employed in hospitals and health systems seeking to address broader societal issues influencing the health status and outcomes of patients and communities.

WASABI: a dynamic iterative framework for gene regulatory network inference.

BACKGROUND: Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. RESULTS: In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. CONCLUSIONS: Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process.

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a "simple" program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

Erythroid differentiation displays a peak of energy consumption concomitant with glycolytic metabolism rearrangements.

Our previous single-cell based gene expression analysis pointed out significant variations of LDHA level during erythroid differentiation. Deeper investigations highlighted that a metabolic switch occurred along differentiation of erythroid cells. More precisely we showed that self-renewing progenitors relied mostly upon lactate-productive glycolysis, and required LDHA activity, whereas differentiating cells, mainly involved mitochondrial oxidative phosphorylation (OXPHOS). These metabolic rearrangements were coming along with a particular temporary event, occurring within the first 24h of erythroid differentiation. The activity of glycolytic metabolism and OXPHOS rose jointly with oxgene consumption dedicated to ATP production at 12-24h of the differentiation process before lactate-productive glycolysis sharply fall down and energy needs decline. Finally, we demonstrated that the metabolic switch mediated through LDHA drop and OXPHOS upkeep might be necessary for erythroid differentiation. We also discuss the possibility that metabolism, gene expression and epigenetics could act together in a circular manner as a driving force for differentiation.

Automated cell cycle and cell size measurements for single-cell gene expression studies.

OBJECTIVES: Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. In order to identify how cell cycle and cell size influences gene expression variability at the single-cell level, we provide an universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. The method consists of isolating cells after a double-stained, analyzing their morphological parameters and performing a transcriptomic analysis on the same identified cells. RESULTS: This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.