Brasier, Allan R.

Effective translation relies on high performance teams integrating diverse disciplines to bring new drugs, medical devices, diagnostics or behavioral interventions into improved health. Despite substantial advancements informed by the Science of Team Science (SciTS), challenges remain in effective translational team (TT) operations, while limited institutional, scholarly, and practitioner legitimacy of team science expertise constrains efforts to address them. Here, we describe an essential member of the TT, the Team Science Professional (TSP). Serving as a team educator and facilitator of team interventions, the TSP plays a pivotal role in facilitating effective team performance. Current challenges faced by this nascent profession are defining career pathways and establishing competencies needed for expert performance. To address these challenges, the authors, representing a spectrum of academic universities organized as a special interest group (SIG) of the Association of Clinical and Translational Science (ACTS), propose a suite of individualized, standardized and pathway-selective micro-credentials (digital badges). Digital badges in “Team Science (TS) Fundamentals,” “TS Practitioner,” “TT Trainer” and “TT Intervention Expert” are described, along with preliminary feasibility and usability evaluations of the submission process. These stackable credentials provide a customizable, portable recognition of skills and help advance career pathways for TSPs. In so doing, the Digital Badge Initiative will enhance the capacity of translational science “Hubs” such as those supported by Clinical and Translational Science Awards to conduct effective and rigorous translation. Moving forward, the ACTS TSP SIG will be examining the impact of digital badging on standardization and its effect on career recognition and retention.

Abstract Histones variant composition and post-translational modifications (PTMs) of core and linker histones jointly orchestrate chromatin architecture and tissue-specific gene regulation. However, capturing the full complexity of the “histone code” in native human tissues remains challenging. Here we present a human cardiac histone proteoform atlas—comprising intact histone variants with combinatorial PTMs—enabled by a streamlined top‑down proteomics workflow. In a single one‑dimensional reversed‑phase liquid chromatography (LC)-mass spectrometry (MS) run, we achieve baseline separation of all four core histone families (H2A, H2B, H3, and H4) together with the linker histone H1 directly from human myocardium. Notably, this intact-protein analysis preserves the connectivity of co-occurring PTMs to individual histone molecules to reveal the combinatorial histone code, enabling proteoform‑level quantification of histone‑variant composition and PTM stoichiometry at the chromatographic scale. Targeted MS/MS directly resolves co-occurring PTM combinations and distinguishes highly homologous histone isoforms. Using this approach, we assemble the first comprehensive cardiac histone proteoform atlas and uncover previously unobserved histone proteoforms. More broadly, this top-down proteomics workflow provides a robust framework for proteoform-resolved analysis of histone variants and PTMs in complex biological systems.

A critical goal of multidisciplinary scientific teams is to integrate knowledge from diverse disciplines for the purpose of developing novel insights and innovations. For example, multidisciplinary translational teams (MTTs) which typically include physicians, biologists, statisticians, and informaticians, aim to integrate biological and clinical knowledge leading to innovations for improving health outcomes. However, such teams face numerous barriers in integrating multidisciplinary knowledge, which is further exacerbated by the explosion of molecular and clinical data generated from millions of patients. Here, we explore the use of a visual analytical representation to help MTTs integrate molecular and clinical data with the goal of accelerating translational insights. The results suggest that the visual analytical representation functioned as a “computational evolving boundary object” which (a) evolved through several emergent states that progressively helped integrate diverse disciplinary knowledge, (b) enabled team members to play primary and supportive roles in evolving the data representation resulting in a more egalitarian team structure, and (c) enabled the team to arrive at novel translational insights leading to domain and methodology publications. However, the interventions also revealed limitations in the approach motivating new visual analytical approaches. These results suggest (a) implications for theory related to modeling computational evolving boundary objects (CEBOs) as an instance of team-centered informatics, and (b) implications for practice related to the design and use of interactive features that enable teams to fluidly evolve CEBOs through emergent states, with the goal of deriving novel insights from large multiomics datasets.

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject‐protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject‐protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker‐based clinical trials, and accelerating the development of personalized approaches to medicine.