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The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.
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Software , Humanos , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Leucócitos Mononucleares/citologia , Genômica/métodos , Análise de DadosRESUMO
One of the characteristics of the central nervous system is the lack of a classical lymphatic drainage system. Although it is now accepted that the central nervous system undergoes constant immune surveillance that takes place within the meningeal compartment, the mechanisms governing the entrance and exit of immune cells from the central nervous system remain poorly understood. In searching for T-cell gateways into and out of the meninges, we discovered functional lymphatic vessels lining the dural sinuses. These structures express all of the molecular hallmarks of lymphatic endothelial cells, are able to carry both fluid and immune cells from the cerebrospinal fluid, and are connected to the deep cervical lymph nodes. The unique location of these vessels may have impeded their discovery to date, thereby contributing to the long-held concept of the absence of lymphatic vasculature in the central nervous system. The discovery of the central nervous system lymphatic system may call for a reassessment of basic assumptions in neuroimmunology and sheds new light on the aetiology of neuroinflammatory and neurodegenerative diseases associated with immune system dysfunction.
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Sistema Nervoso Central/anatomia & histologia , Sistema Nervoso Central/imunologia , Vasos Linfáticos/anatomia & histologia , Vasos Linfáticos/imunologia , Animais , Sistema Nervoso Central/citologia , Cavidades Cranianas/anatomia & histologia , Feminino , Humanos , Tolerância Imunológica/imunologia , Vigilância Imunológica/imunologia , Vasos Linfáticos/citologia , Masculino , Meninges/anatomia & histologia , Meninges/citologia , Meninges/imunologia , Camundongos Endogâmicos C57BL , Linfócitos T/citologia , Linfócitos T/imunologiaRESUMO
We report the case of a patient with X-linked severe combined immunodeficiency (X-SCID) who survived for over 20 years without hematopoietic stem cell transplantation (HSCT) because of a somatic reversion mutation. An important feature of this rare case included the strategy to validate the pathogenicity of a variant of the IL2RG gene when the T and B cell lineages comprised only revertant cells. We studied the X-inactivation of sorted T cells from the mother to show that the pathogenic variant was indeed the cause of his SCID. One interesting feature was a progressive loss of B cells over 20 years. CyTOF (cytometry time of flight) analysis of bone marrow offered a potential explanation of the B cell failure, with expansions of progenitor populations that suggest a developmental block. Another interesting feature was that the patient bore extensive granulomatous disease and skin cancers that contained T cells, despite severe T cell lymphopenia in the blood. Finally, the patient had a few hundred T cells on presentation but his TCRs comprised a very limited repertoire, supporting the important conclusion that repertoire size trumps numbers of T cells.
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Linfócitos B/imunologia , Suscetibilidade a Doenças , Doenças por Imunodeficiência Combinada Ligada ao Cromossomo X/diagnóstico , Doenças por Imunodeficiência Combinada Ligada ao Cromossomo X/etiologia , Linfócitos B/metabolismo , Biomarcadores , Biópsia , Pré-Escolar , Citocinas/metabolismo , Suscetibilidade a Doenças/imunologia , Predisposição Genética para Doença , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Imunofenotipagem , Lactente , Contagem de Linfócitos , Masculino , Fenótipo , Pele/patologia , Linfócitos T/imunologia , Linfócitos T/metabolismo , Sequenciamento do Exoma , Inativação do Cromossomo XRESUMO
The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.
