The tidyomics ecosystem: enhancing omic data analyses.

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.

Structural and functional features of central nervous system lymphatic vessels.

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.

Progressive B Cell Loss in Revertant X-SCID.

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.

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.

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.

Student Education About Pre-exposure Prophylaxis (PrEP) Varies Between Regions of the United States.

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.

The tidyomics ecosystem: Enhancing omic data analyses.

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.

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers.

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.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

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.

CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.

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.