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Inteligência Artificial , Neoplasias , Biologia Computacional , Humanos , Aprendizado de Máquina , Neoplasias/diagnóstico , ProteômicaRESUMO
BACKGROUND: Daily, oral pre-exposure prophylaxis (PrEP) is an effective and safe prevention strategy for people at risk for HIV. However, prescription of PrEP has been limited for patients at the highest risk. Disparities in PrEP prescription are pronounced among racial and gender minority patients. A significant body of literature indicates that practicing healthcare providers have little awareness and knowledge of PrEP. Very little work has investigated the education about PrEP among health professionals in training. OBJECTIVE: The objective of this study was to compare health professions students' awareness of PrEP and education about PrEP between regions of the US, and to determine if correlations between regional HIV incidence and PrEP use were present. DESIGN: Survey study. PARTICIPANTS: A cross-sectional sample of health professions students (N = 1859) representing future prescribers (MD, DO, PA), pharmacists, and nurses in the US. KEY RESULTS: Overall, 83.4% of students were aware of PrEP, but only 62.2% of fourth-year students indicated they had been taught about PrEP at any time during their training. Education about PrEP was most comprehensive in the Northeastern US, the area with the highest PrEP to need ratio (4.7). In all regions, transgender patients and heterosexual men and women were least likely to be presented in education as PrEP candidates, and men who have sex with men were the most frequently presented. CONCLUSIONS: There are marked differences in education regarding PrEP both between academic programs and regions of the USA.
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Fármacos Anti-HIV , Infecções por HIV , Profilaxia Pré-Exposição , Minorias Sexuais e de Gênero , Fármacos Anti-HIV/uso terapêutico , Estudos Transversais , Feminino , Infecções por HIV/tratamento farmacológico , Infecções por HIV/epidemiologia , Infecções por HIV/prevenção & controle , Conhecimentos, Atitudes e Prática em Saúde , Homossexualidade Masculina , Humanos , Masculino , Estudantes , Estados Unidos/epidemiologiaRESUMO
BACKGROUND: The traditional model of promotion and tenure in the health professions relies heavily on formal scholarship through teaching, research, and service. Institutions consider how much weight to give activities in each of these areas and determine a threshold for advancement. With the emergence of social media, scholars can engage wider audiences in creative ways and have a broader impact. Conventional metrics like the h-index do not account for social media impact. Social media engagement is poorly represented in most curricula vitae (CV) and therefore is undervalued in promotion and tenure reviews. OBJECTIVE: The objective was to develop crowdsourced guidelines for documenting social media scholarship. These guidelines aimed to provide a structure for documenting a scholar's general impact on social media, as well as methods of documenting individual social media contributions exemplifying innovation, education, mentorship, advocacy, and dissemination. METHODS: To create unifying guidelines, we created a crowdsourced process that capitalized on the strengths of social media and generated a case example of successful use of the medium for academic collaboration. The primary author created a draft of the guidelines and then sought input from users on Twitter via a publicly accessible Google Document. There was no limitation on who could provide input and the work was done in a democratic, collaborative fashion. Contributors edited the draft over a period of 1 week (September 12-18, 2020). The primary and secondary authors then revised the draft to make it more concise. The guidelines and manuscript were then distributed to the contributors for edits and adopted by the group. All contributors were given the opportunity to serve as coauthors on the publication and were told upfront that authorship would depend on whether they were able to document the ways in which they met the 4 International Committee of Medical Journal Editors authorship criteria. RESULTS: We developed 2 sets of guidelines: Guidelines for Listing All Social Media Scholarship Under Public Scholarship (in Research/Scholarship Section of CV) and Guidelines for Listing Social Media Scholarship Under Research, Teaching, and Service Sections of CV. Institutions can choose which set fits their existing CV format. CONCLUSIONS: With more uniformity, scholars can better represent the full scope and impact of their work. These guidelines are not intended to dictate how individual institutions should weigh social media contributions within promotion and tenure cases. Instead, by providing an initial set of guidelines, we hope to provide scholars and their institutions with a common format and language to document social media scholarship.
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Bolsas de Estudo/normas , Ocupações em Saúde/educação , Mídias Sociais/normas , HumanosRESUMO
[This corrects the article DOI: 10.2196/25070.].
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Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple Instance Learning (MMIL), an expectation maximization method that enables the training and calibration of cell-level classifiers using patient-level labels. Our approach can be used to train e.g. lasso logistic regression models, gradient boosted trees, and neural networks. When applied to clinically-annotated, primary patient samples in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL), our method accurately identifies cancer cells, generalizes across tissues and treatment timepoints, and selects biologically relevant features. In addition, MMIL is capable of incorporating cell labels into model training when they are known, providing a powerful framework for leveraging both labeled and unlabeled data simultaneously. Mixture Modeling for MIL offers a novel approach for cell classification, with significant potential to advance disease understanding and management, especially in scenarios with unknown gold-standard labels and high dimensionality.
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The 2014 NIH Physician-Scientist Workforce Working Group predicted a future shortage of physician-scientists. Subsequent studies have highlighted disparities in MD-PhD admissions based on race, income, and education. Our analysis of data from the Association of American Medical Colleges covering 2014-2021 (15,156 applicants and 6,840 acceptees) revealed that acceptance into US MD-PhD programs correlates with research experience, family income, and research publications. The number of research experiences associated with parental education and family income. Applicants were more likely to be accepted with a family income greater than $50,000 or with one or more publications or presentations. Applicants were less likely to be accepted if they had parents without a graduate degree, were Black/African American, were first-generation college students, or were reapplicants, irrespective of the number of research experiences, publications, or presentations. These findings underscore an admissions bias that favors candidates from affluent and highly educated families, while disadvantaging underrepresented minorities.
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Pesquisa Biomédica , Educação Médica , Médicos , Humanos , Fatores Sociodemográficos , Pesquisa Biomédica/educação , Recursos HumanosRESUMO
The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.
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BACKGROUND: Many health professions faculty members lack training on fundamental lesbian, gay, bisexual, transgender, and queer (LGBTQ+) health topics. Faculty development is needed to address knowledge gaps, improve teaching, and prepare students to competently care for the growing LGBTQ+ population. OBJECTIVE: We conducted a program evaluation of the massive open online course Teaching LGBTQ+ Health: A Faculty Development Course for Health Professions Educators from the Stanford School of Medicine. Our goal was to understand participant demographics, impact, and ongoing maintenance needs to inform decisions about updating the course. METHODS: We evaluated the course for the period from March 27, 2021, to February 24, 2023, guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. We assessed impact using participation numbers, evidence of learning, and likelihood of practice change. Data included participant demographics, performance on a pre- and postcourse quiz, open-text entries throughout the course, continuing medical education (CME) credits awarded, and CME course evaluations. We analyzed demographics using descriptive statistics and pre- and postcourse quiz scores using a paired 2-tailed t test. We conducted a qualitative thematic analysis of open-text responses to prompts within the course and CME evaluation questions. RESULTS: Results were reported using the 5 framework domains. Regarding Reach, 1782 learners participated in the course, and 1516 (85.07%) accessed it through a main course website. Of the different types of participants, most were physicians (423/1516, 27.9%) and from outside the sponsoring institution and target audience (1452/1516, 95.78%). Regarding Effectiveness, the median change in test scores for the 38.1% (679/1782) of participants who completed both the pre- and postcourse tests was 3 out of 10 points, or a 30% improvement (P<.001). Themes identified from CME evaluations included LGBTQ+ health as a distinct domain, inclusivity in practices, and teaching LGBTQ+ health strategies. A minority of participants (237/1782, 13.3%) earned CME credits. Regarding Adoption, themes identified among responses to prompts in the course included LGBTQ+ health concepts and instructional strategies. Most participants strongly agreed with numerous positive statements about the course content, presentation, and likelihood of practice change. Regarding Implementation, the course cost US $57,000 to build and was intramurally funded through grants and subsidies. The course faculty spent an estimated 600 hours on the project, and educational technologists spent another 712 hours. Regarding Maintenance, much of the course is evergreen, and ongoing oversight and quality assurance require minimal faculty time. New content will likely include modules on transgender health and gender-affirming care. CONCLUSIONS: Teaching LGBTQ+ Health improved participants' knowledge of fundamental queer health topics. Overall participation has been modest to date. Most participants indicated an intention to change clinical or teaching practices. Maintenance costs are minimal. The web-based course will continue to be offered, and new content will likely be added.
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Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.
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Neoplasias , Criança , Humanos , Neoplasias/etiologia , Neoplasias/terapia , Oncologia/métodos , ProteômicaRESUMO
Summary: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface. Availability and implementation: {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn.